diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
new file mode 100644
index 0000000000000000000000000000000000000000..22c13a0e838d89dbd1b47607cdaf73aa6912f0aa
--- /dev/null
+++ b/.github/workflows/ci.yml
@@ -0,0 +1,51 @@
+name: CI
+
+on:
+ push:
+ branches: [main]
+ pull_request:
+
+concurrency:
+ group: ${{ github.workflow }}-${{ github.ref }}
+ cancel-in-progress: true
+
+jobs:
+ lint:
+ runs-on: ubuntu-latest
+ timeout-minutes: 5
+ steps:
+ - uses: actions/checkout@v4
+ - uses: astral-sh/setup-uv@v4
+ with:
+ python-version: "3.10"
+ enable-cache: true
+ - run: uv sync --frozen --extra dev
+ - run: uv run black --check skydiscover/
+ - run: uv run isort --check skydiscover/
+
+ test:
+ runs-on: ubuntu-latest
+ timeout-minutes: 10
+ steps:
+ - uses: actions/checkout@v4
+ - uses: astral-sh/setup-uv@v4
+ with:
+ python-version: "3.10"
+ enable-cache: true
+ - run: uv sync --frozen --extra dev
+ - name: Smoke test — package imports cleanly
+ run: uv run python -c "from skydiscover import Runner, run_discovery, discover_solution, __version__; print(f'skydiscover {__version__} OK')"
+ - name: Run tests
+ run: uv run pytest tests/ -v
+
+ build:
+ runs-on: ubuntu-latest
+ timeout-minutes: 5
+ needs: [lint, test]
+ steps:
+ - uses: actions/checkout@v4
+ - uses: astral-sh/setup-uv@v4
+ with:
+ python-version: "3.10"
+ enable-cache: true
+ - run: uv build
diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000000000000000000000000000000000000..bf025a2462fb20c7e578e63b2a02992c5ff2a221
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,60 @@
+# Python
+__pycache__/
+*.py[cod]
+*$py.class
+*.so
+*.egg-info/
+*.egg
+dist/
+build/
+.eggs/
+
+# Virtual environments
+.venv/
+venv/
+env/
+
+# IDE
+.idea/
+.vscode/
+*.swp
+*.swo
+.claude/
+
+# OS
+.DS_Store
+
+# Testing
+.pytest_cache/
+.coverage
+htmlcov/
+
+# Secrets
+.env
+secrets.yaml
+
+# Logs & outputs
+*.log
+*.jsonl
+output*/
+outputs*/
+outputs_*/
+
+
+# Benchmark generated data
+benchmarks/image_gen/sky_festival/sky_festival_output/
+benchmarks/image_gen/sky_festival/sky_festival_paradigm_output_*/
+benchmarks/frontier-cs-eval/Frontier-CS
+benchmarks/ADRS/eplb/expert-load.json
+benchmarks/ADRS/cloudcast/profiles/
+benchmarks/ADRS/cloudcast/examples/
+benchmarks/ADRS/llm_sql/datasets/
+
+# Generated test outputs (re-generate with test_all_benchmarks.sh)
+tests/**/test_outputs_*/
+
+# Evaluation run outputs
+eval_runs/
+
+# Local documentation
+tasks/
diff --git a/.gitmodules b/.gitmodules
new file mode 100644
index 0000000000000000000000000000000000000000..3a5e8bc6a60f21e9d174b6d0f7af2375f28e1c40
--- /dev/null
+++ b/.gitmodules
@@ -0,0 +1,3 @@
+[submodule "benchmarks/ale_bench/ALE-Bench"]
+ path = benchmarks/ale_bench/ALE-Bench
+ url = https://github.com/SakanaAI/ALE-Bench.git
diff --git a/LICENSE b/LICENSE
new file mode 100644
index 0000000000000000000000000000000000000000..842521348dce45184b3fbb7ad3c2152e808eeae9
--- /dev/null
+++ b/LICENSE
@@ -0,0 +1,201 @@
+ Apache License
+ Version 2.0, January 2004
+ http://www.apache.org/licenses/
+
+ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
+
+ 1. Definitions.
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+ for use, reproduction, or distribution of Your modifications, or
+ for any such Derivative Works as a whole, provided Your use,
+ reproduction, and distribution of the Work otherwise complies with
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+
+ 5. Submission of Contributions. Unless You explicitly state otherwise,
+ any Contribution intentionally submitted for inclusion in the Work
+ by You to the Licensor shall be under the terms and conditions of
+ this License, without any additional terms or conditions.
+ Notwithstanding the above, nothing herein shall supersede or modify
+ the terms of any separate license agreement you may have executed
+ with Licensor regarding such Contributions.
+
+ 6. Trademarks. This License does not grant permission to use the trade
+ names, trademarks, service marks, or product names of the Licensor,
+ except as required for reasonable and customary use in describing the
+ origin of the Work and reproducing the content of the NOTICE file.
+
+ 7. Disclaimer of Warranty. Unless required by applicable law or
+ agreed to in writing, Licensor provides the Work (and each
+ Contributor provides its Contributions) on an "AS IS" BASIS,
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+ appropriateness of using or redistributing the Work and assume any
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+
+ 8. Limitation of Liability. In no event and under no legal theory,
+ whether in tort (including negligence), contract, or otherwise,
+ unless required by applicable law (such as deliberate and grossly
+ negligent acts) or agreed to in writing, shall any Contributor be
+ liable to You for damages, including any direct, indirect, special,
+ incidental, or consequential damages of any character arising as a
+ result of this License or out of the use or inability to use the
+ Work (including but not limited to damages for loss of goodwill,
+ work stoppage, computer failure or malfunction, or any and all
+ other commercial damages or losses), even if such Contributor
+ has been advised of the possibility of such damages.
+
+ 9. Accepting Warranty or Additional Liability. While redistributing
+ the Work or Derivative Works thereof, You may choose to offer,
+ and charge a fee for, acceptance of support, warranty, indemnity,
+ or other liability obligations and/or rights consistent with this
+ License. However, in accepting such obligations, You may act only
+ on Your own behalf and on Your sole responsibility, not on behalf
+ of any other Contributor, and only if You agree to indemnify,
+ defend, and hold each Contributor harmless for any liability
+ incurred by, or claims asserted against, such Contributor by reason
+ of your accepting any such warranty or additional liability.
+
+ END OF TERMS AND CONDITIONS
+
+ APPENDIX: How to apply the Apache License to your work.
+
+ To apply the Apache License to your work, attach the following
+ boilerplate notice, with the fields enclosed by brackets "[]"
+ replaced with your own identifying information. (Don't include
+ the brackets!) The text should be enclosed in the appropriate
+ comment syntax for the file format. We also recommend that a
+ file or class name and description of purpose be included on the
+ same "printed page" as the copyright notice for easier
+ identification within third-party archives.
+
+ Copyright [2025] [SkyDiscover Team]
+
+ Licensed under the Apache License, Version 2.0 (the "License");
+ you may not use this file except in compliance with the License.
+ You may obtain a copy of the License at
+
+ http://www.apache.org/licenses/LICENSE-2.0
+
+ Unless required by applicable law or agreed to in writing, software
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\ No newline at end of file
diff --git a/README.md b/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..171752988f20f0cb3b7a7e97858bc85f5e7a67e6
--- /dev/null
+++ b/README.md
@@ -0,0 +1,388 @@
+
+ SkyDiscover
+
+
+
+ A Flexible Framework for AI-Driven Scientific and Algorithmic Discovery
+
+
+
+
+
+
+
+
+
+**SkyDiscover** is a modular framework for AI-driven scientific and algorithmic discovery, providing a unified interface for implementing, running, and fairly comparing discovery algorithms across 200+ optimization tasks.
+
+SkyDiscover introduces two new adaptive optimization algorithms:
+
+- **[AdaEvolve](https://arxiv.org/abs/2602.20133)**, which dynamically adjusts its optimization behavior based on observed progress.
+- **[EvoX](https://arxiv.org/abs/2602.23413)**, which dynamically evolves the optimization (evolution) strategy itself using LLMs on the fly.
+
+SkyDiscover also supports using OpenEvolve, ShinkaEvolve and GEPA to quickly benchmark these algorithms using their own source code. SkyDiscover also hosts native versions of OpenEvolve and GEPA under `openevolve_native` and `gepa_native` algorithms using the modular interface.
+
+SkyDiscover natively supports [Harbor](https://harborframework.com/)-format benchmarks, so you can run external benchmark suites out of the box, including [AlgoTune](https://github.com/oripress/AlgoTune), [EvoEval](https://github.com/evo-eval/evoeval), [HumanEvalFix](https://github.com/bigcode-project/octopack), [BigCodeBench](https://github.com/bigcode-project/bigcodebench), [LiveCodeBench](https://livecodebench.github.io/), [USACO](https://usaco.org/), [CRUSTBench](https://github.com/AInfinity/CRUSTBench), and [CodePDE](https://github.com/).
+> 🚧 This project is under active development.
+
+---
+
+## 🏆 Benchmark Performance
+
+Across ~200 optimization benchmarks, AdaEvolve and EvoX achieve the strongest open-source results: matching or exceeding AlphaEvolve and human SOTA, and outperforming OpenEvolve, GEPA, and ShinkaEvolve under identical generation budgets.
+
+- **Frontier-CS (172 problems)**: ~34% median score improvement over OpenEvolve, GEPA, and ShinkaEvolve
+- **Math + Systems Optimization (14 tasks evaluated)**: Matches or exceeds AlphaEvolve and human-designed SOTA on 6/6 systems and 6/8 math tasks
+- **Real-world systems impact**: 41% lower cross-cloud transfer cost, 14% better GPU load balance for MoE serving, and 29% lower KV-cache pressure via GPU model placement
+
+
+
+
+
+📊 Complete results of AdaEvolve and EvoX (100 iterations)
+
+
+
+
+
+📈 Scaling behavior of AdaEvolve and EvoX
+ Best-so-far score vs. iteration for Signal Processing, Heilbronn Convex, Prism, and Cloudcast (500 iterations, GPT-5).
+
+
+
+
+
+🔗 Evolving AdaEvolve's policy with EvoX (coming soon)
+
+
+Task breakdown across math, systems, and programming challenges
+
+## 🚀 Quick Start
+
+**Prerequisites:** Python >= 3.10, [uv](https://docs.astral.sh/uv/)
+
+```bash
+# Install
+uv sync
+export OPENAI_API_KEY=""
+
+# Try the circle packing benchmark
+uv sync --extra math
+uv run skydiscover-run benchmarks/math/circle_packing/initial_program.py \
+ benchmarks/math/circle_packing/evaluator.py \
+ --config benchmarks/math/circle_packing/config.yaml \
+ --search evox \
+ --iterations 100
+
+uv run skydiscover-run benchmarks/math/circle_packing/initial_program.py \
+ benchmarks/math/circle_packing/evaluator.py \
+ --config benchmarks/math/circle_packing/config.yaml \
+ --search adaevolve \
+ --iterations 100
+
+# Or run on your own problem
+# algo can be "evox", "adaevolve", "openevolve", "gepa", "shinkaevolve"
+uv run skydiscover-run initial_program.py evaluator.py \
+ --search \
+ --model gpt-5 \
+ --iterations 100
+
+# initial_program is optional — omit it to let the LLM start from scratch
+uv run skydiscover-run evaluator.py \
+ --search \
+ --model gpt-5 \
+ --iterations 100
+
+# Run a Harbor benchmark (e.g. AlgoTune) — no seed program needed
+pip install harbor
+harbor datasets download algotune@1.0 -o /tmp/algotune
+uv run skydiscover-run /tmp/algotune//algotune-set-cover \
+ --model anthropic/claude-sonnet-4-6 \
+ --search best_of_n -i 10
+```
+
+Or use the Python API:
+
+```python
+from skydiscover import run_discovery
+
+result = run_discovery(
+ initial_program="initial_program.py",
+ evaluator="evaluator.py",
+ search=[algo], # algo can be "adaevolve", "evox", "openevolve", "gepa", "shinkaevolve"
+ model="gpt-5",
+ iterations=100,
+)
+
+print(result.best_score, result.best_solution)
+```
+
+
+## ✏️ What You Write
+
+### Scoring Function (required)
+
+SkyDiscover supports three evaluator formats — pick whichever fits your use case:
+
+| Format | When to use | What you point `evaluation_file` at |
+|:---|:---|:---|
+| **Python function** | Simple tasks, no system deps | `evaluator.py` |
+| **Containerized** | Custom deps, data files, isolation | `evaluator/` directory (must contain `Dockerfile` + `evaluate.sh`) |
+| **Harbor task** | External benchmark suites (AlgoTune, EvoEval, HumanEvalFix, BigCodeBench, LiveCodeBench, USACO, CRUSTBench, CodePDE, and more) | Task directory (must contain `instruction.md` + `tests/` + `environment/Dockerfile`) |
+
+SkyDiscover auto-detects the format. See [`benchmarks/README.md`](benchmarks/README.md#adding-a-benchmark) for full setup instructions.
+
+**Python evaluator** — a file with an `evaluate(program_path)` function:
+
+```python
+def evaluate(program_path):
+ score = run_and_grade(program_path)
+ return {
+ "combined_score": score, # primary optimization target (maximized)
+ "artifacts": { # optional — stored with the solution for future context
+ "feedback": "Off by one in the loop boundary",
+ },
+ }
+```
+
+**Containerized evaluator** — a directory with a `Dockerfile` and `evaluate.sh` that writes JSON to stdout. Runs in Docker, so it can have arbitrary dependencies.
+
+**Harbor task** — a directory following the [Harbor](https://harborframework.com/) format (`instruction.md`, `environment/Dockerfile`, `tests/test.sh`). Works out of the box with 8+ tested benchmark suites (see [benchmarks/README.md](benchmarks/README.md#tested-harbor-datasets) for the full list).
+
+- **combined_score** drives evolution. If omitted, SkyDiscover averages all numeric values in the dict.
+- **artifacts** is optional — entries are injected into the next LLM prompt as context.
+
+For `search.type: adaevolve`, you can also enable explicit Pareto optimization by configuring `search.database.pareto_objectives` and returning those objective metrics directly from the evaluator. In that mode, `combined_score` becomes optional and is only used as a scalar fallback/proxy when configured.
+
+### Starting Solution (optional)
+
+The initial program is **optional**. When omitted, the LLM generates a solution from scratch. If provided, it marks the region to mutate with EVOLVE-BLOCK markers. Everything outside is left untouched.
+
+```python
+# EVOLVE-BLOCK-START
+def solve(input_data):
+ return input_data # baseline — SkyDiscover will improve this
+# EVOLVE-BLOCK-END
+```
+
+If no markers are present, the entire file is treated as mutatable.
+
+
+## 🧬 Pick an Algorithm
+
+See [Benchmark Performance](#-benchmark-performance) for a detailed comparison of AdaEvolve and EvoX against other algorithms.
+
+| Algorithm | Flag | Description |
+|:---|:---|:---|
+| ⭐ **AdaEvolve** | `--search adaevolve` | Multi-island adaptive search with UCB, migration, and paradigm breakthroughs |
+| 🧠 **EvoX** | `--search evox` | Self-evolving paradigm that co-adapts solution generation and experience management |
+| 📊 **Top-K** | `--search topk` | Selects top-K solutions to refine |
+| 🔍 **Beam Search** | `--search beam_search` | Breadth-first expansion of a beam of top solutions |
+| 🎲 **Best-of-N** | `--search best_of_n` | Generates N variants per iteration, keeps the best |
+| 🧪 **GEPA Native** | `--search gepa_native` | Pareto-efficient search with reflective prompting and LLM-mediated merge |
+| 🗺️ **OpenEvolve Native** | `--search openevolve_native` | MAP-Elites + island-based evolutionary search |
+
+### External backends
+
+Install with `uv sync --extra external`, then use the corresponding flag:
+
+| Backend | Flag | Source |
+|:---|:---|:---|
+| **OpenEvolve** | `--search openevolve` | [codelion/openevolve](https://github.com/codelion/openevolve) |
+| **GEPA** | `--search gepa` | [gepa-ai/gepa](https://github.com/gepa-ai/gepa) |
+| **ShinkaEvolve** | `--search shinkaevolve` | [SakanaAI/ShinkaEvolve](https://github.com/SakanaAI/ShinkaEvolve) (manual install) |
+
+
+ShinkaEvolve manual install
+
+```bash
+git clone --depth 1 https://github.com/SakanaAI/ShinkaEvolve.git external_repos/ShinkaEvolve
+uv pip install -e external_repos/ShinkaEvolve
+```
+
+
+
+
+## ⚙️ Configuration
+
+Pass a YAML config with `-c`. See [configs/](configs/) for full annotated templates.
+
+```yaml
+max_iterations: 100
+llm:
+ models: [{ name: "gemini/gemini-3-pro-preview", weight: 1.0 }]
+search:
+ type: "adaevolve" # or "evox", "topk", "beam_search", "best_of_n"
+prompt:
+ system_message: |
+ You are an expert at optimizing algorithms.
+```
+
+API keys (OPENAI_API_KEY, GEMINI_API_KEY, etc.) are resolved from environment variables automatically.
+
+### 📊 Live Monitor & Human Feedback
+
+Add `monitor: { enabled: true }` to your config. The dashboard URL prints at run start — scatter plot of all programs, code diffs, metrics, and AI summaries. A **Human Feedback** panel lets you steer evolution in real time.
+Replay a completed run:
+
+```bash
+uv run skydiscover-viewer /path/to/checkpoints/checkpoint_100
+```
+
+
+## 📖 Reference
+
+
+CLI flags
+
+
+Python API — discover_solution() (convenience wrapper)
+
+
+Model providers
+
+
+
+---
+
+## 🛠️ Extending SkyDiscover
+
+- **New benchmark** → [`benchmarks/README.md`](benchmarks/README.md#adding-a-benchmark)
+- **New search algorithm** → [`skydiscover/search/README.md`](skydiscover/search/README.md)
+- **New context builder** → [`skydiscover/context_builder/README.md`](skydiscover/context_builder/README.md)
+
+---
+
+## 🔗 Related Work
+SkyDiscover is inspired by [AlphaEvolve](https://deepmind.google/discover/blog/alphaevolve-a-gemini-powered-coding-agent-for-designing-advanced-algorithms/) and incorporates useful code components from open-source efforts such as [OpenEvolve](https://github.com/codelion/openevolve). Its interface is compatible with the [optimize_anything](https://gepa-ai.github.io/gepa/blog/2026/02/18/introducing-optimize-anything/) API.
+
+## ✍️ Citation
+
+```bibtex
+@misc{skydiscover2026,
+ title = {SkyDiscover: A Flexible Framework for AI-Driven Scientific and Algorithmic Discovery},
+ author = {Liu, Shu and Cemri, Mert and Agarwal, Shubham and Krentsel, Alexander and Naren, Ashwin and Mang, Qiuyang and Li, Zhifei and Gupta, Akshat and Maheswaran, Monishwaran and Cheng, Audrey and Pan, Melissa and Boneh, Ethan and Ramchandran, Kannan and Sen, Koushik and Dimakis, Alexandros G. and Zaharia, Matei and Stoica, Ion},
+ year = {2026},
+ url = {https://skydiscover-ai.github.io/blog.html}
+}
+```
+
+## 📬 Contact Us
+For questions or feedback, reach out to us:
+[lshu@berkeley.edu](mailto:lshu@berkeley.edu) · [mert_cemri@berkeley.edu](mailto:mert_cemri@berkeley.edu) · [shubham3@berkeley.edu](mailto:shubham3@berkeley.edu)
diff --git a/benchmarks/kernelbench/__init__.py b/benchmarks/kernelbench/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/benchmarks/kernelbench/requirements.txt b/benchmarks/kernelbench/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..86ab641a8d55a615cfc32c7df04b5631f7bf44dd
--- /dev/null
+++ b/benchmarks/kernelbench/requirements.txt
@@ -0,0 +1,4 @@
+# KernelBench library for problem fetching (resolver)
+# Note: The evaluator uses kernelbench[gpu] which includes GPU support
+# For resolver-only usage (fetching problems), the base package is sufficient
+kernelbench @ git+https://github.com/ScalingIntelligence/KernelBench.git
\ No newline at end of file
diff --git a/docs/.gitignore b/docs/.gitignore
new file mode 100644
index 0000000000000000000000000000000000000000..4867674d668a268c871180146a61fbc75aa197f1
--- /dev/null
+++ b/docs/.gitignore
@@ -0,0 +1,3 @@
+node_modules/
+.next/
+.source/
diff --git a/docs/README.md b/docs/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..67dcf23dd8eb8881fac5e395d9c988cf74c02dfd
--- /dev/null
+++ b/docs/README.md
@@ -0,0 +1,13 @@
+# SkyDiscover Documentation
+
+Built with [Next.js](https://nextjs.org/) + [Fumadocs](https://fumadocs.vercel.app/).
+
+## Local Development
+
+```bash
+cd docs
+npm install
+npm run dev
+```
+
+Then open [http://localhost:3000](http://localhost:3000).
\ No newline at end of file
diff --git a/docs/app/api/search/route.ts b/docs/app/api/search/route.ts
new file mode 100644
index 0000000000000000000000000000000000000000..b02d7cd3a76edb01134abbdb2e1de2314287791a
--- /dev/null
+++ b/docs/app/api/search/route.ts
@@ -0,0 +1,6 @@
+import { source } from '@/lib/source';
+import { createFromSource } from 'fumadocs-core/search/server';
+
+export const { GET } = createFromSource(source, {
+ language: 'english',
+});
diff --git a/docs/app/docs/layout.tsx b/docs/app/docs/layout.tsx
new file mode 100644
index 0000000000000000000000000000000000000000..a373143bf42103e20342b968f6fe63bb474d04f2
--- /dev/null
+++ b/docs/app/docs/layout.tsx
@@ -0,0 +1,11 @@
+import { source } from '@/lib/source';
+import { DocsLayout } from 'fumadocs-ui/layouts/docs';
+import { baseOptions } from '@/lib/layout.shared';
+
+export default function Layout({ children }: LayoutProps<'/docs'>) {
+ return (
+
+ {children}
+
+ );
+}
diff --git a/docs/app/global.css b/docs/app/global.css
new file mode 100644
index 0000000000000000000000000000000000000000..50b3bc2960fb64da8057ea2aedd9357063924221
--- /dev/null
+++ b/docs/app/global.css
@@ -0,0 +1,3 @@
+@import 'tailwindcss';
+@import 'fumadocs-ui/css/neutral.css';
+@import 'fumadocs-ui/css/preset.css';
diff --git a/docs/app/page.tsx b/docs/app/page.tsx
new file mode 100644
index 0000000000000000000000000000000000000000..3b1c125aff09f86cd459acff3f1c285195c5cca6
--- /dev/null
+++ b/docs/app/page.tsx
@@ -0,0 +1,22 @@
+import Link from 'next/link';
+
+export default function HomePage() {
+ return (
+
+
+
SkyDiscover Documentation
+
+ Documentation for SkyDiscover.
+
+
+
+
+ );
+}
diff --git a/docs/content/docs/getting-started/index.mdx b/docs/content/docs/getting-started/index.mdx
new file mode 100644
index 0000000000000000000000000000000000000000..3533e3580ca591ed01747c1ebaa4a45045b115b4
--- /dev/null
+++ b/docs/content/docs/getting-started/index.mdx
@@ -0,0 +1,11 @@
+---
+title: "Getting Started"
+description: "Set up SkyDiscover, run your first discovery task, and learn how to configure it."
+---
+
+Get up and running with SkyDiscover in a few minutes. This section covers
+everything you need to go from zero to your first AI-driven discovery:
+
+- **[Installation](/docs/getting-started/installation)** — install SkyDiscover and set up your API keys
+- **[Quick Start](/docs/getting-started/quick-start)** — run your first task and understand the core workflow
+- **[Configuration](/docs/getting-started/configuration)** — models, algorithms, config files, and the Python API
diff --git a/docs/content/docs/getting-started/installation.mdx b/docs/content/docs/getting-started/installation.mdx
new file mode 100644
index 0000000000000000000000000000000000000000..d819586ebcff2bb61cdda4e1a474c19b8d0259e6
--- /dev/null
+++ b/docs/content/docs/getting-started/installation.mdx
@@ -0,0 +1,51 @@
+---
+title: "Installation"
+description: "Install SkyDiscover and configure your LLM API keys."
+---
+
+## Prerequisites
+
+- **Python** >= 3.10
+- **[uv](https://docs.astral.sh/uv/)** — fast Python package manager
+- **[Docker](https://docs.docker.com/get-docker/)** — for containerized evaluators
+- An **LLM API key** (OpenAI, Gemini, Anthropic, or a local model)
+
+## Install SkyDiscover
+
+```bash
+git clone https://github.com/skydiscover-ai/skydiscover.git
+cd skydiscover
+uv sync
+```
+
+## Verify the installation
+
+```bash
+uv run skydiscover-run --help
+```
+
+You should see the help text with available flags.
+
+## Set your API key
+
+SkyDiscover uses [LiteLLM](https://docs.litellm.ai/) under the hood, so any
+provider works. Set the key for the provider you want to use:
+
+```bash
+export ANTHROPIC_API_KEY="..."
+# Or for other providers:
+# export OPENAI_API_KEY="sk-..."
+# export GEMINI_API_KEY="..."
+```
+
+## Optional extras
+
+Running some comparison benchmarks requires additional dependencies. Install them as needed:
+
+```bash
+uv sync --extra adrs # ADRS systems benchmarks
+uv sync --extra external # OpenEvolve / GEPA / ShinkaEvolve backends
+uv sync --extra math # Math benchmarks (SciPy, JAX, etc.)
+```
+
+You can combine extras: `uv sync --extra external --extra math`
diff --git a/docs/content/docs/getting-started/meta.json b/docs/content/docs/getting-started/meta.json
new file mode 100644
index 0000000000000000000000000000000000000000..647129c70ccdf64c20148a8cdea0fa5678472c88
--- /dev/null
+++ b/docs/content/docs/getting-started/meta.json
@@ -0,0 +1,4 @@
+{
+ "title": "Getting Started",
+ "pages": ["index", "installation", "quick-start", "configuration"]
+}
diff --git a/docs/content/docs/meta.json b/docs/content/docs/meta.json
new file mode 100644
index 0000000000000000000000000000000000000000..6d8f4305d8780e8f4278d09573cb5af0af5370da
--- /dev/null
+++ b/docs/content/docs/meta.json
@@ -0,0 +1,7 @@
+{
+ "title": "Documentation",
+ "pages": [
+ "index",
+ "getting-started"
+ ]
+}
diff --git a/docs/lib/source.ts b/docs/lib/source.ts
new file mode 100644
index 0000000000000000000000000000000000000000..7a964388c853fd66851227d161691e50d5fecc86
--- /dev/null
+++ b/docs/lib/source.ts
@@ -0,0 +1,7 @@
+import { docs } from 'fumadocs-mdx:collections/server';
+import { loader } from 'fumadocs-core/source';
+
+export const source = loader({
+ baseUrl: '/docs',
+ source: docs.toFumadocsSource(),
+});
diff --git a/docs/mdx-components.tsx b/docs/mdx-components.tsx
new file mode 100644
index 0000000000000000000000000000000000000000..a771f0f4d717e078a633fc7e29065f02bbc5cedb
--- /dev/null
+++ b/docs/mdx-components.tsx
@@ -0,0 +1,18 @@
+import defaultMdxComponents from 'fumadocs-ui/mdx';
+import { Callout } from 'fumadocs-ui/components/callout';
+import { File, Files, Folder } from 'fumadocs-ui/components/files';
+import { Tab, Tabs } from 'fumadocs-ui/components/tabs';
+import type { MDXComponents } from 'mdx/types';
+
+export function getMDXComponents(components?: MDXComponents): MDXComponents {
+ return {
+ ...defaultMdxComponents,
+ Callout,
+ File,
+ Files,
+ Folder,
+ Tab,
+ Tabs,
+ ...components,
+ };
+}
diff --git a/docs/next-env.d.ts b/docs/next-env.d.ts
new file mode 100644
index 0000000000000000000000000000000000000000..c4b7818fbb2c2c34c24feb1b627ee824507c5600
--- /dev/null
+++ b/docs/next-env.d.ts
@@ -0,0 +1,6 @@
+/// ``."""
+ if not os.path.isdir(checkpoint_dir):
+ return None
+
+ def parse_iteration(path: str) -> Optional[int]:
+ try:
+ return int(path.rsplit("_", 1)[-1])
+ except (ValueError, IndexError):
+ return None
+
+ candidates = []
+ for name in os.listdir(checkpoint_dir):
+ full_path = os.path.join(checkpoint_dir, name)
+ if not os.path.isdir(full_path):
+ continue
+ iteration = parse_iteration(name)
+ if iteration is None:
+ continue
+ candidates.append((iteration, full_path))
+
+ if not candidates:
+ return None
+
+ return max(candidates, key=lambda item: item[0])[1]
+
+
+if __name__ == "__main__":
+ sys.exit(main())
diff --git a/skydiscover/config.py b/skydiscover/config.py
new file mode 100644
index 0000000000000000000000000000000000000000..9f3d1330422b91c322d33953f2bc9a617284c9de
--- /dev/null
+++ b/skydiscover/config.py
@@ -0,0 +1,986 @@
+"""
+Configuration handling for SkyDiscover
+"""
+
+from __future__ import annotations
+
+import logging
+import os
+import re
+from dataclasses import dataclass, field, fields
+from pathlib import Path
+from typing import Any, Callable, Dict, List, Optional, Union
+
+import yaml
+
+logger = logging.getLogger(__name__)
+
+
+# ═══════════════════════════════════════════════════════════════════════
+# Internal — provider resolution helpers
+# ═══════════════════════════════════════════════════════════════════════
+
+_PROVIDERS: Dict[str, tuple] = {
+ "openai": ("https://api.openai.com/v1", ["OPENAI_API_KEY"]),
+ "azure": ("https://api.openai.com/v1", ["AZURE_API_KEY", "OPENAI_API_KEY"]),
+ "gemini": (
+ "https://generativelanguage.googleapis.com/v1beta/openai/",
+ ["GEMINI_API_KEY", "GOOGLE_API_KEY"],
+ ),
+ "anthropic": ("https://api.anthropic.com/v1/", ["ANTHROPIC_API_KEY"]),
+ "deepseek": ("https://api.deepseek.com/v1", ["DEEPSEEK_API_KEY"]),
+ "mistral": ("https://api.mistral.ai/v1", ["MISTRAL_API_KEY"]),
+ "cohere": ("https://api.cohere.com/v1", ["CO_API_KEY", "COHERE_API_KEY"]),
+ "huggingface": (None, ["HF_TOKEN", "HUGGINGFACE_API_KEY"]),
+ "ollama": (None, []),
+ "vllm": (None, []),
+}
+
+# Bare model-name prefixes → provider (backwards compat for --model gpt-5, etc.)
+_BARE_PREFIX_MAP: Dict[str, str] = {
+ "gpt-": "openai",
+ "o1": "openai",
+ "o3": "openai",
+ "o4": "openai",
+ "gemini-": "gemini",
+ "claude-": "anthropic",
+ "deepseek-": "deepseek",
+ "mistral-": "mistral",
+ "command-": "cohere",
+}
+
+
+def _parse_model_spec(model_str: str) -> tuple:
+ """Parse a model string into ``(provider, model_name, default_api_base, env_vars)``.
+
+ Supports:
+ - ``provider/model`` (e.g. ``gemini/gemini-3-pro``)
+ - bare names with known prefix (e.g. ``gemini-3-pro`` → gemini)
+ - unknown bare names default to ``openai``
+ """
+ if "/" in model_str:
+ provider, _, model_name = model_str.partition("/")
+ provider_lower = provider.lower()
+ if provider_lower in _PROVIDERS:
+ api_base, env_vars = _PROVIDERS[provider_lower]
+ return provider_lower, model_name, api_base, env_vars
+
+ for prefix, provider in _BARE_PREFIX_MAP.items():
+ if model_str.startswith(prefix):
+ api_base, env_vars = _PROVIDERS[provider]
+ return provider, model_str, api_base, env_vars
+
+ api_base, env_vars = _PROVIDERS["openai"]
+ return "openai", model_str, api_base, env_vars
+
+
+def _resolve_api_key_from_env(env_vars: Optional[List[str]] = None) -> Optional[str]:
+ """Return the first API key found in *env_vars*, falling back to ``OPENAI_API_KEY``.
+
+ *env_vars* typically comes from ``_parse_model_spec()``.
+ """
+ for var in env_vars or []:
+ key = os.environ.get(var)
+ if key:
+ return key
+ return os.environ.get("OPENAI_API_KEY")
+
+
+def _expand_env_vars(text: str) -> str:
+ """Expand ${VAR} patterns in text with environment variable values."""
+
+ def _replacer(match):
+ return os.environ.get(match.group(1), match.group(0))
+
+ return re.sub(r"\$\{(\w+)\}", _replacer, text)
+
+
+# ═══════════════════════════════════════════════════════════════════════
+# 1. Context Builder — assembles LLM prompts (prompt/)
+# ═══════════════════════════════════════════════════════════════════════
+
+
+@dataclass
+class ContextBuilderConfig:
+ """Configuration for prompt generation"""
+
+ template: str = "default" # "default", "evox"
+ template_dir: Optional[str] = None
+ system_message: str = "system_message"
+ evaluator_system_message: str = "evaluator_system_message"
+
+ suggest_simplification_after_chars: Optional[int] = 500
+
+
+# ═══════════════════════════════════════════════════════════════════════
+# 2. Solution Generator — produces candidates via LLM calls (llm/)
+# ═══════════════════════════════════════════════════════════════════════
+
+
+@dataclass
+class LLMModelConfig:
+ """Configuration for a single LLM model"""
+
+ # API configuration
+ api_base: Optional[str] = None
+ api_key: Optional[str] = None
+ name: Optional[str] = None
+
+ # Custom LLM client
+ init_client: Optional[Callable] = None
+
+ # Weight for model in pool, default to random sampling model based on weight
+ weight: float = 1.0
+
+ # Generation parameters
+ system_message: Optional[str] = None
+ temperature: Optional[float] = None
+ top_p: Optional[float] = None
+ max_tokens: Optional[int] = None
+
+ # Request parameters
+ timeout: Optional[int] = None
+ retries: Optional[int] = None
+ retry_delay: Optional[int] = None
+
+ # Reasoning parameters
+ reasoning_effort: Optional[str] = None
+
+
+@dataclass
+class LLMConfig(LLMModelConfig):
+ """Configuration for LLM models"""
+
+ # API configuration
+ api_base: str = _PROVIDERS["openai"][0]
+
+ # Generation parameters
+ system_message: Optional[str] = "system_message"
+ temperature: Optional[float] = 0.7
+ top_p: Optional[float] = None
+ max_tokens: int = 32000
+
+ # Request parameters
+ timeout: int = 600
+ retries: int = 3
+ retry_delay: int = 5
+
+ # model(s) for solution discovery
+ models: List[LLMModelConfig] = field(default_factory=list)
+
+ # model(s) for evaluator
+ evaluator_models: List[LLMModelConfig] = field(default_factory=lambda: [])
+
+ # model(s) for guide tasks (idea generation, paradigm breakthroughs, etc.)
+ # If not specified, falls back to using the main 'models' list
+ guide_models: List[LLMModelConfig] = field(default_factory=lambda: [])
+
+ # Reasoning parameters (inherited from LLMModelConfig but can be overridden)
+ reasoning_effort: Optional[str] = None
+
+ def __post_init__(self):
+ """Post-initialization to set up model configurations"""
+ # If no evaluator models are defined, use the same models as for solution discovery
+ if not self.evaluator_models:
+ self.evaluator_models = self.models.copy()
+
+ # If no guide models are defined, use the same models as for solution discovery
+ if not self.guide_models:
+ self.guide_models = self.models.copy()
+
+ # Resolve per-model api_base, api_key, and bare name from provider prefix
+ # Check if user explicitly set api_base at the LLMConfig level
+ # (i.e. it differs from the hardcoded default). When a custom api_base
+ # is provided, we should NOT override it with the provider default so
+ # that update_model_params() below can propagate the user's value.
+ user_set_api_base = self.api_base.rstrip("/") != _PROVIDERS["openai"][0].rstrip("/")
+ for model in self.models + self.evaluator_models + self.guide_models:
+ if model.name and model.api_base is None:
+ provider, bare_name, provider_base, env_vars = _parse_model_spec(model.name)
+ # Skip provider URL only for unrecognized bare names that fell
+ # through to the OpenAI default — never for an explicitly-prefixed
+ # provider (e.g. "anthropic/claude-3-sonnet") or a known bare prefix.
+ is_fallback = provider == "openai" and not (
+ model.name.startswith("openai/")
+ or any(model.name.startswith(p) for p in _BARE_PREFIX_MAP)
+ )
+ if provider_base and not (user_set_api_base and is_fallback):
+ model.api_base = provider_base
+ if model.api_key is None:
+ model.api_key = _resolve_api_key_from_env(env_vars)
+ # Strip provider prefix so the API receives the bare model name
+ if "/" in model.name and provider != "openai":
+ model.name = bare_name
+
+ # Update models with shared configuration values
+ shared_config = {
+ "api_base": self.api_base,
+ "api_key": self.api_key,
+ "temperature": self.temperature,
+ "top_p": self.top_p,
+ "max_tokens": self.max_tokens,
+ "timeout": self.timeout,
+ "retries": self.retries,
+ "retry_delay": self.retry_delay,
+ "reasoning_effort": self.reasoning_effort,
+ }
+ self.update_model_params(shared_config)
+
+ def update_model_params(self, args: Dict[str, Any], overwrite: bool = False) -> None:
+ """Update model parameters for all models (including guide_models)."""
+ all_models = self.models + self.evaluator_models + self.guide_models
+ for model in all_models:
+ for key, value in args.items():
+ if overwrite or getattr(model, key, None) is None:
+ setattr(model, key, value)
+
+
+@dataclass
+class AgenticConfig:
+ """Configuration for agentic solution generation.
+
+ When enabled, replaces the single-shot LLM call with a multi-turn
+ tool-calling agent loop that can read files and search the codebase
+ before outputting the discovered solution.
+ """
+
+ enabled: bool = False
+ codebase_root: Optional[str] = None
+
+ # Agent loop limits
+ max_steps: int = 5
+
+ # Timeouts (seconds)
+ per_step_timeout: float = 60.0
+ overall_timeout: float = 300.0
+
+ # Context management
+ max_context_chars: int = 400_000
+ max_file_chars: int = 50_000
+ max_search_results: int = 50
+ max_files_read: int = 20
+
+ # Regex safety
+ regex_timeout: float = 2.0
+ max_regex_length: int = 200
+
+ # Repo map — a depth-limited directory tree injected into the agent's first
+ # message so it knows what files are available to read_file/search.
+ repo_map_max_depth: int = 4
+
+ # File access
+ allowed_extensions: tuple = (
+ ".py",
+ ".txt",
+ ".md",
+ ".json",
+ ".yaml",
+ ".yml",
+ ".toml",
+ ".cfg",
+ ".ini",
+ ".js",
+ ".ts",
+ ".java",
+ ".cpp",
+ ".c",
+ ".h",
+ ".rs",
+ ".go",
+ )
+ excluded_dirs: tuple = (
+ ".git",
+ "__pycache__",
+ "node_modules",
+ ".venv",
+ "venv",
+ ".mypy_cache",
+ ".pytest_cache",
+ "dist",
+ "build",
+ )
+
+
+# ═══════════════════════════════════════════════════════════════════════
+# 3. Evaluator — scores candidates and logs metadata (evaluation/)
+# ═══════════════════════════════════════════════════════════════════════
+
+
+@dataclass
+class EvaluatorConfig:
+ """Configuration for program evaluation"""
+
+ evaluation_file: Optional[str] = None
+ file_suffix: str = ".py"
+ is_image_mode: bool = False
+
+ timeout: int = 360
+ max_retries: int = 3
+
+ # Evaluation strategies
+ cascade_evaluation: bool = True
+ cascade_thresholds: List[float] = field(default_factory=lambda: [0.3, 0.6])
+
+ # When True, the evaluator source code (or instruction.md for Harbor
+ # tasks) is prepended to the LLM system message so the model can see
+ # exactly how solutions are scored. Disabled by default to avoid
+ # leaking implementation details that may introduce noise.
+ inject_evaluator_context: bool = False
+
+ # LLM-as-a-judge: when True, an LLMJudge scores programs alongside the
+ # evaluator and appends llm_* metrics to the result.
+ # This will read from prompt.evaluator_system_message if provided, otherwise use the default system prompt.
+ llm_as_judge: bool = False
+
+
+# ═════════════════════════════════════════════════════════════════════════════════════════════
+# 4. Solution Selector — maintains database and strategy to pick prior programs (search/)
+# ═════════════════════════════════════════════════════════════════════════════════════════════
+
+
+@dataclass
+class DatabaseConfig:
+ """Base configuration shared by all database types."""
+
+ db_path: Optional[str] = None
+ log_prompts: bool = True
+
+
+@dataclass
+class EvolveDatabaseConfig(DatabaseConfig):
+ """Read database from a file."""
+
+ database_file_path: Optional[str] = None
+
+
+@dataclass
+class EvoxDatabaseConfig(EvolveDatabaseConfig):
+ """Evox (co-evolution) database config with built-in defaults."""
+
+ evaluation_file: Optional[str] = None
+ config_path: Optional[str] = None
+ auto_generate_variation_operators: bool = True
+
+ _evox_config_dir = Path(__file__).parent / "search" / "evox" / "config"
+ _evox_database_dir = Path(__file__).parent / "search" / "evox" / "database"
+
+ def __post_init__(self):
+ if self.database_file_path is None:
+ # Initial guide strategy for the solution discovery
+ self.database_file_path = str(self._evox_database_dir / "initial_search_strategy.py")
+ if self.evaluation_file is None:
+ # Dummy evaluator for the guide strategy
+ self.evaluation_file = str(self._evox_database_dir / "search_strategy_evaluator.py")
+ if self.config_path is None:
+ # Default config for the guide strategy
+ self.config_path = str(self._evox_config_dir / "search.yaml")
+
+
+@dataclass
+class BeamSearchDatabaseConfig(DatabaseConfig):
+ """Beam search database config."""
+
+ beam_width: int = 5
+ beam_selection_strategy: str = "diversity_weighted"
+ beam_diversity_weight: float = 0.3
+ beam_temperature: float = 1.0
+ beam_depth_penalty: float = 0.0
+
+
+@dataclass
+class BestOfNDatabaseConfig(DatabaseConfig):
+ """Best-of-N database config."""
+
+ best_of_n: int = 5
+
+
+@dataclass
+class AdaEvolveDatabaseConfig(DatabaseConfig):
+ """AdaEvolve adaptive multi-island database config."""
+
+ population_size: int = 20
+ num_islands: int = 2
+ decay: float = 0.9
+ intensity_min: float = 0.15
+ intensity_max: float = 0.5
+ use_adaptive_search: bool = True
+ use_ucb_selection: bool = True
+ use_migration: bool = True
+ use_unified_archive: bool = True
+ fixed_intensity: float = 0.4
+ migration_interval: int = 15
+ migration_count: int = 5
+ local_context_program_ratio: float = 0.6
+ archive_elite_ratio: float = 0.2
+ pareto_weight: float = 0.4
+ fitness_weight: float = 1.0
+ novelty_weight: float = 0.0
+ k_neighbors: int = 5
+ diversity_strategy: str = "code"
+ use_dynamic_islands: bool = True
+ max_islands: int = 5
+ spawn_productivity_threshold: float = 0.015
+ spawn_cooldown_iterations: int = 30
+ use_paradigm_breakthrough: bool = True
+ paradigm_window_size: int = 10
+ paradigm_improvement_threshold: float = 0.12
+ paradigm_max_uses: int = 2
+ paradigm_num_to_generate: int = 3
+ paradigm_max_tried: int = 10
+
+ # Stagnation handling
+ stagnation_threshold: int = 10
+ stagnation_multi_child_count: int = 3
+
+ # Sibling context
+ sibling_context_limit: int = 5
+
+ # Error retry
+ enable_error_retry: bool = True
+ max_error_retries: int = 2
+
+ # Archive
+ archive_size: int = 100
+
+ # Metric direction
+ higher_is_better: Dict[str, bool] = field(default_factory=dict)
+ fitness_key: Optional[str] = None
+ pareto_objectives: List[str] = field(default_factory=list)
+ pareto_objectives_weight: float = 0.0
+
+
+@dataclass
+class OpenEvolveNativeDatabaseConfig(DatabaseConfig):
+ """OpenEvolve Native: MAP-Elites + island-based search config."""
+
+ num_islands: int = 5
+ population_size: int = 40
+ archive_size: int = 100
+ exploration_ratio: float = 0.2
+ exploitation_ratio: float = 0.7
+ elite_selection_ratio: float = 0.1
+ feature_dimensions: List[str] = field(default_factory=lambda: ["complexity", "diversity"])
+ feature_bins: int = 10
+ diversity_reference_size: int = 20
+ migration_interval: int = 10
+ migration_rate: float = 0.1
+ random_seed: Optional[int] = 42
+
+
+@dataclass
+class ClaudeCodeConfig(DatabaseConfig):
+ """Configuration for the Claude Code baseline.
+
+ Claude Code runs autonomously inside a Docker container, iterating on
+ the solution using the evaluator directly. max_turns maps to the
+ --max-turns flag passed to the claude CLI.
+ """
+
+ max_turns: int = 50
+ docker_image: str = "skydiscover-claude-code:latest"
+
+
+@dataclass
+class GEPANativeDatabaseConfig(DatabaseConfig):
+ """Configuration for GEPA Native search database.
+
+ GEPA (Guided Evolution for Program Adaptation) uses an elite pool with
+ epsilon-greedy selection, acceptance gating, and LLM-mediated merge.
+ """
+
+ population_size: int = 40
+ candidate_selection_strategy: str = "epsilon_greedy" # "epsilon_greedy", "best", "pareto"
+ epsilon: float = 0.1
+ max_rejection_history: int = 20
+
+ # Controller-read settings (stored here for single config source)
+ acceptance_gating: bool = True
+ use_merge: bool = True
+ merge_after_stagnation: int = 15
+ max_merge_attempts: int = 10
+ max_recent_failures: int = 5
+ random_seed: Optional[int] = 42
+
+
+_DB_CONFIG_BY_TYPE: Dict[str, type] = {
+ "evox": EvoxDatabaseConfig,
+ "beam_search": BeamSearchDatabaseConfig,
+ "best_of_n": BestOfNDatabaseConfig,
+ "topk": DatabaseConfig,
+ "adaevolve": AdaEvolveDatabaseConfig,
+ "openevolve_native": OpenEvolveNativeDatabaseConfig,
+ "gepa_native": GEPANativeDatabaseConfig,
+ "claude_code": ClaudeCodeConfig,
+}
+
+
+@dataclass
+class SearchConfig:
+ """General Configuration for All Search Algorithms"""
+
+ type: str = "topk"
+ database: DatabaseConfig = field(default_factory=DatabaseConfig)
+ num_context_programs: int = 4
+ output_dir: Optional[str] = None
+ switch_interval: Optional[int] = (
+ None # EvoX: stagnation iters before strategy switch. Auto-calculated if None.
+ )
+ share_llm: bool = (
+ False # EvoX: if True, meta-level search evolution uses the same LLM as the main discovery process.
+ )
+
+
+# ═══════════════════════════════════════════════════════════════════════
+# Extras — live monitor dashboard
+# ═══════════════════════════════════════════════════════════════════════
+
+
+@dataclass
+class MonitorConfig:
+ """Configuration for the live run monitor dashboard"""
+
+ enabled: bool = False
+ port: int = 8765
+ host: str = "127.0.0.1"
+ max_solution_length: int = 10000
+
+ # AI summary settings
+ summary_model: str = "gpt-5-mini"
+ summary_api_key: Optional[str] = None # Falls back to OPENAI_API_KEY
+ summary_api_base: str = _PROVIDERS["openai"][0]
+ summary_top_k: int = 3
+ summary_interval: int = 0 # Auto-generate every N programs (0 = manual)
+
+
+# ═══════════════════════════════════════════════════════════════════════
+# Benchmark Loader
+# ═══════════════════════════════════════════════════════════════════════
+
+
+@dataclass
+class BenchmarkConfig:
+ """Configuration for loading problems from external benchmark datasets.
+
+ When enabled, allows SkyDiscover to fetch problems from external
+ benchmark datasets (e.g., KernelBench, Frontier-CS) without requiring
+ explicit initial_program paths.
+
+ Benchmark specification and evaluation parameters (e.g., target problem)
+ are stored in a `params` dictionary.
+ """
+
+ enabled: bool = False
+ name: Optional[str] = None
+ resolver: Optional[str] = (
+ None # Python import path to resolver module (e.g., 'benchmarks.kernelbench.resolver')
+ )
+ params: Dict[str, Any] = field(default_factory=dict) # Benchmark-specific parameters
+
+
+# ═══════════════════════════════════════════════════════════════════════
+# Master Configuration
+# ═══════════════════════════════════════════════════════════════════════
+
+
+@dataclass
+class Config:
+ """Master configuration for SkyDiscover"""
+
+ # General settings
+ max_iterations: int = 100
+ checkpoint_interval: int = 10
+ log_level: str = "INFO"
+ log_dir: Optional[str] = None
+ language: Optional[str] = None
+ file_suffix: str = ".py"
+
+ # Component configurations
+ llm: LLMConfig = field(default_factory=LLMConfig)
+ context_builder: ContextBuilderConfig = field(default_factory=ContextBuilderConfig)
+ search: SearchConfig = field(default_factory=SearchConfig)
+ evaluator: EvaluatorConfig = field(default_factory=EvaluatorConfig)
+ agentic: AgenticConfig = field(default_factory=AgenticConfig)
+ benchmark: BenchmarkConfig = field(default_factory=BenchmarkConfig)
+
+ # Live monitor dashboard
+ monitor: MonitorConfig = field(default_factory=MonitorConfig)
+
+ # Human feedback settings
+ human_feedback_enabled: bool = False
+ human_feedback_file: Optional[str] = None
+ human_feedback_mode: str = "append" # "append" or "replace"
+
+ # Generation settings
+ diff_based_generation: bool = True
+ max_solution_length: int = 60000
+
+ # Parallelism — how many iterations run concurrently.
+ # 1 = sequential (default, current behaviour).
+ # >1 = N iterations overlap via asyncio tasks: while one evaluates,
+ # others can sample/generate, giving near-linear speedup.
+ max_parallel_iterations: int = 1
+
+ # Runtime-only: system prompt override (set by apply_overrides, read by external backends)
+ system_prompt_override: Optional[str] = None
+
+ @classmethod
+ def from_yaml(cls, path: Union[str, Path]) -> Config:
+ """Load configuration from a YAML file"""
+ config_path = Path(path)
+ config_dir = config_path.parent
+
+ with open(path, "r") as f:
+ raw = f.read()
+ config_dict = yaml.safe_load(_expand_env_vars(raw))
+
+ # Handle file references for system_message
+ if "prompt" in config_dict and "system_message" in config_dict["prompt"]:
+ system_message = config_dict["prompt"]["system_message"]
+ if (
+ isinstance(system_message, str)
+ and "\n" not in system_message.strip()
+ and len(system_message.strip()) < 256
+ ):
+ file_path = config_dir / system_message
+ try:
+ if file_path.exists() and file_path.is_file():
+ with open(file_path, "r") as f:
+ config_dict["prompt"]["system_message"] = f.read()
+ except OSError:
+ logger.debug("Could not read system_message from %s", file_path, exc_info=True)
+
+ return cls.from_dict(config_dict)
+
+ def to_yaml(self, path: Union[str, Path]) -> None:
+ """Save configuration to a YAML file"""
+ with open(path, "w") as f:
+ yaml.dump(self.to_dict(), f, default_flow_style=False)
+
+ @classmethod
+ def from_dict(cls, config_dict: Dict[str, Any]) -> Config:
+ """Create configuration from a dictionary"""
+ # Handle nested configurations
+ config = Config()
+
+ # Update top-level fields
+ for key, value in config_dict.items():
+ if key not in [
+ "llm",
+ "prompt",
+ "database",
+ "search",
+ "evaluator",
+ "agentic",
+ "benchmark",
+ "monitor",
+ ] and hasattr(config, key):
+ setattr(config, key, value)
+
+ # Update nested configs
+ if "llm" in config_dict:
+ llm_dict = config_dict["llm"]
+ if "models" in llm_dict:
+ llm_dict["models"] = [LLMModelConfig(**m) for m in llm_dict["models"]]
+ if "evaluator_models" in llm_dict:
+ llm_dict["evaluator_models"] = [
+ LLMModelConfig(**m) for m in llm_dict["evaluator_models"]
+ ]
+ if "guide_models" in llm_dict:
+ llm_dict["guide_models"] = [LLMModelConfig(**m) for m in llm_dict["guide_models"]]
+ config.llm = LLMConfig(**llm_dict)
+ if "prompt" in config_dict:
+ config.context_builder = ContextBuilderConfig(**config_dict["prompt"])
+
+ if "search" in config_dict:
+ search_dict = config_dict["search"]
+ search_type = search_dict.get("type", "topk")
+ db_config_cls = _DB_CONFIG_BY_TYPE.get(search_type, DatabaseConfig)
+ if "database" in search_dict:
+ db_dict = search_dict["database"]
+ # Separate known fields from algorithm-specific extras
+ # (e.g., adaevolve's decay, intensity_min, use_adaptive_search, etc.)
+ known_fields = {f.name for f in fields(db_config_cls)}
+ db_known = {k: v for k, v in db_dict.items() if k in known_fields}
+ db_extras = {k: v for k, v in db_dict.items() if k not in known_fields}
+ db_config = db_config_cls(**db_known)
+ for k, v in db_extras.items():
+ setattr(db_config, k, v)
+ search_dict["database"] = db_config
+ else:
+ search_dict["database"] = db_config_cls()
+ config.search = SearchConfig(**search_dict)
+
+ if "evaluator" in config_dict:
+ config.evaluator = EvaluatorConfig(**config_dict["evaluator"])
+ if "agentic" in config_dict:
+ agentic_dict = dict(config_dict["agentic"]) # copy to avoid mutating input
+ # Convert list fields to tuples for the dataclass
+ for tuple_field in ("allowed_extensions", "excluded_dirs"):
+ if tuple_field in agentic_dict and isinstance(agentic_dict[tuple_field], list):
+ agentic_dict[tuple_field] = tuple(agentic_dict[tuple_field])
+ config.agentic = AgenticConfig(**agentic_dict)
+ if "benchmark" in config_dict:
+ benchmark_dict = config_dict["benchmark"]
+ # Separate known dataclass fields from benchmark-specific parameters
+ known_fields = {f.name for f in fields(BenchmarkConfig) if f.name != "params"}
+ benchmark_known = {k: v for k, v in benchmark_dict.items() if k in known_fields}
+ benchmark_params = {k: v for k, v in benchmark_dict.items() if k not in known_fields}
+ config.benchmark = BenchmarkConfig(**benchmark_known, params=benchmark_params)
+ if "monitor" in config_dict:
+ config.monitor = MonitorConfig(**config_dict["monitor"])
+
+ return config
+
+ def to_dict(self) -> Dict[str, Any]:
+ """Convert configuration to a dictionary"""
+ return {
+ # General settings
+ "max_iterations": self.max_iterations,
+ "checkpoint_interval": self.checkpoint_interval,
+ "log_level": self.log_level,
+ "log_dir": self.log_dir,
+ # Component configurations
+ "llm": {
+ "models": self.llm.models,
+ "evaluator_models": self.llm.evaluator_models,
+ "api_base": self.llm.api_base,
+ "temperature": self.llm.temperature,
+ "top_p": self.llm.top_p,
+ "max_tokens": self.llm.max_tokens,
+ "timeout": self.llm.timeout,
+ "retries": self.llm.retries,
+ "retry_delay": self.llm.retry_delay,
+ },
+ "prompt": {
+ "template": self.context_builder.template,
+ "template_dir": self.context_builder.template_dir,
+ "system_message": self.context_builder.system_message,
+ "evaluator_system_message": self.context_builder.evaluator_system_message,
+ },
+ "search": {
+ "type": self.search.type,
+ "num_context_programs": self.search.num_context_programs,
+ "database": {
+ f.name: getattr(self.search.database, f.name)
+ for f in fields(self.search.database)
+ },
+ },
+ "evaluator": {
+ "evaluation_file": self.evaluator.evaluation_file,
+ "file_suffix": self.evaluator.file_suffix,
+ "is_image_mode": self.evaluator.is_image_mode,
+ "timeout": self.evaluator.timeout,
+ "max_retries": self.evaluator.max_retries,
+ "cascade_evaluation": self.evaluator.cascade_evaluation,
+ "cascade_thresholds": self.evaluator.cascade_thresholds,
+ "inject_evaluator_context": self.evaluator.inject_evaluator_context,
+ "llm_as_judge": self.evaluator.llm_as_judge,
+ },
+ # Agentic generation
+ "agentic": {
+ "enabled": self.agentic.enabled,
+ "codebase_root": self.agentic.codebase_root,
+ "max_steps": self.agentic.max_steps,
+ "per_step_timeout": self.agentic.per_step_timeout,
+ "overall_timeout": self.agentic.overall_timeout,
+ "max_context_chars": self.agentic.max_context_chars,
+ "max_file_chars": self.agentic.max_file_chars,
+ "max_search_results": self.agentic.max_search_results,
+ "max_files_read": self.agentic.max_files_read,
+ "regex_timeout": self.agentic.regex_timeout,
+ "max_regex_length": self.agentic.max_regex_length,
+ "repo_map_max_depth": self.agentic.repo_map_max_depth,
+ "allowed_extensions": list(self.agentic.allowed_extensions),
+ "excluded_dirs": list(self.agentic.excluded_dirs),
+ },
+ # Live monitor
+ "monitor": {
+ "enabled": self.monitor.enabled,
+ "port": self.monitor.port,
+ "host": self.monitor.host,
+ "max_solution_length": self.monitor.max_solution_length,
+ "summary_model": self.monitor.summary_model,
+ "summary_top_k": self.monitor.summary_top_k,
+ "summary_interval": self.monitor.summary_interval,
+ },
+ # Human-in-the-loop
+ "human_feedback_enabled": self.human_feedback_enabled,
+ "human_feedback_file": self.human_feedback_file,
+ # Generation settings
+ "diff_based_generation": self.diff_based_generation,
+ "max_solution_length": self.max_solution_length,
+ # Parallelism
+ "max_parallel_iterations": self.max_parallel_iterations,
+ }
+
+
+def load_config(config_path: Optional[Union[str, Path]] = None) -> Config:
+ """Load configuration from a YAML file or use defaults"""
+ if config_path:
+ if not os.path.exists(config_path):
+ raise FileNotFoundError(f"Config file not found: {config_path}")
+ config = Config.from_yaml(config_path)
+ else:
+ config = Config()
+
+ # Update api_base from environment if provided — use overwrite=True
+ # because __post_init__ already pushed the hardcoded default to all models.
+ api_base = os.environ.get("OPENAI_API_BASE") or os.environ.get("OPENAI_BASE_URL")
+ if api_base:
+ config.llm.api_base = api_base
+ config.llm.update_model_params({"api_base": api_base}, overwrite=True)
+
+ # Determine which API key to use (provider-aware)
+ if not config.llm.api_key:
+ env_vars = None
+ if config.llm.models:
+ first_model_name = config.llm.models[0].name
+ if first_model_name:
+ _, _, _, env_vars = _parse_model_spec(first_model_name)
+ api_key = _resolve_api_key_from_env(env_vars)
+ if api_key:
+ config.llm.api_key = api_key
+ config.llm.update_model_params({"api_key": api_key})
+
+ # Make the system message available to the individual models, in case it is not provided from the prompt sampler
+ config.llm.update_model_params({"system_message": config.context_builder.system_message})
+
+ # Bridge provider env vars so that downstream configs (e.g. evox search.yaml)
+ # can resolve ${OPENAI_API_KEY} from the environment.
+ bridge_provider_env(config)
+
+ return config
+
+
+def bridge_provider_env(config: Config) -> None:
+ """
+ Set provider-specific env vars from resolved config.
+
+ External backends read credentials from environment variables directly.
+ """
+ if not config.llm.models:
+ return
+ model = config.llm.models[0]
+ if not model.api_key:
+ return
+
+ # Use _parse_model_spec to get the right env vars for this model
+ _, _, _, env_vars = _parse_model_spec(model.name or "")
+ for var in env_vars:
+ os.environ.setdefault(var, model.api_key)
+
+ # Always ensure OPENAI_API_KEY is set — many tools (ShinkaEvolve, etc.) expect it
+ os.environ.setdefault("OPENAI_API_KEY", model.api_key)
+
+ # Set OPENAI_API_BASE so backends that check it can find the endpoint
+ if model.api_base:
+ os.environ.setdefault("OPENAI_API_BASE", model.api_base)
+
+
+def build_output_dir(search_type: str, initial_program_path: str, base_dir: str = "outputs") -> str:
+ """Build a standardized output directory: outputs//_/"""
+ from datetime import datetime
+
+ problem_name = (
+ os.path.basename(os.path.dirname(os.path.abspath(initial_program_path))) or "unknown"
+ )
+ timestamp = datetime.now().strftime("%m%d_%H%M")
+ return os.path.join(base_dir, search_type, f"{problem_name}_{timestamp}")
+
+
+# ═══════════════════════════════════════════════════════════════════════
+# Runtime overrides — shared by the public API and CLI
+# ═══════════════════════════════════════════════════════════════════════
+
+
+def apply_overrides(
+ config: Config,
+ *,
+ model: Optional[str] = None,
+ api_base: Optional[str] = None,
+ agentic: bool = False,
+ search: Optional[str] = None,
+ system_prompt: Optional[str] = None,
+) -> None:
+ """Apply runtime overrides (model, api_base, etc.) to a loaded Config in place."""
+ if model:
+ # Parse the model string into a list of model specifications
+ specs = [s.strip() for s in model.split(",")]
+ models: List[LLMModelConfig] = []
+ for spec in specs:
+ provider, model_name, default_api_base, env_vars = _parse_model_spec(spec)
+ effective_base = api_base or default_api_base
+ if effective_base is None:
+ raise ValueError(
+ f"Provider '{provider}' requires an explicit api_base.\n"
+ f"Example: model='{spec}', api_base='http://localhost:8000/v1'"
+ )
+ resolved_key = _resolve_api_key_from_env(env_vars)
+ models.append(
+ LLMModelConfig(
+ name=model_name,
+ api_base=effective_base,
+ api_key=resolved_key,
+ )
+ )
+
+ config.llm.api_base = models[0].api_base
+ if models[0].api_key:
+ config.llm.api_key = models[0].api_key
+ config.llm.models = models
+ config.llm.evaluator_models = [
+ LLMModelConfig(name=m.name, api_base=m.api_base, api_key=m.api_key) for m in models
+ ]
+ config.llm.guide_models = [
+ LLMModelConfig(name=m.name, api_base=m.api_base, api_key=m.api_key) for m in models
+ ]
+ elif api_base:
+ config.llm.api_base = api_base
+ config.llm.update_model_params({"api_base": api_base}, overwrite=True)
+
+ # API key (api_base-only; multi-model already resolved above)
+ if not model and api_base:
+ parsed_env_vars: Optional[List[str]] = None
+ for _prefix, (base_url, env_list) in _PROVIDERS.items():
+ if base_url and config.llm.api_base.startswith(base_url.rstrip("/")):
+ parsed_env_vars = env_list
+ break
+ resolved_key = _resolve_api_key_from_env(parsed_env_vars)
+ if resolved_key:
+ config.llm.api_key = resolved_key
+ config.llm.update_model_params({"api_key": resolved_key}, overwrite=True)
+
+ # Propagate shared generation/request settings
+ if model or api_base:
+ config.llm.update_model_params(
+ {
+ "temperature": config.llm.temperature,
+ "top_p": config.llm.top_p,
+ "max_tokens": config.llm.max_tokens,
+ "timeout": config.llm.timeout,
+ "retries": config.llm.retries,
+ "retry_delay": config.llm.retry_delay,
+ "reasoning_effort": config.llm.reasoning_effort,
+ },
+ overwrite=True,
+ )
+ # Fill api_base/api_key only where a model doesn't already have them
+ config.llm.update_model_params(
+ {"api_base": config.llm.api_base, "api_key": config.llm.api_key},
+ overwrite=False,
+ )
+
+ if agentic:
+ config.agentic.enabled = True
+
+ if search:
+ if not hasattr(config, "search"):
+ config.search = SearchConfig()
+ config.search.type = search
+ new_db_cls = _DB_CONFIG_BY_TYPE.get(search)
+ if new_db_cls and not isinstance(config.search.database, new_db_cls):
+ config.search.database = new_db_cls()
+
+ if system_prompt:
+ config.context_builder.system_message = system_prompt
+ config.system_prompt_override = system_prompt
diff --git a/skydiscover/context_builder/adaevolve/__init__.py b/skydiscover/context_builder/adaevolve/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e8e07cd2532422e80175885ed1de016375c78cc5
--- /dev/null
+++ b/skydiscover/context_builder/adaevolve/__init__.py
@@ -0,0 +1,7 @@
+"""
+AdaEvolve context builder: adaptive search with paradigm breakthrough guidance.
+"""
+
+from .builder import AdaEvolveContextBuilder
+
+__all__ = ["AdaEvolveContextBuilder"]
diff --git a/skydiscover/context_builder/adaevolve/builder.py b/skydiscover/context_builder/adaevolve/builder.py
new file mode 100644
index 0000000000000000000000000000000000000000..3ae1d42c5e20162c096acbb346e0d24351f231b1
--- /dev/null
+++ b/skydiscover/context_builder/adaevolve/builder.py
@@ -0,0 +1,493 @@
+"""
+AdaEvolve context builder for SkyDiscover.
+
+Extends DefaultContextBuilder with AdaEvolve-specific prompt sections:
+- Evaluator feedback from parent artifacts
+- Paradigm breakthrough guidance
+- Sibling context (previous mutations of the same parent)
+- Error retry context
+
+These are assembled into a ``search_guidance`` string and injected into
+AdaEvolve-specific templates via the ``{search_guidance}`` placeholder.
+"""
+
+import logging
+import re
+from pathlib import Path
+from typing import Any, Dict, List, Optional, Union
+
+from skydiscover.config import Config
+from skydiscover.context_builder.default import DefaultContextBuilder
+from skydiscover.context_builder.utils import TemplateManager, prog_attr
+from skydiscover.search.base_database import Program
+from skydiscover.utils.metrics import compute_proxy_score, get_score
+
+logger = logging.getLogger(__name__)
+
+
+class AdaEvolveContextBuilder(DefaultContextBuilder):
+ """
+ Context builder for AdaEvolve's adaptive evolutionary search.
+
+ Adds a ``{search_guidance}`` section to the prompt containing:
+ - Evaluator diagnostic feedback (from parent's artifacts)
+ - Paradigm breakthrough guidance (when search is globally stagnating)
+ - Sibling context (previous mutations of the same parent)
+ - Error retry context (when retrying after a failed generation)
+
+ The controller passes raw data via the ``context`` dict:
+ - ``context["paradigm"]``: paradigm dict or None
+ - ``context["siblings"]``: list of Program objects
+ - ``context["error_context"]``: error string or None
+
+ Evaluator feedback is extracted from the parent program's artifacts.
+ """
+
+ def __init__(self, config: Config):
+ super().__init__(config)
+ default_templates = str(Path(__file__).parent.parent / "default" / "templates")
+ adaevolve_templates = str(Path(__file__).parent / "templates")
+ self.template_manager = TemplateManager(
+ default_templates, adaevolve_templates, self.context_config.template_dir
+ )
+
+ def _db_config(self) -> Any:
+ return getattr(self.config.search, "database", None)
+
+ def _is_multiobjective_enabled(self) -> bool:
+ return bool(getattr(self._db_config(), "pareto_objectives", None) or [])
+
+ def _objective_descriptions(self) -> List[str]:
+ db_config = self._db_config()
+ higher_is_better = getattr(db_config, "higher_is_better", None) or {}
+ descriptions = []
+ for objective in getattr(db_config, "pareto_objectives", None) or []:
+ direction = "maximize" if higher_is_better.get(objective, True) else "minimize"
+ descriptions.append(f"{objective} ({direction})")
+ return descriptions
+
+ def _metric_to_maximization_value(self, metric_name: str, value: Any) -> Optional[float]:
+ from skydiscover.utils.metrics import normalize_metric_value
+
+ higher_is_better = getattr(self._db_config(), "higher_is_better", None) or {}
+ return normalize_metric_value(metric_name, value, higher_is_better)
+
+ _PROGRESS_SCORE_MISSING = float("-inf")
+
+ def _get_progress_score(self, metrics: Dict[str, Any]) -> float:
+ """Scalar proxy used only for prompt-side progress descriptions.
+
+ Returns ``_PROGRESS_SCORE_MISSING`` (``-inf``) for empty/missing metrics
+ so that callers can distinguish "no data" from "score is zero".
+ """
+ db_config = self._db_config()
+ pareto_objectives = getattr(db_config, "pareto_objectives", None) or None
+ return compute_proxy_score(
+ metrics,
+ fitness_key=getattr(db_config, "fitness_key", None),
+ pareto_objectives=pareto_objectives,
+ higher_is_better=getattr(db_config, "higher_is_better", None) or {},
+ )
+
+ def _task_objective_text(self) -> str:
+ subject = (
+ "prompt" if (self.config.language or "").lower() in ("text", "prompt") else "program"
+ )
+ if not self._is_multiobjective_enabled():
+ return f"Suggest improvements to the {subject} that will improve its COMBINED_SCORE."
+ return (
+ f"Suggest improvements to the {subject} that improve its Pareto trade-offs across: "
+ + ", ".join(self._objective_descriptions())
+ + "."
+ )
+
+ def _diversity_dimensions_text(self) -> str:
+ if not self._is_multiobjective_enabled():
+ return "The system maintains diversity across these dimensions: score, complexity."
+ return "The system maintains diversity across Pareto trade-offs, novelty, and solution structure."
+
+ def _diversity_note_text(self) -> str:
+ if not self._is_multiobjective_enabled():
+ return "Different solutions with similar combined_score but different features are valuable."
+ return "Different solutions with similar overall trade-offs but different objective balances are valuable."
+
+ def build_prompt(
+ self,
+ current_program: Union[Program, Dict[str, Program]],
+ context: Dict[str, Any] = None,
+ **kwargs: Any,
+ ) -> Dict[str, str]:
+ """
+ Build prompt with AdaEvolve-specific search guidance.
+
+ Computes the ``search_guidance`` string from AdaEvolve context keys,
+ then delegates to the parent's ``build_prompt`` which fills the
+ ``{search_guidance}`` placeholder in AdaEvolve templates.
+ """
+ context = context or {}
+
+ # Build the search guidance from AdaEvolve-specific context
+ search_guidance = self._build_search_guidance(current_program, context)
+
+ # Override any caller-supplied search_guidance with our computed one
+ kwargs.pop("search_guidance", None)
+
+ # Pass search_guidance through **kwargs to template.format()
+ result = super().build_prompt(
+ current_program,
+ context,
+ search_guidance=search_guidance,
+ task_objective=self._task_objective_text(),
+ diversity_dimensions=self._diversity_dimensions_text(),
+ diversity_note=self._diversity_note_text(),
+ **kwargs,
+ )
+
+ return result
+
+ # =========================================================================
+ # Suppress default artifact feedback rendering
+ # =========================================================================
+
+ def _format_current_program(
+ self,
+ current_program: Union[Program, Dict[str, Program]],
+ language: str,
+ ) -> str:
+ """Override to suppress artifacts["feedback"] from {current_program}.
+
+ AdaEvolve renders evaluator feedback explicitly via _build_search_guidance
+ into {search_guidance}, so we strip it here to avoid duplication.
+ """
+ # Remove feedback from artifacts so parent renderer skips it (rendered via search_guidance instead)
+ if isinstance(current_program, dict):
+ program = list(current_program.values())[0]
+ else:
+ program = current_program
+
+ artifacts = getattr(program, "artifacts", None)
+ saved_feedback = None
+ if isinstance(artifacts, dict) and "feedback" in artifacts:
+ saved_feedback = artifacts.pop("feedback")
+
+ try:
+ return super()._format_current_program(current_program, language)
+ finally:
+ if saved_feedback is not None and isinstance(artifacts, dict):
+ artifacts["feedback"] = saved_feedback
+
+ # =========================================================================
+ # Search Guidance Assembly
+ # =========================================================================
+
+ def _build_search_guidance(
+ self,
+ current_program: Union[Program, Dict[str, Program]],
+ context: Dict[str, Any],
+ ) -> str:
+ """
+ Assemble all AdaEvolve-specific guidance sections into one string.
+
+ Sections are included in priority order:
+ 1. Evaluator feedback (highest value — shows why parent fails)
+ 2. Paradigm breakthrough guidance (when globally stagnating)
+ 3. Sibling context (previous mutations of this parent)
+ 4. Error retry context (when retrying after failure)
+ """
+ # Extract parent program from current_program dict
+ if isinstance(current_program, dict):
+ parent_program = list(current_program.values())[0]
+ else:
+ parent_program = current_program
+
+ language = self.config.language or "python"
+ paradigm = context.get("paradigm")
+ siblings = context.get("siblings", [])
+ error_context = context.get("error_context")
+
+ sections: List[str] = []
+
+ # 1. Evaluator feedback from parent artifacts
+ feedback_section = self._format_evaluator_feedback(parent_program)
+ if feedback_section:
+ sections.append(feedback_section)
+
+ # 2. Paradigm breakthrough guidance
+ if paradigm:
+ sections.append(self._format_paradigm_guidance(paradigm, language))
+
+ # 3. Sibling context
+ if siblings:
+ sibling_section = self._format_sibling_context(siblings, parent_program)
+ if sibling_section:
+ sections.append(sibling_section)
+
+ # 4. Error retry context
+ if error_context:
+ sections.append(self._format_error_context(error_context))
+
+ if not sections:
+ return ""
+
+ return "\n\n".join(sections)
+
+ def _identify_improvement_areas(
+ self,
+ current_program: str,
+ metrics: Dict[str, float],
+ previous_programs: List[Program],
+ ) -> str:
+ """Generate improvement bullets for scalar or Pareto mode."""
+ if not self._is_multiobjective_enabled():
+ return super()._identify_improvement_areas(current_program, metrics, previous_programs)
+
+ improvement_areas = [
+ "Focus on Pareto trade-offs across: " + ", ".join(self._objective_descriptions())
+ ]
+
+ current_score = self._get_progress_score(metrics)
+ if previous_programs:
+ prev_metrics = prog_attr(previous_programs[-1], "metrics", {}) or {}
+ prev_score = self._get_progress_score(prev_metrics)
+ # Only show delta text when both scores are valid (not missing).
+ missing = self._PROGRESS_SCORE_MISSING
+ if current_score != missing and prev_score != missing:
+ if current_score > prev_score + 1e-6:
+ improvement_areas.append(
+ f"Pareto proxy improved: {prev_score:.4f} -> {current_score:.4f}"
+ )
+ elif current_score < prev_score - 1e-6:
+ improvement_areas.append(
+ f"Pareto proxy declined: {prev_score:.4f} -> {current_score:.4f}. Revisit recent trade-offs."
+ )
+ else:
+ improvement_areas.append(f"Pareto proxy unchanged at {current_score:.4f}")
+ elif current_score != missing:
+ improvement_areas.append(f"Pareto proxy at {current_score:.4f} (first measurement)")
+
+ threshold = self.context_config.suggest_simplification_after_chars
+ if threshold and len(current_program) > threshold:
+ improvement_areas.append(
+ f"Consider simplifying - solution length exceeds {threshold} characters"
+ )
+
+ return "\n".join(f"- {area}" for area in improvement_areas)
+
+ # =========================================================================
+ # Section Formatters
+ # =========================================================================
+
+ @staticmethod
+ def _format_evaluator_feedback(parent_program: Program) -> Optional[str]:
+ """
+ Format evaluator feedback from parent's artifacts.
+
+ The evaluator may return diagnostic feedback (e.g. analysis of failed
+ examples) in artifacts["feedback"]. This is injected into the prompt
+ so the LLM can make targeted improvements instead of guessing.
+ """
+ artifacts = getattr(parent_program, "artifacts", None)
+ if not artifacts:
+ return None
+
+ feedback = artifacts.get("feedback")
+ if not feedback or not isinstance(feedback, str):
+ return None
+
+ # Truncate very long feedback to keep prompt focused
+ max_len = 2000
+ if len(feedback) > max_len:
+ feedback = feedback[:max_len] + "\n... (truncated)"
+
+ return (
+ "## EVALUATOR FEEDBACK ON CURRENT PROGRAM\n"
+ "The evaluator analyzed cases where the current program failed "
+ "and produced the following diagnostic feedback. "
+ "Use this to make targeted improvements:\n\n"
+ f"{feedback}"
+ )
+
+ @staticmethod
+ def _format_paradigm_guidance(paradigm: Dict[str, Any], language: str) -> str:
+ """
+ Format paradigm breakthrough guidance for the LLM.
+
+ Uses different framing for prompt optimization vs code optimization.
+ """
+ is_prompt_opt = (language or "").lower() in ("text", "prompt")
+
+ idea = paradigm.get("idea", "N/A")
+ description = paradigm.get("description", "N/A")
+ target = paradigm.get("what_to_optimize", "score")
+ cautions = paradigm.get("cautions", "N/A")
+ approach_type = paradigm.get("approach_type", "N/A")
+
+ if is_prompt_opt:
+ header = "## BREAKTHROUGH STRATEGY - APPLY THIS"
+ intro = "The search has stagnated globally. You MUST apply this breakthrough prompt strategy:"
+ fields = (
+ f"**STRATEGY:** {idea}\n\n"
+ f"**HOW TO APPLY:**\n{description}\n\n"
+ f"**TARGET:** {target}\n\n"
+ f"**CAUTIONS:** {cautions}\n\n"
+ f"**APPROACH TYPE:** {approach_type}"
+ )
+ critical_bullets = (
+ "- You MUST rewrite the prompt using this strategy\n"
+ "- The strategy must be reflected in the actual prompt structure and content\n"
+ "- Keep the prompt clear and well-structured\n"
+ "- Do not add unnecessary verbosity — every sentence should serve a purpose\n"
+ "- Ensure the prompt still addresses the core task"
+ )
+ else:
+ header = "## BREAKTHROUGH IDEA - IMPLEMENT THIS"
+ intro = "The search has stagnated globally. You MUST implement this breakthrough idea:"
+ fields = (
+ f"**IDEA:** {idea}\n\n"
+ f"**HOW TO IMPLEMENT:**\n{description}\n\n"
+ f"**TARGET METRIC:** {target}\n\n"
+ f"**CAUTIONS:** {cautions}\n\n"
+ f"**APPROACH TYPE:** {approach_type}"
+ )
+ critical_bullets = (
+ "- You MUST implement the breakthrough idea\n"
+ "- Ensure the paradigm is actually used in your implementation (not just mentioned in comments)\n"
+ "- Correctness is essential - your implementation must be correct and functional\n"
+ "- Verify output format matches evaluator requirements\n"
+ "- Make purposeful changes that implement the idea\n"
+ "- Test your implementation logic carefully"
+ )
+
+ return f"{header}\n\n{intro}\n\n{fields}\n\n**CRITICAL:**\n{critical_bullets}"
+
+ def _format_sibling_context(
+ self, siblings: List[Program], parent_program: Program
+ ) -> Optional[str]:
+ """
+ Format sibling context showing previous mutations of the parent.
+
+ Shows what mutations have been tried before, whether they improved
+ or regressed, so the LLM can avoid repeating failed approaches.
+ """
+ if not siblings:
+ return None
+
+ parent_fitness = self._get_progress_score(getattr(parent_program, "metrics", {}))
+ missing = self._PROGRESS_SCORE_MISSING
+
+ improved, regressed, unchanged = 0, 0, 0
+ entries: List[str] = []
+
+ for i, child in enumerate(siblings, 1):
+ child_fitness = self._get_progress_score(getattr(child, "metrics", {}))
+
+ if parent_fitness == missing or child_fitness == missing:
+ entries.append(f" {i}. (metrics unavailable) [UNKNOWN]")
+ unchanged += 1
+ continue
+
+ delta = child_fitness - parent_fitness
+
+ if delta > 0.001:
+ status = "IMPROVED"
+ improved += 1
+ elif delta < -0.001:
+ status = "REGRESSED"
+ regressed += 1
+ else:
+ status = "NO CHANGE"
+ unchanged += 1
+
+ entries.append(
+ f" {i}. {parent_fitness:.4f} -> {child_fitness:.4f} " f"({delta:+.4f}) [{status}]"
+ )
+
+ lines = [
+ "## PREVIOUS ATTEMPTS ON THIS PARENT",
+ f"Summary: {improved} improved, {unchanged} unchanged, {regressed} regressed",
+ *entries,
+ "Avoid repeating approaches that didn't work.",
+ ]
+ return "\n".join(lines)
+
+ def _format_previous_attempts(
+ self, previous_programs: List[Program], num_previous_attempts: int = 3
+ ) -> str:
+ """Format recent attempts using AdaEvolve's scalar proxy in Pareto mode."""
+ if not self._is_multiobjective_enabled():
+ return super()._format_previous_attempts(previous_programs, num_previous_attempts)
+
+ if not previous_programs:
+ return "No previous attempts yet."
+
+ try:
+ previous_attempt_template = self.template_manager.get_template("previous_attempt")
+ except (ValueError, KeyError):
+ previous_attempt_template = "### Attempt {attempt_number}\n- Changes: {changes}\n- Metrics: {performance}\n- Outcome: {outcome}"
+
+ previous_programs = sorted(
+ previous_programs,
+ key=lambda program: self._get_progress_score(prog_attr(program, "metrics", {}) or {}),
+ reverse=True,
+ )
+ selected = previous_programs[: min(num_previous_attempts, len(previous_programs))]
+
+ lines = []
+ for i, program in enumerate(reversed(selected)):
+ attempt_number = len(selected) - i
+ metadata = prog_attr(program, "metadata", {}) or {}
+ metrics = prog_attr(program, "metrics", {}) or {}
+
+ changes = metadata.get("changes", "Unknown changes")
+ performance_parts = []
+ for name, value in metrics.items():
+ if not isinstance(value, bool) and isinstance(value, (int, float)):
+ try:
+ performance_parts.append(f"{name}: {value:.4f}")
+ except (ValueError, TypeError):
+ performance_parts.append(f"{name}: {value}")
+ else:
+ performance_parts.append(f"{name}: {value}")
+ performance_str = ", ".join(performance_parts) if performance_parts else "No metrics"
+
+ parent_metrics = metadata.get("parent_metrics", {})
+ outcome = self._determine_outcome(metrics, parent_metrics)
+
+ lines.append(
+ previous_attempt_template.format(
+ attempt_number=attempt_number,
+ changes=changes,
+ performance=performance_str,
+ outcome=outcome,
+ )
+ + "\n\n"
+ )
+
+ return "".join(lines)
+
+ def _determine_outcome(
+ self, program_metrics: Dict[str, Any], parent_metrics: Dict[str, Any]
+ ) -> str:
+ """Describe attempt outcome using the configured scalar proxy in Pareto mode."""
+ if not self._is_multiobjective_enabled():
+ return super()._determine_outcome(program_metrics, parent_metrics)
+
+ prog_value = self._get_progress_score(program_metrics)
+ parent_value = self._get_progress_score(parent_metrics)
+ missing = self._PROGRESS_SCORE_MISSING
+ if prog_value == missing or parent_value == missing:
+ return "Insufficient metrics for comparison"
+ if prog_value > parent_value + 1e-6:
+ return "Improvement in Pareto proxy"
+ if prog_value < parent_value - 1e-6:
+ return "Regression in Pareto proxy"
+ return "No change in Pareto proxy"
+
+ @staticmethod
+ def _format_error_context(error_context: str) -> str:
+ """Format retry error context."""
+ return (
+ "## RETRY CONTEXT\n"
+ f"Previous attempt failed with error:\n```\n{error_context}\n```\n"
+ "Please fix this issue in your response."
+ )
diff --git a/skydiscover/context_builder/adaevolve/templates/diff_user_message.txt b/skydiscover/context_builder/adaevolve/templates/diff_user_message.txt
new file mode 100644
index 0000000000000000000000000000000000000000..ab52b5bfe01ed2e0932a9337bdc04d4cd2579230
--- /dev/null
+++ b/skydiscover/context_builder/adaevolve/templates/diff_user_message.txt
@@ -0,0 +1,52 @@
+# Current Solution Information
+- Main Metrics:
+{metrics}
+- Focus areas: {improvement_areas}
+
+# Program Generation History
+## Previous Attempts
+
+{previous_attempts}
+
+{other_context_programs}
+
+# Current Solution
+{current_program}
+
+{search_guidance}
+
+# Task
+{task_objective}
+{diversity_dimensions}
+{diversity_note}
+
+You MUST use the exact SEARCH/REPLACE diff format shown below to indicate changes:
+
+<<<<<<< SEARCH
+# Original code to find and replace (must match exactly)
+=======
+# New replacement code
+>>>>>>> REPLACE
+
+Example of valid diff format:
+<<<<<<< SEARCH
+for i in range(m):
+ for j in range(p):
+ for k in range(n):
+ C[i, j] += A[i, k] * B[k, j]
+=======
+# Reorder loops for better memory access pattern
+for i in range(m):
+ for k in range(n):
+ for j in range(p):
+ C[i, j] += A[i, k] * B[k, j]
+>>>>>>> REPLACE
+
+**CRITICAL**: You can suggest multiple changes. Each SEARCH section must EXACTLY match code in "# Current Solution" - copy it character-for-character, preserving all whitespace and indentation. Do NOT paraphrase or reformat.
+Be thoughtful about your changes and explain your reasoning thoroughly.
+Include a concise docstring at the start of functions describing the exact approach taken.
+
+IMPORTANT: If an instruction header of "## IMPORTANT: ..." is given below the "# Current Solution", you MUST follow it. Otherwise,
+focus on targeted improvements of the program.
+
+{timeout_warning}
\ No newline at end of file
diff --git a/skydiscover/context_builder/adaevolve/templates/full_rewrite_prompt_opt_user_message.txt b/skydiscover/context_builder/adaevolve/templates/full_rewrite_prompt_opt_user_message.txt
new file mode 100644
index 0000000000000000000000000000000000000000..2d80bf7048e89a1574d50d935704329265a0fdef
--- /dev/null
+++ b/skydiscover/context_builder/adaevolve/templates/full_rewrite_prompt_opt_user_message.txt
@@ -0,0 +1,44 @@
+# Current Prompt Information
+- Main Metrics:
+{metrics}
+- Focus areas: {improvement_areas}
+
+# Prompt Generation History
+## Previous Attempts
+
+{previous_attempts}
+
+{other_context_programs}
+
+# Current Prompt
+{current_program}
+
+{search_guidance}
+
+# Task
+{task_objective}
+{diversity_dimensions}
+{diversity_note}
+
+Provide the complete rewritten prompt.
+
+IMPORTANT: Your rewritten prompt must serve the same task as the original prompt,
+but with improved instructions, structure, or reasoning guidance.
+Do NOT change the task the prompt is designed for - only improve HOW it instructs the LLM.
+
+```{language}
+# Your rewritten prompt here
+```
+
+Be thoughtful about your changes and explain your reasoning thoroughly.
+Consider these prompt engineering strategies:
+- Clearer step-by-step reasoning instructions
+- Better output format constraints
+- More precise task framing and role definitions
+- Adding or refining examples
+- Reducing ambiguity in instructions
+
+IMPORTANT: If an instruction header of "## IMPORTANT: ..." is given below the "# Current Prompt", you MUST follow it. Otherwise,
+focus on targeted improvements of the prompt.
+
+{timeout_warning}
\ No newline at end of file
diff --git a/skydiscover/context_builder/adaevolve/templates/full_rewrite_user_message.txt b/skydiscover/context_builder/adaevolve/templates/full_rewrite_user_message.txt
new file mode 100644
index 0000000000000000000000000000000000000000..5717575fb603b6006cecaf6d7dfb9b190cbd72a1
--- /dev/null
+++ b/skydiscover/context_builder/adaevolve/templates/full_rewrite_user_message.txt
@@ -0,0 +1,38 @@
+# Current Solution Information
+- Main Metrics:
+{metrics}
+- Focus areas: {improvement_areas}
+
+# Program Generation History
+## Previous Attempts
+
+{previous_attempts}
+
+{other_context_programs}
+
+# Current Solution
+{current_program}
+
+{search_guidance}
+
+# Task
+{task_objective}
+{diversity_dimensions}
+{diversity_note}
+
+Provide the complete new program solution.
+
+IMPORTANT: Make sure your rewritten program maintains the same inputs and outputs
+as the original program, but with improved internal implementation.
+
+```{language}
+# Your rewritten program here
+```
+
+**CRITICAL**: Be thoughtful about your changes and explain your reasoning thoroughly.
+Include a concise docstring at the start of functions describing the exact approach taken.
+
+IMPORTANT: If an instruction header of "## IMPORTANT: ..." is given below the "# Current Solution", you MUST follow it. Otherwise,
+focus on targeted improvements of the program.
+
+{timeout_warning}
\ No newline at end of file
diff --git a/skydiscover/context_builder/default/builder.py b/skydiscover/context_builder/default/builder.py
new file mode 100644
index 0000000000000000000000000000000000000000..33c22af00bc641c16bef1c08ec36b76a187e24b8
--- /dev/null
+++ b/skydiscover/context_builder/default/builder.py
@@ -0,0 +1,464 @@
+"""Default context builder for SkyDiscover.
+
+Builds LLM prompts with the following structure:
+
+System message:
+ Role/instructions loaded from config or template override.
+
+User message (populated from a template, all templates share this layout):
+ 1. Metrics & focus areas — current combined_score + per-metric breakdown,
+ and suggested improvement directions.
+ 2. Previous attempts — top recent programs with their changes, metrics,
+ and whether they improved/regressed.
+ 3. context programs — other programs from the database
+ shown with their metrics and code.
+ 4. Current program — the current program's code, metrics, score
+ breakdown, and evaluator feedback (if any).
+ 5. Task instructions — how to respond (diff format, full rewrite, or
+ image generation depending on the template).
+
+Each section is formatted by a dedicated helper (_format_metrics,
+_format_previous_attempts, _format_other_context_programs, _format_current_program).
+"""
+
+import logging
+from pathlib import Path
+from typing import Any, Dict, List, Optional, Union
+
+from skydiscover.config import Config
+from skydiscover.context_builder.base import ContextBuilder
+from skydiscover.context_builder.utils import TemplateManager, format_artifacts, prog_attr
+from skydiscover.search.base_database import Program
+
+logger = logging.getLogger(__name__)
+
+_TEMPLATES_DIR = str(Path(__file__).parent / "templates")
+_TEXT_LANGUAGES = {"text", "prompt", "text/plain"}
+
+
+def _filter_other_metrics(metrics: dict) -> dict:
+ return {k: v for k, v in metrics.items() if k not in {"combined_score", "error"}}
+
+
+class DefaultContextBuilder(ContextBuilder):
+ """
+ Builds LLM prompts from current program, metrics, and context programs.
+ """
+
+ def __init__(self, config: Config):
+ super().__init__(config)
+ self.system_template_override = None
+ self.user_template_override = None
+ self.template_manager = TemplateManager(_TEMPLATES_DIR, self.context_config.template_dir)
+
+ def set_templates(
+ self, system_template: Optional[str] = None, user_template: Optional[str] = None
+ ) -> None:
+ """Override the default system/user template keys.
+
+ Pass None for either argument to keep the current value.
+ """
+ self.system_template_override = system_template
+ self.user_template_override = user_template
+ logger.info(f"Templates set: system={system_template}, user={user_template}")
+
+ # ------------------------------------------------------------------
+ # Main Prompt Builder
+ # ------------------------------------------------------------------
+
+ def build_prompt(
+ self,
+ current_program: Union[Program, Dict[str, Program]],
+ context: Dict[str, Any] = None,
+ **kwargs: Any,
+ ) -> Dict[str, str]:
+ """
+ Build a prompt dict with "system" and "user" keys.
+
+ Args:
+ current_program: Program or {info: Program} to evolve from.
+ context: optional dict with program_metrics, other_context_programs,
+ previous_programs, etc.
+
+ Returns:
+ {"system": str, "user": str} ready for LLM.generate().
+ """
+ context = context or {}
+
+ # EXPERIENCES: information needed from the database
+ program_metrics = context.get("program_metrics", {})
+ other_context_programs = context.get("other_context_programs", {})
+ previous_programs = context.get("previous_programs", [])
+
+ # Information needed from the config
+ language = self.config.language or "python"
+ diff_based_generation = self.config.diff_based_generation
+
+ # Format experiences
+ metrics_str = self._format_metrics(program_metrics)
+ previous_attempts_section = self._format_previous_attempts(previous_programs)
+ other_context_section = self._format_other_context_programs(
+ other_context_programs, language
+ )
+ current_program_section = self._format_current_program(current_program, language)
+ has_current_program = bool(current_program_section)
+
+ if isinstance(current_program, dict) and current_program:
+ actual_program = list(current_program.values())[0]
+ current_solution = prog_attr(actual_program, "solution")
+ else:
+ current_solution = prog_attr(current_program, "solution")
+
+ improvement_areas = self._identify_improvement_areas(
+ current_solution, program_metrics, previous_programs
+ )
+
+ if context.get("errors"):
+ other_context_section += self._format_failed_attempts(context["errors"], language)
+
+ evaluator_timeout = getattr(self.config.evaluator, "timeout", None)
+ timeout_warning = (
+ f"- Time limit: Programs should complete execution within {evaluator_timeout} seconds; otherwise, they will timeout."
+ if evaluator_timeout
+ else ""
+ )
+
+ user_template_key = self._select_template_key(
+ language, diff_based_generation, has_current_program
+ )
+ user_template = self.template_manager.get_template(user_template_key)
+
+ user_message = user_template.format(
+ current_program=current_program_section,
+ metrics=metrics_str,
+ previous_attempts=previous_attempts_section,
+ other_context_programs=other_context_section,
+ improvement_areas=improvement_areas,
+ language=language,
+ timeout_warning=timeout_warning,
+ **kwargs,
+ )
+
+ return {"system": self._get_system_message(), "user": user_message}
+
+ def _select_template_key(
+ self, language: str, diff_based: bool, has_current_program: bool = True
+ ) -> str:
+ """Pick template: override > auto (from_scratch / image / diff / full rewrite)."""
+ if self.user_template_override:
+ return self.user_template_override
+
+ if not has_current_program:
+ return "from_scratch_user_message"
+
+ if language == "image":
+ return "image_user_message"
+
+ if diff_based:
+ return "diff_user_message"
+
+ if language.lower() in _TEXT_LANGUAGES:
+ return "full_rewrite_prompt_opt_user_message"
+ return "full_rewrite_user_message"
+
+ def _get_system_message(self) -> str:
+ """Return system message from override, template, or raw config string."""
+ if self.system_template_override:
+ return self.template_manager.get_template(self.system_template_override)
+ system_msg = self.context_config.system_message
+ if system_msg in self.template_manager.templates:
+ return self.template_manager.get_template(system_msg)
+ return system_msg
+
+ # ------------------------------------------------------------------
+ # Formatting helpers
+ # ------------------------------------------------------------------
+
+ def _format_current_program(
+ self, current_program: Union[Program, Dict[str, Program]], language: str
+ ) -> str:
+ """Format parent program with heading, score breakdown, code, and feedback.
+
+ Returns empty string when there is no current program so the heading
+ is omitted entirely from the rendered prompt.
+ """
+ if not current_program:
+ return ""
+
+ if isinstance(current_program, dict):
+ info = list(current_program.keys())[0]
+ program = list(current_program.values())[0]
+ else:
+ info = ""
+ program = current_program
+
+ solution = prog_attr(program, "solution")
+ metrics = prog_attr(program, "metrics", {})
+
+ lines = ["# Current Solution\n"]
+ if info:
+ lines.append(f"\n{info}\n")
+
+ lines.append("\n## Program Information\n")
+ if metrics:
+ combined = metrics.get("combined_score")
+ if combined is not None and isinstance(combined, (int, float)):
+ lines.append(f"combined_score: {combined:.4f}\n")
+
+ error = metrics.get("error")
+ if error:
+ lines.append(f"error: {error}\n")
+
+ other_metrics = _filter_other_metrics(metrics)
+ if other_metrics:
+ lines.append("Score breakdown:")
+ for key, value in other_metrics.items():
+ if isinstance(value, float):
+ lines.append(f"\n - {key}: {value:.4f}")
+ elif isinstance(value, (int, str, bool)):
+ lines.append(f"\n - {key}: {value}")
+ lines.append("\n")
+
+ if language != "image":
+ lines.append(f"\n```{language}\n{solution}\n```\n")
+
+ feedback_section = format_artifacts(program, heading="##")
+ if feedback_section:
+ lines.append(feedback_section)
+
+ return "".join(lines)
+
+ def _identify_improvement_areas(
+ self,
+ current_program: str,
+ metrics: Dict[str, float],
+ previous_programs: List[Program],
+ ) -> str:
+ """Generate bullet points: score trend vs previous attempt, simplification hint."""
+ improvement_areas = []
+
+ current_score = metrics.get("combined_score", 0.0)
+ if not isinstance(current_score, (int, float)):
+ try:
+ current_score = float(current_score)
+ except (ValueError, TypeError):
+ current_score = 0.0
+
+ if previous_programs:
+ prev = previous_programs[-1]
+ prev_metrics = prog_attr(prev, "metrics", {})
+ prev_score = prev_metrics.get("combined_score", 0.0)
+ if not isinstance(prev_score, (int, float)):
+ try:
+ prev_score = float(prev_score)
+ except (ValueError, TypeError):
+ prev_score = 0.0
+
+ if current_score > prev_score:
+ improvement_areas.append(
+ f"Combined score improved: {prev_score:.4f} → {current_score:.4f}"
+ )
+ elif current_score < prev_score:
+ improvement_areas.append(
+ f"Combined score declined: {prev_score:.4f} → {current_score:.4f}. Consider revising recent changes."
+ )
+ elif abs(current_score - prev_score) < 1e-6:
+ improvement_areas.append(f"Combined score unchanged at {current_score:.4f}")
+
+ threshold = self.context_config.suggest_simplification_after_chars
+ if threshold and len(current_program) > threshold:
+ improvement_areas.append(
+ f"Consider simplifying - solution length exceeds {threshold} characters"
+ )
+
+ if not improvement_areas:
+ improvement_areas.append("Focus on improving the combined_score")
+
+ return "\n".join(f"- {area}" for area in improvement_areas)
+
+ def _format_single_context_program(
+ self, program: Program, index: int, language: str, lines: list
+ ) -> None:
+ """Append one context program's header, metrics, and code to lines."""
+ if program is None:
+ return
+
+ solution = prog_attr(program, "solution")
+ metrics = prog_attr(program, "metrics", {})
+
+ combined = metrics.get("combined_score") if metrics else None
+ if combined is not None and isinstance(combined, (int, float)):
+ lines.append(f"### Program {index} (combined_score: {combined:.4f})\n")
+ else:
+ lines.append(f"### Program {index}\n")
+
+ if metrics:
+ error = metrics.get("error")
+ if error:
+ lines.append(f"- error: {error}\n")
+
+ other_metrics = _filter_other_metrics(metrics)
+ if other_metrics:
+ lines.append("Score breakdown:")
+ for key, value in other_metrics.items():
+ if isinstance(value, float):
+ lines.append(f" - {key}: {value:.4f}")
+ elif isinstance(value, (int, str, bool)):
+ lines.append(f" - {key}: {value}")
+ lines.append("\n")
+
+ if language != "image":
+ lines.append(f"\n```{language}\n{solution}\n```\n")
+ lines.append("\n")
+
+ def _format_other_context_programs(
+ self,
+ other_context_programs: Union[List[Program], Dict[str, List[Program]]],
+ language: str,
+ ) -> str:
+ """Format all context programs, grouped by key when dict-wrapped."""
+ if not other_context_programs:
+ return ""
+
+ lines = []
+ if isinstance(other_context_programs, dict):
+ for label, programs in other_context_programs.items():
+ if not programs:
+ continue
+ lines.append(f"\n## {label or 'Other Context Solutions'}\n")
+ lines.append(
+ "These programs represent diverse approaches and creative solutions that may be relevant to the current task:\n\n"
+ )
+ for i, program in enumerate(programs, start=1):
+ self._format_single_context_program(program, i, language, lines)
+ else:
+ lines.append(
+ "These programs represent diverse approaches and creative solutions that may inspire new ideas:\n"
+ )
+ for i, program in enumerate(other_context_programs, start=1):
+ self._format_single_context_program(program, i, language, lines)
+
+ return "".join(lines)
+
+ def _format_failed_attempts(self, errors: list, language: str) -> str:
+ """Format failed retry attempts for the prompt."""
+ lines = ["\n## ❌ Previous Failed Attempts (this retry):\n"]
+ lines.append("The following attempts failed. Avoid these errors:\n\n")
+ for attempt in errors:
+ err_msg = attempt.get("metadata", {}).get("error", "Unknown error")
+ attempt_num = attempt.get("metadata", {}).get("attempt_number", "?")
+ lines.append(f"### Attempt {attempt_num}:\n")
+ lines.append(f"**Error:** {err_msg}\n")
+
+ failed_solution = attempt.get("solution", "")
+ llm_response = attempt.get("llm_response", "")
+
+ if "SEARCH" in err_msg and llm_response:
+ if len(llm_response) > 1500:
+ llm_response = llm_response[:1500] + "\n... (truncated)"
+ lines.append(f"**Your response that failed:**\n```\n{llm_response}\n```\n\n")
+ elif failed_solution:
+ if len(failed_solution) > 1500:
+ failed_solution = failed_solution[:1500] + "\n... (truncated)"
+ lines.append(
+ f"**Generated solution that failed:**\n```{language}\n{failed_solution}\n```\n"
+ )
+
+ traceback_str = attempt.get("metadata", {}).get("traceback", "")
+ if traceback_str:
+ if len(traceback_str) > 800:
+ traceback_str = "... (truncated)\n" + traceback_str[-800:]
+ lines.append(f"**Traceback:**\n```\n{traceback_str}\n```\n\n")
+ else:
+ lines.append("\n")
+ return "".join(lines)
+
+ def _format_previous_attempts(
+ self, previous_programs: List[Program], num_previous_attempts: int = 3
+ ) -> str:
+ """Format top N previous attempts with their changes, metrics, and outcome."""
+ if not previous_programs:
+ return "No previous attempts yet."
+
+ try:
+ previous_attempt_template = self.template_manager.get_template("previous_attempt")
+ except (ValueError, KeyError):
+ previous_attempt_template = "### Attempt {attempt_number}\n- Changes: {changes}\n- Metrics: {performance}\n- Outcome: {outcome}"
+
+ previous_programs = sorted(
+ previous_programs,
+ key=lambda p: prog_attr(p, "metrics", {}).get("combined_score", 0.0),
+ reverse=True,
+ )
+ selected = previous_programs[: min(num_previous_attempts, len(previous_programs))]
+
+ lines = []
+ for i, program in enumerate(reversed(selected)):
+ attempt_number = len(selected) - i
+ metadata = prog_attr(program, "metadata", {}) or {}
+ metrics = prog_attr(program, "metrics", {}) or {}
+
+ changes = metadata.get("changes", "Unknown changes")
+ performance_parts = []
+ for name, value in metrics.items():
+ if isinstance(value, (int, float)):
+ try:
+ performance_parts.append(f"{name}: {value:.4f}")
+ except (ValueError, TypeError):
+ performance_parts.append(f"{name}: {value}")
+ else:
+ performance_parts.append(f"{name}: {value}")
+ performance_str = ", ".join(performance_parts) if performance_parts else "No metrics"
+
+ parent_metrics = metadata.get("parent_metrics", {})
+ outcome = self._determine_outcome(metrics, parent_metrics)
+
+ lines.append(
+ previous_attempt_template.format(
+ attempt_number=attempt_number,
+ changes=changes,
+ performance=performance_str,
+ outcome=outcome,
+ )
+ + "\n\n"
+ )
+
+ return "".join(lines)
+
+ @staticmethod
+ def _determine_outcome(program_metrics: Dict[str, Any], parent_metrics: Dict[str, Any]) -> str:
+ """Compare combined_score to parent: 'Improvement', 'Regression', or 'No change'."""
+ prog_value = program_metrics.get("combined_score")
+ parent_value = parent_metrics.get("combined_score", 0)
+ if isinstance(prog_value, (int, float)) and isinstance(parent_value, (int, float)):
+ if prog_value > parent_value:
+ return "Improvement in combined_score"
+ elif prog_value < parent_value:
+ return "Regression in combined_score"
+ return "No change in combined_score"
+
+ def _format_metrics(self, metrics: Dict[str, float]) -> str:
+ """Format metrics: combined_score first, then error, then per-metric breakdown."""
+ if not metrics:
+ return "No metrics available"
+
+ lines = []
+ combined_score = metrics.get("combined_score")
+ if combined_score is not None:
+ lines.append(f"- combined_score: {combined_score:.4f}")
+
+ error = metrics.get("error")
+ if error:
+ lines.append(f"- error: {error}")
+
+ other_metrics = _filter_other_metrics(metrics)
+ if other_metrics:
+ lines.append("")
+ lines.append("Metrics:")
+ for key, value in other_metrics.items():
+ if isinstance(value, float):
+ lines.append(f" - {key}: {value:.4f}")
+ elif isinstance(value, (int, str, bool)):
+ lines.append(f" - {key}: {value}")
+
+ return "\n".join(lines) if lines else "No metrics available"
diff --git a/skydiscover/context_builder/default/templates/evaluator_system_message.txt b/skydiscover/context_builder/default/templates/evaluator_system_message.txt
new file mode 100644
index 0000000000000000000000000000000000000000..1946392b7acfc0e194893ac19e23d00d9b55c4b9
--- /dev/null
+++ b/skydiscover/context_builder/default/templates/evaluator_system_message.txt
@@ -0,0 +1,2 @@
+You are an expert solution reviewer.
+Your job is to analyze the provided solution and evaluate it systematically.
\ No newline at end of file
diff --git a/skydiscover/context_builder/default/templates/evaluator_user_message.txt b/skydiscover/context_builder/default/templates/evaluator_user_message.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e9c9dd2406dc11d3c5a45b5af4335eb7376bf22c
--- /dev/null
+++ b/skydiscover/context_builder/default/templates/evaluator_user_message.txt
@@ -0,0 +1,19 @@
+Evaluate the following code on a scale of 0.0 to 1.0 for the following metrics:
+1. Readability: How easy is the code to read and understand?
+2. Maintainability: How easy would the code be to maintain and modify?
+3. Efficiency: How efficient is the code in terms of time and space complexity?
+
+For each metric, provide a score between 0.0 and 1.0, where 1.0 is best.
+
+Code to evaluate:
+```python
+{current_program}
+```
+
+Return your evaluation as a JSON object with the following format:
+{{
+ "readability": [score],
+ "maintainability": [score],
+ "efficiency": [score],
+ "reasoning": "[brief explanation of scores]"
+}}
\ No newline at end of file
diff --git a/skydiscover/context_builder/default/templates/from_scratch_user_message.txt b/skydiscover/context_builder/default/templates/from_scratch_user_message.txt
new file mode 100644
index 0000000000000000000000000000000000000000..69ef5a1c86494a4789216734788b4811ffb818f9
--- /dev/null
+++ b/skydiscover/context_builder/default/templates/from_scratch_user_message.txt
@@ -0,0 +1,13 @@
+{other_context_programs}
+
+# Task
+Generate a new solution that achieves a high COMBINED_SCORE.
+
+```{language}
+# Your solution here
+```
+
+**CRITICAL**: Be thoughtful and explain your reasoning thoroughly.
+Include a concise docstring at the start of functions describing the exact approach taken.
+
+{timeout_warning}
\ No newline at end of file
diff --git a/skydiscover/context_builder/default/templates/full_rewrite_prompt_opt_user_message.txt b/skydiscover/context_builder/default/templates/full_rewrite_prompt_opt_user_message.txt
new file mode 100644
index 0000000000000000000000000000000000000000..f80e482d430a8463999a1c7b4284c154f209df92
--- /dev/null
+++ b/skydiscover/context_builder/default/templates/full_rewrite_prompt_opt_user_message.txt
@@ -0,0 +1,41 @@
+# Current Prompt Information
+- Main Metrics:
+{metrics}
+- Focus areas: {improvement_areas}
+
+# Prompt Generation History
+## Previous Attempts
+
+{previous_attempts}
+
+{other_context_programs}
+
+{current_program}
+
+# Task
+Suggest improvements to the prompt that will improve its COMBINED_SCORE.
+The system maintains diversity across these dimensions: score, complexity.
+Different prompts with similar combined_score but different strategies are valuable.
+
+Provide the complete rewritten prompt.
+
+IMPORTANT: Your rewritten prompt must serve the same task as the original prompt,
+but with improved instructions, structure, or reasoning guidance.
+Do NOT change the task the prompt is designed for - only improve HOW it instructs the LLM.
+
+```{language}
+# Your rewritten prompt here
+```
+
+Be thoughtful about your changes and explain your reasoning thoroughly.
+Consider these prompt engineering strategies:
+- Clearer step-by-step reasoning instructions
+- Better output format constraints
+- More precise task framing and role definitions
+- Adding or refining examples
+- Reducing ambiguity in instructions
+
+IMPORTANT: If an instruction header of "## IMPORTANT: ..." is given below the "# Current Solution", you MUST follow it. Otherwise,
+focus on targeted improvements of the prompt.
+
+{timeout_warning}
\ No newline at end of file
diff --git a/skydiscover/context_builder/default/templates/image_user_message.txt b/skydiscover/context_builder/default/templates/image_user_message.txt
new file mode 100644
index 0000000000000000000000000000000000000000..deaf7b7dcb81acdd9c751d5c7dbdf40b4a9fb337
--- /dev/null
+++ b/skydiscover/context_builder/default/templates/image_user_message.txt
@@ -0,0 +1,23 @@
+# Current Image Scores
+{metrics}
+- Focus areas: {improvement_areas}
+
+# Generation History
+## Previous Attempts
+{previous_attempts}
+
+{other_context_programs}
+
+{current_program}
+
+# Task
+You MUST follow the Target Image Description above exactly.
+You are shown the current best image(s) from the database along with their scores.
+Analyze what works well in the high-scoring images and identify weaknesses
+relative to the target description.
+Then generate a NEW, improved image that better matches ALL requirements
+in the target description and pushes the combined_score higher.
+
+Provide brief text reasoning about your approach and what you changed.
+
+{timeout_warning}
\ No newline at end of file
diff --git a/skydiscover/context_builder/default/templates/previous_attempt.txt b/skydiscover/context_builder/default/templates/previous_attempt.txt
new file mode 100644
index 0000000000000000000000000000000000000000..3d944c148263795af7a6f869e7f2674d7bbe7f9f
--- /dev/null
+++ b/skydiscover/context_builder/default/templates/previous_attempt.txt
@@ -0,0 +1,4 @@
+### Attempt {attempt_number}
+- Changes: {changes}
+- Metrics: {performance}
+- Outcome: {outcome}
\ No newline at end of file
diff --git a/skydiscover/context_builder/default/templates/system_message.txt b/skydiscover/context_builder/default/templates/system_message.txt
new file mode 100644
index 0000000000000000000000000000000000000000..d6182b512eeca183dc54717d874778af1b0f11eb
--- /dev/null
+++ b/skydiscover/context_builder/default/templates/system_message.txt
@@ -0,0 +1,3 @@
+You are an expert tasked with iteratively improving a solution.
+Your goal is to maximize the COMBINED SCORE while exploring diverse approaches.
+The system maintains a collection of diverse solutions - both high combined score AND diversity are valuable.
\ No newline at end of file
diff --git a/skydiscover/context_builder/evox/README.md b/skydiscover/context_builder/evox/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..3fe0f76851c9f8795dd54b44b74ce69390a7ac3c
--- /dev/null
+++ b/skydiscover/context_builder/evox/README.md
@@ -0,0 +1,34 @@
+# EvoxContextBuilder
+
+Extends `DefaultContextBuilder` with LLM-generated summaries and statistical insights injected into every prompt. Used by the EvoX co-evolutionary search algorithm.
+
+## What it adds
+
+Before building the prompt, `EvoxContextBuilder` makes parallel LLM calls to a cheaper guide model to generate:
+
+- Population statistics insights (score trends, diversity, improvement rates)
+- Problem context summary (description of the task derived from the evaluator)
+- Batch summaries of context programs
+
+These are assembled into the prompt alongside the standard sections (metrics, previous attempts, current program).
+
+## Files
+
+| File | What it does |
+|------|-------------|
+| `builder.py` | `EvoxContextBuilder` class, parallel LLM calls, template assembly |
+| `formatters.py` | Pure formatting functions (population state, execution trace, DB stats diff) |
+| `templates/` | Prompt templates (override default ones with the same filename) |
+
+## Templates
+
+`EvoxContextBuilder` loads both `default/templates/` and `evox/templates/`. Files with the same name in `evox/templates/` override the defaults.
+
+| File | Role | Purpose |
+|------|------|---------|
+| `system_message.txt` | system | Overrides the default system prompt for search evolution |
+| `search_evolution_user_message.txt` | user | Main user prompt for search algorithm discovery |
+| `problem_template.txt` | fragment | Problem description fed to the guide LLM |
+| `problem_context_summary_system_message.txt` | system (guide LLM) | Instructs guide LLM to summarize the problem context |
+| `stats_insight_system_message.txt` | system (guide LLM) | Instructs guide LLM to generate population statistics insights |
+| `batch_summary_prompt.txt` | system + user (guide LLM) | Instructs guide LLM to summarize context programs in batch |
diff --git a/skydiscover/context_builder/evox/templates/batch_summary_prompt.txt b/skydiscover/context_builder/evox/templates/batch_summary_prompt.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e2af0c0427b3380b36135038797a663c6662b017
--- /dev/null
+++ b/skydiscover/context_builder/evox/templates/batch_summary_prompt.txt
@@ -0,0 +1,62 @@
+===SYSTEM===
+You are summarizing previous search algorithm attempts.
+
+For EACH program, provide:
+
+1) TECHNIQUE: (1-2 short sentences)
+ Describe the parent and context selection approach.
+ • Parent selection: what parents were typically chosen recently?
+ • Context selection: what context programs were used recently?
+ • How is the label used?
+
+2) KEY TAKEAWAYS: (3-5 bullet points)
+ • Success or not (improved the downstream problem score or not). Compare scores across parent, context, and resulting child programs.
+ • Highlight any notable patterns in the numbers (e.g., score gaps to SOTA, recent score trends
+ [slowing down, plateau, ...], regressions, etc.).
+ • Describe observable patterns in the technique used, parent/context selection (e.g., score ranges, diversity, selection frequency, best program inclusion/exclusion, context lineage diversity, empty context usage, ...)
+ and any correlations with child program outcomes.
+ • Label usage analysis:
+ - Report the COUNT of DIVERGE_LABEL and REFINE_LABEL uses, and the child scores for each.
+ - Label-guided generations are inherently high-variance. Individual attempts
+ often regress — this is expected. Breakthroughs typically require 3-5+ tries with varied parents and contexts.
+ Do NOT treat individual regressions as evidence that "labels don't work".
+ - If any labels were UNDER-UTILIZED during LONG STAGNATION, flag this as a MISSED OPPORTUNITY.
+ - Flag if the same parent program or same contexts are reused repeatedly with labels and no improvement is observed.
+
+Keep under 70 words per program. Report facts only.
+Ground all statements in the actual statistics provided.
+
+===PER_PROGRAM===
+TASK DESCRIPTION:
+{task_description}
+
+--------------------------------------------------------
+SEARCH ALGORITHM CODE:
+```python
+{solution}
+```
+
+--------------------------------------------------------
+STATS (includes execution trace with parent/context scores):
+{db_stats_text}
+
+===INSTRUCTIONS===
+Below are {num_programs} PREVIOUS SEARCH ALGORITHM ATTEMPTS.
+
+{combined_content}
+--------------------------------------------------------
+For EACH program, provide analysis in this EXACT format:
+
+[PROGRAM 1]
+
+SIGNALS OBSERVED: <...>
+
+WHAT SEEMED TO HELP (ONLY LIST WHAT HELPED): <...>
+
+POTENTIAL KEY INSIGHT: <...>
+
+[PROGRAM 2]
+
+...and so on. Use the exact [PROGRAM N] markers.
+
+Keep under 60 words per program. Ground observations in actual statistics.
diff --git a/skydiscover/context_builder/evox/templates/search_evolution_user_message.txt b/skydiscover/context_builder/evox/templates/search_evolution_user_message.txt
new file mode 100644
index 0000000000000000000000000000000000000000..7f3041d88368e46d5afb527f6ef99cb507e3530c
--- /dev/null
+++ b/skydiscover/context_builder/evox/templates/search_evolution_user_message.txt
@@ -0,0 +1,39 @@
+# DOWNSTREAM PROBLEM CONTEXT
+Your search algorithm evolves solutions for the downstream problem. Use this to inform your search strategy.
+
+{problem_template}
+
+---------------------------
+
+# Search Algorithm Information
+
+## Your Algorithm's Search Window
+{search_window_context}
+
+## Solution Population Statistics
+This describes the current state of the solution database your algorithm will work with.
+
+{population_state}
+
+# What You Are Writing
+
+{current_program}
+
+{other_context_programs}
+
+---------------------------
+# Task
+
+Rewrite the program to improve search algorithm score based on the population state.
+
+Provide the complete new program solution, and explain high-level 1-2 principals on what you have done
+and why.
+
+Keep things SIMPLE.
+
+IMPORTANT: Make sure your rewritten program maintains the same inputs and outputs
+as the original program, but with improved internal implementation.
+
+```{language}
+# Your rewritten search algorithm here
+```
\ No newline at end of file
diff --git a/skydiscover/context_builder/evox/templates/system_message.txt b/skydiscover/context_builder/evox/templates/system_message.txt
new file mode 100644
index 0000000000000000000000000000000000000000..d6182b512eeca183dc54717d874778af1b0f11eb
--- /dev/null
+++ b/skydiscover/context_builder/evox/templates/system_message.txt
@@ -0,0 +1,3 @@
+You are an expert tasked with iteratively improving a solution.
+Your goal is to maximize the COMBINED SCORE while exploring diverse approaches.
+The system maintains a collection of diverse solutions - both high combined score AND diversity are valuable.
\ No newline at end of file
diff --git a/skydiscover/context_builder/gepa_native/__init__.py b/skydiscover/context_builder/gepa_native/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e6dbe8edf470aa4bf43d44431724b767eab3c815
--- /dev/null
+++ b/skydiscover/context_builder/gepa_native/__init__.py
@@ -0,0 +1,5 @@
+"""GEPA Native context builder: reflective prompting with rejection history."""
+
+from .builder import GEPANativeContextBuilder
+
+__all__ = ["GEPANativeContextBuilder"]
diff --git a/skydiscover/context_builder/gepa_native/builder.py b/skydiscover/context_builder/gepa_native/builder.py
new file mode 100644
index 0000000000000000000000000000000000000000..252591819372bc1fefaca6a3fd6e9d20ab60a782
--- /dev/null
+++ b/skydiscover/context_builder/gepa_native/builder.py
@@ -0,0 +1,189 @@
+"""
+GEPA Native context builder for SkyDiscover.
+
+Extends DefaultContextBuilder with GEPA-specific reflective prompting:
+- Reflective analysis framing (instructs the LLM to reason about failures)
+- Rejection history (recently rejected programs with scores, errors, and code)
+
+These are assembled into a ``search_guidance`` string and injected into
+templates via the ``{search_guidance}`` placeholder.
+
+Metrics and evaluator diagnostics are already rendered by the default
+template in the ``{current_program}`` section, so they are NOT duplicated
+in the reflective guidance.
+"""
+
+import logging
+import re
+from pathlib import Path
+from typing import Any, Dict, List, Optional, Union
+
+from skydiscover.config import Config
+from skydiscover.context_builder.default import DefaultContextBuilder
+from skydiscover.context_builder.utils import TemplateManager
+from skydiscover.search.base_database import Program
+from skydiscover.utils.metrics import get_score
+
+logger = logging.getLogger(__name__)
+
+
+class GEPANativeContextBuilder(DefaultContextBuilder):
+ """
+ Context builder for GEPA Native's reflective evolutionary search.
+
+ Adds a ``{search_guidance}`` section to the prompt containing:
+ - Reflective analysis framing (tells the LLM to reason about failures)
+ - Recent rejected programs (code that didn't improve on the parent)
+
+ The controller passes raw data via the ``context`` dict:
+ - ``context["rejection_history"]``: list of rejected Program objects
+ - ``context["rejection_parent_scores"]``: dict mapping parent_id -> float score
+
+ Metrics and evaluator diagnostics are already in {current_program}
+ via the default template, so they are not repeated here.
+ """
+
+ def __init__(self, config: Config):
+ super().__init__(config)
+ default_templates = str(Path(__file__).parent.parent / "default" / "templates")
+ gepa_templates = str(Path(__file__).parent / "templates")
+ self.template_manager = TemplateManager(
+ default_templates, gepa_templates, self.context_config.template_dir
+ )
+
+ def build_prompt(
+ self,
+ current_program: Union[Program, Dict[str, Program]],
+ context: Dict[str, Any] = None,
+ **kwargs: Any,
+ ) -> Dict[str, str]:
+ """
+ Build prompt with GEPA reflective search guidance.
+
+ Computes the ``search_guidance`` string from GEPA context keys,
+ then delegates to the parent's ``build_prompt`` which fills the
+ ``{search_guidance}`` placeholder in the template.
+ """
+ context = context or {}
+
+ search_guidance = self._build_search_guidance(current_program, context)
+
+ kwargs.pop("search_guidance", None)
+
+ result = super().build_prompt(
+ current_program,
+ context,
+ search_guidance=search_guidance,
+ **kwargs,
+ )
+
+ # Collapse 3+ consecutive newlines to 2 (empty search_guidance leaves extra blank lines)
+ if "user" in result:
+ result["user"] = re.sub(r"\n{3,}", "\n\n", result["user"])
+
+ return result
+
+ # =========================================================================
+ # Search Guidance Assembly
+ # =========================================================================
+
+ def _build_search_guidance(
+ self,
+ current_program: Union[Program, Dict[str, Program]],
+ context: Dict[str, Any],
+ ) -> str:
+ """
+ Assemble GEPA-specific reflective guidance into one string.
+
+ Sections:
+ 1. Reflective analysis framing (always present when there's content)
+ 2. Rejection history (recently rejected programs)
+ """
+ rejection_history = context.get("rejection_history", [])
+ rejection_parent_scores = context.get("rejection_parent_scores", {})
+
+ sections: List[str] = []
+
+ # Rejection history
+ if rejection_history:
+ rejection_section = self._format_rejection_history(
+ rejection_history, rejection_parent_scores
+ )
+ if rejection_section:
+ sections.append(rejection_section)
+
+ if not sections:
+ return ""
+
+ # Prepend reflective framing header
+ header = (
+ "## Reflective Analysis\n"
+ "Review the evaluation results and diagnostics in the program "
+ "information above. Identify root causes and domain-specific "
+ "insights. Address these failure modes in your solution."
+ )
+
+ return header + "\n\n" + "\n\n".join(sections)
+
+ # =========================================================================
+ # Section Formatters
+ # =========================================================================
+
+ @staticmethod
+ def _format_rejection_history(
+ rejected: List[Program],
+ parent_scores: Dict[str, float],
+ ) -> Optional[str]:
+ """
+ Format recently rejected programs for reflective prompting.
+
+ Shows what mutations were tried and rejected (didn't improve on parent),
+ including their scores, error messages, changes descriptions, and code
+ snippets so the LLM can avoid repeating the same mistakes.
+ """
+ if not rejected:
+ return None
+
+ entries: List[str] = []
+
+ for i, prog in enumerate(rejected, 1):
+ prog_score = get_score(prog.metrics) if prog.metrics else 0.0
+
+ parent_score_str = ""
+ if prog.parent_id and prog.parent_id in parent_scores:
+ parent_score_str = f", parent_score: {parent_scores[prog.parent_id]:.4f}"
+
+ error_msg = ""
+ if prog.metrics:
+ error_msg = prog.metrics.get("error", "")
+ if not error_msg:
+ error_msg = prog.metrics.get("error_message", "")
+
+ changes = ""
+ if prog.metadata:
+ changes = prog.metadata.get("changes", "")
+
+ entry_lines = [f"#### Attempt {i} (score: {prog_score:.4f}{parent_score_str})"]
+ if changes:
+ entry_lines.append(f"Changes: {changes}")
+ if error_msg:
+ entry_lines.append(f"Error: {error_msg}")
+
+ # Code snippet so the LLM can see what was tried
+ if prog.solution:
+ code_lines = prog.solution.splitlines()
+ if len(code_lines) > 30:
+ snippet = "\n".join(code_lines[:30]) + "\n... (truncated)"
+ else:
+ snippet = prog.solution
+ entry_lines.append(f"Code tried:\n```\n{snippet}\n```")
+
+ entries.append("\n".join(entry_lines))
+
+ lines = [
+ "### Recent Rejected Attempts",
+ "The following mutations were rejected because they did not "
+ "improve on the parent. Avoid repeating these approaches:",
+ *entries,
+ ]
+ return "\n\n".join(lines)
diff --git a/skydiscover/context_builder/gepa_native/templates/full_rewrite_prompt_opt_user_message.txt b/skydiscover/context_builder/gepa_native/templates/full_rewrite_prompt_opt_user_message.txt
new file mode 100644
index 0000000000000000000000000000000000000000..b1c2727527ba8e6b6577f0b99acaaca3f4fd5ca1
--- /dev/null
+++ b/skydiscover/context_builder/gepa_native/templates/full_rewrite_prompt_opt_user_message.txt
@@ -0,0 +1,44 @@
+# Current Prompt Information
+- Main Metrics:
+{metrics}
+- Focus areas: {improvement_areas}
+
+# Prompt Generation History
+## Previous Attempts
+
+{previous_attempts}
+
+{other_context_programs}
+
+# Current Prompt
+{current_program}
+
+{search_guidance}
+
+# Task
+Suggest improvements to the prompt that will improve its COMBINED_SCORE.
+The system maintains diversity across these dimensions: score, complexity.
+Different prompts with similar combined_score but different strategies are valuable.
+
+Provide the complete rewritten prompt.
+
+IMPORTANT: Your rewritten prompt must serve the same task as the original prompt,
+but with improved instructions, structure, or reasoning guidance.
+Do NOT change the task the prompt is designed for - only improve HOW it instructs the LLM.
+
+```{language}
+# Your rewritten prompt here
+```
+
+Be thoughtful about your changes and explain your reasoning thoroughly.
+Consider these prompt engineering strategies:
+- Clearer step-by-step reasoning instructions
+- Better output format constraints
+- More precise task framing and role definitions
+- Adding or refining examples
+- Reducing ambiguity in instructions
+
+IMPORTANT: If an instruction header of "## IMPORTANT: ..." is given below the "# Current Prompt", you MUST follow it. Otherwise,
+focus on targeted improvements of the prompt.
+
+{timeout_warning}
\ No newline at end of file
diff --git a/skydiscover/context_builder/gepa_native/templates/full_rewrite_user_message.txt b/skydiscover/context_builder/gepa_native/templates/full_rewrite_user_message.txt
new file mode 100644
index 0000000000000000000000000000000000000000..be1c5037ed4aadc96c3514bb782eaa411996fa6d
--- /dev/null
+++ b/skydiscover/context_builder/gepa_native/templates/full_rewrite_user_message.txt
@@ -0,0 +1,38 @@
+# Current Solution Information
+- Main Metrics:
+{metrics}
+- Focus areas: {improvement_areas}
+
+# Program Generation History
+## Previous Attempts
+
+{previous_attempts}
+
+{other_context_programs}
+
+# Current Solution
+{current_program}
+
+{search_guidance}
+
+# Task
+Suggest improvements to the program that will improve its COMBINED_SCORE.
+The system maintains diversity across these dimensions: score, complexity.
+Different solutions with similar combined_score but different features are valuable.
+
+Provide the complete new program solution.
+
+IMPORTANT: Make sure your rewritten program maintains the same inputs and outputs
+as the original program, but with improved internal implementation.
+
+```{language}
+# Your rewritten program here
+```
+
+**CRITICAL**: Be thoughtful about your changes and explain your reasoning thoroughly.
+Include a concise docstring at the start of functions describing the exact approach taken.
+
+IMPORTANT: If an instruction header of "## IMPORTANT: ..." is given below the "# Current Solution", you MUST follow it. Otherwise,
+focus on targeted improvements of the program.
+
+{timeout_warning}
\ No newline at end of file
diff --git a/skydiscover/runner.py b/skydiscover/runner.py
new file mode 100644
index 0000000000000000000000000000000000000000..76c2b7bdedabe96a8985342d065d0687aaeb750d
--- /dev/null
+++ b/skydiscover/runner.py
@@ -0,0 +1,478 @@
+import json
+import logging
+import os
+import signal
+import sys
+import time
+import uuid
+from typing import Optional
+
+from skydiscover.config import Config, build_output_dir, load_config
+from skydiscover.search.base_database import Program
+from skydiscover.search.default_discovery_controller import (
+ DiscoveryController,
+ DiscoveryControllerInput,
+)
+from skydiscover.search.registry import create_database, get_program
+from skydiscover.search.route import get_discovery_controller
+from skydiscover.search.utils.logging_utils import setup_search_logging
+from skydiscover.utils.code_utils import extract_solution_language
+from skydiscover.utils.metrics import format_metrics, get_score
+
+logger = logging.getLogger(__name__)
+
+
+class Runner:
+ """Top-level entry point for a discovery run.
+
+ Loads config, creates the database and discovery controller, runs the
+ search loop, and saves checkpoints + best program.
+
+ Args:
+ initial_program_path: path to the starting solution file.
+ evaluation_file: path to the user's evaluator script (must define evaluate()).
+ config_path: optional YAML config file (ignored if config is provided).
+ config: optional pre-built Config object (takes priority over config_path).
+ output_dir: where to write logs, checkpoints, and best program.
+ Auto-generated from search type + problem name if omitted.
+ """
+
+ def __init__(
+ self,
+ evaluation_file: str,
+ initial_program_path: Optional[str] = None,
+ config_path: Optional[str] = None,
+ config: Optional[Config] = None,
+ output_dir: Optional[str] = None,
+ evaluator_env_vars: Optional[dict[str, str]] = None,
+ ):
+ self.config = config if config is not None else load_config(config_path)
+ self.name = self.config.search.type
+ self.output_dir = output_dir or build_output_dir(
+ self.name, initial_program_path or "scratch"
+ )
+ os.makedirs(self.output_dir, exist_ok=True)
+ self._setup_logging()
+
+ # Load the initial program (can be optional)
+ self.initial_program_path = initial_program_path
+ self.initial_program_solution = (
+ self._load_initial_program() if initial_program_path else None
+ )
+ if self.initial_program_solution and not self.config.language:
+ self.config.language = extract_solution_language(self.initial_program_solution)
+ if not self.config.language:
+ self.config.language = "python"
+
+ # Set the file extension
+ ext = os.path.splitext(initial_program_path)[1] if initial_program_path else ".py"
+ ext = ext or ".py"
+ self.file_extension = ext if ext.startswith(".") else f".{ext}"
+ if self.config.file_suffix == ".py":
+ self.config.file_suffix = self.file_extension
+
+ # Create the database
+ self.database = create_database(self.config.search.type, self.config.search.database)
+ self.database.language = self.config.language or "python"
+ self.evaluation_file = evaluation_file
+ self.evaluator_env_vars = dict(evaluator_env_vars or {})
+
+ # Initialize the discovery controller
+ self.discovery_controller: Optional[DiscoveryController] = None
+
+ logger.info(f"Runner ready: search={self.name}, program={self.initial_program_path}")
+
+ @property
+ def initial_score(self) -> Optional[float]:
+ """Score of the seed program, or None if unavailable."""
+ if not self.database or not self.database.programs or not self.initial_program_solution:
+ return None
+
+ seed_solution = self.initial_program_solution
+ seed_prog = None
+ for prog in self.database.programs.values():
+ if prog.solution == seed_solution:
+ seed_prog = prog
+ break
+ if seed_prog is None:
+ for prog in self.database.programs.values():
+ if prog.iteration_found == 0:
+ seed_prog = prog
+ break
+
+ if seed_prog and seed_prog.metrics:
+ return get_score(seed_prog.metrics)
+ return None
+
+ async def run(
+ self,
+ iterations: Optional[int] = None,
+ checkpoint_path: Optional[str] = None,
+ ) -> Optional[Program]:
+ """Entrypoint for the discovery process.
+
+ Args:
+ iterations: max iterations (uses config.max_iterations if None).
+ checkpoint_path: resume from this checkpoint directory if provided.
+
+ Returns:
+ Best Program found, or None if no valid programs were produced.
+ """
+ max_iterations = iterations if iterations is not None else self.config.max_iterations
+
+ start_iteration = 0
+ if checkpoint_path and os.path.exists(checkpoint_path):
+ self._load_checkpoint(checkpoint_path)
+ start_iteration = self.database.last_iteration + 1
+ logger.info(f"Resuming from iteration {start_iteration}")
+ else:
+ start_iteration = self.database.last_iteration
+
+ # Create the discovery controller input
+ controller_input = DiscoveryControllerInput(
+ config=self.config,
+ evaluation_file=self.evaluation_file,
+ database=self.database,
+ file_suffix=self.config.file_suffix,
+ output_dir=self.output_dir,
+ evaluator_env_vars=self.evaluator_env_vars,
+ )
+
+ # Get the discovery controller
+ self.discovery_controller = get_discovery_controller(controller_input)
+
+ # Add initial program to database if not resuming
+ should_add_initial = (
+ start_iteration == 0
+ and len(self.database.programs) == 0
+ and self.initial_program_solution is not None
+ )
+
+ if should_add_initial:
+ await self._add_initial_program(start_iteration)
+ else:
+ logger.info(
+ f"Resuming from iteration {start_iteration} with {len(self.database.programs)} programs"
+ )
+
+ # Start the monitor
+ monitor_server = None
+ try:
+ monitor_server = self._start_monitor(max_iterations)
+ self._setup_human_feedback(monitor_server)
+ self._setup_monitor_summary(monitor_server)
+ self._push_existing_to_monitor()
+ self._install_signal_handlers()
+
+ discovery_start = start_iteration + 1 if should_add_initial else start_iteration
+ self.database.log_status()
+
+ def checkpoint_cb(iteration: int) -> None:
+ self._sync_database()
+ self._save_checkpoint(iteration)
+
+ # MAIN LOOP: Run the discovery
+ await self.discovery_controller.run_discovery(
+ discovery_start,
+ max_iterations,
+ checkpoint_callback=checkpoint_cb,
+ )
+
+ self._sync_database()
+ final_iteration = discovery_start + max_iterations - 1
+ if final_iteration > 0:
+ self._save_checkpoint(final_iteration)
+
+ # Re-evaluate best program in test mode (authoritative score).
+ best = self._get_best_program()
+ if best:
+ try:
+ test_result = await self.discovery_controller.evaluator.evaluate_program(
+ best.solution, best.id, mode="test"
+ )
+ for k, v in test_result.metrics.items():
+ best.metrics[f"test_{k}"] = v
+ logger.info(
+ f"Test evaluation for best program: {format_metrics(test_result.metrics)}"
+ )
+ # Persist test metrics to disk so they survive the run.
+ self._save_best_program(best)
+ except Exception as e:
+ logger.warning(f"Test-mode re-evaluation failed: {e}")
+
+ finally:
+ # Stop the monitor
+ early_stopped = (
+ self.discovery_controller is not None
+ and self.discovery_controller.early_stopping_triggered
+ )
+ if self.discovery_controller is not None:
+ self.discovery_controller.close()
+ self.discovery_controller = None
+
+ if monitor_server:
+ try:
+ reason = "early_stopping" if early_stopped else "completed"
+ monitor_server.push_event({"type": "discovery_complete", "reason": reason})
+ time.sleep(0.5)
+ monitor_server.stop()
+ except Exception:
+ logger.debug("Failed to stop monitor server", exc_info=True)
+
+ # Get the best program
+ best_program = self._get_best_program()
+ if best_program:
+ status = "early stopping" if early_stopped else "completed"
+ logger.info(f"Discovery {status}. Best: {format_metrics(best_program.metrics)}")
+ self._save_best_program(best_program)
+ return best_program
+
+ logger.warning("No valid programs found")
+ return None
+
+ # ------------------------------------------------------------------
+ # Initial program
+ # ------------------------------------------------------------------
+
+ async def _add_initial_program(self, start_iteration: int) -> None:
+ logger.info("Adding initial program to database")
+ program_id = str(uuid.uuid4())
+
+ initial_image_path = None
+ if self.config.language == "image":
+ logger.info("Generating initial image from seed text...")
+ img_dir = os.path.join(self.output_dir, "generated_images")
+ try:
+ result = await self.discovery_controller.llms.generate(
+ system_message="Generate an image based on the following description. Also provide brief reasoning about your creative choices.",
+ messages=[{"role": "user", "content": self.initial_program_solution}],
+ image_output=True,
+ output_dir=img_dir,
+ program_id=program_id,
+ )
+ initial_image_path = result.image_path
+ logger.info(f"Initial image: {initial_image_path}")
+ except Exception as e:
+ logger.warning(f"Failed to generate initial image: {e}")
+
+ eval_input = (
+ initial_image_path
+ if self.config.language == "image" and initial_image_path
+ else self.initial_program_solution
+ )
+ eval_result = await self.discovery_controller.evaluator.evaluate_program(
+ eval_input, program_id
+ )
+ metrics = eval_result.metrics
+
+ if not initial_image_path and isinstance(metrics.get("image_path"), str):
+ initial_image_path = metrics.pop("image_path")
+
+ program = get_program(
+ self.config, self.initial_program_solution, program_id, metrics, start_iteration
+ )
+ program.artifacts = eval_result.artifacts
+
+ if initial_image_path:
+ program.metadata = program.metadata or {}
+ program.metadata["image_path"] = initial_image_path
+
+ self.database.add(program)
+ try:
+ self.database.initial_program_id = program.id
+ self.database.initial_program_score = get_score(program.metrics or {})
+ except Exception as e:
+ logger.warning(f"Failed to set initial program score: {e}")
+
+ # ------------------------------------------------------------------
+ # Monitor and feedback setup
+ # ------------------------------------------------------------------
+
+ def _start_monitor(self, max_iterations: int):
+ if not self.config.monitor.enabled:
+ return None
+ try:
+ from skydiscover.extras.monitor import MonitorServer, create_monitor_callback
+
+ server = MonitorServer(
+ host=self.config.monitor.host,
+ port=self.config.monitor.port,
+ max_solution_length=self.config.monitor.max_solution_length,
+ )
+ server.set_config_summary(f"{self.name} | max_iter={max_iterations}")
+ server.start()
+
+ callback = create_monitor_callback(server, self.database, time.time())
+ self.discovery_controller.monitor_callback = callback
+
+ url = f"http://localhost:{server.port}/"
+ print(f"\n Live monitor: {url}\n", flush=True)
+ logger.info(f"Live monitor: {url}")
+ return server
+ except Exception as e:
+ logger.warning(f"Failed to start monitor: {e}")
+ return None
+
+ def _setup_human_feedback(self, monitor_server) -> None:
+ if not (self.config.human_feedback_enabled or monitor_server):
+ return
+ try:
+ from skydiscover.context_builder import HumanFeedbackReader
+
+ path = self.config.human_feedback_file or os.path.join(
+ self.output_dir, "human_feedback.md"
+ )
+ mode = getattr(self.config, "human_feedback_mode", "append")
+ reader = HumanFeedbackReader(path, mode=mode)
+ self.discovery_controller.feedback_reader = reader
+ if monitor_server:
+ monitor_server.set_feedback_reader(reader)
+ logger.info(f"Human feedback: {path}")
+ except Exception as e:
+ logger.warning(f"Failed to set up human feedback: {e}")
+
+ def _setup_monitor_summary(self, monitor_server) -> None:
+ if not (monitor_server and self.config.monitor.summary_model):
+ return
+ try:
+ monitor_server.configure_summary(
+ model=self.config.monitor.summary_model,
+ api_key=self.config.monitor.summary_api_key or "",
+ api_base=self.config.monitor.summary_api_base,
+ top_k=self.config.monitor.summary_top_k,
+ interval=self.config.monitor.summary_interval,
+ )
+ except Exception as e:
+ logger.warning(f"Failed to configure AI summary: {e}")
+
+ def _push_existing_to_monitor(self) -> None:
+ if not (self.discovery_controller.monitor_callback and self.database.programs):
+ return
+ for prog in self.database.programs.values():
+ try:
+ self.discovery_controller.monitor_callback(
+ prog, getattr(prog, "iteration_found", 0)
+ )
+ except Exception:
+ logger.debug("Monitor callback failed for program %s", prog.id, exc_info=True)
+ logger.info(f"Pushed {len(self.database.programs)} existing program(s) to monitor")
+
+ def _install_signal_handlers(self) -> None:
+ def on_signal(signum, frame):
+ logger.info(f"Signal {signum} received, shutting down...")
+ if self.discovery_controller is not None:
+ self.discovery_controller.request_shutdown()
+
+ def force_exit(signum, frame):
+ sys.exit(128 + signum)
+
+ # After the first termination signal, ensure subsequent SIGINT/SIGTERM
+ # cause an immediate exit instead of re-running the soft handler.
+ signal.signal(signal.SIGINT, force_exit)
+ signal.signal(signal.SIGTERM, force_exit)
+
+ signal.signal(signal.SIGINT, on_signal)
+ signal.signal(signal.SIGTERM, on_signal)
+
+ # ------------------------------------------------------------------
+ # Checkpointing and saving
+ # ------------------------------------------------------------------
+
+ def _sync_database(self) -> None:
+ """Ensure we have the controller's latest database"""
+ db = getattr(self.discovery_controller, "database", None)
+ if db is not None and db is not self.database:
+ self.database = db
+
+ def _setup_logging(self) -> None:
+ log_dir = self.config.log_dir or os.path.join(self.output_dir, "logs")
+ setup_search_logging(log_level=self.config.log_level, log_dir=log_dir, name=self.name)
+
+ def _load_initial_program(self) -> str:
+ with open(self.initial_program_path, "r") as f:
+ return f.read()
+
+ def _save_checkpoint(self, iteration: int) -> None:
+ checkpoint_dir = os.path.join(self.output_dir, "checkpoints")
+ checkpoint_path = os.path.join(checkpoint_dir, f"checkpoint_{iteration}")
+ os.makedirs(checkpoint_path, exist_ok=True)
+
+ self.database.save(checkpoint_path, iteration)
+
+ best = self._get_best_program()
+ if best:
+ with open(
+ os.path.join(checkpoint_path, f"best_program{self.file_extension}"), "w"
+ ) as f:
+ f.write(best.solution)
+ with open(os.path.join(checkpoint_path, "best_program_info.json"), "w") as f:
+ from skydiscover.search.utils.checkpoint_manager import SafeJSONEncoder
+
+ json.dump(
+ {
+ "id": best.id,
+ "generation": best.generation,
+ "iteration": best.iteration_found,
+ "current_iteration": iteration,
+ "metrics": best.metrics,
+ "language": best.language,
+ "timestamp": best.timestamp,
+ "saved_at": time.time(),
+ },
+ f,
+ indent=2,
+ cls=SafeJSONEncoder,
+ )
+ logger.info(f"Checkpoint {iteration}: best={format_metrics(best.metrics)}")
+
+ logger.info(f"Checkpoint saved to {checkpoint_path}")
+
+ def _load_checkpoint(self, checkpoint_path: str) -> None:
+ if not os.path.exists(checkpoint_path):
+ raise FileNotFoundError(f"Checkpoint not found: {checkpoint_path}")
+ self.database.load(checkpoint_path)
+ logger.info(f"Loaded checkpoint (iteration {self.database.last_iteration})")
+
+ def _get_best_program(self) -> Optional[Program]:
+ if self.database.best_program_id:
+ prog = self.database.get(self.database.best_program_id)
+ if prog:
+ return prog
+ return self.database.get_best_program()
+
+ def _save_best_program(self, program: Program) -> None:
+ best_dir = os.path.join(self.output_dir, "best")
+ os.makedirs(best_dir, exist_ok=True)
+
+ code_path = os.path.join(best_dir, f"best_program{self.file_extension}")
+ with open(code_path, "w") as f:
+ f.write(program.solution)
+
+ info_path = os.path.join(best_dir, "best_program_info.json")
+ with open(info_path, "w") as f:
+ from skydiscover.search.utils.checkpoint_manager import SafeJSONEncoder
+
+ json.dump(
+ {
+ "id": program.id,
+ "generation": program.generation,
+ "iteration": program.iteration_found,
+ "timestamp": program.timestamp,
+ "parent_id": program.parent_id,
+ "metrics": program.metrics,
+ "language": program.language,
+ "saved_at": time.time(),
+ },
+ f,
+ indent=2,
+ cls=SafeJSONEncoder,
+ )
+
+ if self.config.language == "image" and program.metadata:
+ img = program.metadata.get("image_path")
+ if img and os.path.exists(img):
+ import shutil
+
+ shutil.copy2(img, os.path.join(best_dir, "best_image" + os.path.splitext(img)[1]))
+
+ logger.info(f"Best program saved to {best_dir}")
diff --git a/skydiscover/search/README.md b/skydiscover/search/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..0ade4adf1d3c55a07ddaff662a2f2f2dd5063813
--- /dev/null
+++ b/skydiscover/search/README.md
@@ -0,0 +1,226 @@
+# Search Algorithms
+
+This is the core of skydiscover — where algorithms decide *which* programs to evolve and *how* to evolve them.
+
+Every algorithm plugs into the same loop: `sample → prompt → LLM → evaluate → add`.
+You only implement what changes; everything else is inherited.
+
+There are two levels of customization:
+
+| Level | What you build | When you need it |
+|-------|---------------|-----------------|
+| **Database only** | `add()` and `sample()` | Different parent selection or storage logic |
+| **Database + Controller** | `run_discovery()` loop | Cross-iteration behavior: stagnation response, island rotation, acceptance gating |
+
+---
+
+## Level 1: Database only
+
+Subclass `ProgramDatabase` and implement two methods. The default controller runs the loop unchanged.
+
+```python
+from skydiscover.search.base_database import Program, ProgramDatabase
+
+class MyDatabase(ProgramDatabase):
+
+ def __init__(self, name: str, config):
+ # read any custom config attributes here, before super().__init__,
+ # if they are needed during load() -> add() on startup
+ super().__init__(name, config)
+
+ def add(self, program: Program, iteration=None, **kwargs) -> str:
+ self.programs[program.id] = program
+ if iteration is not None:
+ self.last_iteration = max(self.last_iteration, iteration)
+ if self.config.db_path:
+ self._save_program(program)
+ self._update_best_program(program) # required
+ return program.id
+
+ def sample(self, num_context_programs=4, **kwargs):
+ parent = ... # pick the program to mutate from
+ context = ... # pick additional programs to show as examples
+ return parent, context
+```
+
+Register in `route.py`:
+
+```python
+from skydiscover.search.my_algo.database import MyDatabase
+register_database("my_algo", MyDatabase)
+```
+
+That is all. `--search my_algo` now works.
+
+Examples at this level: `topk/` (56 lines), `best_of_n/` (85 lines), `beam_search/` (527 lines).
+
+### `ProgramDatabase` helpers
+
+| Method / attribute | What it does |
+|--------------------|-------------|
+| `self.programs` | `dict[str, Program]` storing all programs |
+| `self._update_best_program(program)` | Updates the global best. Call this in every `add()`. |
+| `self._save_program(program)` | Persists a program to disk (no-op if no db_path) |
+| `self.get_top_programs(n)` | Returns top n programs by score |
+| `self.get_best_program()` | Returns the highest-scoring program seen |
+
+### `Program` fields
+
+| Field | Type | Notes |
+|-------|------|-------|
+| `id` | `str` | UUID. Use as dict key. |
+| `parent_id` | `str` or None | ID of the program this was mutated from |
+| `solution` | `str` | Source code or prompt text |
+| `metrics` | `dict` | Evaluation results, includes `combined_score` |
+| `iteration_found` | `int` | Iteration that produced this program |
+| `metadata` | `dict` | Arbitrary extra data |
+
+---
+
+## Level 2: Database + Controller
+
+Use this when you need behavior that spans across iterations: tracking improvement history, reacting to stagnation, filtering results before they enter the population.
+
+The key point: you do not rewrite the generate-evaluate logic. You call `_run_iteration()`, which runs the full `sample → prompt → LLM → evaluate` cycle, and then decide what to do with the result.
+
+```python
+from skydiscover.search.default_discovery_controller import DiscoveryController, DiscoveryControllerInput
+
+class MyController(DiscoveryController):
+
+ def __init__(self, controller_input: DiscoveryControllerInput):
+ super().__init__(controller_input)
+
+ async def run_discovery(self, start_iteration, max_iterations, **kwargs):
+ for iteration in range(start_iteration, start_iteration + max_iterations):
+ if self.shutdown_event.is_set():
+ break
+
+ result = await self._run_iteration(iteration)
+
+ if result.error:
+ continue
+
+ # optional: filter before storing
+ self._process_iteration_result(result, iteration, kwargs.get("checkpoint_callback"))
+
+ # optional: cross-iteration logic here
+
+ return self.database.get_best_program()
+```
+
+Register both:
+
+```python
+register_database("my_algo", MyDatabase)
+register_controller("my_algo", MyController)
+```
+
+Examples at this level: `adaevolve/` (multi-island UCB search), `gepa_native/` (acceptance gating + merge), `evox/` (co-evolves the search algorithm itself).
+
+### Controller primitives
+
+| Method | What it does |
+|--------|-------------|
+| `await self._run_iteration(iteration)` | Full single-step cycle. Returns `SerializableResult`. |
+| `self._process_iteration_result(result, iteration, cb)` | Stores to DB, logs, triggers checkpoint. |
+| `self.database.get_best_program()` | Returns the best program seen so far. |
+| `self.shutdown_event.is_set()` | True when graceful shutdown is requested. |
+
+`SerializableResult` fields: `error` (str or None), `child_program_dict` (dict or None), `parent_id` (str or None).
+
+---
+
+## Config dataclass (optional)
+
+If your algorithm has custom settings, add a dataclass in `skydiscover/config.py`:
+
+```python
+@dataclass
+class MyDatabaseConfig(DatabaseConfig):
+ my_param: float = 1.0
+```
+
+Add it to `_DB_CONFIG_BY_TYPE`:
+
+```python
+"my_algo": MyDatabaseConfig,
+```
+
+Then users can set it in `config.yaml`:
+
+```yaml
+search:
+ type: my_algo
+ database:
+ my_param: 2.0
+```
+
+---
+
+## Registration Reference
+
+All registrations happen in `route.py`:
+
+```python
+# Level 1: database only (uses default DiscoveryController)
+register_database("topk", TopKDatabase)
+register_database("best_of_n", BestOfNDatabase)
+register_database("beam_search", BeamSearchDatabase)
+register_database("openevolve_native", OpenEvolveNativeDatabase)
+
+# Level 2: database + controller
+register_database("adaevolve", AdaEvolveDatabase)
+register_controller("adaevolve", AdaEvolveController)
+
+register_database("gepa_native", GEPANativeDatabase)
+register_controller("gepa_native", GEPANativeController)
+
+# Level 2: controller + dynamic database
+register_controller("evox", CoEvolutionController)
+register_database("evox_meta", SearchStrategyDatabase)
+```
+
+`get_discovery_controller()` in `route.py` looks up the controller registry: if a controller is registered for the search type it is used, otherwise the default `DiscoveryController` is returned.
+
+---
+
+## `ProgramDatabase` API
+
+Override the abstract methods; the rest are inherited.
+
+| Method | Abstract? | Purpose |
+|--------|-----------|---------|
+| `add(program, iteration)` | Yes | Store a scored program |
+| `sample(num_context_programs)` | Yes | Select parent and context |
+| `save(path, iteration)` | No | Checkpoint to disk |
+| `load(path)` | No | Restore from checkpoint |
+| `_update_best_program(program)` | No | Track best program (call from `add`) |
+| `get_best_program()` | No | Return highest-scoring program |
+| `get_top_programs(n)` | No | Return top N programs by score |
+| `get(program_id)` | No | Retrieve by ID |
+| `log_status()` | No | Log database summary |
+| `get_statistics()` | No | Return stats dict for prompt context |
+| `log_prompt(...)` | No | Store prompt and response for a program |
+
+---
+
+## Directory structure
+
+```
+search/
+ base_database.py Program dataclass + ProgramDatabase ABC
+ default_discovery_controller.py Default loop + iteration primitives
+ registry.py register_database / register_controller / register_program
+ route.py --search flag to class mapping
+
+ topk/ Level 1: simplest example
+ best_of_n/ Level 1
+ beam_search/ Level 1
+
+ adaevolve/ Level 2: database + controller + components
+ gepa_native/ Level 2: database + controller
+ evox/ Level 2: co-evolutionary search
+
+ utils/ Shared: checkpointing, logging, serialization
+```
diff --git a/skydiscover/search/base_database.py b/skydiscover/search/base_database.py
new file mode 100644
index 0000000000000000000000000000000000000000..f24975aef9b703fd08df3458f6ee6e678d0aad65
--- /dev/null
+++ b/skydiscover/search/base_database.py
@@ -0,0 +1,563 @@
+"""
+Clean interfaces for ProgramDatabase classes.
+
+This module defines abstract base classes that capture the essential operations
+needed for program database operations.
+"""
+
+from __future__ import annotations
+
+import logging
+import os
+import time
+from abc import ABC, abstractmethod
+from dataclasses import asdict, dataclass, field, fields
+from typing import Any, Dict, List, Optional, Tuple, Union
+
+from skydiscover.config import DatabaseConfig
+from skydiscover.utils.metrics import format_metrics, get_score
+
+logger = logging.getLogger(__name__)
+
+
+@dataclass
+class Program:
+ """Represents a program in the database"""
+
+ # Program identification
+ id: str
+ solution: str
+ language: str = "python"
+
+ # Performance
+ metrics: Dict[str, Any] = field(default_factory=dict)
+
+ # Tracking information
+ iteration_found: int = 0
+ parent_id: Optional[str] = None # Parent program ID it mutates from
+ other_context_ids: Optional[List[str]] = (
+ None # other program IDs to provide as context to generate this program
+ )
+ parent_info: Optional[Tuple[str, str]] = None # information about the parent program
+ context_info: Optional[List[Tuple[str, str]]] = None # information about the context programs
+
+ timestamp: float = field(default_factory=time.time)
+
+ # Metadata
+ metadata: Dict[str, Any] = field(default_factory=dict)
+ artifacts: Dict[str, Any] = field(default_factory=dict)
+
+ # Prompts
+ prompts: Optional[Dict[str, Any]] = None
+ generation: int = 0
+
+ def to_dict(self) -> Dict[str, Any]:
+ """Convert to dictionary representation"""
+ return asdict(self)
+
+ @classmethod
+ def from_dict(cls, data: Dict[str, Any]) -> Program:
+ """Create from dictionary representation"""
+ # Get the valid field names for the Program dataclass
+ valid_fields = {f.name for f in fields(cls)}
+
+ # Filter the data to only include valid fields
+ filtered_data = {k: v for k, v in data.items() if k in valid_fields}
+
+ # Log if we're filtering out any fields
+ if len(filtered_data) != len(data):
+ filtered_out = set(data.keys()) - set(filtered_data.keys())
+ logger.debug(f"Filtered out unsupported fields when loading Program: {filtered_out}")
+
+ return cls(**filtered_data)
+
+
+class ProgramDatabase(ABC):
+ """
+ Abstract base class for program storage and sampling.
+
+ This interface captures the essential operations needed for any discovery process:
+ - Add a program to the database
+ - Sample a program and context programs to learn from past experiences for the next discovery step
+ """
+
+ def __init__(self, name: str, config: DatabaseConfig, **kwargs: Any):
+ self.name = name
+ self.config = config
+
+ # In-memory program storage
+ self.programs: Dict[str, Program] = {}
+
+ # Track the last iteration number (for resuming)
+ self.last_iteration: int = 0
+
+ # Optionally track initial program info (set by controller on first add)
+ self.initial_program_id: Optional[str] = None
+ self.initial_program_score: Optional[float] = None
+
+ # Best program tracking
+ self.best_program_id: Optional[str] = None
+
+ # Prompt log
+ self.prompts_by_program: Optional[Dict[str, Dict[str, Dict[str, str]]]] = None
+
+ # Initialize checkpoint manager (imported here to avoid circular imports)
+ from skydiscover.search.utils.checkpoint_manager import CheckpointManager
+
+ self.checkpoint_manager = CheckpointManager(self.config)
+
+ # Load database from disk if path is provided
+ if config.db_path and os.path.exists(config.db_path):
+ self.load(config.db_path)
+
+ # ------------------------------------------------------------------
+ # Abstract methods — implement these in subclasses
+ # ------------------------------------------------------------------
+
+ @abstractmethod
+ def add(self, program: Program, iteration: Optional[int] = None, **kwargs: Any) -> str:
+ """Add a program to the database.
+
+ Args:
+ program: Program to add.
+ iteration: Current iteration (for tracking).
+
+ Returns:
+ Program ID.
+ """
+ ...
+
+ @abstractmethod
+ def sample(
+ self,
+ num_context_programs: Optional[int] = 4,
+ **kwargs: Any,
+ ) -> Tuple[
+ Union[Program, Dict[str, Program]],
+ Union[List[Program], Dict[str, List[Program]]],
+ ]:
+ """Sample a parent program and context programs for discovery.
+
+ Args:
+ num_context_programs: Number of context programs to sample.
+ **kwargs: Search-specific parameters.
+
+ Returns:
+ (parent, context_programs) — each can be plain or dict-wrapped.
+ Plain: (Program, [Program, ...])
+ Dict-wrapped: ({info: Program}, {info: [Program, ...]})
+ where the key is additional information about the program.
+ """
+ ...
+
+ # ------------------------------------------------------------------
+ # Save and load
+ # ------------------------------------------------------------------
+ def save(self, path: Optional[str] = None, iteration: int = 0) -> None:
+ """
+ Save the database to disk
+
+ Args:
+ path: Path to save to (uses config.db_path if None)
+ iteration: Current iteration number
+ """
+ self.checkpoint_manager.save(
+ programs=self.programs,
+ prompts_by_program=self.prompts_by_program,
+ best_program_id=self.best_program_id,
+ last_iteration=iteration if iteration is not None else self.last_iteration,
+ path=path,
+ )
+
+ def load(self, path: str) -> None:
+ """
+ Load the database from disk
+
+ Args:
+ path: Path to load from
+ """
+ programs, best_id, last_iter = self.checkpoint_manager.load(path)
+ self.programs = programs
+ self.best_program_id = best_id
+ self.last_iteration = last_iter
+
+ self.log_status()
+
+ def _save_program(
+ self,
+ program: Program,
+ base_path: Optional[str] = None,
+ prompts: Optional[Dict[str, Dict[str, str]]] = None,
+ ) -> None:
+ """
+ Save a single program to disk.
+
+ This is a convenience method that delegates to CheckpointManager.
+ Subclasses should use this method when they need to save individual programs
+ (e.g., during add() operations).
+
+ Args:
+ program: Program to save
+ base_path: Base path to save to (uses config.db_path if None)
+ prompts: Optional prompts to save with the program
+ """
+ self.checkpoint_manager._save_program(program, base_path, prompts)
+
+ # ------------------------------------------------------------------
+ # Best program tracking
+ # ------------------------------------------------------------------
+
+ def _is_better(self, program1: Program, program2: Program) -> bool:
+ """Determine if program1 has better fitness than program2."""
+ if not program1.metrics and not program2.metrics:
+ # No evidence either way — keep the current best.
+ return False
+ if program1.metrics and not program2.metrics:
+ return True
+ if not program1.metrics and program2.metrics:
+ return False
+ return get_score(program1.metrics) > get_score(program2.metrics)
+
+ def _update_best_program(self, program: Program) -> None:
+ """Update the best program tracking after a new program is added."""
+ if self.best_program_id is None:
+ self.best_program_id = program.id
+ logger.debug(f"Set initial best program to {program.id}")
+ return
+ # If the best program is not in the database, set it to the new program
+ if self.best_program_id not in self.programs:
+ self.best_program_id = program.id
+ return
+
+ current_best = self.programs[self.best_program_id]
+
+ # If the new program is better than the current best, set it to the new program
+ if self._is_better(program, current_best):
+ self.best_program_id = program.id
+
+ def get_best_program(self, metric: Optional[str] = None) -> Optional[Program]:
+ """Get the best program, optionally by a specific metric."""
+ if not self.programs:
+ return None
+
+ if metric is None and self.best_program_id:
+ if self.best_program_id in self.programs:
+ return self.programs[self.best_program_id]
+ else:
+ logger.warning(
+ f"Tracked best program {self.best_program_id} no longer exists, will recalculate"
+ )
+ self.best_program_id = None
+
+ if metric:
+ sorted_programs = sorted(
+ [p for p in self.programs.values() if metric in p.metrics],
+ key=lambda p: p.metrics[metric],
+ reverse=True,
+ )
+ else:
+ sorted_programs = sorted(
+ self.programs.values(),
+ key=lambda p: get_score(p.metrics),
+ reverse=True,
+ )
+
+ if sorted_programs and (
+ self.best_program_id is None or sorted_programs[0].id != self.best_program_id
+ ):
+ self.best_program_id = sorted_programs[0].id
+
+ return sorted_programs[0] if sorted_programs else None
+
+ def get_top_programs(self, n: int = 10, metric: Optional[str] = None) -> List[Program]:
+ """Get the top N programs, optionally by a specific metric."""
+ if not self.programs:
+ return []
+
+ if metric:
+ sorted_programs = sorted(
+ [p for p in self.programs.values() if metric in p.metrics],
+ key=lambda p: p.metrics[metric],
+ reverse=True,
+ )
+ else:
+ sorted_programs = sorted(
+ self.programs.values(),
+ key=lambda p: get_score(p.metrics),
+ reverse=True,
+ )
+
+ return sorted_programs[:n]
+
+ def get(self, program_id: str) -> Optional[Program]:
+ """Get a program by ID"""
+ return self.programs.get(program_id)
+
+ # ------------------------------------------------------------------
+ # Prompt logging
+ # ------------------------------------------------------------------
+ def log_prompt(
+ self,
+ program_id: str,
+ template_key: str,
+ prompt: Dict[str, str],
+ responses: Optional[List[str]] = None,
+ ) -> None:
+ """
+ Log a prompt for a program.
+ Only logs if self.config.log_prompts is True.
+
+ Args:
+ program_id: ID of the program to log the prompt for
+ template_key: Key for the prompt template
+ prompt: Prompts in the format {template_key: { 'system': str, 'user': str }}.
+ responses: Optional list of responses to the prompt, if available.
+ """
+
+ if not self.config.log_prompts:
+ return
+
+ if responses is None:
+ responses = []
+ prompt["responses"] = responses
+
+ if self.prompts_by_program is None:
+ self.prompts_by_program = {}
+
+ if program_id not in self.prompts_by_program:
+ self.prompts_by_program[program_id] = {}
+ self.prompts_by_program[program_id][template_key] = prompt
+
+ def log_status(self) -> None:
+ """Log the status of the database"""
+ best_program = self.get_best_program()
+ if best_program and best_program.metrics:
+ score_str = format_metrics(best_program.metrics)
+ else:
+ score_str = "N/A"
+ logger.info(
+ f"Database has {len(self.programs)} programs, best program score is {score_str}"
+ )
+
+ def get_statistics(
+ self, num_recent_iterations: int = 100, k: int = 20, improvement_threshold: float = 0.10
+ ) -> Dict[str, Any]:
+ """
+ Get statistics about the database population.
+
+ Args:
+ num_recent_iterations: Number of recent iterations to include in trajectory stats
+ k: Number of top scores to return
+ improvement_threshold: Minimum score improvement to count as meaningful improvement.
+ """
+ import statistics
+
+ population_size = len(self.programs)
+
+ if self.programs:
+ actual_last_iter = max(
+ (
+ p.iteration_found
+ for p in self.programs.values()
+ if isinstance(p.iteration_found, int)
+ ),
+ default=0,
+ )
+ self.last_iteration = max(self.last_iteration, actual_last_iter)
+ else:
+ actual_last_iter = 0
+
+ scores = [
+ p.metrics.get("combined_score")
+ for p in self.programs.values()
+ if p.metrics.get("combined_score") is not None
+ ]
+
+ if scores:
+ scores_sorted = sorted(scores, reverse=True)
+ n = len(scores_sorted)
+ if n >= 4:
+ quartiles = statistics.quantiles(scores, n=4)
+ q25, q50, q75 = quartiles[0], quartiles[1], quartiles[2]
+ else:
+ q25 = q50 = q75 = statistics.median(scores)
+ score_stats = {
+ "best": scores_sorted[0],
+ "q75": q75,
+ "q50": q50,
+ "q25": q25,
+ "worst": scores_sorted[-1],
+ }
+
+ top_scores = scores_sorted[:k]
+ n = len(scores)
+
+ pct_top = sum(1 for s in scores if s >= q75) / n * 100
+ pct_upper_mid = sum(1 for s in scores if q50 <= s < q75) / n * 100
+ pct_lower_mid = sum(1 for s in scores if q25 <= s < q50) / n * 100
+ pct_bottom = sum(1 for s in scores if s < q25) / n * 100
+
+ unique_scores = len(set(round(s, 4) for s in scores))
+
+ score_stats["score_tiers"] = {
+ "top": {"threshold": f"score >= {q75:.4f}", "pct_programs": pct_top},
+ "upper_mid": {
+ "threshold": f"{q50:.4f} <= score < {q75:.4f}",
+ "pct_programs": pct_upper_mid,
+ },
+ "lower_mid": {
+ "threshold": f"{q25:.4f} <= score < {q50:.4f}",
+ "pct_programs": pct_lower_mid,
+ },
+ "bottom": {"threshold": f"score < {q25:.4f}", "pct_programs": pct_bottom},
+ }
+ score_stats["unique_scores"] = unique_scores
+ else:
+ score_stats = {"best": None, "q75": None, "q50": None, "q25": None, "worst": None}
+ top_scores = []
+
+ programs_with_parents = [p for p in self.programs.values() if p.parent_id is not None]
+ unique_parents = len({p.parent_id for p in programs_with_parents})
+ avg_solutions_per_parent = (
+ len(programs_with_parents) / unique_parents if unique_parents > 0 else 0.0
+ )
+
+ iterations_without_improvement = 0
+ if scores:
+ best_score = max(scores)
+ near_best_programs = [
+ p
+ for p in self.programs.values()
+ if p.metrics.get("combined_score") is not None
+ and p.metrics.get("combined_score") >= best_score - improvement_threshold
+ ]
+ if near_best_programs:
+ iteration_near_best_achieved = min(
+ p.iteration_found
+ for p in near_best_programs
+ if isinstance(p.iteration_found, int)
+ )
+ iterations_without_improvement = actual_last_iter - iteration_near_best_achieved
+
+ if not self.programs:
+ recent_stats = {}
+ recent_programs = []
+ else:
+ recent_programs = [
+ p
+ for p in self.programs.values()
+ if p.metrics.get("combined_score") is not None
+ and isinstance(p.iteration_found, int)
+ and p.iteration_found > actual_last_iter - num_recent_iterations
+ ]
+ recent_programs.sort(key=lambda p: p.iteration_found)
+
+ execution_trace = []
+ recent_scores = []
+ parent_scores = []
+
+ for p in recent_programs:
+ prog_id = p.id
+ prog_score = p.metrics.get("combined_score")
+ recent_scores.append(prog_score)
+
+ if p.parent_id is not None:
+ parent_id = p.parent_id
+ parent_label = None
+ if p.parent_info is not None:
+ parent_label = p.parent_info[0]
+ if parent_id in self.programs:
+ parent_program = self.programs[parent_id]
+ parent_score = parent_program.metrics.get("combined_score")
+ parent_scores.append(parent_score)
+ else:
+ parent_score = None
+ parent_scores.append(None)
+ parent_tuple = (parent_label, parent_id, parent_score)
+ else:
+ parent_tuple = None
+ parent_scores.append(None)
+
+ context_tuples = []
+ if p.other_context_ids:
+ context_label_map = {}
+ if p.context_info is not None:
+ for label, ctx_id in p.context_info:
+ context_label_map[ctx_id] = label
+
+ for other_context_id in p.other_context_ids:
+ ctx_label = context_label_map.get(other_context_id)
+ if other_context_id in self.programs:
+ ctx_score = self.programs[other_context_id].metrics.get(
+ "combined_score"
+ )
+ context_tuples.append((ctx_label, other_context_id, ctx_score))
+ else:
+ context_tuples.append((ctx_label, other_context_id, None))
+
+ trace_entry = {
+ "iteration": p.iteration_found,
+ "program": (prog_id, prog_score),
+ "parent": parent_tuple,
+ "context": context_tuples if context_tuples else None,
+ }
+ execution_trace.append(trace_entry)
+
+ from collections import Counter
+
+ recent_programs_with_parents = [p for p in recent_programs if p.parent_id is not None]
+
+ most_reused_parent_ratio = 0.0
+ most_reused_parent_score = None
+ most_reused_context_ratio = 0.0
+ most_reused_context_score = None
+
+ if recent_programs_with_parents:
+ parent_counts = Counter(
+ p.parent_id for p in recent_programs_with_parents if p.parent_id
+ )
+ if parent_counts:
+ top_parent_id, parent_count = parent_counts.most_common(1)[0]
+ most_reused_parent_ratio = parent_count / len(recent_programs_with_parents)
+ if top_parent_id in self.programs:
+ most_reused_parent_score = self.programs[top_parent_id].metrics.get(
+ "combined_score"
+ )
+
+ if recent_programs:
+ context_counts = Counter()
+ programs_with_context = 0
+ for p in recent_programs:
+ if p.other_context_ids:
+ programs_with_context += 1
+ for ctx_id in p.other_context_ids:
+ context_counts[ctx_id] += 1
+ if context_counts and programs_with_context > 0:
+ top_ctx_id, context_count = context_counts.most_common(1)[0]
+ most_reused_context_ratio = context_count / programs_with_context
+ if top_ctx_id in self.programs:
+ most_reused_context_score = self.programs[top_ctx_id].metrics.get(
+ "combined_score"
+ )
+
+ recent_stats = {
+ "num_recent_iterations": min(num_recent_iterations, len(recent_scores)),
+ "execution_trace": execution_trace,
+ "score_trajectory": recent_scores,
+ "parent_scores": parent_scores,
+ "iterations_without_improvement": iterations_without_improvement,
+ "improvement_threshold": improvement_threshold,
+ "most_reused_parent_ratio": most_reused_parent_ratio,
+ "most_reused_parent_score": most_reused_parent_score,
+ "most_reused_context_ratio": most_reused_context_ratio,
+ "most_reused_context_score": most_reused_context_score,
+ }
+
+ return {
+ "previous_programs": recent_programs,
+ "population_size": population_size,
+ "solution_score_summary": score_stats,
+ "avg_solutions_per_parent": avg_solutions_per_parent,
+ "top_solution_scores": top_scores,
+ "recent_solution_stats": recent_stats,
+ }
diff --git a/skydiscover/search/beam_search/README.md b/skydiscover/search/beam_search/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..54dce3b9a46d5d55ffddbacb10d41cc0b3504143
--- /dev/null
+++ b/skydiscover/search/beam_search/README.md
@@ -0,0 +1,44 @@
+# Beam Search
+
+Maintains a fixed-width beam of the most promising programs. At each iteration the parent is selected from the beam using one of four strategies; the beam is then pruned back to `beam_width` by fitness and optional diversity.
+
+## Algorithm
+
+```
+add(program):
+ add to beam
+ if len(beam) > beam_width:
+ prune: keep top beam_width by fitness (+ diversity bonus if beam_diversity_weight > 0)
+
+sample():
+ parent = select from beam using beam_selection_strategy
+ other context = top programs globally (may overlap with beam)
+```
+
+Beam depth is tracked per program via parent linkage and can be penalized in scoring via `beam_depth_penalty`.
+
+## Selection strategies
+
+| Strategy | Behavior |
+|----------|----------|
+| `diversity_weighted` | Softmax over `(1 - w) * fitness + w * diversity_from_recently_expanded` (default) |
+| `stochastic` | Softmax-weighted random sampling by score at temperature `beam_temperature` |
+| `round_robin` | Cycles through beam members in score order |
+| `best` | Always picks the highest-scoring beam member |
+
+## Config
+
+```yaml
+search:
+ type: "beam_search"
+ database:
+ beam_width: 5
+ beam_selection_strategy: "diversity_weighted"
+ beam_diversity_weight: 0.3 # weight for diversity term (0 = pure fitness)
+ beam_temperature: 1.0 # temperature for stochastic / diversity_weighted
+ beam_depth_penalty: 0.0 # exponential penalty per depth level (0 = disabled)
+```
+
+## When to use
+
+Beam search prevents the population from collapsing to a single lineage, which Top-K is prone to. `diversity_weighted` is the default and generally works well. Increase `beam_diversity_weight` if runs tend to converge prematurely; decrease it on tasks where exploitation matters more.
diff --git a/skydiscover/search/beam_search/__init__.py b/skydiscover/search/beam_search/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..052a394a3530ee79ac8e9bfd26a66b7ba11984a9
--- /dev/null
+++ b/skydiscover/search/beam_search/__init__.py
@@ -0,0 +1,10 @@
+"""
+Beam Search module for selecting programs from the solution database.
+
+This module provides a beam search-based database for parent selection,
+maintaining a fixed-width beam of the most promising candidates.
+"""
+
+from skydiscover.search.beam_search.database import BeamSearchDatabase
+
+__all__ = ["BeamSearchDatabase"]
diff --git a/skydiscover/search/beam_search/database.py b/skydiscover/search/beam_search/database.py
new file mode 100644
index 0000000000000000000000000000000000000000..3f17b3ec18d3c72436e50f9949e4017e5f9584a9
--- /dev/null
+++ b/skydiscover/search/beam_search/database.py
@@ -0,0 +1,698 @@
+"""
+Beam Search Database for selecting programs from the solution database.
+
+Beam search maintains a fixed-width "beam" of the most promising candidates,
+exploring multiple paths in parallel while pruning less promising directions.
+
+Key features:
+- Maintains top-K candidates (beam) at each depth level
+- Supports multiple parent selection strategies
+- Tracks search tree depth for analysis
+- Optional diversity bonus to prevent beam collapse
+"""
+
+import json
+import logging
+import math
+import os
+import random
+from collections import defaultdict
+from typing import Any, Dict, List, Optional, Set, Tuple
+
+from skydiscover.config import DatabaseConfig
+from skydiscover.search.base_database import Program, ProgramDatabase
+
+logger = logging.getLogger(__name__)
+
+
+class BeamSearchDatabase(ProgramDatabase):
+ """
+ Database implementing beam search for parent selection.
+
+ Beam search maintains a fixed number of "active" candidates (the beam),
+ expanding from the most promising programs while pruning others.
+
+ Configuration options (via DatabaseConfig attributes):
+ beam_width: Number of candidates to keep in beam (default: 5)
+ beam_selection_strategy: How to pick parent from beam
+ - "best": Always pick highest scoring
+ - "stochastic": Weighted random by score
+ - "round_robin": Cycle through beam members
+ - "diversity_weighted": Balance score and diversity (default)
+ beam_diversity_weight: Weight for diversity in selection (default: 0.3)
+ beam_temperature: Temperature for stochastic selection (default: 1.0)
+ beam_depth_penalty: Penalty factor for deep programs (default: 0.0)
+ """
+
+ def __init__(self, name: str, config: DatabaseConfig):
+ # Initialize beam-specific attributes BEFORE super().__init__()
+ # because super().__init__() may call load() which needs these
+ self.beam_width = getattr(config, "beam_width", 5)
+ self.selection_strategy = getattr(config, "beam_selection_strategy", "diversity_weighted")
+ self.diversity_weight = getattr(config, "beam_diversity_weight", 0.3)
+ self.temperature = getattr(config, "beam_temperature", 1.0)
+ self.depth_penalty = getattr(config, "beam_depth_penalty", 0.0)
+
+ # Track program depths in search tree
+ self.depth: Dict[str, int] = {}
+
+ # Current beam (set of program IDs)
+ self.beam: Set[str] = set()
+
+ # Track which programs have been expanded (had children generated)
+ self.expanded: Set[str] = set()
+
+ # Round-robin state
+ self._rr_index = 0
+
+ # Statistics for analysis
+ self.stats: Dict[str, Any] = {
+ "total_expansions": 0,
+ "max_depth_reached": 0,
+ "beam_updates": 0,
+ "diversity_scores": [],
+ }
+
+ # Now call super().__init__() which may trigger load()
+ super().__init__(name, config)
+
+ logger.info(
+ f"BeamSearchDatabase initialized: width={self.beam_width}, "
+ f"strategy={self.selection_strategy}, diversity_weight={self.diversity_weight}"
+ )
+
+ def add(self, program: Program, iteration: Optional[int] = None, **kwargs) -> str:
+ """
+ Add a program to the database and update the beam.
+
+ The beam is updated to always contain the top beam_width programs,
+ considering both fitness scores and optionally diversity.
+
+ Args:
+ program: Program to add
+ iteration: Current iteration (for tracking)
+
+ Returns:
+ Program ID
+ """
+ # Store the program
+ self.programs[program.id] = program
+
+ # Track iteration
+ if iteration is not None:
+ self.last_iteration = max(self.last_iteration, iteration)
+
+ # Calculate depth from parent
+ if program.parent_id and program.parent_id in self.depth:
+ self.depth[program.id] = self.depth[program.parent_id] + 1
+ else:
+ self.depth[program.id] = 0
+
+ # Update max depth statistic
+ self.stats["max_depth_reached"] = max(
+ self.stats["max_depth_reached"], self.depth[program.id]
+ )
+
+ # Update the beam
+ self._update_beam(program)
+
+ # Update best program tracking (from base class)
+ self._update_best_program(program)
+
+ # Save to disk if configured
+ if self.config.db_path:
+ self._save_program(program)
+
+ logger.debug(
+ f"Added program {program.id} at depth {self.depth[program.id]}, "
+ f"beam size: {len(self.beam)}"
+ )
+
+ return program.id
+
+ def _update_beam(self, new_program: Program) -> None:
+ """
+ Update the beam to include the new program if it's good enough.
+
+ The beam always contains the best beam_width programs based on
+ fitness score, with optional diversity consideration.
+ """
+ # Add to beam candidates
+ self.beam.add(new_program.id)
+
+ # If beam exceeds width, prune to best beam_width
+ if len(self.beam) > self.beam_width:
+ self._prune_beam()
+ self.stats["beam_updates"] += 1
+
+ def _prune_beam(self) -> None:
+ """
+ Prune beam to beam_width, keeping the best candidates.
+
+ Uses a combination of fitness score and diversity to select
+ which programs to keep.
+ """
+ if len(self.beam) <= self.beam_width:
+ return
+
+ # Get all beam programs with their scores
+ beam_programs = []
+ for pid in self.beam:
+ prog = self.programs.get(pid)
+ if prog:
+ score = self._get_program_score(prog)
+ beam_programs.append((pid, prog, score))
+
+ if self.diversity_weight > 0:
+ # Use diversity-aware selection
+ selected = self._diverse_selection(beam_programs, self.beam_width)
+ else:
+ # Pure fitness-based selection
+ beam_programs.sort(key=lambda x: x[2], reverse=True)
+ selected = [bp[0] for bp in beam_programs[: self.beam_width]]
+
+ self.beam = set(selected)
+
+ def _diverse_selection(self, candidates: List[Tuple[str, Program, float]], k: int) -> List[str]:
+ """
+ Select k programs balancing fitness and diversity.
+
+ Uses a greedy algorithm that iteratively selects the program
+ that maximizes a combination of fitness and minimum distance
+ to already selected programs.
+
+ Args:
+ candidates: List of (id, program, score) tuples
+ k: Number of programs to select
+
+ Returns:
+ List of selected program IDs
+ """
+ if len(candidates) <= k:
+ return [c[0] for c in candidates]
+
+ selected = []
+ remaining = list(candidates)
+
+ # Always include the best program
+ remaining.sort(key=lambda x: x[2], reverse=True)
+ selected.append(remaining.pop(0))
+
+ # Greedily add remaining programs
+ while len(selected) < k and remaining:
+ best_idx = -1
+ best_combined_score = -float("inf")
+
+ for i, (pid, prog, score) in enumerate(remaining):
+ # Calculate diversity as min distance to selected
+ min_diversity = min(
+ self._solution_distance(prog.solution, self.programs[s[0]].solution)
+ for s in selected
+ )
+
+ # Normalize score (assume scores are in [0, 1] or similar)
+ normalized_score = score
+
+ # Combined score
+ combined = (
+ 1 - self.diversity_weight
+ ) * normalized_score + self.diversity_weight * min_diversity
+
+ if combined > best_combined_score:
+ best_combined_score = combined
+ best_idx = i
+
+ if best_idx >= 0:
+ selected.append(remaining.pop(best_idx))
+
+ return [s[0] for s in selected]
+
+ def _solution_distance(self, solution1: str, solution2: str) -> float:
+ """
+ Calculate normalized distance between two code strings.
+
+ Uses a simple character-level comparison. For production use,
+ consider AST-based or embedding-based similarity.
+
+ Returns:
+ Distance in [0, 1] where 1 means completely different
+ """
+ if not solution1 or not solution2:
+ return 1.0
+
+ # Simple Jaccard distance on character n-grams
+ n = 3
+
+ def get_ngrams(s: str, n: int) -> Set[str]:
+ return set(s[i : i + n] for i in range(len(s) - n + 1))
+
+ ngrams1 = get_ngrams(solution1, n)
+ ngrams2 = get_ngrams(solution2, n)
+
+ if not ngrams1 and not ngrams2:
+ return 0.0
+
+ intersection = len(ngrams1 & ngrams2)
+ union = len(ngrams1 | ngrams2)
+
+ similarity = intersection / union if union > 0 else 0
+ return 1.0 - similarity
+
+ def _get_program_score(self, program: Program) -> float:
+ """
+ Get the fitness score for a program.
+
+ Uses combined_score if available, otherwise averages all metrics.
+ Applies depth penalty if configured.
+
+ Args:
+ program: Program to score
+
+ Returns:
+ Fitness score (higher is better)
+ """
+ if not program.metrics:
+ return 0.0
+
+ # Get base score
+ if "combined_score" in program.metrics:
+ score = program.metrics["combined_score"]
+ elif "score" in program.metrics:
+ score = program.metrics["score"]
+ else:
+ # Average of all metrics
+ values = [v for v in program.metrics.values() if isinstance(v, (int, float))]
+ score = sum(values) / len(values) if values else 0.0
+
+ # Apply depth penalty if configured
+ if self.depth_penalty > 0 and program.id in self.depth:
+ depth = self.depth[program.id]
+ score = score * math.exp(-self.depth_penalty * depth)
+
+ return score
+
+ def sample(
+ self, num_context_programs: Optional[int] = 4, **kwargs
+ ) -> Tuple[Program, List[Program]]:
+ """
+ Sample a parent program and context programs using beam search strategy.
+
+ The parent is selected from the current beam using the configured
+ selection strategy. context programs are drawn from top programs.
+
+ Args:
+ num_context_programs: Number of context programs to return
+
+ Returns:
+ Tuple of (parent, other_context_programs).
+ """
+ if not self.beam:
+ # Fallback: use best program if beam is empty
+ best = self.get_best_program()
+ if best:
+ self.beam.add(best.id)
+ else:
+ raise ValueError("Cannot sample: no programs in database")
+
+ # Select parent based on strategy
+ parent = self._select_parent()
+
+ # Mark as expanded
+ self.expanded.add(parent.id)
+ self.stats["total_expansions"] += 1
+
+ # Get context programs from top programs, excluding the parent
+ n = num_context_programs or 4
+ top_programs = self.get_top_programs(n + 1)
+ other_context_programs = [p for p in top_programs if p.id != parent.id][:n]
+
+ logger.info(
+ f"Beam search: selected parent {parent.id} (depth={self.depth.get(parent.id, 0)}, "
+ f"score={self._get_program_score(parent):.4f}), "
+ f"beam_size={len(self.beam)}, other_context_programs={len(other_context_programs)}"
+ )
+
+ return parent, other_context_programs
+
+ def _select_parent(self) -> Program:
+ """
+ Select a parent program from the beam using the configured strategy.
+
+ Returns:
+ Selected parent program
+ """
+ beam_list = [self.programs[pid] for pid in self.beam if pid in self.programs]
+
+ if not beam_list:
+ raise ValueError("Beam is empty, cannot select parent")
+
+ if self.selection_strategy == "best":
+ return self._select_best(beam_list)
+ elif self.selection_strategy == "stochastic":
+ return self._select_stochastic(beam_list)
+ elif self.selection_strategy == "round_robin":
+ return self._select_round_robin(beam_list)
+ elif self.selection_strategy == "diversity_weighted":
+ return self._select_diversity_weighted(beam_list)
+ else:
+ logger.warning(f"Unknown strategy {self.selection_strategy}, using best")
+ return self._select_best(beam_list)
+
+ def _select_best(self, candidates: List[Program]) -> Program:
+ """Select the highest scoring program."""
+ return max(candidates, key=self._get_program_score)
+
+ def _select_stochastic(self, candidates: List[Program]) -> Program:
+ """
+ Select using softmax-weighted random sampling.
+
+ Higher temperature = more uniform distribution.
+ Lower temperature = more greedy selection.
+ """
+ scores = [self._get_program_score(p) for p in candidates]
+
+ # Apply temperature and softmax
+ if self.temperature > 0:
+ # Shift scores for numerical stability
+ max_score = max(scores)
+ exp_scores = [math.exp((s - max_score) / self.temperature) for s in scores]
+ total = sum(exp_scores)
+ probs = [e / total for e in exp_scores]
+ else:
+ # Temperature = 0 means greedy
+ return self._select_best(candidates)
+
+ # Weighted random selection
+ r = random.random()
+ cumsum = 0
+ for i, prob in enumerate(probs):
+ cumsum += prob
+ if r <= cumsum:
+ return candidates[i]
+
+ return candidates[-1]
+
+ def _select_round_robin(self, candidates: List[Program]) -> Program:
+ """
+ Select in round-robin order through the beam.
+
+ Ensures all beam members get expanded equally.
+ """
+ # Sort by score for consistent ordering
+ sorted_candidates = sorted(candidates, key=self._get_program_score, reverse=True)
+
+ selected = sorted_candidates[self._rr_index % len(sorted_candidates)]
+ self._rr_index += 1
+
+ return selected
+
+ def _select_diversity_weighted(self, candidates: List[Program]) -> Program:
+ """
+ Select balancing exploitation (high score) and exploration (diversity).
+
+ Programs that are more different from recently expanded programs
+ get a diversity bonus.
+ """
+ if not self.expanded:
+ # No expansion history, use stochastic
+ return self._select_stochastic(candidates)
+
+ # Calculate combined scores
+ combined_scores = []
+ for prog in candidates:
+ fitness = self._get_program_score(prog)
+
+ # Calculate diversity from expanded programs
+ recent_expanded = list(self.expanded)[-10:] # Last 10 expanded
+ if recent_expanded:
+ diversity = sum(
+ self._solution_distance(prog.solution, self.programs[eid].solution)
+ for eid in recent_expanded
+ if eid in self.programs
+ ) / len(recent_expanded)
+ else:
+ diversity = 1.0
+
+ combined = (1 - self.diversity_weight) * fitness + self.diversity_weight * diversity
+ combined_scores.append(combined)
+
+ # Select using softmax on combined scores
+ if self.temperature > 0:
+ max_score = max(combined_scores)
+ exp_scores = [math.exp((s - max_score) / self.temperature) for s in combined_scores]
+ total = sum(exp_scores)
+ probs = [e / total for e in exp_scores]
+
+ r = random.random()
+ cumsum = 0
+ for i, prob in enumerate(probs):
+ cumsum += prob
+ if r <= cumsum:
+ return candidates[i]
+
+ # Fallback to best combined score
+ best_idx = combined_scores.index(max(combined_scores))
+ return candidates[best_idx]
+
+ def get_beam_programs(self) -> List[Program]:
+ """
+ Get all programs currently in the beam.
+
+ Returns:
+ List of programs in the beam, sorted by score (descending)
+ """
+ beam_programs = [self.programs[pid] for pid in self.beam if pid in self.programs]
+ return sorted(beam_programs, key=self._get_program_score, reverse=True)
+
+ def get_unexpanded_beam(self) -> List[Program]:
+ """
+ Get beam programs that haven't been expanded yet.
+
+ Useful for analysis or alternative expansion strategies.
+
+ Returns:
+ List of unexpanded beam programs
+ """
+ unexpanded = [
+ self.programs[pid]
+ for pid in self.beam
+ if pid in self.programs and pid not in self.expanded
+ ]
+ return sorted(unexpanded, key=self._get_program_score, reverse=True)
+
+ def get_search_stats(self) -> Dict:
+ """
+ Get statistics about the beam search progress.
+
+ Returns:
+ Dictionary with search statistics
+ """
+ return {
+ "beam_size": len(self.beam),
+ "total_programs": len(self.programs),
+ "total_expansions": self.stats["total_expansions"],
+ "max_depth_reached": self.stats["max_depth_reached"],
+ "beam_updates": self.stats["beam_updates"],
+ "unexpanded_in_beam": len(self.beam - self.expanded),
+ "avg_beam_depth": (
+ sum(self.depth.get(pid, 0) for pid in self.beam) / len(self.beam)
+ if self.beam
+ else 0
+ ),
+ }
+
+ def log_status(self) -> None:
+ """Log the status of the beam search database."""
+ stats = self.get_search_stats()
+ logger.info(
+ f"BeamSearchDatabase status: {stats['total_programs']} programs, "
+ f"beam_size={stats['beam_size']}, max_depth={stats['max_depth_reached']}, "
+ f"expansions={stats['total_expansions']}"
+ )
+
+ # Log beam contents
+ if self.beam:
+ beam_progs = self.get_beam_programs()
+ logger.info("Current beam:")
+ for i, prog in enumerate(beam_progs[:5]): # Show top 5
+ logger.info(
+ f" {i+1}. {prog.id}: score={self._get_program_score(prog):.4f}, "
+ f"depth={self.depth.get(prog.id, 0)}"
+ )
+
+ # ------------------------------------------------------------------
+ # Save and Load
+ # ------------------------------------------------------------------
+
+ def save(self, path: Optional[str] = None, iteration: int = 0) -> None:
+ """
+ Save the database to disk, including beam search state.
+
+ Args:
+ path: Path to save to (uses config.db_path if None)
+ iteration: Current iteration number
+ """
+ save_path = path or self.config.db_path
+ if not save_path:
+ logger.warning("No database path specified, skipping save")
+ return
+
+ # Create directory if it doesn't exist
+ os.makedirs(save_path, exist_ok=True)
+
+ # Save each program
+ for program in self.programs.values():
+ prompts = None
+ if (
+ self.config.log_prompts
+ and self.prompts_by_program
+ and program.id in self.prompts_by_program
+ ):
+ prompts = self.prompts_by_program[program.id]
+ self._save_program(program, save_path, prompts=prompts)
+
+ # Save metadata including beam search state
+ metadata = {
+ "best_program_id": self.best_program_id,
+ "last_iteration": iteration if iteration is not None else self.last_iteration,
+ # Beam search specific state
+ "beam": list(self.beam),
+ "depth": self.depth,
+ "expanded": list(self.expanded),
+ "rr_index": self._rr_index,
+ "stats": self.stats,
+ }
+
+ with open(os.path.join(save_path, "metadata.json"), "w") as f:
+ json.dump(metadata, f, indent=2)
+
+ logger.info(
+ f"Saved BeamSearchDatabase with {len(self.programs)} programs, "
+ f"beam_size={len(self.beam)} to {save_path}"
+ )
+
+ def load(self, path: str) -> None:
+ """
+ Load the database from disk, restoring beam search state.
+
+ Args:
+ path: Path to load from
+ """
+ if not os.path.exists(path):
+ logger.warning(f"Database path {path} does not exist, skipping load")
+ return
+
+ # Load metadata first
+ metadata_path = os.path.join(path, "metadata.json")
+ if os.path.exists(metadata_path):
+ with open(metadata_path, "r") as f:
+ metadata = json.load(f)
+
+ self.best_program_id = metadata.get("best_program_id")
+ self.last_iteration = metadata.get("last_iteration", 0)
+
+ # Restore beam search state
+ self.beam = set(metadata.get("beam", []))
+ self.depth = metadata.get("depth", {})
+ self.expanded = set(metadata.get("expanded", []))
+ self._rr_index = metadata.get("rr_index", 0)
+ saved_stats = metadata.get("stats", {})
+ self.stats.update(saved_stats)
+
+ logger.info(
+ f"Loaded metadata: last_iteration={self.last_iteration}, "
+ f"beam_size={len(self.beam)}"
+ )
+
+ # Load programs
+ programs_dir = os.path.join(path, "programs")
+ if os.path.exists(programs_dir):
+ for program_file in os.listdir(programs_dir):
+ if program_file.endswith(".json"):
+ program_path = os.path.join(programs_dir, program_file)
+ try:
+ with open(program_path, "r") as f:
+ program_data = json.load(f)
+
+ program = Program.from_dict(program_data)
+ self.programs[program.id] = program
+ except Exception as e:
+ logger.warning(f"Error loading program {program_file}: {str(e)}")
+
+ # Validate and reconstruct beam if needed
+ self._validate_and_reconstruct_beam()
+
+ logger.info(f"Loaded BeamSearchDatabase with {len(self.programs)} programs from {path}")
+ self.log_status()
+
+ def _validate_and_reconstruct_beam(self) -> None:
+ """
+ Validate beam state after loading and reconstruct if necessary.
+
+ This handles cases where:
+ - Beam contains IDs that no longer exist in programs
+ - Beam is empty but programs exist
+ - Depth information is missing for some programs
+ """
+ # Remove invalid beam entries (programs that don't exist)
+ valid_beam = {pid for pid in self.beam if pid in self.programs}
+ if len(valid_beam) != len(self.beam):
+ removed = len(self.beam) - len(valid_beam)
+ logger.warning(f"Removed {removed} invalid entries from beam")
+ self.beam = valid_beam
+
+ # Remove invalid expanded entries
+ valid_expanded = {pid for pid in self.expanded if pid in self.programs}
+ self.expanded = valid_expanded
+
+ # Reconstruct depth for programs missing it
+ missing_depth = [pid for pid in self.programs if pid not in self.depth]
+ if missing_depth:
+ logger.info(f"Reconstructing depth for {len(missing_depth)} programs")
+ self._reconstruct_depths()
+
+ # If beam is empty but we have programs, reconstruct beam from top programs
+ if not self.beam and self.programs:
+ logger.info("Beam is empty, reconstructing from top programs")
+ top_programs = self.get_top_programs(self.beam_width)
+ self.beam = {p.id for p in top_programs}
+ logger.info(f"Reconstructed beam with {len(self.beam)} programs")
+
+ def _reconstruct_depths(self) -> None:
+ """
+ Reconstruct depth information for all programs based on parent relationships.
+
+ Uses BFS from root programs (those without parents) to assign depths.
+ """
+ # Find all root programs (no parent or parent not in database)
+ roots = []
+ for pid, prog in self.programs.items():
+ if not prog.parent_id or prog.parent_id not in self.programs:
+ self.depth[pid] = 0
+ roots.append(pid)
+
+ # BFS to assign depths
+ queue = list(roots)
+ visited = set(roots)
+
+ # Build child lookup
+ children: Dict[str, List[str]] = defaultdict(list)
+ for pid, prog in self.programs.items():
+ if prog.parent_id and prog.parent_id in self.programs:
+ children[prog.parent_id].append(pid)
+
+ while queue:
+ current = queue.pop(0)
+ current_depth = self.depth.get(current, 0)
+
+ for child_id in children[current]:
+ if child_id not in visited:
+ self.depth[child_id] = current_depth + 1
+ visited.add(child_id)
+ queue.append(child_id)
+
+ # Handle orphaned programs (shouldn't happen but just in case)
+ for pid in self.programs:
+ if pid not in self.depth:
+ self.depth[pid] = 0
+ logger.warning(f"Orphaned program {pid}, assigned depth 0")
diff --git a/skydiscover/search/best_of_n/README.md b/skydiscover/search/best_of_n/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..a64367e0a559f404230f26ca88b855dff385761e
--- /dev/null
+++ b/skydiscover/search/best_of_n/README.md
@@ -0,0 +1,31 @@
+# Best-of-N
+
+Generates N independent mutations from the same parent before switching parents. This gives the search N attempts to improve a single program, after which it commits to the current global best and repeats.
+
+## Algorithm
+
+```
+sample():
+ if parent_iteration_count >= N or no current parent:
+ parent = argmax combined_score across all programs # switch to best
+ reset counter
+ else:
+ parent = current_parent # reuse
+ increment counter
+ other context programs = random sample from top programs (excluding parent)
+```
+
+Other context programs are re-sampled from the top pool every iteration regardless of the parent reuse cycle.
+
+## Config
+
+```yaml
+search:
+ type: "best_of_n"
+ database:
+ best_of_n: 5 # number of iterations to reuse the same parent
+```
+
+## When to use
+
+Useful when single-step improvements are noisy and you want to give the model multiple shots at the same problem state before moving on. Larger `best_of_n` increases exploitation depth; smaller values approach Top-K behavior.
diff --git a/skydiscover/search/best_of_n/__init__.py b/skydiscover/search/best_of_n/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..a10db322f0001f8c9e33f288ab42b07b68f2634f
--- /dev/null
+++ b/skydiscover/search/best_of_n/__init__.py
@@ -0,0 +1,10 @@
+"""
+Best of N module for selecting programs from the solution database.
+
+This module provides a "best of N" database that reuses the same parent
+for N consecutive iterations before sampling a new parent.
+"""
+
+from skydiscover.search.best_of_n.database import BestOfNDatabase
+
+__all__ = ["BestOfNDatabase"]
diff --git a/skydiscover/search/best_of_n/database.py b/skydiscover/search/best_of_n/database.py
new file mode 100644
index 0000000000000000000000000000000000000000..4fbbbb50f1ef3cbf6068346607e069d63a31f360
--- /dev/null
+++ b/skydiscover/search/best_of_n/database.py
@@ -0,0 +1,127 @@
+import logging
+import random
+from typing import Dict, List, Optional, Tuple
+
+from skydiscover.config import DatabaseConfig
+from skydiscover.search.base_database import Program, ProgramDatabase
+
+logger = logging.getLogger(__name__)
+
+
+class BestOfNDatabase(ProgramDatabase):
+ """
+ Database implementing "best of N" strategy.
+
+ Reuses the same parent for N consecutive iterations before sampling a new parent.
+ This allows exploring multiple variations from the same starting point.
+
+ Configuration options (via DatabaseConfig attributes):
+ best_of_n: Number of iterations to reuse the same parent (default: 5)
+ """
+
+ def __init__(self, name: str, config: DatabaseConfig):
+ super().__init__(name, config)
+
+ # Get N parameter from config, default to 5
+ self.n = getattr(config, "best_of_n", 5)
+
+ # Track current parent and iteration count
+ self.current_parent_id: Optional[str] = None
+ self.parent_iteration_count: int = 0
+
+ logger.info(f"BestOfNDatabase initialized: N={self.n}")
+
+ def add(self, program: Program, iteration: Optional[int] = None, **kwargs) -> str:
+ """
+ Add a program to the database and increment parent iteration count.
+ """
+ # Store the program
+ self.programs[program.id] = program
+
+ # Track last iteration if provided
+ if iteration is not None:
+ self.last_iteration = max(self.last_iteration, iteration)
+
+ # Increment counter for current parent
+ if self.current_parent_id is not None:
+ self.parent_iteration_count += 1
+
+ # Save to disk if configured
+ if self.config.db_path:
+ self._save_program(program)
+
+ # Update the absolute best program tracking
+ self._update_best_program(program)
+
+ logger.debug(
+ f"Added program {program.id} to best-of-N database (count={self.parent_iteration_count}/{self.n})"
+ )
+ return program.id
+
+ def sample(
+ self, num_context_programs: Optional[int] = 4, **kwargs
+ ) -> Tuple[Program, List[Program]]:
+ """
+ Sample a parent program and context programs for evolution.
+
+ Reuses the same parent for N iterations, then samples a new parent.
+ context programs are sampled fresh each iteration from top programs.
+
+ Args:
+ num_context_programs: Number of context programs to sample (defaults to 5)
+ **kwargs: Additional keyword arguments
+
+ Returns:
+ Tuple of (parent, other_context_programs).
+ """
+ if not self.programs:
+ raise ValueError("Cannot sample: no programs in database")
+
+ # Check if we need to sample a new parent
+ if (
+ self.current_parent_id is None
+ or self.parent_iteration_count >= self.n
+ or self.current_parent_id not in self.programs
+ ):
+ # Sample new parent (best program)
+ def safe_score(p):
+ score = p.metrics.get("combined_score") if p.metrics else None
+ if not isinstance(score, (int, float)):
+ return float("-inf")
+ return float(score)
+
+ parent = max(self.programs.values(), key=safe_score)
+ self.current_parent_id = parent.id
+ self.parent_iteration_count = 0
+
+ logger.info(
+ f"Best-of-N: sampled new parent {parent.id} (score={safe_score(parent):.4f})"
+ )
+ else:
+ # Reuse current parent
+ parent = self.programs[self.current_parent_id]
+ logger.debug(
+ f"Best-of-N: reusing parent {parent.id} (count={self.parent_iteration_count}/{self.n})"
+ )
+
+ # Sample context from top programs (excluding parent)
+ # Get more top programs than needed to ensure we have enough after excluding parent
+ top_pool_size = max(num_context_programs * 2, 10)
+ top_programs = self.get_top_programs(top_pool_size)
+
+ # Filter out parent and sample
+ candidates = [p for p in top_programs if p.id != parent.id]
+ num_to_sample = min(num_context_programs, len(candidates))
+
+ if num_to_sample > 0:
+ other_context_programs = random.sample(candidates, num_to_sample)
+ else:
+ # Fallback: use all available programs except parent
+ all_candidates = [p for p in self.programs.values() if p.id != parent.id]
+ other_context_programs = (
+ random.sample(all_candidates, min(num_context_programs, len(all_candidates)))
+ if all_candidates
+ else []
+ )
+
+ return parent, other_context_programs
diff --git a/skydiscover/search/default_discovery_controller.py b/skydiscover/search/default_discovery_controller.py
new file mode 100644
index 0000000000000000000000000000000000000000..da2eaa6749720279092e6f12845e29d13038cbe5
--- /dev/null
+++ b/skydiscover/search/default_discovery_controller.py
@@ -0,0 +1,993 @@
+"""
+Discovery controller for running discovery processes.
+
+Provides the default execution loop for discovery processes (sample → prompt → LLM → evaluate).
+Subclasses only need to override ``run_discovery`` to change orchestration
+(e.g. co-evolution interleaves solution and search-algorithm evolution).
+"""
+
+import asyncio
+import logging
+import multiprocessing as mp
+import os
+import time
+import uuid
+from dataclasses import dataclass
+from typing import Any, Callable, Dict, List, Optional, Tuple, Union
+
+from skydiscover.config import Config
+from skydiscover.context_builder.default import DefaultContextBuilder
+from skydiscover.context_builder.evox import EvoxContextBuilder
+from skydiscover.evaluation import create_evaluator
+from skydiscover.evaluation.llm_judge import LLMJudge
+from skydiscover.llm.base import LLMResponse
+from skydiscover.llm.llm_pool import LLMPool
+from skydiscover.search.base_database import Program, ProgramDatabase
+from skydiscover.search.utils.discovery_utils import SerializableResult, build_image_content
+from skydiscover.utils.code_utils import (
+ apply_diff,
+ extract_diffs,
+ format_diff_summary,
+ parse_full_rewrite,
+)
+
+logger = logging.getLogger(__name__)
+
+
+@dataclass
+class DiscoveryControllerInput:
+ """Input to the discovery controller"""
+
+ config: Config
+ evaluation_file: str
+ database: ProgramDatabase
+ file_suffix: str = ".py"
+ output_dir: Optional[str] = None
+ evaluator_env_vars: Optional[Dict[str, str]] = None
+
+
+class DiscoveryController:
+ """
+ Discovery controller with a default sequential execution strategy.
+
+ Handles the full generate-evaluate cycle: prompt building, LLM calls,
+ response parsing, evaluation, and result processing.
+
+ The default ``run_discovery`` runs iterations sequentially. Subclasses
+ (e.g. CoEvolutionController) can override it for different orchestration
+ while reusing the shared iteration primitives.
+ """
+
+ def __init__(self, controller_input: DiscoveryControllerInput):
+ self.config = controller_input.config
+ self.evaluation_file = controller_input.evaluation_file
+ self.database = controller_input.database
+ self.file_suffix = controller_input.file_suffix
+ self.output_dir = controller_input.output_dir
+ self.evaluator_env_vars = controller_input.evaluator_env_vars
+
+ self.shutdown_event = mp.Event()
+ self.early_stopping_triggered = False
+
+ self.llms = LLMPool(self.config.llm.models)
+ self.evaluator_llms = LLMPool(self.config.llm.evaluator_models)
+ self.guide_llms = LLMPool(self.config.llm.guide_models)
+
+ self._init_context_builder()
+
+ self.config.evaluator.evaluation_file = self.evaluation_file
+ self.config.evaluator.file_suffix = self.file_suffix
+ self.config.evaluator.is_image_mode = self.config.language == "image"
+
+ llm_judge = None
+ if self.config.evaluator.llm_as_judge:
+ ctx = DefaultContextBuilder(self.config)
+ ctx.set_templates("evaluator_system_message")
+ llm_judge = LLMJudge(self.evaluator_llms, ctx, self.database)
+
+ self.evaluator = create_evaluator(
+ self.config.evaluator,
+ llm_judge=llm_judge,
+ max_concurrent=max(self.config.max_parallel_iterations, 4),
+ env_vars=controller_input.evaluator_env_vars,
+ )
+
+ self.agentic_generator = None
+ if self.config.agentic.enabled:
+ from skydiscover.llm.agentic_generator import AgenticGenerator
+
+ self.agentic_generator = AgenticGenerator(self.llms, self.config.agentic)
+ logger.info(f"Agentic mode enabled (codebase: {self.config.agentic.codebase_root})")
+
+ self.num_context_programs = controller_input.config.search.num_context_programs
+
+ self.monitor_callback: Optional[Callable] = None
+ self.feedback_reader: Optional[Any] = None
+ self._prompt_context: Dict[str, Any] = {}
+
+ # Load evaluator/task description and inject into system message so
+ # the LLM knows what problem to solve (especially for from-scratch).
+ self._inject_evaluator_context()
+
+ logger.info(
+ f"DiscoveryController initialized: num_context_programs={self.num_context_programs}"
+ )
+
+ def close(self):
+ """Release resources held by the evaluator (e.g. Docker containers)."""
+ if hasattr(self.evaluator, "close"):
+ self.evaluator.close()
+
+ # ------------------------------------------------------------------
+ # Initialisation helpers
+ # ------------------------------------------------------------------
+
+ def _inject_evaluator_context(self):
+ """Load evaluator/task description and prepend to the system message.
+
+ For Harbor tasks this loads instruction.md; for containerized benchmarks
+ it loads the evaluator source files. The content gives the LLM essential
+ context about the problem it needs to solve.
+
+ Controlled by ``evaluator.inject_evaluator_context`` (default False).
+ """
+ if not self.config.evaluator.inject_evaluator_context:
+ return
+
+ from skydiscover.search.utils.discovery_utils import load_evaluator_code
+
+ task_description = load_evaluator_code(self.evaluation_file)
+ if not task_description:
+ return
+
+ ctx = self.config.context_builder
+ existing = ctx.system_message or ""
+ # Prepend the task description so the LLM always sees it.
+ ctx.system_message = (
+ f"# Task Description\n\n{task_description}\n\n{existing}"
+ if existing
+ else f"# Task Description\n\n{task_description}"
+ )
+
+ def _init_context_builder(self):
+ """Initialize the appropriate context builder based on config."""
+ if getattr(self.config.context_builder, "template", "default") == "evox":
+ self.context_builder = EvoxContextBuilder(self.config)
+ template_name = "search_evolution_user_message"
+ self.context_builder.set_templates(user_template=template_name)
+ else:
+ self.context_builder = DefaultContextBuilder(self.config)
+
+ async def _call_llm(self, system_message: str, user_message: str, **kwargs) -> LLMResponse:
+ """Call the LLM, using agentic mode if enabled (text-only)."""
+ if self.agentic_generator and not kwargs.get("image_output"):
+ text = await self.agentic_generator.generate(system_message, user_message)
+ if text:
+ return LLMResponse(text=text)
+ return await self.llms.generate(
+ system_message, [{"role": "user", "content": user_message}], **kwargs
+ )
+
+ # ------------------------------------------------------------------
+ # Main discovery loop
+ # ------------------------------------------------------------------
+
+ async def run_discovery(
+ self,
+ start_iteration: int,
+ max_iterations: int,
+ checkpoint_callback: Optional[Callable[[int], None]] = None,
+ post_process_result: Optional[bool] = True,
+ retry_times: Optional[int] = 3,
+ ) -> Optional[Union[Program, SerializableResult]]:
+ """
+ Run the discovery process.
+
+ When ``config.max_parallel_iterations == 1`` (default), iterations
+ run sequentially — same behaviour as before.
+
+ When ``> 1``, up to *N* iterations run concurrently as asyncio
+ tasks, bounded by a semaphore. Generation and evaluation naturally
+ overlap across iterations: while iteration *i* evaluates, iteration
+ *i+1* can generate, and iteration *i+2* can sample.
+
+ Args:
+ start_iteration: The iteration to start from.
+ max_iterations: The number of iterations to run.
+ checkpoint_callback: Optional callback for checkpointing.
+ post_process_result: If True, add results to the database and
+ return the best Program. If False, return the raw
+ ``SerializableResult`` from the last iteration.
+ retry_times: Number of retry attempts per iteration.
+
+ Returns:
+ Best ``Program`` found (post_process_result=True) or raw
+ ``SerializableResult`` (post_process_result=False).
+ """
+ max_parallel = self.config.max_parallel_iterations
+
+ if max_parallel > 1:
+ return await self._run_discovery_parallel(
+ start_iteration,
+ max_iterations,
+ checkpoint_callback,
+ post_process_result,
+ retry_times,
+ max_parallel,
+ )
+
+ return await self._run_discovery_sequential(
+ start_iteration,
+ max_iterations,
+ checkpoint_callback,
+ post_process_result,
+ retry_times,
+ )
+
+ # ------------------------------------------------------------------
+ # Sequential loop (original behaviour, max_parallel_iterations=1)
+ # ------------------------------------------------------------------
+
+ async def _run_discovery_sequential(
+ self,
+ start_iteration: int,
+ max_iterations: int,
+ checkpoint_callback: Optional[Callable[[int], None]] = None,
+ post_process_result: Optional[bool] = True,
+ retry_times: Optional[int] = 3,
+ ) -> Optional[Union[Program, SerializableResult]]:
+ total_iterations = start_iteration + max_iterations
+
+ result = None
+ for iteration in range(start_iteration, total_iterations):
+ if self.shutdown_event.is_set():
+ logger.info("Shutdown requested, stopping discovery loop early")
+ break
+
+ try:
+ result = await self._run_iteration(iteration, retry_times=retry_times)
+ if result.error:
+ logger.warning(f"Iteration {iteration} failed: {result.error}")
+ continue
+
+ if post_process_result:
+ self._process_iteration_result(result, iteration, checkpoint_callback)
+
+ except Exception as e:
+ logger.exception(f"Error in iteration {iteration}: {e}")
+
+ if not post_process_result:
+ return result
+
+ return self._finalize_discovery()
+
+ # ------------------------------------------------------------------
+ # Parallel loop (max_parallel_iterations > 1)
+ # ------------------------------------------------------------------
+
+ async def _run_discovery_parallel(
+ self,
+ start_iteration: int,
+ max_iterations: int,
+ checkpoint_callback: Optional[Callable[[int], None]] = None,
+ post_process_result: Optional[bool] = True,
+ retry_times: Optional[int] = 3,
+ max_parallel: int = 4,
+ ) -> Optional[Union[Program, SerializableResult]]:
+ total_iterations = start_iteration + max_iterations
+ sem = asyncio.Semaphore(max_parallel)
+ pending: set = set()
+ last_result: Optional[SerializableResult] = None
+
+ logger.info(
+ f"Parallel discovery: up to {max_parallel} iterations in flight "
+ f"({start_iteration}..{total_iterations - 1})"
+ )
+
+ async def _bounded_iteration(iteration: int) -> Tuple[int, Optional[SerializableResult]]:
+ """Run one iteration under the semaphore, then process its result.
+
+ Result processing (database.add) happens here rather than being
+ collected later so that subsequent iterations see the latest DB
+ state as soon as the ``await`` inside ``_run_iteration`` yields.
+ """
+ async with sem:
+ if self.shutdown_event.is_set():
+ return iteration, None
+ try:
+ result = await self._run_iteration(iteration, retry_times=retry_times)
+ except Exception as e:
+ logger.exception(f"Error in parallel iteration {iteration}: {e}")
+ return iteration, None
+
+ # Process outside the semaphore — database.add() is sync and
+ # completes atomically between await-points, so no lock needed.
+ if result and not result.error and post_process_result:
+ self._process_iteration_result(result, iteration, checkpoint_callback)
+ elif result and result.error:
+ logger.warning(f"Iteration {iteration} failed: {result.error}")
+
+ return iteration, result
+
+ for iteration in range(start_iteration, total_iterations):
+ if self.shutdown_event.is_set():
+ break
+
+ task = asyncio.create_task(_bounded_iteration(iteration), name=f"iter_{iteration}")
+ pending.add(task)
+ task.add_done_callback(pending.discard)
+
+ # When the pipeline is full, wait for at least one to finish
+ # before scheduling more — this provides backpressure.
+ if len(pending) >= max_parallel:
+ done, pending = await asyncio.wait(
+ pending,
+ return_when=asyncio.FIRST_COMPLETED,
+ )
+ for t in done:
+ try:
+ _, res = t.result()
+ if res is not None:
+ last_result = res
+ except Exception as e:
+ logger.warning(
+ f"A task in parallel discovery failed with an exception: {e}"
+ )
+
+ # Drain remaining tasks
+ if pending:
+ done, _ = await asyncio.wait(pending)
+ for t in done:
+ try:
+ _, res = t.result()
+ if res is not None:
+ last_result = res
+ except Exception as e:
+ logger.warning(
+ f"A task in parallel discovery (drain) failed with an exception: {e}"
+ )
+
+ if not post_process_result:
+ return last_result
+
+ return self._finalize_discovery()
+
+ # ------------------------------------------------------------------
+ # Shared helpers
+ # ------------------------------------------------------------------
+
+ def _finalize_discovery(self) -> Optional[Program]:
+ if self.shutdown_event.is_set():
+ logger.info(
+ f"✅ Discovery process completed "
+ f"(search strategy = {self.database.name}) - Shutdown requested"
+ )
+ else:
+ logger.info(
+ f"✅ Discovery process completed "
+ f"(search strategy = {self.database.name}) - Maximum iterations reached"
+ )
+ return self.database.get_best_program()
+
+ # ------------------------------------------------------------------
+ # Single-iteration primitives (shared by all controllers)
+ # ------------------------------------------------------------------
+
+ async def _run_from_scratch_iteration(self, iteration: int) -> SerializableResult:
+ """Generate a first solution from scratch when the database is empty."""
+ try:
+ iteration_start = time.time()
+
+ prompt = self.context_builder.build_prompt(current_program=None, context={})
+
+ if self.feedback_reader:
+ self.feedback_reader.set_current_prompt(prompt["system"])
+ feedback = self.feedback_reader.read()
+ if feedback:
+ prompt = self.feedback_reader.apply_feedback(prompt)
+
+ llm_generation_time = 0.0
+ llm_start = time.time()
+ result = await self._call_llm(prompt["system"], prompt["user"])
+ llm_generation_time = time.time() - llm_start
+ llm_response = result.text
+ if not llm_response:
+ return SerializableResult(error="Empty LLM response", iteration=iteration)
+
+ child_solution = parse_full_rewrite(llm_response, self.config.language)
+ if not child_solution:
+ return SerializableResult(
+ error="No valid solution in response",
+ iteration=iteration,
+ prompt=prompt,
+ llm_response=llm_response,
+ )
+
+ child_id = str(uuid.uuid4())
+ eval_start = time.time()
+ eval_result = await self.evaluator.evaluate_program(child_solution, child_id)
+ eval_time = time.time() - eval_start
+
+ child = Program(
+ id=child_id,
+ solution=child_solution,
+ language=self.config.language,
+ parent_id=None,
+ metrics=eval_result.metrics,
+ iteration_found=iteration,
+ metadata={"changes": "Generated from scratch"},
+ artifacts=eval_result.artifacts or {},
+ )
+
+ return SerializableResult(
+ child_program_dict=child.to_dict(),
+ parent_id=None,
+ other_context_ids=[],
+ iteration_time=time.time() - iteration_start,
+ llm_generation_time=llm_generation_time,
+ eval_time=eval_time,
+ prompt=prompt,
+ llm_response=llm_response,
+ iteration=iteration,
+ )
+ except Exception as e:
+ logger.exception(f"From-scratch generation failed: {e}")
+ return SerializableResult(error=str(e), iteration=iteration)
+
+ async def _run_iteration(
+ self,
+ iteration: int,
+ retry_times: int = 1,
+ ) -> SerializableResult:
+ """Run a single generate-evaluate iteration."""
+ try:
+ if not self.database.programs:
+ return await self._run_from_scratch_iteration(iteration)
+
+ raw_parent, raw_context_programs = self.database.sample(
+ num_context_programs=self.num_context_programs
+ )
+
+ # Normalize sample() result — databases may return plain or dict-wrapped
+ if isinstance(raw_parent, dict):
+ if len(raw_parent) != 1:
+ raise ValueError(
+ f"sample() must return exactly one parent, got {len(raw_parent)}"
+ )
+ parent_info_key = list(raw_parent.keys())[0]
+ parent = list(raw_parent.values())[0]
+ else:
+ parent_info_key = ""
+ parent = raw_parent
+
+ # Other context programs that are relevant
+ if isinstance(raw_context_programs, dict):
+ context_programs_dict = raw_context_programs
+ else:
+ context_programs_dict = {"": raw_context_programs}
+
+ parent_info = (parent_info_key, parent.id)
+ context_info = [
+ (key, p.id) for key, programs in context_programs_dict.items() for p in programs
+ ]
+ context_program_ids = [
+ p.id for programs in context_programs_dict.values() for p in programs
+ ]
+
+ logger.debug(
+ f"Iteration {iteration}: parent {parent.id} ({parent_info_key}), "
+ f"other_context_programs keys: {list(context_programs_dict.keys())}"
+ )
+
+ iteration_start = time.time()
+
+ failed_attempts = []
+ child_solution, child_id, child_metrics, llm_response, changes_summary = (
+ None,
+ None,
+ None,
+ None,
+ None,
+ )
+
+ image_path = None # set by image mode or evaluator
+ eval_time = 0.0
+
+ # Build prompt with parent and context programs
+ for retry in range(retry_times):
+ prompt = self._build_prompt(
+ current_program=raw_parent,
+ context_programs=context_programs_dict,
+ failed_attempts=failed_attempts,
+ )
+
+ if failed_attempts:
+ logger.info(
+ f"Retry {retry + 1}/{retry_times}: rebuilding prompt with {len(failed_attempts)} failed attempt(s)"
+ )
+
+ # Apply human feedback (append or replace mode)
+ if self.feedback_reader:
+ self.feedback_reader.set_current_prompt(prompt["system"])
+ feedback = self.feedback_reader.read()
+ if feedback:
+ prompt = self.feedback_reader.apply_feedback(prompt)
+ self.feedback_reader.log_usage(
+ iteration, feedback, self.feedback_reader.mode
+ )
+
+ try:
+ llm_generation_time = 0.0
+ llm_start = time.time()
+ if self.config.language == "image":
+ child_id = str(uuid.uuid4())
+ user_content = build_image_content(
+ prompt["user"], parent, context_programs_dict
+ )
+ result = await self._call_llm(
+ prompt["system"],
+ user_content,
+ image_output=True,
+ output_dir=self._get_image_output_dir(),
+ program_id=child_id,
+ )
+ llm_response = result.text or ""
+ image_path = result.image_path
+ if image_path:
+ child_solution = result.text or "(image generated)"
+ changes_summary = "Image generation"
+ parse_error = None
+ else:
+ child_solution = None
+ changes_summary = None
+ parse_error = "VLM did not generate an image"
+ else:
+ result = await self._call_llm(prompt["system"], prompt["user"])
+ llm_response = result.text
+ llm_generation_time = time.time() - llm_start
+ except Exception as e:
+ logger.error(f"LLM generation failed: {e}")
+ return SerializableResult(
+ error=f"LLM generation failed: {str(e)}",
+ iteration=iteration,
+ attempts_used=retry + 1,
+ )
+
+ if self.config.language != "image":
+ # Text/code mode: parse LLM response
+ if llm_response is None:
+ return SerializableResult(
+ error="LLM returned None response",
+ iteration=iteration,
+ attempts_used=retry + 1,
+ )
+
+ child_solution, changes_summary, parse_error = self._parse_llm_response(
+ llm_response, parent.solution, iteration, retry + 1, retry_times
+ )
+
+ if child_solution and len(child_solution) > self.config.max_solution_length:
+ logger.warning(
+ "Generated solution exceeds maximum length (iteration=%s, attempt %s/%s): %s > %s",
+ iteration,
+ retry + 1,
+ retry_times,
+ len(child_solution),
+ self.config.max_solution_length,
+ )
+ parse_error = f"Generated solution exceeds maximum length ({len(child_solution)} > {self.config.max_solution_length})"
+ child_solution = None
+
+ if parse_error:
+ failed_attempts.append(
+ {
+ "solution": child_solution or "",
+ "llm_response": llm_response,
+ "metrics": {},
+ "metadata": {
+ "error": parse_error,
+ "attempt_number": retry + 1,
+ },
+ }
+ )
+ if retry < retry_times - 1:
+ continue
+ logger.error(
+ "All %s retry attempts failed due to parse/validation error: %s",
+ retry_times,
+ parse_error,
+ )
+ return SerializableResult(
+ error=f"{parse_error} (after {retry_times} attempts)",
+ iteration=iteration,
+ prompt=prompt,
+ llm_response=llm_response,
+ attempts_used=retry_times,
+ )
+
+ if self.config.language != "image":
+ child_id = str(uuid.uuid4())
+
+ eval_input = image_path if self.config.language == "image" else child_solution
+ eval_start = time.time()
+ child_eval_result = await self.evaluator.evaluate_program(eval_input, child_id)
+ eval_time = time.time() - eval_start
+ child_metrics = child_eval_result.metrics
+ # Extract image_path from evaluator metrics (non-image mode fallback)
+ if not image_path:
+ image_path = (
+ child_metrics.pop("image_path", None)
+ if isinstance(child_metrics.get("image_path"), str)
+ else None
+ )
+
+ if (
+ child_metrics.get("validity") in (0, -1)
+ or (
+ child_metrics.get("timeout") is True
+ and child_metrics.get("validity") is None
+ )
+ or (
+ child_metrics.get("combined_score") == 0
+ and child_metrics.get("error") is not None
+ )
+ ):
+ error_msg = (
+ (
+ child_metrics.get("error")
+ if isinstance(child_metrics.get("error"), str)
+ else None
+ )
+ or child_metrics.get("error_message")
+ or "Evaluation failed (validity=0)"
+ )
+
+ logger.warning(
+ "Evaluation failed (attempt %s/%s): validity=%s, error=%s",
+ retry + 1,
+ retry_times,
+ child_metrics.get("validity"),
+ error_msg,
+ )
+ logger.debug(
+ "Failed solution (attempt %s/%s):\n%s",
+ retry + 1,
+ retry_times,
+ child_solution,
+ )
+
+ failed_attempts.append(
+ {
+ "solution": child_solution,
+ "metrics": child_metrics,
+ "metadata": {
+ "changes": changes_summary,
+ "parent_metrics": parent.metrics,
+ "error": error_msg,
+ "attempt_number": retry + 1,
+ },
+ }
+ )
+
+ if retry < retry_times - 1:
+ continue
+ logger.error(
+ "All %s retry attempts failed. Final error: %s", retry_times, error_msg
+ )
+ iteration_time = time.time() - iteration_start
+ failed_extra = {"failed_attempts": failed_attempts}
+ if image_path:
+ failed_extra["image_path"] = image_path
+ failed_child_program = self._create_child_program(
+ child_id=child_id,
+ child_solution=child_solution,
+ parent=parent,
+ context_program_ids=context_program_ids,
+ parent_info=parent_info,
+ context_info=context_info,
+ child_metrics=child_metrics or {},
+ iteration=iteration,
+ changes_summary=changes_summary,
+ extra_metadata=failed_extra,
+ artifacts=child_eval_result.artifacts,
+ )
+ return SerializableResult(
+ error=f"Evaluator failed after {retry_times} attempts: {error_msg}",
+ iteration=iteration,
+ child_program_dict=failed_child_program.to_dict(),
+ parent_id=parent.id,
+ other_context_ids=context_program_ids,
+ iteration_time=iteration_time,
+ llm_generation_time=llm_generation_time,
+ eval_time=eval_time,
+ prompt=prompt,
+ llm_response=llm_response,
+ attempts_used=retry_times,
+ )
+ break
+
+ extra_meta = {}
+ if image_path:
+ extra_meta["image_path"] = image_path
+ child_program = self._create_child_program(
+ child_id=child_id,
+ child_solution=child_solution,
+ parent=parent,
+ context_program_ids=context_program_ids,
+ parent_info=parent_info,
+ context_info=context_info,
+ child_metrics=child_metrics,
+ iteration=iteration,
+ changes_summary=changes_summary,
+ extra_metadata=extra_meta if extra_meta else None,
+ artifacts=child_eval_result.artifacts,
+ )
+ iteration_time = time.time() - iteration_start
+
+ return SerializableResult(
+ child_program_dict=child_program.to_dict(),
+ parent_id=parent.id,
+ other_context_ids=context_program_ids,
+ iteration_time=iteration_time,
+ llm_generation_time=llm_generation_time,
+ eval_time=eval_time,
+ prompt=prompt,
+ llm_response=llm_response,
+ iteration=iteration,
+ attempts_used=retry + 1,
+ )
+ except Exception as e:
+ logger.exception(f"Error in iteration {iteration}")
+ return SerializableResult(error=str(e), iteration=iteration, attempts_used=1)
+
+ # ------------------------------------------------------------------
+ # Prompt / parsing / program creation helpers
+ # ------------------------------------------------------------------
+
+ def _build_prompt(
+ self,
+ current_program: Union[Program, Dict[str, Program]],
+ context_programs: Union[List[Program], Dict[str, List[Program]]],
+ failed_attempts: list,
+ ) -> Dict[str, str]:
+ """Build the prompt for LLM generation."""
+ parent = (
+ list(current_program.values())[0]
+ if isinstance(current_program, dict)
+ else current_program
+ )
+ db_stats = self._prompt_context.get("db_stats") or self.database.get_statistics()
+
+ # Build context with parent program and any other relevant information
+ context = {
+ "program_metrics": parent.metrics,
+ "other_context_programs": context_programs,
+ "previous_programs": db_stats.get("previous_programs", []),
+ "db_stats": db_stats,
+ }
+ for k, v in self._prompt_context.items():
+ if k not in context:
+ context[k] = v
+
+ if failed_attempts:
+ context["errors"] = failed_attempts
+
+ return self.context_builder.build_prompt(current_program=current_program, context=context)
+
+ def _parse_llm_response(
+ self,
+ llm_response: str,
+ parent_solution: str,
+ iteration: int,
+ attempt: int,
+ retry_times: int,
+ ) -> Tuple[Optional[str], Optional[str], Optional[str]]:
+ """
+ Parse LLM response to extract child solution.
+
+ Returns:
+ Tuple of (child_solution, changes_summary, parse_error)
+ """
+ if self.config.diff_based_generation:
+ diff_blocks = extract_diffs(llm_response)
+ if not diff_blocks:
+ preview = llm_response[:2000] + (
+ "\n... (truncated) ..." if len(llm_response) > 2000 else ""
+ )
+ logger.warning(
+ "No valid diffs found in LLM response (iteration=%s, attempt %s/%s). "
+ "Expected SEARCH/REPLACE blocks. Preview:\n%s",
+ iteration,
+ attempt,
+ retry_times,
+ preview,
+ )
+ return None, None, "No valid diffs found in response"
+
+ child_solution = apply_diff(parent_solution, llm_response)
+ changes_summary = format_diff_summary(diff_blocks)
+
+ if child_solution == parent_solution:
+ logger.warning(
+ "Diff blocks found but none matched parent solution (iteration=%s, attempt %s/%s).",
+ iteration,
+ attempt,
+ retry_times,
+ )
+ return (
+ None,
+ None,
+ "Diff SEARCH blocks did not match parent solution - no changes applied",
+ )
+
+ return child_solution, changes_summary, None
+ else:
+ new_solution = parse_full_rewrite(llm_response, self.config.language)
+ if not new_solution:
+ logger.warning(
+ "No valid solution found in LLM response (iteration=%s, attempt %s/%s).",
+ iteration,
+ attempt,
+ retry_times,
+ )
+ return None, None, "No valid solution found in response"
+ return new_solution, "Full rewrite", None
+
+ def _create_child_program(
+ self,
+ child_id: str,
+ child_solution: str,
+ parent: Program,
+ context_program_ids: list,
+ parent_info: tuple,
+ context_info: list,
+ child_metrics: Dict[str, Any],
+ iteration: int,
+ changes_summary: Optional[str],
+ extra_metadata: Optional[Dict[str, Any]] = None,
+ artifacts: Optional[Dict[str, Any]] = None,
+ ) -> Program:
+ """Create a child program with the given attributes."""
+ metadata = {
+ "changes": changes_summary,
+ "parent_metrics": parent.metrics,
+ }
+ if extra_metadata:
+ metadata.update(extra_metadata)
+
+ return Program(
+ id=child_id,
+ solution=child_solution,
+ language=self.config.language,
+ parent_id=parent.id,
+ other_context_ids=context_program_ids,
+ parent_info=parent_info,
+ context_info=context_info,
+ metrics=child_metrics,
+ iteration_found=iteration,
+ metadata=metadata,
+ artifacts=artifacts or {},
+ )
+
+ # ------------------------------------------------------------------
+ # Post-processing
+ # ------------------------------------------------------------------
+
+ async def postprocess_result(
+ self, result: SerializableResult, iteration_number: int, verbose: bool = True
+ ):
+ """
+ Process the iteration result and return the best program from the database.
+
+ Used by co-evolution where evaluation can be delayed.
+ """
+ self._process_iteration_result(
+ result, iteration_number, checkpoint_callback=None, verbose=verbose
+ )
+ return self.database.get_best_program()
+
+ # ------------------------------------------------------------------
+ # Utilities
+ # ------------------------------------------------------------------
+
+ def _get_image_output_dir(self) -> str:
+ """Return the directory for saving VLM-generated images."""
+ base = self.output_dir or "."
+ d = os.path.join(base, "generated_images")
+ os.makedirs(d, exist_ok=True)
+ return d
+
+ def request_shutdown(self) -> None:
+ """Request graceful shutdown"""
+ logger.info("Graceful shutdown requested...")
+ self.shutdown_event.set()
+
+ def _process_iteration_result(
+ self,
+ result: Any,
+ iteration: int,
+ checkpoint_callback: Optional[Callable[[int], None]] = None,
+ verbose: bool = True,
+ ) -> None:
+ """
+ Process the result from a single iteration.
+
+ Args:
+ result: The iteration result to process.
+ iteration: Current iteration number.
+ checkpoint_callback: Optional callback for checkpoint intervals.
+ verbose: If True, log progress and metrics; if False, suppress logging.
+ """
+ if result.error:
+ if verbose:
+ logger.warning(f"Iteration {iteration} failed: {result.error}")
+ return
+
+ program_class = getattr(self.database, "_program_class", Program)
+ child_program = program_class(**result.child_program_dict)
+
+ self.database.add(child_program, iteration=iteration)
+
+ # Fire monitor callback (live dashboard)
+ if self.monitor_callback:
+ try:
+ self.monitor_callback(child_program, iteration)
+ except Exception:
+ logger.debug("Monitor callback error", exc_info=True)
+
+ if result.prompt:
+ self.database.log_prompt(
+ template_key=(
+ "full_rewrite_user_message"
+ if not self.config.diff_based_generation
+ else "diff_user_message"
+ ),
+ program_id=child_program.id,
+ prompt=result.prompt,
+ responses=[result.llm_response] if result.llm_response else [],
+ )
+
+ if verbose:
+ logger.info(
+ f"Iteration {iteration}: "
+ f"Program {child_program.id} "
+ f"(parent: {result.parent_id}) "
+ f"completed in {result.iteration_time:.2f}s"
+ f" (llm: {result.llm_generation_time:.2f}s,"
+ f" eval: {result.eval_time:.2f}s)"
+ )
+
+ if iteration > 0 and iteration % self.config.checkpoint_interval == 0:
+ if verbose:
+ logger.info(f"[CHECKPOINT] Checkpoint interval reached at iteration {iteration}")
+
+ self.database.log_status()
+ if checkpoint_callback:
+ checkpoint_callback(iteration)
+
+ if child_program.metrics:
+ if verbose:
+ metrics_str = ", ".join(
+ f"{k}={v:.4f}" if isinstance(v, (int, float)) else f"{k}={v}"
+ for k, v in child_program.metrics.items()
+ )
+ logger.info(f"Metrics: {metrics_str}")
+
+ if not hasattr(self, "_warned_about_combined_score"):
+ self._warned_about_combined_score = False
+
+ if (
+ "combined_score" not in child_program.metrics
+ and not self._warned_about_combined_score
+ ):
+ if verbose:
+ logger.warning(
+ "⚠️ No 'combined_score' metric found in evaluation results. "
+ "Using 0.0 for discovery process guidance. "
+ "For better solution discovery results, please modify your evaluator to return a 'combined_score' "
+ "metric that properly weights different aspects of program performance."
+ )
+ self._warned_about_combined_score = True
+
+ if self.database.best_program_id == child_program.id and verbose:
+ logger.info(f"🌟 New best solution found at iteration {iteration}")
diff --git a/skydiscover/search/evox/README.md b/skydiscover/search/evox/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..c0df8f8fec0ba53e25034784892e724080821a0b
--- /dev/null
+++ b/skydiscover/search/evox/README.md
@@ -0,0 +1,89 @@
+# EvoX: Self-Evolving Search Strategies
+
+Paper: [EvoX](https://arxiv.org/abs/2602.23413)
+
+## Idea
+
+Evolutionary LLM search systems rely on fixed strategies — static explore–exploit ratios, rigid sampling heuristics, predefined selection logic — that remain unchanged as the search landscape shifts. EvoX treats the search strategy itself as a **co-evolving program**, jointly evolving candidate solutions and the search algorithms used to generate them, analogous to learning a learning rate schedule but for the entire search procedure.
+
+## Two Levels of Co-Evolution
+
+**Inner — solution discovery.** A solution database stores candidates and defines sampling (parent + context programs selection). The LLM generates new candidates; the evaluator scores them. The database implementation is not fixed — it gets hot-swapped by the outer loop.
+
+**Outer — search strategy evolution.** When solutions stagnate for `switch_interval` iterations (~10% of total), the LLM generates an entirely new `EvolvedProgramDatabase` class (as Python code) — a new sampling/selection strategy. The new strategy is validated, all programs are migrated, and solution discovery resumes. Search algorithms are scored by:
+
+```
+score = improvement * (1 + log(1 + start_score)) / sqrt(horizon)
+```
+
+Higher `start_score` → larger weight → algorithms that improve an already-strong solution are rewarded more.
+
+## Algorithm
+
+```
+for each iteration:
+ 1. Sample parent + context programs from current solution database
+ 2. Generate solution candidate via LLM, evaluate, store
+ 3. Track stagnation — if improvement < 0.01 for switch_interval consecutive iterations:
+ a. Score current search algorithm by solution improvement in its window
+ b. Generate new EvolvedProgramDatabase class via LLM
+ c. Validate new database (structural + functional checks)
+ d. Migrate all programs to new database, resume
+ 4. If new database fails at runtime: restore previous database, preserve new programs
+```
+
+## Code Structure
+
+```
+evox/
+├── coevolve_controller.py # CoEvolutionController — solution + search co-evolution,
+│ # stagnation detection, database hot-swapping,
+│ # fallback/restore on failure
+├── database/
+│ ├── initial_search_strategy.py # EvolvedProgramDatabase — seed solution database
+│ │ # (the code the LLM evolves into new strategies)
+│ ├── search_strategy_db.py # SearchStrategyDatabase — stores evolved search
+│ │ # strategy programs, samples best for refinement
+│ └── search_strategy_evaluator.py # Validates LLM-generated database implementations
+│ # (structural checks, metric preservation tests)
+├── config/
+│ ├── search.yaml # Search-side evolution config (loaded automatically)
+│ └── evox_search_sys_prompt.txt # System prompt for search strategy generation
+└── utils/
+ ├── search_scorer.py # LogWindowScorer — log-weighted scoring of search
+ │ # algorithms by solution improvement over horizon
+ ├── variation_operator_generator.py # variation operator that determines how to present candidate
+ ├── template.py # Default variation operator templates
+ └── coevolve_logging.py # Artifact saving (code, metadata, failed attempts)
+```
+
+## Usage
+
+```bash
+uv run skydiscover-run initial_program.py evaluator.py \
+ --config config.yaml --search evox --iterations 100
+```
+
+```python
+from skydiscover import run_discovery
+result = run_discovery(
+ initial_program="initial_program.py",
+ evaluator="evaluator.py",
+ search="evox",
+ model="gpt-5",
+ iterations=100,
+)
+```
+
+## Config
+
+See `configs/evox.yaml` for the full template. Key settings:
+
+```yaml
+search:
+ type: "evox"
+ database:
+ auto_generate_variation_operators: true
+```
+
+Ablation flag: `auto_generate_variation_operators` — set to `false` to use default templates for variation operator instead of LLM-generated ones.
diff --git a/skydiscover/search/evox/__init__.py b/skydiscover/search/evox/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..4cf11c0449d26a81ac27c0f7ff82fc66c5db8113
--- /dev/null
+++ b/skydiscover/search/evox/__init__.py
@@ -0,0 +1,7 @@
+"""
+EvoX: Co-evolution of search algorithms alongside solution programs.
+"""
+
+from skydiscover.search.evox.controller import CoEvolutionController
+
+__all__ = ["CoEvolutionController"]
diff --git a/skydiscover/search/evox/config/evox_search_sys_prompt.txt b/skydiscover/search/evox/config/evox_search_sys_prompt.txt
new file mode 100644
index 0000000000000000000000000000000000000000..f2051fbf242543e15e37c75bf1ee2a5227f87378
--- /dev/null
+++ b/skydiscover/search/evox/config/evox_search_sys_prompt.txt
@@ -0,0 +1,172 @@
+You are an expert coder evolving a search algorithm for program optimization.
+Your task is to improve the EvolvedProgramDatabase class, which implements a search algorithm that helps the LLM leverage past experience to generate better solutions.
+
+========================
+HIGH-LEVEL CONTEXT
+========================
+- You are evolving the *search algorithm itself*.
+- The search algorithm is implemented as a database that:
+ (1) manages candidate programs (solutions), and
+ (2) decides which program to mutate next and how, and which programs to sample (if any) as context for the next program generation
+- The search algorithm will run on the downstream problem for a number of iterations.
+- Search algorithms that fail to improve past a meaningful threshold within a time window may be replaced.
+
+========================
+SCORING OBJECTIVE
+========================
+Your search algorithm is evaluated based on how much the best solution improves over time. The quality of your search algorithm is measured by:
+
+ Search Algorithm Score = ((best_score - initial_score) * (1 + log(1 + initial_score))) / sqrt(iterations)
+
+ - Where best_score is the best score found on the downstream problem, and initial_score is the starting score before search begins.
+
+========================
+CLASS & FUNCTIONS
+========================
+You must implement a class named EvolvedProgramDatabase that inherits from ProgramDatabase.
+
+This class defines the search algorithm by deciding:
+- add() function: How programs are added to the population
+- sample() function
+ - Which program to mutate next and how (the parent)
+ - What past programs to provide as context (if any) to the LLM when generating new candidate programs
+
+EvolvedProgram extends Program (from skydiscover.search.base_database).
+
+Important Program fields:
+- id: str
+- solution: str
+ - The solution to the problem, can be code, prompt, etc.
+- language: str = "python"
+- metrics: Dict[str, Any]
+ - Task evaluation metrics; the main metric is "combined_score"
+- iteration_found: int
+- parent_id: str
+ - ID of the parent program it was mutated from
+- other_context_ids: List[str]
+ - IDs of other programs used as context for this program generation
+- parent_info: Tuple[str, str]
+ - (label, id) tuple tracking the label assigned to the parent for generating this program.
+ - If label is non-empty, it indicates either `self.DIVERGE_LABEL` or `self.REFINE_LABEL` was used.
+- timestamp: float
+- metadata: Dict[str, Any]
+
+NOTE:
+- You can read these attributes directly from any EvolvedProgram.
+- The program passed to add() already has these fields populated. You can use them as needed, but do
+not overwrite them.
+
+EvolvedProgramDatabase extends ProgramDatabase.
+
+Available attributes and helpers from ProgramDatabase:
+- self.programs: Dict[str, EvolvedProgram] - access to all programs
+- self.DIVERGE_LABEL: str - a predefined label for signaling divergence from parent program (DO NOT modify)
+- self.REFINE_LABEL: str - a predefined label for signaling refinement over the parent program (DO NOT modify)
+- Helper methods (use these rather than re-implementing):
+ get(program_id: str) -> Optional[EvolvedProgram] - get Program by its program_id
+
+========================
+REQUIRED METHODS
+========================
+You MUST implement the following methods exactly (do not change signatures):
+
+1) add
+ def add(self, program: EvolvedProgram, iteration: Optional[int] = None, **kwargs: Any) -> str
+
+The add() method controls how programs enter and remain in the population.
+
+2) sample
+ def sample(self, num_context_programs: Optional[int] = 4, **kwargs) -> Tuple[
+ Dict[str, EvolvedProgram],
+ Dict[str, List[EvolvedProgram]]
+ ]
+
+The sample() method selects which program to mutate next and how, and which past programs to use as context.
+
+ Returns a tuple of:
+ - parent_dict: Dict[str, EvolvedProgram]
+ A dict mapping a label string to EXACTLY ONE program to mutate.
+
+ - context_programs_dict: Dict[str, List[EvolvedProgram]]
+ A dict mapping label strings to lists of past programs to provide as additional context for the next program mutation.
+
+ LABEL RULES FOR PARENT and CONTEXT:
+ - Parent labels MUST be empty string "" by default.
+ - Context labels MUST ALWAYS be empty strings "".
+
+ PARENT + CONTEXT SELECTION PRINCIPLES:
+
+ The parent and context together determine what the LLM sees when generating the next solution.
+
+ 1) Parent selection: what to mutate
+ - Main program to mutate for the next generation.
+ 2) Context selection: what else the LLM sees
+ - Complementary perspectives to inform the next generation. E.g. different approaches, contrasting examples, different score ranges.
+ 3) Avoid deterministic selections that would always pick the same program across iterations.
+
+================================================
+PREDEFINED LABELS (DIVERGE & REFINE)
+================================================
+- Parent labels MUST be empty string "" by default.
+
+- `self.DIVERGE_LABEL`: Signals the LLM to try something fundamentally different.
+- `self.REFINE_LABEL`: Signals the LLM to refine the current approach.
+
+- **USE LABELS ONLY WHEN APPROPRIATE**:
+ - Default should be empty string "" for parent label - let smart parent/context selection do the work.
+ - Use DIVERGE_LABEL or REFINE_LABEL when progress is stagnating -- consider why:
+ - Fundamentally limited approaches may need a new direction.
+ - A promising candidate solution may need refinement to reach its potential.
+ - A new idea often needs a few iterations of refinement to work.
+ - Be careful not to overuse any particular program to apply the labels.
+ - Choose label and the targeted program based on the search state, not a fixed rule.
+
+- ONLY WHEN using self.DIVERGE_LABEL or self.REFINE_LABEL, you can optionally return empty {} context for targeted divergence or refinement on the parent program.
+
+========================
+DESIGN IDEAS
+========================
+KEEP IT SIMPLE: Match your algorithm to the population size and iteration window (Read "Your Algorithm's Search Window").
+ - NOTE: You can define helper state in __init__ for use in add/sample.
+
+PARENT and CONTEXT SELECTION DIVERSITY (PRIMARY MECHANISM):
+ - Use `parent_id`, `other_context_ids`, or `parent_info` to check which parent and context were used, or whether a label has been tried.
+ - Think about different combinations of diverse parents and varied context, and consider what works best.
+ - Sometimes failure programs can also provide useful insights.
+ - Be careful not to overuse any particular program or set of programs from the database.
+ - Adjust the search strategy based on the search state - try different approaches (parent, label, and context selection) when progress is deeply stuck.
+
+READING THE POPULATION:
+ - Exploit or explore?
+ - Consolidate gains or explore more?
+ - What parents/contexts led to improvements? Which led nowhere or showed little improvement?
+ - How far are we from SOTA? When close to SOTA, what parent should I select and refine?
+ - How are the scores distributed?
+ - Is the population diverse or converging? Consider whether diversity itself (not just score) should be a selection signal.
+ - When progress stalls, ask: is the issue with parent and context selection, or a fundamental plateau (reasonable
+ variations exhausted)? Adjust the selection strategy, and consider using DIVERGE and REFINE labels as part of your toolkit.
+
+========================
+CONSTRAINTS
+========================
+- DO NOT change class names (EvolvedProgramDatabase, EvolvedProgram) or method signatures. Preserve all imports and dataclass definitions.
+- All variables used in add() or sample() MUST be initialized in __init__ with safe default values to avoid runtime errors.
+- DO NOT modify, overwrite, or recompute any existing values in program.metrics.
+ - Existing metrics (e.g., "combined_score") MUST remain unchanged.
+ - You may add new internal metrics or metadata keys if useful.
+- When accessing metrics:
+ - Always validate the type before use: check isinstance(value, (int, float)) before converting to float or performing arithmetic.
+ - Filter out non-numeric values (like error strings) when computing averages or scores.
+- LABEL CONSTRAINTS (CRITICAL):
+ - Parent label: MUST be "" (empty string) by default. Use self.DIVERGE_LABEL or self.REFINE_LABEL when population signals warrant it.
+ - Context labels: MUST ALWAYS be "" (empty string). No exceptions.
+ - self.DIVERGE_LABEL and self.REFINE_LABEL are predefined constants. DO NOT redefine or modify them.
+- Track progress state (e.g., best score history, parent usage) in add(), not sample(). This persists correctly across checkpoint resumes.
+- Be aware that all programs share a single root ancestor (the initial seed); the lineage forms a tree, so tracing parent_id always leads back to the same root.
+- Program objects are NOT hashable: do not put them in sets or use them as dict keys. Use program.id (a string) for deduplication and lookups instead.
+
+========================
+CRITICAL INSTRUCTION
+========================
+- Design a search algorithm that performs parent and context selection wisely and adaptively.
+- Only count improvements as meaningful if they exceed 1% relative or 0.01 absolute.
\ No newline at end of file
diff --git a/skydiscover/search/evox/config/search.yaml b/skydiscover/search/evox/config/search.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..10bb5a3eb1d030a9c2ec3b5d6f89d231506d54a8
--- /dev/null
+++ b/skydiscover/search/evox/config/search.yaml
@@ -0,0 +1,40 @@
+
+# General settings
+max_iterations: 100
+# NOTE: evolve search checkpoint interval = 1, evolve program checkpoint interval = 10
+checkpoint_interval: 1
+log_level: "INFO"
+
+llm:
+ models:
+ - name: "gpt-5"
+ weight: 1.0
+ api_base: "https://api.openai.com/v1"
+ api_key: ${OPENAI_API_KEY}
+ temperature: 0.7
+ # top_p: 0.95 # omitted by default; some providers (e.g. Anthropic) reject both temperature and top_p
+ max_tokens: 32000
+ timeout: 600
+ guide_models:
+ - name: "gpt-5-mini"
+ max_tokens: 8000
+ timeout: 300
+
+search:
+ type: "evox_meta"
+ num_context_programs: 2
+
+# Prompt configuration
+prompt:
+ template: "evox"
+ system_message: evox_search_sys_prompt.txt
+
+# Evaluator configuration
+evaluator:
+ timeout: 3600
+ max_retries: 3
+ cascade_evaluation: false
+
+# Generation settings
+diff_based_generation: false
+max_solution_length: 60000
diff --git a/skydiscover/search/evox/controller.py b/skydiscover/search/evox/controller.py
new file mode 100644
index 0000000000000000000000000000000000000000..575d80cade86a8bc5cb14e697fad1862406c10da
--- /dev/null
+++ b/skydiscover/search/evox/controller.py
@@ -0,0 +1,591 @@
+"""
+Co-evolution controller.
+
+Runs evolution for the main *solution* database while also evolving a
+separate *search* program/database in the same process.
+"""
+
+import logging
+import os
+import tempfile
+import uuid
+from pathlib import Path
+from typing import Any, Dict, List, Optional
+
+from skydiscover.search.default_discovery_controller import (
+ DiscoveryController,
+ DiscoveryControllerInput,
+)
+from skydiscover.search.evox.utils.coevolve_logging import (
+ handle_generation_failure,
+ log_search_algorithm_generated,
+ make_json_serializable,
+ update_saved_search_algorithm_score,
+)
+from skydiscover.search.evox.utils.search_scorer import LogWindowScorer
+from skydiscover.search.evox.utils.variation_operator_generator import generate_variation_operators
+from skydiscover.search.registry import get_program, setup_search
+from skydiscover.search.utils.discovery_utils import SerializableResult, load_database_from_file
+
+logger = logging.getLogger(__name__)
+
+
+class CoEvolutionController(DiscoveryController):
+ """
+ Co-evolves solution programs alongside search algorithms.
+
+ The solution database uses an evolving search algorithm for sampling,
+ while the search algorithm itself is scored based on solution improvements.
+ """
+
+ # Adaptive mode defaults
+ DEFAULT_SWITCH_RATIO = 0.10 # Evolve search after 10% of total iterations stagnate
+ DEFAULT_IMPROVEMENT_THRESHOLD = 0.01
+
+ def __init__(self, controller_input: DiscoveryControllerInput):
+ super().__init__(controller_input)
+
+ self._init_search_evolution_controller()
+ self._init_output_dir(controller_input)
+
+ def _init_search_evolution_controller(self) -> None:
+ """Initialize search controller, scorer, and load initial algorithm."""
+ db_cfg = self.config.search.database
+ if not db_cfg.database_file_path:
+ raise ValueError(
+ "config.search.database.database_file_path is required for co-evolution"
+ )
+
+ controller_input, self._search_initial_code = setup_search(
+ initial_program_path=db_cfg.database_file_path,
+ evaluation_file=db_cfg.evaluation_file,
+ config_path=db_cfg.config_path,
+ output_dir=self.config.search.output_dir,
+ evaluator_env_vars=self.evaluator_env_vars,
+ parent_llm_config=self.config.llm if self.config.search.share_llm else None,
+ )
+ self.search_controller = DiscoveryController(controller_input)
+ self.search_scorer = LogWindowScorer()
+ self._active_search_algorithm_code = self._search_initial_code
+
+ self._log_coevolution_setup(db_cfg)
+ self._init_search_tracking()
+
+ def _init_search_tracking(self) -> None:
+ """Initialize search evolution tracking state."""
+ self._pending_search_result: Optional[SerializableResult] = None
+ self._best_search_score: Optional[float] = None
+ self._num_search_evolutions = 0
+
+ self._switch_interval = getattr(self.config.search, "switch_interval", None)
+ self._stagnant_count = 0
+ self._last_tracked_best_score: Optional[float] = None
+
+ self._diverge_label = ""
+ self._refine_label = ""
+
+ self._fallback_database = None
+ self._fallback_search_code = None
+
+ def _init_output_dir(self, controller_input: DiscoveryControllerInput) -> None:
+ base_dir = controller_input.output_dir or os.path.join(
+ os.path.dirname(controller_input.evaluation_file),
+ "outputs",
+ self.config.search.type,
+ )
+ self.search_outputs_dir = os.path.join(base_dir, "search")
+ os.makedirs(self.search_outputs_dir, exist_ok=True)
+
+ async def run_discovery(
+ self,
+ start_iteration: int,
+ max_iterations: int,
+ checkpoint_callback=None,
+ post_process_result: Optional[bool] = True,
+ ):
+ """Run co-evolution of solution programs and search algorithms."""
+ self.total_solution_iterations = start_iteration + max_iterations
+ self._max_solution_iterations = max_iterations
+
+ if self._switch_interval is None:
+ self._switch_interval = max(1, int(max_iterations * self.DEFAULT_SWITCH_RATIO))
+ logger.info(f"Switch if {self._switch_interval} iterations of stagnation detected")
+
+ self.start_db_stats = self.database.get_statistics(
+ improvement_threshold=self.DEFAULT_IMPROVEMENT_THRESHOLD
+ )
+
+ # Set up search window and labels
+ self._reset_search_window()
+
+ # Generate variation labels for the search algorithm
+ await self._generate_variation_operators()
+
+ # Run co-evolution
+ iteration = start_iteration
+ while iteration < self.total_solution_iterations:
+ if self.shutdown_event.is_set():
+ logger.info("Shutdown requested")
+ break
+
+ try:
+ # Run solution iteration
+ result = await self._run_iteration(iteration, retry_times=3)
+ attempts_used = getattr(result, "attempts_used", 1)
+
+ if result.error:
+ logger.warning(
+ f"Iteration {iteration} failed (used {attempts_used} attempts): {result.error}"
+ )
+ # Database error after a switch: fall back and retry
+ if self._fallback_database is not None and result.prompt is None:
+ self._restore_fallback_database()
+ continue # Retry same iteration with restored database
+ else:
+ self._process_iteration_result(result, iteration, checkpoint_callback)
+
+ for _ in range(attempts_used):
+ self._record_search_window_step()
+
+ completed_solution_iter = iteration
+ iteration += attempts_used
+
+ # Co-evolve search strategy if needed (skip on final iteration)
+ if iteration < self.total_solution_iterations and self._should_evolve_search():
+ logger.info(
+ f"Stagnation detected -> evolving search strategy (solution_iter={completed_solution_iter})"
+ )
+ await self._evolve_search(completed_solution_iter)
+
+ except Exception as e:
+ logger.error(f"Error in iteration {iteration}: {e}", exc_info=True)
+ # Exception from database.add() after a switch: fall back and retry
+ if self._fallback_database is not None:
+ self._restore_fallback_database()
+ continue # Retry same iteration
+ iteration += 1 # Normal error — advance
+
+ if self._pending_search_result:
+ await self._finalize_pending_search()
+
+ logger.info(f"[SOLUTION EVOLUTION] Evolution completed: {self.database.name}")
+ return self.database.get_best_program()
+
+ def _should_evolve_search(self) -> bool:
+ """Check if it's time to evolve the search algorithm (stagnation-based)."""
+ current = self._get_best_score()
+
+ if self._last_tracked_best_score is None:
+ self._stagnant_count = 0
+ elif (current - self._last_tracked_best_score) > self.DEFAULT_IMPROVEMENT_THRESHOLD:
+ self._stagnant_count = 0
+ else:
+ self._stagnant_count += 1
+
+ self._last_tracked_best_score = current
+
+ if self._stagnant_count >= self._switch_interval:
+ self._stagnant_count = 0
+ return True
+
+ return False
+
+ async def _evolve_search(self, solution_iter: int) -> None:
+ """Handle search evolution: score previous algorithm, generate and switch to new one."""
+
+ if not self.search_controller.database.programs:
+ await self._initialize_first_search_program(solution_iter)
+ return
+
+ # If there is a pending search result, finalize it (as search window is reset)
+ if self._pending_search_result:
+ await self._finalize_pending_search()
+
+ self._reset_search_window()
+ await self._generate_and_validate_search_algorithm(solution_iter)
+
+ async def _finalize_pending_search(self) -> None:
+ """Score the pending search algorithm and add it to the search strategy database."""
+ pending_iteration = self._num_search_evolutions
+ is_new_best = self._assign_search_score()
+
+ await update_saved_search_algorithm_score(
+ self.search_outputs_dir,
+ pending_iteration,
+ self._pending_search_result,
+ is_new_best=is_new_best,
+ db_stats=self.database.get_statistics(),
+ )
+ await self.search_controller.postprocess_result(
+ self._pending_search_result, self._num_search_evolutions, verbose=False
+ )
+
+ self._pending_search_result = None
+ self._num_search_evolutions += 1
+
+ async def _initialize_first_search_program(self, solution_iter: int) -> None:
+ """Initialize and score the first (file-based) search program."""
+ start_score = (
+ self.search_scorer.get_start_score()
+ or getattr(self.database, "initial_program_score", None)
+ or 0.0
+ )
+ metrics = self._compute_search_metrics(
+ start_score=start_score,
+ best_scores=None,
+ horizon=self._switch_interval,
+ start_iteration=0,
+ )
+ search_score = float(metrics.get("combined_score", 0.0) or 0.0)
+
+ initial_program = get_program(
+ self.search_controller.config,
+ self._search_initial_code,
+ str(uuid.uuid4()),
+ metrics,
+ self._num_search_evolutions,
+ )
+ initial_program.metadata = initial_program.metadata or {}
+ initial_program.metadata["start_db_stats"] = make_json_serializable(self.start_db_stats)
+ initial_program.metadata["end_db_stats"] = make_json_serializable(
+ self.database.get_statistics(improvement_threshold=self.DEFAULT_IMPROVEMENT_THRESHOLD)
+ )
+
+ initial_result = SerializableResult(
+ child_program_dict=initial_program.to_dict(), iteration=self._num_search_evolutions
+ )
+ self._best_search_score = search_score
+ await self.search_controller.postprocess_result(
+ initial_result, self._num_search_evolutions, verbose=False
+ )
+
+ self.search_controller.database.initial_program_id = initial_program.id
+ self.search_controller.database.initial_program_score = search_score
+ self._num_search_evolutions += 1
+
+ self._reset_search_window()
+ await self._generate_and_validate_search_algorithm(solution_iter)
+
+ async def _generate_variation_operators(self) -> None:
+ """Generate diverge/refine labels once and assign to the current database."""
+ if self._diverge_label and self._refine_label:
+ self._assign_labels_to_db(self.database)
+ return
+
+ db_cfg = self.config.search.database
+ if not getattr(db_cfg, "auto_generate_variation_operators", True):
+ from skydiscover.search.evox.utils.template import (
+ DEFAULT_DIVERGE_TEMPLATE,
+ DEFAULT_REFINE_TEMPLATE,
+ )
+
+ self._diverge_label = DEFAULT_DIVERGE_TEMPLATE
+ self._refine_label = DEFAULT_REFINE_TEMPLATE
+ logger.info(
+ "Using default variation operators (auto_generate_variation_operators=false)"
+ )
+ self._assign_labels_to_db(self.database)
+ return
+
+ system_message = self.config.context_builder.system_message or ""
+ from skydiscover.search.utils.discovery_utils import load_evaluator_code
+
+ evaluator_code = load_evaluator_code(self.evaluation_file)
+
+ try:
+ problem_dir = Path(self.evaluation_file).parent if self.evaluation_file else None
+ label_llms = self.search_controller.guide_llms
+ model_names = ", ".join(m.name for m in label_llms.models_cfg)
+ logger.info(f"Label generation: using guide_model = [{model_names}]")
+ self._diverge_label, self._refine_label = await generate_variation_operators(
+ system_message,
+ evaluator_code,
+ problem_dir=problem_dir,
+ llm_pool=label_llms,
+ )
+ logger.info(
+ f"Generated variation operator labels ({len(self._diverge_label)}/{len(self._refine_label)} chars)"
+ )
+ except Exception as e:
+ self._diverge_label = ""
+ self._refine_label = ""
+ logger.error(f"Label generation failed: {e}, setting labels to empty strings")
+
+ self._assign_labels_to_db(self.database)
+
+ def _assign_labels_to_db(self, db) -> None:
+ """Assign the variation operators to a database instance."""
+ db.DIVERGE_LABEL = self._diverge_label
+ db.REFINE_LABEL = self._refine_label
+
+ async def _generate_and_validate_search_algorithm(self, solution_iter: int) -> None:
+ """Generate a new search algorithm and switch to it if valid."""
+ iteration = self._num_search_evolutions
+ search_stats = self._build_search_stats(solution_iter)
+
+ self.search_controller._prompt_context = {
+ "search_stats": search_stats["search_algorithm_stats"],
+ "db_stats": search_stats["db_stats"],
+ }
+ result = await self.search_controller.run_discovery(
+ start_iteration=iteration,
+ max_iterations=1,
+ post_process_result=False,
+ )
+
+ if not result or result.error:
+ await handle_generation_failure(
+ self.search_outputs_dir,
+ self._active_search_algorithm_code,
+ iteration,
+ result,
+ solution_iter,
+ )
+ self._num_search_evolutions += 1
+ return
+
+ result.child_program_dict.setdefault("metadata", {})["start_db_stats"] = (
+ make_json_serializable(search_stats["db_stats"])
+ )
+ await log_search_algorithm_generated(
+ self.search_outputs_dir,
+ result,
+ iteration,
+ diverge_label=self._diverge_label,
+ refine_label=self._refine_label,
+ )
+
+ if not self._switch_to_new_search_algorithm(result):
+ await handle_generation_failure(
+ self.search_outputs_dir,
+ self._active_search_algorithm_code,
+ iteration,
+ result,
+ solution_iter,
+ "validation",
+ )
+ self._num_search_evolutions += 1
+ return
+
+ self._pending_search_result = result
+ self._reset_search_window(start_iteration=solution_iter)
+
+ def _build_search_stats(self, solution_iter: int) -> Dict[str, Any]:
+ """Build statistics dict for search algorithm generation."""
+ return {
+ "search_algorithm_stats": {
+ "window_start_iteration": solution_iter,
+ "total_iterations": self._max_solution_iterations,
+ "search_window_horizon": self._switch_interval,
+ "problem_description": self.config.context_builder.system_message,
+ "evaluator_context": self.evaluation_file,
+ "improvement_threshold": self.DEFAULT_IMPROVEMENT_THRESHOLD,
+ },
+ "db_stats": self.database.get_statistics(
+ improvement_threshold=self.DEFAULT_IMPROVEMENT_THRESHOLD
+ ),
+ }
+
+ def _switch_to_new_search_algorithm(self, result: SerializableResult) -> bool:
+ """Switch solution database to use the new search algorithm."""
+ child_dict = result.child_program_dict or {}
+ search_code = child_dict.get("solution")
+ if not search_code:
+ logger.warning("No solution in search result; skipping transition")
+ return False
+
+ search_program_id = child_dict.get("id", "unknown")
+ fd, file_path = tempfile.mkstemp(suffix=".py", prefix="evox_search_")
+ try:
+ with os.fdopen(fd, "w") as f:
+ f.write(search_code)
+
+ # Load the new search algorithm database
+ new_db_class, prog_class = load_database_from_file(file_path)
+ # Ensure labels exist for databases that use them in __init__ (before _assign_labels_to_db)
+ if not hasattr(new_db_class, "DIVERGE_LABEL"):
+ new_db_class.DIVERGE_LABEL = ""
+ if not hasattr(new_db_class, "REFINE_LABEL"):
+ new_db_class.REFINE_LABEL = ""
+ new_db = new_db_class(self.config.search.type, self.config.search.database)
+ new_db._program_class = prog_class
+
+ # Assign labels to the new search algorithm database
+ self._assign_labels_to_db(new_db)
+
+ # Migrate programs and prompts from the current database to the new database
+ migrated_count = self._migrate_to_db(new_db)
+
+ self._wrap_add_method(new_db)
+ new_db.get_best_program() # Sets best_program_id (None -> actual best)
+
+ self._fallback_database = self.database
+ self._fallback_search_code = self._active_search_algorithm_code
+
+ self.database = new_db
+ if self.evaluator.llm_judge:
+ self.evaluator.llm_judge.database = new_db
+ logger.info(
+ f"Switched to search algorithm {search_program_id} ({migrated_count} programs migrated)"
+ )
+
+ self._active_search_algorithm_code = search_code
+ return True
+
+ except Exception as e:
+ logger.error(f"Failed to load search algorithm {search_program_id}: {e}")
+ return False
+ finally:
+ if os.path.exists(file_path):
+ os.unlink(file_path)
+
+ def _restore_fallback_database(self) -> None:
+ """Restore the previous search strategy after a failed switch."""
+ broken_db = self.database
+ old_db = self._fallback_database
+
+ # Migrate new programs found during the broken strategy's successful runs
+ old_ids = set(old_db.programs)
+ migrated = 0
+ for pid, program in broken_db.programs.items():
+ if pid not in old_ids:
+ try:
+ old_db.add(program, iteration=program.iteration_found)
+ migrated += 1
+ except Exception:
+ logger.debug("Migration failed for program %s", program.id, exc_info=True)
+
+ logger.warning(
+ "New search strategy caused database error — "
+ f"restoring previous search strategy ({migrated} new programs preserved)"
+ )
+ self.database = old_db
+ if self.evaluator.llm_judge:
+ self.evaluator.llm_judge.database = old_db
+ self._active_search_algorithm_code = self._fallback_search_code
+ self._pending_search_result = None
+ self._num_search_evolutions += 1 # Count the failed attempt
+ self._fallback_database = None
+ self._fallback_search_code = None
+
+ def _migrate_to_db(self, new_db) -> int:
+ """Migrate all programs and prompts from current database to new database."""
+ prog_class = getattr(new_db, "_program_class", None)
+ for program in sorted(self.database.programs.values(), key=lambda x: x.iteration_found):
+ converted = prog_class(**program.to_dict()) if prog_class else program
+ new_db.add(converted, iteration=program.iteration_found)
+ migrated = len(self.database.programs)
+
+ # Migrate prompts
+ if self.database.config.log_prompts:
+ if new_db.prompts_by_program is None:
+ new_db.prompts_by_program = {}
+
+ old_prompts = self.database.prompts_by_program or {}
+ new_db.prompts_by_program.update(
+ {k: v for k, v in old_prompts.items() if k not in new_db.prompts_by_program}
+ )
+
+ for p in new_db.programs.values():
+ if p.prompts and p.id not in new_db.prompts_by_program:
+ new_db.prompts_by_program[p.id] = p.prompts
+
+ return migrated
+
+ def _get_best_score(self) -> float:
+ """Get the current best solution score (combined_score metric)."""
+
+ best = self.database.get_best_program()
+
+ if best and best.metrics:
+ score = best.metrics.get("combined_score")
+ return float(score) if isinstance(score, (int, float)) else 0.0
+ return getattr(self.database, "initial_program_score", None) or 0.0
+
+ def _reset_search_window(self, start_iteration: Optional[int] = None) -> None:
+ """Start a fresh scoring window for the active search algorithm."""
+ self.search_scorer.reset_window(self._get_best_score(), start_iteration=start_iteration)
+
+ def _record_search_window_step(self) -> None:
+ """Record current best score for search algorithm scoring."""
+
+ if self.search_scorer.get_start_score() is None:
+ self._reset_search_window()
+
+ self.search_scorer.record_step(self._get_best_score())
+
+ def _compute_search_metrics(
+ self,
+ start_score: Optional[float] = None,
+ best_scores: Optional[List[float]] = None,
+ horizon: Optional[int] = None,
+ start_iteration: Optional[int] = None,
+ ) -> Dict[str, Any]:
+ """Compute scoring metrics using the configured scorer."""
+
+ return self.search_scorer.compute_metrics(
+ start_score=start_score,
+ best_scores=best_scores,
+ horizon=horizon,
+ total_iterations=self._max_solution_iterations,
+ start_iteration=start_iteration,
+ )
+
+ def _wrap_add_method(self, db) -> None:
+ """Wrap database.add() to ensure _update_best_program is always called."""
+ original_add = db.add
+
+ def wrapped_add(program, iteration=None, **kwargs):
+ result = original_add(program, iteration=iteration, **kwargs)
+ db._update_best_program(program) # Idempotent safety for LLM-generated databases
+ return result
+
+ db.add = wrapped_add
+
+ def _assign_search_score(self) -> bool:
+ """Assign score to pending search algorithm. Returns True if new best."""
+ if not self._pending_search_result:
+ return False
+
+ if self.search_scorer.get_window_size() > 0:
+ metrics = self._compute_search_metrics(horizon=self._switch_interval)
+ else:
+ start = self.search_scorer.get_start_score() or 0.0
+ metrics = self._compute_search_metrics(
+ start_score=start,
+ best_scores=[self._get_best_score()],
+ horizon=self._switch_interval,
+ )
+
+ score = float(metrics.get("combined_score", 0.0) or 0.0)
+
+ child_dict = self._pending_search_result.child_program_dict or {}
+ child_dict.setdefault("metrics", {}).update(metrics)
+ child_dict.setdefault("metadata", {})["end_db_stats"] = make_json_serializable(
+ self.database.get_statistics(improvement_threshold=self.DEFAULT_IMPROVEMENT_THRESHOLD)
+ )
+ self._pending_search_result.child_program_dict = child_dict
+
+ is_new_best = self._best_search_score is not None and score > self._best_search_score
+ if is_new_best:
+ logger.info(
+ f"New best search score: {score:.6f} (+{score - self._best_search_score:.6f})"
+ )
+ if is_new_best or self._best_search_score is None:
+ self._best_search_score = score
+ return is_new_best
+
+ def _log_coevolution_setup(self, db_cfg) -> None:
+ logger.info("=" * 70)
+ logger.info("[EVOX CO-EVOLUTION SETUP]")
+ logger.info("-" * 70)
+ logger.info(f" [SOLUTION EVOLUTION]")
+ logger.info(f" Initial search strategy file : {db_cfg.database_file_path}")
+ logger.info(f" Solution database class : {self.database.__class__.__name__}")
+ logger.info(f" [META EVOLUTION OF SEARCH STRATEGY]")
+ logger.info(
+ f" Search strategy database class: {self.search_controller.database.__class__.__name__}"
+ )
+ logger.info(f" Search strategy evaluator : {db_cfg.evaluation_file}")
+ logger.info(f" Search strategy config : {db_cfg.config_path}")
+ logger.info("=" * 70)
diff --git a/skydiscover/search/evox/database/__init__.py b/skydiscover/search/evox/database/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/skydiscover/search/evox/database/initial_search_strategy.py b/skydiscover/search/evox/database/initial_search_strategy.py
new file mode 100644
index 0000000000000000000000000000000000000000..fbe69071d9df77c5833aabb572727fab16202e66
--- /dev/null
+++ b/skydiscover/search/evox/database/initial_search_strategy.py
@@ -0,0 +1,67 @@
+# EVOLVE-BLOCK-START
+import logging
+import random
+from dataclasses import dataclass
+from typing import Dict, List, Optional, Tuple
+
+from skydiscover.config import DatabaseConfig
+from skydiscover.search.base_database import Program, ProgramDatabase
+
+logger = logging.getLogger(__name__)
+
+
+@dataclass
+class EvolvedProgram(Program):
+ """Program for the evolved database."""
+
+
+class EvolvedProgramDatabase(ProgramDatabase):
+ """Initial search strategy database."""
+
+ def __init__(self, name: str, config: DatabaseConfig):
+ super().__init__(name, config)
+ self.initial_program = None
+
+ def add(self, program: EvolvedProgram, iteration: Optional[int] = None, **kwargs) -> str:
+ """Add a program to the database."""
+ if iteration == 0 or program.iteration_found == 0:
+ self.initial_program = program
+
+ self.programs[program.id] = program
+
+ if iteration is not None:
+ self.last_iteration = max(self.last_iteration, iteration)
+
+ if self.config.db_path:
+ self._save_program(program)
+
+ self._update_best_program(program)
+
+ logger.debug(f"Added program {program.id} to the evolve database")
+ return program.id
+
+ def sample(
+ self, num_context_programs: Optional[int] = 4, **kwargs
+ ) -> Tuple[Dict[str, EvolvedProgram], Dict[str, List[EvolvedProgram]]]:
+ """
+ Picks a random parent and set of context programs.
+ """
+ candidates = list(self.programs.values())
+
+ if len(candidates) == 0:
+ raise ValueError("No candidates available for sampling")
+
+ parent = random.choice(candidates)
+
+ sample_size = min(num_context_programs + 1, len(candidates))
+ examples = random.sample(candidates, sample_size)
+
+ examples = [p for p in examples if p.id != parent.id][:num_context_programs]
+
+ parent_dict = {"": parent}
+ context_programs_dict = {"": examples}
+
+ return parent_dict, context_programs_dict
+
+
+# EVOLVE-BLOCK-END
diff --git a/skydiscover/search/evox/database/search_strategy_db.py b/skydiscover/search/evox/database/search_strategy_db.py
new file mode 100644
index 0000000000000000000000000000000000000000..adb4d42103e8f341c5252ad739f7c28cbc6bbb0d
--- /dev/null
+++ b/skydiscover/search/evox/database/search_strategy_db.py
@@ -0,0 +1,61 @@
+import logging
+import random
+from dataclasses import dataclass
+from typing import Dict, List, Optional, Tuple
+
+from skydiscover.config import DatabaseConfig
+from skydiscover.search.base_database import Program, ProgramDatabase
+
+logger = logging.getLogger(__name__)
+
+
+@dataclass
+class SearchStrategy(Program):
+ """Program entry for the search strategy database."""
+
+
+class SearchStrategyDatabase(ProgramDatabase):
+ """Database for storing and sampling evolved search strategy programs."""
+
+ def __init__(self, name: str, config: DatabaseConfig):
+ super().__init__(name, config)
+
+ def add(self, program: SearchStrategy, iteration: Optional[int] = None, **kwargs) -> str:
+ """Add a program to the database."""
+ self.programs[program.id] = program
+
+ if iteration is not None:
+ self.last_iteration = max(self.last_iteration, iteration)
+
+ if self.config.db_path:
+ self._save_program(program)
+
+ self._update_best_program(program)
+
+ logger.debug(f"Added program {program.id} to evolve database")
+ return program.id
+
+ def sample(
+ self, num_context_programs: Optional[int] = 4, **kwargs
+ ) -> Tuple[Dict[str, SearchStrategy], Dict[str, List[SearchStrategy]]]:
+ """
+ Sample a search strategy to refine and other context strategies for evolution.
+ """
+
+ def safe_score(p):
+ score = p.metrics.get("combined_score") if p.metrics else None
+ if not isinstance(score, (int, float)):
+ logger.warning(
+ f"Program {p.id} has invalid combined_score: {score}, metrics: {p.metrics}"
+ )
+ return float(score) if isinstance(score, (int, float)) else float("-inf")
+
+ parent = max(self.programs.values(), key=safe_score)
+ available_programs = list(self.programs.values())
+ num_to_sample = max(0, min(num_context_programs, len(available_programs)))
+
+ other_context_programs = (
+ random.sample(available_programs, num_to_sample) if num_to_sample > 0 else []
+ )
+ other_context_programs = [p for p in other_context_programs if p.id != parent.id]
+ return {"": parent}, {"": other_context_programs}
diff --git a/skydiscover/search/evox/database/search_strategy_evaluator.py b/skydiscover/search/evox/database/search_strategy_evaluator.py
new file mode 100644
index 0000000000000000000000000000000000000000..7475cb3e6d64970cc6ef5488e338d3f3dbea6290
--- /dev/null
+++ b/skydiscover/search/evox/database/search_strategy_evaluator.py
@@ -0,0 +1,777 @@
+import inspect
+import traceback
+from concurrent.futures import ThreadPoolExecutor, as_completed
+from typing import Any, Dict, List, Tuple
+
+from skydiscover.config import DatabaseConfig
+from skydiscover.search.base_database import Program, ProgramDatabase
+from skydiscover.search.utils.discovery_utils import load_database_from_file
+
+
+def _error_response(error_message: str) -> Dict[str, Any]:
+ """Create a standardized error response dictionary."""
+ return {"validity": 0, "error": error_message, "combined_score": None}
+
+
+def _verify_metrics_preserved(
+ original_metrics: Dict[str, Any],
+ stored_metrics: Dict[str, Any],
+ operation: str,
+ program_id: str,
+) -> str:
+ """
+ Verify that all metrics from original_metrics are present in stored_metrics
+ with the same values. Returns error message if verification fails, empty string if OK.
+ """
+ original_keys = set(original_metrics.keys())
+
+ for key in original_keys:
+ if key not in stored_metrics:
+ return (
+ f"Metric '{key}' was deleted from program '{program_id}' during {operation}. "
+ f"Original metrics: {list(original_keys)}, stored metrics: {list(stored_metrics.keys())}"
+ )
+
+ original_value = original_metrics[key]
+ stored_value = stored_metrics[key]
+
+ if isinstance(original_value, (int, float)) and isinstance(stored_value, (int, float)):
+ if abs(float(original_value) - float(stored_value)) > 1e-10:
+ return (
+ f"Metric '{key}' value was modified in program '{program_id}' during {operation}: "
+ f"original={original_value}, stored={stored_value}. "
+ f"Metric values must remain unchanged."
+ )
+ elif original_value != stored_value:
+ return (
+ f"Metric '{key}' value was modified in program '{program_id}' during {operation}: "
+ f"original={original_value!r}, stored={stored_value!r}. "
+ f"Metric values must remain unchanged."
+ )
+
+ return ""
+
+
+def evaluate(program_path: str, fast_mode: bool = False) -> Dict[str, Any]:
+ """
+ Validate a search algorithm by testing its database implementation.
+
+ Checks:
+ 1. Structural: Class name (EvolvedProgramDatabase), inheritance, sample() signature
+ 2. add() operations: Metric preservation for single and multiple programs
+ 3. sample() return type: Tuple[Dict[str, Program], Dict[str, List[Program]]]
+ 4. sample() metrics preservation: Returned and stored programs maintain all original metrics
+ 5. Edge cases: Programs with error strings in metrics
+ 6. Migration compatibility: Can handle base Program instances (not just EvolvedProgram)
+
+ Returns:
+ If valid: {"validity": 1, "combined_score": 0.0}
+ If invalid: {"validity": 0, "error": error_message, "combined_score": None}
+ """
+ NUM_PROGRAMS_TO_ADD = 5 if fast_mode else 10
+ NUM_SAMPLE_ITERATIONS = 2 if fast_mode else 10
+ NUM_ERROR_SAMPLE_ITERATIONS = 2 if fast_mode else 5
+ NUM_MIGRATION_PROGRAMS = 3 if fast_mode else 5
+ NUM_MIGRATION_SAMPLES = 1 if fast_mode else 3
+
+ try:
+ try:
+ database_class, program_class = load_database_from_file(program_path)
+ except Exception as e:
+ return _error_response(f"Failed to load database class: {str(e)}")
+
+ try:
+ if database_class.__name__ != "EvolvedProgramDatabase":
+ return _error_response(
+ f"Database class must be named 'EvolvedProgramDatabase', "
+ f"got '{database_class.__name__}'"
+ )
+
+ if not issubclass(database_class, ProgramDatabase):
+ return _error_response("EvolvedProgramDatabase must inherit from ProgramDatabase")
+
+ if not issubclass(program_class, Program):
+ return _error_response(
+ "EvolvedProgram must inherit from Program (base_database.Program)"
+ )
+
+ sample_sig = inspect.signature(database_class.sample)
+ params = list(sample_sig.parameters.values())
+ if len(params) < 2 or params[1].name != "num_context_programs":
+ return _error_response(
+ "sample() must have signature sample(self, num_context_programs: Optional[int] = 4, **kwargs). "
+ "Expected second parameter named 'num_context_programs'."
+ )
+
+ return_annotation = sample_sig.return_annotation
+ if return_annotation != inspect.Signature.empty:
+ import typing
+
+ if hasattr(typing, "get_args") and hasattr(typing, "get_origin"):
+ origin = typing.get_origin(return_annotation)
+ args = typing.get_args(return_annotation)
+ if origin is tuple or (hasattr(typing, "Tuple") and origin is typing.Tuple):
+ if len(args) != 2:
+ return _error_response(
+ f"sample() return type must be Tuple[Dict[str, Program], Dict[str, List[Program]]] "
+ f"(2 elements), got {return_annotation} with {len(args)} elements"
+ )
+ except Exception as e:
+ return _error_response(f"Failed structural checks for EvolvedProgramDatabase: {str(e)}")
+
+ # Ensure DIVERGE_LABEL and REFINE_LABEL exist for evolved databases that use them in __init__
+ # (coevolve_controller assigns real values via _assign_labels_to_db; evaluator uses empty defaults)
+ if not hasattr(database_class, "DIVERGE_LABEL"):
+ database_class.DIVERGE_LABEL = ""
+ if not hasattr(database_class, "REFINE_LABEL"):
+ database_class.REFINE_LABEL = ""
+
+ try:
+ test_config = DatabaseConfig()
+ db = database_class("test_db", test_config)
+ except Exception as e:
+ return _error_response(f"Failed to initialize database: {str(e)}")
+
+ all_original_metrics = {}
+
+ # Test: Add one program and verify
+ try:
+ test_program = program_class(
+ id="test_program_1",
+ solution="def test(): return 1",
+ language="python",
+ metrics={"score": 0.5, "combined_score": 0.5},
+ )
+ test_program.iteration_found = 0
+ original_metrics = test_program.metrics.copy()
+ all_original_metrics["test_program_1"] = original_metrics.copy()
+
+ program_id = db.add(test_program, iteration=0)
+ if program_id != "test_program_1":
+ return _error_response(f"add() returned unexpected ID: {program_id}")
+
+ db.initial_program_id = program_id
+
+ stored_program = db.get("test_program_1")
+ if stored_program is None:
+ return _error_response("Program not found in database after add()")
+
+ error_msg = _verify_metrics_preserved(
+ original_metrics, stored_program.metrics, "add()", "test_program_1"
+ )
+ if error_msg:
+ return _error_response(error_msg)
+
+ error_msg = _verify_metrics_preserved(
+ original_metrics, test_program.metrics, "add() (original object)", "test_program_1"
+ )
+ if error_msg:
+ return _error_response(error_msg)
+ except Exception as e:
+ return _error_response(f"Failed to add or verify program: {str(e)}")
+
+ # Test: Add programs and sample from them
+ try:
+ for i in range(NUM_PROGRAMS_TO_ADD):
+ program = program_class(
+ id=f"program_{i}",
+ solution=f"def func_{i}(): return {i}",
+ language="python",
+ metrics={"score": float(i) / 10.0, "combined_score": float(i) / 10.0},
+ )
+ program.iteration_found = i
+ original_metrics = program.metrics.copy()
+ all_original_metrics[f"program_{i}"] = original_metrics.copy()
+ db.add(program, iteration=i)
+
+ if not fast_mode or i == NUM_PROGRAMS_TO_ADD - 1:
+ stored_program = db.get(f"program_{i}")
+ if stored_program is None:
+ return _error_response(
+ f"Program 'program_{i}' not found in database after add()"
+ )
+
+ error_msg = _verify_metrics_preserved(
+ all_original_metrics[f"program_{i}"],
+ stored_program.metrics,
+ "add()",
+ f"program_{i}",
+ )
+ if error_msg:
+ return _error_response(error_msg)
+
+ if len(db.programs) < NUM_PROGRAMS_TO_ADD:
+ return _error_response(
+ f"Expected at least {NUM_PROGRAMS_TO_ADD} programs, found {len(db.programs)}"
+ )
+
+ for sample_iter in range(NUM_SAMPLE_ITERATIONS):
+ sample_result = db.sample(num_context_programs=4)
+
+ if not isinstance(sample_result, tuple):
+ return _error_response(
+ f"sample() must return a tuple, got {type(sample_result)} on iteration {sample_iter}"
+ )
+ if len(sample_result) != 2:
+ return _error_response(
+ f"sample() must return a tuple of 2 elements (parent_dict, context_programs_dict), got {len(sample_result)} elements on iteration {sample_iter}"
+ )
+
+ parent_dict, context_programs_dict = sample_result
+
+ if not isinstance(parent_dict, dict):
+ return _error_response(
+ f"sample() first element must be a Dict[str, Program], got {type(parent_dict)} on iteration {sample_iter}"
+ )
+
+ if len(parent_dict) != 1:
+ return _error_response(
+ f"sample() must return exactly one parent program in parent_dict, "
+ f"got {len(parent_dict)} parents with keys {list(parent_dict.keys())} on iteration {sample_iter}"
+ )
+
+ parent_label = list(parent_dict.keys())[0]
+ parent = parent_dict[parent_label]
+
+ if parent is None:
+ return _error_response(
+ f"sample() returned None for parent on iteration {sample_iter}"
+ )
+ if not isinstance(parent, Program):
+ return _error_response(
+ f"sample() parent_dict value must be a Program, got {type(parent)} on iteration {sample_iter}"
+ )
+ if parent.id not in db.programs:
+ return _error_response(f"Sampled parent (id={parent.id}) not found in database")
+
+ if not isinstance(context_programs_dict, dict):
+ return _error_response(
+ f"sample() second element must be a Dict[str, List[Program]], got {type(context_programs_dict)} on iteration {sample_iter}"
+ )
+
+ all_context_programs = []
+ for label, prog_list in context_programs_dict.items():
+ if not isinstance(prog_list, list):
+ return _error_response(
+ f"sample() context_programs_dict['{label}'] must be a list, got {type(prog_list)} on iteration {sample_iter}"
+ )
+ for prog in prog_list:
+ if not isinstance(prog, Program):
+ return _error_response(
+ f"sample() context_programs_dict['{label}'] contains non-Program object: {type(prog)} on iteration {sample_iter}"
+ )
+ all_context_programs.extend(prog_list)
+
+ requested_num = 4
+ max_possible = min(requested_num, len(db.programs))
+ if len(all_context_programs) > max_possible:
+ return _error_response(
+ f"sample() returned {len(all_context_programs)} total context programs, which exceeds the maximum possible "
+ f"({max_possible} given {len(db.programs)} programs in database) "
+ f"for num_context_programs={requested_num} on iteration {sample_iter}."
+ )
+
+ stored_parent = db.get(parent.id)
+ if stored_parent is None:
+ return _error_response(
+ f"Sampled parent (id={parent.id}) not found in database via get()"
+ )
+
+ if parent.id in all_original_metrics:
+ original_parent_metrics = all_original_metrics[parent.id]
+ else:
+ return _error_response(
+ f"Program '{parent.id}' not found in original_metrics tracking. "
+ f"This indicates a bug in the test - all programs should have their original metrics tracked."
+ )
+
+ error_msg = _verify_metrics_preserved(
+ original_parent_metrics, parent.metrics, "sample() (parent returned)", parent.id
+ )
+ if error_msg:
+ return _error_response(error_msg)
+
+ error_msg = _verify_metrics_preserved(
+ original_parent_metrics,
+ stored_parent.metrics,
+ "sample() (parent stored)",
+ parent.id,
+ )
+ if error_msg:
+ return _error_response(error_msg)
+
+ for context_prog in all_context_programs:
+ if context_prog.id not in db.programs:
+ return _error_response(
+ f"Sampled context program (id={context_prog.id}) not found in database"
+ )
+
+ stored_context = db.get(context_prog.id)
+ if stored_context is None:
+ return _error_response(
+ f"Sampled context program (id={context_prog.id}) not found in database via get()"
+ )
+
+ if context_prog.id in all_original_metrics:
+ original_context_metrics = all_original_metrics[context_prog.id]
+ else:
+ return _error_response(
+ f"Program '{context_prog.id}' not found in original_metrics tracking. "
+ f"This indicates a bug in the test - all programs should have their original metrics tracked."
+ )
+
+ error_msg = _verify_metrics_preserved(
+ original_context_metrics,
+ context_prog.metrics,
+ "sample() (context program returned)",
+ context_prog.id,
+ )
+ if error_msg:
+ return _error_response(error_msg)
+
+ error_msg = _verify_metrics_preserved(
+ original_context_metrics,
+ stored_context.metrics,
+ "sample() (context program stored)",
+ context_prog.id,
+ )
+ if error_msg:
+ return _error_response(error_msg)
+ except Exception as e:
+ return _error_response(f"Failed to add 10 programs and sample: {str(e)}")
+
+ # Test: Handle programs with error strings in metrics
+ try:
+ error_program = program_class(
+ id="error_program",
+ solution="def error_func(): return 0",
+ language="python",
+ metrics={
+ "combined_score": 0.0,
+ "error": "Stage 1 error: cannot access local variable 'r' where it is not associated with a value",
+ "runs_successfully": 0.0,
+ },
+ )
+ error_program.iteration_found = 10
+ original_error_metrics = error_program.metrics.copy()
+ all_original_metrics["error_program"] = original_error_metrics.copy()
+ db.add(error_program, iteration=10)
+
+ stored_error_program = db.get("error_program")
+ if stored_error_program is None:
+ return _error_response("Program with error string in metrics not found after add()")
+
+ error_msg = _verify_metrics_preserved(
+ original_error_metrics, stored_error_program.metrics, "add()", "error_program"
+ )
+ if error_msg:
+ return _error_response(error_msg)
+
+ for sample_iter in range(NUM_ERROR_SAMPLE_ITERATIONS):
+ sample_result = db.sample(num_context_programs=3)
+
+ if not isinstance(sample_result, tuple):
+ return _error_response(
+ f"sample() must return a tuple when testing error strings, got {type(sample_result)} on iteration {sample_iter}"
+ )
+ if len(sample_result) != 2:
+ return _error_response(
+ f"sample() must return a tuple of 2 elements when testing error strings, got {len(sample_result)} elements on iteration {sample_iter}"
+ )
+
+ parent_dict, context_programs_dict = sample_result
+
+ if not isinstance(parent_dict, dict):
+ return _error_response(
+ f"sample() first element must be a Dict[str, Program] when testing error strings, got {type(parent_dict)} on iteration {sample_iter}"
+ )
+
+ if len(parent_dict) != 1:
+ return _error_response(
+ f"sample() must return exactly one parent program when testing error strings, "
+ f"got {len(parent_dict)} parents with keys {list(parent_dict.keys())} on iteration {sample_iter}"
+ )
+
+ parent_label = list(parent_dict.keys())[0]
+ parent = parent_dict[parent_label]
+
+ if parent is None:
+ return _error_response(
+ f"sample() returned None for parent when testing error strings (iteration {sample_iter})"
+ )
+ if not isinstance(parent, Program):
+ return _error_response(
+ f"sample() parent_dict value must be a Program when testing error strings, got {type(parent)} on iteration {sample_iter}"
+ )
+
+ if not isinstance(context_programs_dict, dict):
+ return _error_response(
+ f"sample() second element must be a Dict[str, List[Program]] when testing error strings, got {type(context_programs_dict)} on iteration {sample_iter}"
+ )
+
+ all_context_programs = []
+ for label, prog_list in context_programs_dict.items():
+ if not isinstance(prog_list, list):
+ return _error_response(
+ f"sample() context_programs_dict['{label}'] must be a list when testing error strings, got {type(prog_list)} on iteration {sample_iter}"
+ )
+ for prog in prog_list:
+ if not isinstance(prog, Program):
+ return _error_response(
+ f"sample() context_programs_dict['{label}'] contains non-Program object when testing error strings: {type(prog)} on iteration {sample_iter}"
+ )
+ all_context_programs.extend(prog_list)
+
+ requested_num = 3
+ max_possible = min(requested_num, len(db.programs))
+ if len(all_context_programs) > max_possible:
+ return _error_response(
+ f"sample() returned {len(all_context_programs)} total context programs when testing error strings, which exceeds the maximum possible "
+ f"({max_possible} given {len(db.programs)} programs in database) "
+ f"for num_context_programs={requested_num} on iteration {sample_iter}."
+ )
+
+ stored_parent = db.get(parent.id)
+ if stored_parent is None:
+ return _error_response(
+ f"Parent (id={parent.id}) not found in database when testing error strings"
+ )
+
+ if parent.id in all_original_metrics:
+ original_parent_metrics = all_original_metrics[parent.id]
+ else:
+ return _error_response(
+ f"Program '{parent.id}' not found in original_metrics tracking when testing error strings."
+ )
+
+ error_msg = _verify_metrics_preserved(
+ original_parent_metrics,
+ parent.metrics,
+ "sample() (parent with error strings)",
+ parent.id,
+ )
+ if error_msg:
+ return _error_response(error_msg)
+
+ error_msg = _verify_metrics_preserved(
+ original_parent_metrics,
+ stored_parent.metrics,
+ "sample() (parent stored with error strings)",
+ parent.id,
+ )
+ if error_msg:
+ return _error_response(error_msg)
+
+ for context_prog in all_context_programs:
+ stored_context = db.get(context_prog.id)
+ if stored_context is None:
+ return _error_response(
+ f"Other context program (id={context_prog.id}) not found in database when testing error strings"
+ )
+
+ if context_prog.id in all_original_metrics:
+ original_context_metrics = all_original_metrics[context_prog.id]
+ else:
+ return _error_response(
+ f"Program '{context_prog.id}' not found in original_metrics tracking when testing error strings."
+ )
+
+ error_msg = _verify_metrics_preserved(
+ original_context_metrics,
+ context_prog.metrics,
+ "sample() (context program with error strings)",
+ context_prog.id,
+ )
+ if error_msg:
+ return _error_response(error_msg)
+
+ error_msg = _verify_metrics_preserved(
+ original_context_metrics,
+ stored_context.metrics,
+ "sample() (context program stored with error strings)",
+ context_prog.id,
+ )
+ if error_msg:
+ return _error_response(error_msg)
+ except Exception as e:
+ return _error_response(
+ f"Failed to handle programs with error strings in metrics: {str(e)}"
+ )
+
+ # Test: Verify existing metrics are not modified by add()
+ try:
+ metrics_test_program = program_class(
+ id="metrics_test_program",
+ solution="def test_metrics(): return 42",
+ language="python",
+ metrics={
+ "combined_score": 0.85,
+ "correlation": 0.92,
+ "success_rate": 1.0,
+ "error": "No error",
+ "execution_time": 0.123,
+ },
+ )
+ metrics_test_program.iteration_found = 11
+
+ original_metrics = metrics_test_program.metrics.copy()
+ all_original_metrics["metrics_test_program"] = original_metrics.copy()
+
+ db.add(metrics_test_program, iteration=11)
+
+ stored_metrics_program = db.get("metrics_test_program")
+ if stored_metrics_program is None:
+ return _error_response("Metrics test program not found in database after add()")
+
+ error_msg = _verify_metrics_preserved(
+ original_metrics, stored_metrics_program.metrics, "add()", "metrics_test_program"
+ )
+ if error_msg:
+ return _error_response(error_msg)
+
+ error_msg = _verify_metrics_preserved(
+ original_metrics,
+ metrics_test_program.metrics,
+ "add() (original object)",
+ "metrics_test_program",
+ )
+ if error_msg:
+ return _error_response(error_msg)
+ except Exception as e:
+ return _error_response(f"Failed to verify metrics immutability: {str(e)}")
+
+ # Test: Migration scenario - add base Program instances (not EvolvedProgram)
+ try:
+ migration_test_db = database_class("migration_test_db", test_config)
+
+ for i in range(NUM_MIGRATION_PROGRAMS):
+ base_program = Program(
+ id=f"migrated_program_{i}",
+ solution=f"def migrated_func_{i}(): return {i * 10}",
+ language="python",
+ metrics={"combined_score": float(i) / 5.0, "score": float(i) / 5.0},
+ )
+ base_program.iteration_found = i
+ migration_test_db.add(base_program, iteration=i)
+
+ migration_test_db.initial_program_id = "migrated_program_0"
+
+ if len(migration_test_db.programs) < NUM_MIGRATION_PROGRAMS:
+ return _error_response(
+ f"Migration test: Expected at least {NUM_MIGRATION_PROGRAMS} programs after adding base Program instances, "
+ f"found {len(migration_test_db.programs)}"
+ )
+
+ for sample_iter in range(NUM_MIGRATION_SAMPLES):
+ try:
+ sample_result = migration_test_db.sample(num_context_programs=3)
+ except ValueError as e:
+ if "No candidates to sample" in str(e):
+ return _error_response(
+ f"Migration test FAILED: sample() raised 'No candidates to sample' when database "
+ f"contains {len(migration_test_db.programs)} programs. This typically happens when "
+ f"the implementation uses 'isinstance(p, EvolvedProgram)' to filter programs, "
+ f"which fails for migrated programs that are instances of the base Program class. "
+ f"Fix: Use 'self.programs.values()' directly without isinstance checks, or check "
+ f"against the base Program class instead."
+ )
+ raise
+
+ if not isinstance(sample_result, tuple) or len(sample_result) != 2:
+ return _error_response(
+ f"Migration test: sample() must return a tuple of 2 elements, "
+ f"got {type(sample_result)} with {len(sample_result) if isinstance(sample_result, tuple) else 'N/A'} elements"
+ )
+
+ parent_dict, context_programs_dict = sample_result
+
+ if not isinstance(parent_dict, dict) or len(parent_dict) != 1:
+ return _error_response(
+ f"Migration test: sample() must return exactly one parent in parent_dict, "
+ f"got {len(parent_dict) if isinstance(parent_dict, dict) else type(parent_dict)}"
+ )
+
+ parent = list(parent_dict.values())[0]
+ if parent is None:
+ return _error_response(
+ f"Migration test: sample() returned None for parent on iteration {sample_iter}"
+ )
+ if not isinstance(parent, Program):
+ return _error_response(
+ f"Migration test: sample() parent must be a Program, got {type(parent)}"
+ )
+
+ if not isinstance(context_programs_dict, dict):
+ return _error_response(
+ f"Migration test: sample() second element must be a dict, got {type(context_programs_dict)}"
+ )
+
+ for label, prog_list in context_programs_dict.items():
+ if not isinstance(prog_list, list):
+ return _error_response(
+ f"Migration test: context_programs_dict['{label}'] must be a list, got {type(prog_list)}"
+ )
+ for prog in prog_list:
+ if not isinstance(prog, Program):
+ return _error_response(
+ f"Migration test: context program must be a Program, got {type(prog)}"
+ )
+ except Exception as e:
+ if "No candidates to sample" in str(e):
+ return _error_response(
+ f"Migration test FAILED: {str(e)}. "
+ f"The database cannot handle programs that are instances of the base Program class "
+ f"(not EvolvedProgram). This breaks evox migration. "
+ f"Fix: Remove isinstance(p, EvolvedProgram) checks in sampling logic."
+ )
+ return _error_response(f"Migration test failed: {str(e)}")
+
+ # Test: Verify num_context_programs contract
+ try:
+ sample_sig = inspect.signature(db.sample)
+ if "num_context_programs" in sample_sig.parameters:
+ requested_num = 3
+ sample_result = db.sample(num_context_programs=requested_num)
+
+ if not isinstance(sample_result, tuple):
+ return _error_response(
+ f"sample() must return a tuple, got {type(sample_result)}"
+ )
+ if len(sample_result) != 2:
+ return _error_response(
+ f"sample() must return a tuple of 2 elements (parent_dict, context_programs_dict), got {len(sample_result)} elements"
+ )
+
+ parent_dict, context_programs_dict = sample_result
+
+ if not isinstance(parent_dict, dict):
+ return _error_response(
+ f"sample() first element must be a Dict[str, Program], got {type(parent_dict)}"
+ )
+ if len(parent_dict) != 1:
+ return _error_response(
+ f"sample() must return exactly one parent program in parent_dict, "
+ f"got {len(parent_dict)} parents with keys {list(parent_dict.keys())}"
+ )
+
+ parent_label = list(parent_dict.keys())[0]
+ parent = parent_dict[parent_label]
+ if parent is None or not isinstance(parent, Program):
+ return _error_response(
+ f"sample() parent_dict value must be a Program, got {type(parent)}"
+ )
+
+ if not isinstance(context_programs_dict, dict):
+ return _error_response(
+ f"sample() second element must be a Dict[str, List[Program]], got {type(context_programs_dict)}"
+ )
+
+ all_context_programs = []
+ for label, prog_list in context_programs_dict.items():
+ if not isinstance(prog_list, list):
+ return _error_response(
+ f"sample() context_programs_dict['{label}'] must be a list, got {type(prog_list)}"
+ )
+ for prog in prog_list:
+ if not isinstance(prog, Program):
+ return _error_response(
+ f"sample() context_programs_dict['{label}'] contains non-Program object: {type(prog)}"
+ )
+ all_context_programs.extend(prog_list)
+
+ max_possible = min(requested_num, len(db.programs))
+ if len(all_context_programs) > max_possible:
+ return _error_response(
+ f"sample() returned {len(all_context_programs)} total context programs, which exceeds the maximum possible "
+ f"({max_possible} given {len(db.programs)} programs in database) "
+ f"for num_context_programs={requested_num}."
+ )
+ except Exception as e:
+ return _error_response(
+ f"Failed to verify num_context_programs contract in sample(): {str(e)}"
+ )
+
+ return {
+ "validity": 1,
+ "combined_score": 0.0,
+ }
+
+ except Exception as e:
+ error_trace = traceback.format_exc()
+ return _error_response(f"Unexpected error: {str(e)}\n{error_trace}")
+
+
+def evaluate_batch(
+ program_paths: List[str],
+ fast_mode: bool = True,
+ max_workers: int = 4,
+ timeout_per_file: float = 30.0,
+) -> Dict[str, Dict[str, Any]]:
+ """Evaluate multiple database implementations in parallel."""
+ results = {}
+
+ def eval_with_timeout(path: str) -> Tuple[str, Dict[str, Any]]:
+ try:
+ result = evaluate(path, fast_mode=fast_mode)
+ return path, result
+ except Exception as e:
+ return path, _error_response(f"Evaluation failed: {str(e)}")
+
+ with ThreadPoolExecutor(max_workers=max_workers) as executor:
+ future_to_path = {executor.submit(eval_with_timeout, path): path for path in program_paths}
+
+ for future in as_completed(future_to_path, timeout=timeout_per_file * len(program_paths)):
+ try:
+ path, result = future.result(timeout=timeout_per_file)
+ results[path] = result
+ except Exception as e:
+ path = future_to_path[future]
+ results[path] = _error_response(f"Evaluation timed out or failed: {str(e)}")
+
+ return results
+
+
+if __name__ == "__main__":
+ """
+ Usage: uv run skydiscover/search/evox/database/database_evaluator.py /path/to/database_impl.py
+ """
+ import json
+ import sys
+ from pathlib import Path
+
+ args = [a for a in sys.argv[1:] if not a.startswith("--")]
+ flags = [a for a in sys.argv[1:] if a.startswith("--")]
+
+ fast_mode = "--fast" in flags
+
+ max_workers = 4
+ for flag in flags:
+ if flag.startswith("--workers="):
+ try:
+ max_workers = int(flag.split("=")[1])
+ except ValueError:
+ pass
+
+ if not args:
+ print(
+ "Usage: uv run skydiscover/search/evox/database/database_evaluator.py [--fast] [--workers=N]"
+ )
+ sys.exit(1)
+
+ program_paths = [str(Path(p).resolve()) for p in args]
+
+ if len(program_paths) == 1:
+ result = evaluate(program_paths[0], fast_mode=fast_mode)
+ print(json.dumps(result, indent=2, sort_keys=True))
+ else:
+ import time
+
+ start = time.time()
+ results = evaluate_batch(program_paths, fast_mode=fast_mode, max_workers=max_workers)
+ elapsed = time.time() - start
+
+ valid_count = sum(1 for r in results.values() if r.get("validity") == 1)
+ print(
+ f"Evaluated {len(results)} files in {elapsed:.2f}s ({valid_count}/{len(results)} valid)"
+ )
+ print(json.dumps(results, indent=2, sort_keys=True))
diff --git a/skydiscover/search/evox/utils/__init__.py b/skydiscover/search/evox/utils/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/skydiscover/search/evox/utils/coevolve_logging.py b/skydiscover/search/evox/utils/coevolve_logging.py
new file mode 100644
index 0000000000000000000000000000000000000000..e7b0ef1f18710c65c2761370463796f8081d7e53
--- /dev/null
+++ b/skydiscover/search/evox/utils/coevolve_logging.py
@@ -0,0 +1,280 @@
+"""
+Utility functions for co-evolution logging and JSON serialization.
+
+All search-algorithm logging lives here as plain async functions so that
+CoEvolutionController stays focused on orchestration.
+"""
+
+import json
+import logging
+import os
+from typing import Any, Dict, List, Optional
+
+from skydiscover.search.utils.discovery_utils import SerializableResult
+
+logger = logging.getLogger(__name__)
+
+
+def make_json_serializable(obj: Any) -> Any:
+ """Recursively convert objects to JSON-serializable types."""
+ if obj is None:
+ return None
+ if isinstance(obj, bool):
+ return obj
+ if isinstance(obj, (str, int, float)):
+ return obj
+ if isinstance(obj, dict):
+ return {str(k): make_json_serializable(v) for k, v in obj.items()}
+ if isinstance(obj, (list, tuple)):
+ return [make_json_serializable(item) for item in obj]
+ if isinstance(obj, (set, frozenset)):
+ return sorted([make_json_serializable(item) for item in obj], key=lambda x: str(x))
+ if hasattr(obj, "to_dict"):
+ return make_json_serializable(obj.to_dict())
+ return str(obj)
+
+
+async def log_search_algorithm_generated(
+ outputs_dir: str,
+ result: SerializableResult,
+ iteration: int,
+ diverge_label: str = "",
+ refine_label: str = "",
+) -> None:
+ """Save newly generated search algorithm details to files (before scoring)."""
+ if result.error:
+ logger.warning(f"Search strategy generation failed (iteration {iteration}): {result.error}")
+ return
+
+ child_dict = result.child_program_dict or {}
+
+ await save_search_algorithm(
+ outputs_dir=outputs_dir,
+ iteration=iteration,
+ program_id=child_dict.get("id", "unknown"),
+ solution=child_dict.get("solution", ""),
+ score=None,
+ metrics={},
+ diverge_label=diverge_label,
+ refine_label=refine_label,
+ pending_score=True,
+ )
+
+ iteration_dir = os.path.join(outputs_dir, f"iteration_{iteration}")
+ logger.info(
+ f"New search algorithm generated (iteration {iteration}) - "
+ f"saved to {os.path.abspath(iteration_dir)} (score pending)"
+ )
+
+
+async def save_search_algorithm(
+ outputs_dir: str,
+ iteration: int,
+ program_id: str,
+ solution: str,
+ score: Optional[float],
+ metrics: Dict[str, Any],
+ system_prompt: str = "",
+ user_prompt: str = "",
+ llm_response: str = "",
+ diverge_label: str = "",
+ refine_label: str = "",
+ pending_score: bool = False,
+) -> None:
+ """Save search algorithm details to files in search/iteration_x/."""
+ iteration_dir = os.path.join(outputs_dir, f"iteration_{iteration}")
+ os.makedirs(iteration_dir, exist_ok=True)
+
+ metadata = {
+ "iteration": iteration,
+ "program_id": program_id,
+ "score": score,
+ "metrics": metrics,
+ "pending_score": pending_score,
+ }
+
+ with open(os.path.join(iteration_dir, "metadata.json"), "w") as f:
+ json.dump(metadata, f, indent=2)
+
+ if solution:
+ with open(os.path.join(iteration_dir, "code.py"), "w") as f:
+ f.write(solution)
+
+ prompts = {
+ k: v
+ for k, v in [
+ ("system_prompt", system_prompt),
+ ("user_prompt", user_prompt),
+ ("llm_response", llm_response),
+ ]
+ if v
+ }
+ if prompts:
+ with open(os.path.join(iteration_dir, "prompts.json"), "w") as f:
+ json.dump(prompts, f, indent=2)
+
+ if iteration == 1:
+ labels_path = os.path.join(iteration_dir, "labels.yaml")
+ try:
+ labels_text = (
+ f"iteration: {iteration}\n"
+ f"diverge_label_length: {len(diverge_label)}\n"
+ f"refine_label_length: {len(refine_label)}\n\n"
+ f"diverge_label: |\n"
+ )
+ for line in diverge_label.splitlines():
+ labels_text += f" {line}\n"
+ labels_text += "\nrefine_label: |\n"
+ for line in refine_label.splitlines():
+ labels_text += f" {line}\n"
+ with open(labels_path, "w") as f:
+ f.write(labels_text)
+ logger.info(
+ f"Saved labels to {labels_path} ({len(diverge_label)}/{len(refine_label)} chars)"
+ )
+ except Exception as e:
+ logger.warning(f"Failed to save labels.yaml: {e}")
+
+
+async def update_saved_search_algorithm_score(
+ outputs_dir: str,
+ iteration: int,
+ result: SerializableResult,
+ is_new_best: bool,
+ db_stats: Dict[str, Any],
+) -> None:
+ """Update the saved metadata file with the newly assigned score."""
+ metadata_path = os.path.join(outputs_dir, f"iteration_{iteration}", "metadata.json")
+
+ if not os.path.exists(metadata_path):
+ logger.warning(
+ f"Metadata file not found for iteration {iteration}, cannot update search strategy score"
+ )
+ return
+
+ with open(metadata_path, "r") as f:
+ metadata = json.load(f)
+
+ child_dict = result.child_program_dict or {}
+ metrics = child_dict.get("metrics", {})
+ metadata["combined_score"] = metrics.get("combined_score")
+ metadata["metrics"] = make_json_serializable(metrics)
+ metadata["pending_score"] = False
+ metadata["is_new_best"] = bool(is_new_best)
+
+ start_db_stats = child_dict.get("metadata", {}).get("start_db_stats")
+ if start_db_stats:
+ metadata["start_db_stats"] = make_json_serializable(start_db_stats)
+
+ metadata["end_db_stats"] = make_json_serializable(db_stats)
+
+ with open(metadata_path, "w") as f:
+ json.dump(metadata, f, indent=2)
+
+
+async def handle_generation_failure(
+ outputs_dir: str,
+ active_search_code: str,
+ iteration: int,
+ result: Optional[SerializableResult],
+ solution_iter: int,
+ stage: str = "generation",
+) -> None:
+ """Handle failed search algorithm generation or validation."""
+ error_msg = result.error if result else "Unknown error"
+ await log_failed_attempt(
+ outputs_dir,
+ iteration,
+ result,
+ error_msg if stage == "generation" else "Failed to load/validate",
+ stage,
+ solution_iter=solution_iter,
+ )
+ if stage == "generation":
+ logger.warning(f"Failed to generate search algorithm: {error_msg}")
+ await log_active_algorithm(outputs_dir, active_search_code, iteration)
+
+
+async def log_failed_attempt(
+ outputs_dir: str,
+ iteration: int,
+ result: Optional[SerializableResult],
+ error: str,
+ stage: str,
+ solution_iter: Optional[int] = None,
+) -> None:
+ """Log a single failed attempt."""
+ iteration_dir = os.path.join(outputs_dir, f"iteration_{iteration}")
+ os.makedirs(iteration_dir, exist_ok=True)
+
+ child_dict = (result.child_program_dict or {}) if result else {}
+ prompt = (result.prompt or {}) if result else {}
+ llm_response = (result.llm_response or "") if result else ""
+
+ failed_file = os.path.join(iteration_dir, "failed_attempts.json")
+ failed_attempts: List[Dict[str, Any]] = []
+ if os.path.exists(failed_file):
+ with open(failed_file, "r") as f:
+ failed_attempts = json.load(f).get("failed_attempts", [])
+
+ attempt_number = len(failed_attempts) + 1
+ attempt_data: Dict[str, Any] = {
+ "attempt_number": attempt_number,
+ "solution_iter": solution_iter,
+ "error": error,
+ "stage": stage,
+ "solution": child_dict.get("solution", ""),
+ "program_id": child_dict.get("id", "unknown"),
+ }
+
+ if llm_response:
+ llm_filename = f"failed_attempt_{attempt_number}_llm_response.txt"
+ with open(os.path.join(iteration_dir, llm_filename), "w") as f:
+ f.write(llm_response)
+ attempt_data["llm_response_file"] = llm_filename
+ attempt_data["llm_response_preview"] = llm_response[:2000]
+
+ if isinstance(prompt, dict) and (prompt.get("system") or prompt.get("user")):
+ prompt_filename = f"failed_attempt_{attempt_number}_prompt.json"
+ with open(os.path.join(iteration_dir, prompt_filename), "w") as f:
+ json.dump(
+ {"system": prompt.get("system", ""), "user": prompt.get("user", "")}, f, indent=2
+ )
+ attempt_data["prompt_file"] = prompt_filename
+
+ failed_attempts.append(attempt_data)
+ with open(failed_file, "w") as f:
+ json.dump({"iteration": iteration, "failed_attempts": failed_attempts}, f, indent=2)
+
+ if attempt_data["solution"]:
+ code_file = os.path.join(iteration_dir, f"failed_attempt_{attempt_number}.py")
+ with open(code_file, "w") as f:
+ f.write(attempt_data["solution"])
+
+
+async def log_active_algorithm(
+ outputs_dir: str,
+ active_search_code: str,
+ iteration: int,
+) -> None:
+ """Log the active algorithm on the solution side (fallback if generation/validation failed)."""
+ active_code = active_search_code or ""
+
+ iteration_dir = os.path.join(outputs_dir, f"iteration_{iteration}")
+ os.makedirs(iteration_dir, exist_ok=True)
+
+ candidate_code_path = os.path.join(iteration_dir, "code.py")
+ active_code_filename = "active_code.py" if os.path.exists(candidate_code_path) else "code.py"
+ with open(os.path.join(iteration_dir, active_code_filename), "w") as f:
+ f.write(active_code)
+
+ metadata_path = os.path.join(iteration_dir, "metadata.json")
+ if os.path.exists(metadata_path):
+ with open(metadata_path, "r") as f:
+ metadata = json.load(f)
+ else:
+ metadata = {"iteration": iteration}
+
+ metadata["is_fallback"] = True
+ with open(metadata_path, "w") as f:
+ json.dump(metadata, f, indent=2)
diff --git a/skydiscover/search/evox/utils/search_scorer.py b/skydiscover/search/evox/utils/search_scorer.py
new file mode 100644
index 0000000000000000000000000000000000000000..9d2f00716336a646d90163a00502ab6601cf57a0
--- /dev/null
+++ b/skydiscover/search/evox/utils/search_scorer.py
@@ -0,0 +1,83 @@
+"""
+Search algorithm scoring metrics for co-evolution.
+"""
+
+import logging
+import math
+from typing import Any, Dict, List, Optional
+
+logger = logging.getLogger(__name__)
+
+
+class LogWindowScorer:
+ """
+ Log-weighted scorer for search algorithms.
+ """
+
+ def __init__(self, algorithm_id: Optional[str] = None):
+ self.algorithm_id = algorithm_id or "unknown"
+ self._start_score: Optional[float] = None
+ self._start_iteration: Optional[int] = None
+ self._best_scores: List[float] = []
+
+ def reset_window(
+ self,
+ start_score: Optional[float],
+ algorithm_id: Optional[str] = None,
+ start_iteration: Optional[int] = None,
+ ) -> None:
+ self._start_score = float(start_score) if start_score is not None else 0.0
+ self._start_iteration = start_iteration
+ self._best_scores = []
+ if algorithm_id:
+ self.algorithm_id = algorithm_id
+
+ def record_step(self, best_score: Optional[float]) -> None:
+ if self._start_score is None:
+ self.reset_window(best_score)
+ if best_score is None:
+ best_score = self._best_scores[-1] if self._best_scores else self._start_score
+ self._best_scores.append(float(best_score))
+
+ def get_window_size(self) -> int:
+ return len(self._best_scores)
+
+ def get_start_score(self) -> Optional[float]:
+ return self._start_score
+
+ def compute_metrics(
+ self,
+ start_score: Optional[float] = None,
+ best_scores: Optional[List[float]] = None,
+ horizon: Optional[int] = None,
+ start_iteration: Optional[int] = None,
+ total_iterations: Optional[int] = None,
+ ) -> Dict[str, Any]:
+ if start_iteration is None:
+ start_iteration = self._start_iteration
+ start = float(start_score if start_score is not None else (self._start_score or 0.0))
+ scores_to_use = best_scores if best_scores is not None else self._best_scores
+ T_obs = len(scores_to_use) if scores_to_use else 0
+ horizon_int = int(horizon) if horizon else max(1, T_obs)
+
+ running_best = start
+ for s in scores_to_use:
+ running_best = max(running_best, float(s))
+
+ improvement = running_best - start
+ log_weight = 1.0 + math.log(1.0 + max(0.0, start))
+ combined_score = improvement * log_weight / math.sqrt(horizon_int)
+
+ logger.info(
+ f"Search strategy score: combined={combined_score:.6f}, "
+ f"improvement={improvement:.6f}, start={start:.6f}, "
+ f"end={running_best:.6f}, horizon={horizon_int}"
+ )
+
+ return {
+ "combined_score": combined_score,
+ "window_start_iteration": start_iteration,
+ "search_window_start_score": start,
+ "search_window_end_score": running_best,
+ "search_horizon": horizon_int,
+ }
diff --git a/skydiscover/search/gepa_native/__init__.py b/skydiscover/search/gepa_native/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..afa2176c0e0c1861009151eeebc78b03ba926848
--- /dev/null
+++ b/skydiscover/search/gepa_native/__init__.py
@@ -0,0 +1,4 @@
+from skydiscover.search.gepa_native.controller import GEPANativeController
+from skydiscover.search.gepa_native.database import GEPANativeDatabase
+
+__all__ = ["GEPANativeDatabase", "GEPANativeController"]
diff --git a/skydiscover/search/gepa_native/controller.py b/skydiscover/search/gepa_native/controller.py
new file mode 100644
index 0000000000000000000000000000000000000000..c14299b7cab0cb3fb0299ed92cb8e96964f57adf
--- /dev/null
+++ b/skydiscover/search/gepa_native/controller.py
@@ -0,0 +1,484 @@
+"""
+GEPA Native Controller - Discovery loop with reflective prompting,
+acceptance gating, and LLM-mediated merge.
+
+Implements GEPA's three core ideas as a SkyDiscover search strategy:
+1. Reflective prompting — surfaces evaluator diagnostics and rejected-program
+ code as actionable feedback in the LLM prompt.
+2. Acceptance gating — rejects mutations that don't strictly improve on the
+ parent, preventing population pollution.
+3. LLM-mediated merge — combines two complementary programs both proactively
+ (after each acceptance) and reactively (on stagnation).
+
+Configuration options (via GEPANativeDatabaseConfig):
+ acceptance_gating: Enable strict parent-improvement gating (default: True)
+ use_merge: Enable LLM-mediated merge (default: True)
+ merge_after_stagnation: Iterations without improvement before merge (default: 15)
+ max_merge_attempts: Total merge budget per run (default: 10)
+ max_recent_failures: Rejected programs shown in reflective section (default: 5)
+"""
+
+import logging
+import time
+import uuid
+from typing import Any, Callable, Dict, List, Optional, Set, Tuple, Union
+
+from skydiscover.context_builder.gepa_native import GEPANativeContextBuilder
+from skydiscover.search.base_database import Program
+from skydiscover.search.default_discovery_controller import (
+ DiscoveryController,
+ DiscoveryControllerInput,
+)
+from skydiscover.search.utils.discovery_utils import SerializableResult
+from skydiscover.utils.code_utils import parse_full_rewrite
+from skydiscover.utils.metrics import get_score
+
+logger = logging.getLogger(__name__)
+
+
+class GEPANativeController(DiscoveryController):
+ """
+ Discovery controller implementing GEPA's guided evolution.
+
+ Key Features:
+ 1. Reflective prompting: Structures evaluation failures and evaluator
+ diagnostics as actionable feedback in the LLM prompt.
+ 2. Acceptance gating: Rejects mutations that don't improve on the parent,
+ preventing population pollution.
+ 3. LLM-mediated merge: Combines two complementary programs both
+ proactively (after each acceptance) and reactively (on stagnation).
+ 4. Merge deduplication: Tracks tried pairs to avoid redundant merges.
+ 5. Merge budget: Caps total merge attempts to bound LLM cost.
+ """
+
+ def __init__(self, controller_input: DiscoveryControllerInput):
+ super().__init__(controller_input)
+
+ # Override context builder with GEPA-specific one
+ self.context_builder = GEPANativeContextBuilder(self.config)
+
+ db_config = self.config.search.database
+ self.acceptance_gating: bool = getattr(db_config, "acceptance_gating", True)
+ self.use_merge: bool = getattr(db_config, "use_merge", True)
+ self.merge_after_stagnation: int = getattr(db_config, "merge_after_stagnation", 15)
+ self.max_recent_failures: int = getattr(db_config, "max_recent_failures", 5)
+ self.max_merge_attempts: int = getattr(db_config, "max_merge_attempts", 10)
+
+ # Stagnation tracking
+ self._best_score_seen: float = -float("inf")
+ self._iterations_without_improvement: int = 0
+
+ # Merge state
+ self._merge_due: bool = False
+ self._merge_attempts_used: int = 0
+ self._merge_pairs_tried: Set[Tuple[str, str]] = set()
+
+ logger.info(
+ f"GEPANativeController initialized: "
+ f"acceptance_gating={self.acceptance_gating}, "
+ f"use_merge={self.use_merge}, "
+ f"merge_after_stagnation={self.merge_after_stagnation}, "
+ f"max_merge_attempts={self.max_merge_attempts}"
+ )
+
+ # ------------------------------------------------------------------
+ # Main discovery loop
+ # ------------------------------------------------------------------
+
+ async def run_discovery(
+ self,
+ start_iteration: int,
+ max_iterations: int,
+ checkpoint_callback: Optional[Callable[[int], None]] = None,
+ post_process_result: Optional[bool] = True,
+ retry_times: Optional[int] = 3,
+ ) -> Optional[Union[Program, SerializableResult]]:
+ """Run GEPA discovery with acceptance gating and merge scheduling.
+
+ The loop follows this order each iteration:
+ 1. Proactive merge (if scheduled from a prior acceptance).
+ 2. Normal mutation via ``_run_iteration``.
+ 3. Acceptance gating — reject if child <= parent.
+ 4. If accepted: process result, schedule next proactive merge.
+ 5. Track improvement; trigger stagnation merge if stuck.
+ """
+ total_iterations = start_iteration + max_iterations
+
+ # Initialise best-score from database if resuming
+ best = self.database.get_best_program()
+ if best and best.metrics:
+ self._best_score_seen = get_score(best.metrics)
+
+ result = None
+ for iteration in range(start_iteration, total_iterations):
+ if self.shutdown_event.is_set():
+ logger.info("Shutdown requested, stopping discovery loop early")
+ break
+
+ try:
+ # Proactive merge: attempt before normal iteration if scheduled
+ if self._merge_due:
+ self._merge_due = False
+ await self._attempt_merge(iteration)
+
+ result = await self._run_iteration(iteration, retry_times=retry_times)
+
+ if result.error:
+ logger.warning(f"Iteration {iteration} failed: {result.error}")
+ self._iterations_without_improvement += 1
+
+ if self._should_merge():
+ await self._attempt_merge(iteration)
+ continue
+
+ # --- Acceptance gating (skip for from-scratch programs) ---
+ if self.acceptance_gating and result.child_program_dict and result.parent_id:
+ accepted = self._acceptance_gate(result, iteration)
+ if not accepted:
+ if self._should_merge():
+ await self._attempt_merge(iteration)
+ continue
+
+ # --- Accept: process normally ---
+ if post_process_result:
+ self._process_iteration_result(result, iteration, checkpoint_callback)
+
+ # Schedule a proactive merge after successful acceptance
+ if self.use_merge and self._merge_attempts_used < self.max_merge_attempts:
+ self._merge_due = True
+
+ # Track improvement
+ if result.child_program_dict:
+ child_score = get_score(result.child_program_dict.get("metrics", {}))
+ if child_score > self._best_score_seen:
+ self._best_score_seen = child_score
+ self._iterations_without_improvement = 0
+ else:
+ self._iterations_without_improvement += 1
+ else:
+ self._iterations_without_improvement += 1
+
+ # Stagnation-triggered merge (fallback)
+ if self._should_merge():
+ await self._attempt_merge(iteration)
+
+ except Exception as e:
+ logger.exception(f"Error in iteration {iteration}: {e}")
+ self._iterations_without_improvement += 1
+
+ if not post_process_result:
+ return result
+
+ if self.shutdown_event.is_set():
+ logger.info(
+ f"Discovery completed (search strategy = {self.database.name}) "
+ "- Shutdown requested"
+ )
+ else:
+ logger.info(
+ f"Discovery completed (search strategy = {self.database.name}) "
+ "- Maximum iterations reached"
+ )
+
+ return self.database.get_best_program()
+
+ # ------------------------------------------------------------------
+ # Reflective prompting (via GEPANativeContextBuilder)
+ # ------------------------------------------------------------------
+
+ def _build_prompt(
+ self,
+ current_program: Dict[str, Program],
+ context_programs: Union[List, Dict[str, list]],
+ failed_attempts: list,
+ ) -> Dict[str, str]:
+ """Build prompt with GEPA reflective context.
+
+ Gathers rejection history from the database and passes it through
+ the context dict so GEPANativeContextBuilder can format it into
+ the {search_guidance} template placeholder.
+ """
+ parent = (
+ list(current_program.values())[0]
+ if isinstance(current_program, dict)
+ else current_program
+ )
+ db_stats = self._prompt_context.get("db_stats") or self.database.get_statistics()
+
+ # Gather rejection history for reflective prompting
+ rejected = self.database.get_rejection_history(limit=self.max_recent_failures)
+
+ # Pre-compute parent scores for rejected programs
+ rejection_parent_scores: Dict[str, float] = {}
+ for prog in rejected:
+ if prog.parent_id and prog.parent_id in self.database.programs:
+ p = self.database.programs[prog.parent_id]
+ rejection_parent_scores[prog.parent_id] = get_score(p.metrics)
+
+ context: Dict[str, Any] = {
+ "program_metrics": parent.metrics,
+ "other_context_programs": context_programs,
+ "previous_programs": db_stats.get("previous_programs", []),
+ "db_stats": db_stats,
+ # GEPA-specific keys (consumed by GEPANativeContextBuilder)
+ "rejection_history": rejected,
+ "rejection_parent_scores": rejection_parent_scores,
+ }
+ for k, v in self._prompt_context.items():
+ if k not in context:
+ context[k] = v
+
+ if failed_attempts:
+ context["errors"] = failed_attempts
+
+ return self.context_builder.build_prompt(current_program=current_program, context=context)
+
+ # ------------------------------------------------------------------
+ # Acceptance gating
+ # ------------------------------------------------------------------
+
+ def _acceptance_gate(self, result: SerializableResult, iteration: int) -> bool:
+ """Apply GEPA acceptance gating.
+
+ A child is accepted only if its fitness strictly exceeds the parent's.
+ Rejected children are stored in the database's rejection history for
+ use in reflective prompting.
+
+ Returns:
+ True if the child is accepted, False otherwise.
+ """
+ child_score = get_score(result.child_program_dict.get("metrics", {}))
+
+ parent_score = 0.0
+ if result.parent_id and result.parent_id in self.database.programs:
+ parent = self.database.programs[result.parent_id]
+ parent_score = get_score(parent.metrics)
+
+ if child_score <= parent_score:
+ child = Program.from_dict(result.child_program_dict)
+ self.database.add_rejected(child)
+
+ logger.info(
+ f"Iteration {iteration}: REJECTED child "
+ f"(child_score={child_score:.4f} <= parent_score={parent_score:.4f})"
+ )
+ self._iterations_without_improvement += 1
+ return False
+
+ return True
+
+ # ------------------------------------------------------------------
+ # LLM-mediated merge
+ # ------------------------------------------------------------------
+
+ def _should_merge(self) -> bool:
+ """Check if a stagnation-triggered merge should be attempted."""
+ return (
+ self.use_merge
+ and self._iterations_without_improvement >= self.merge_after_stagnation
+ and self._merge_attempts_used < self.max_merge_attempts
+ )
+
+ async def _attempt_merge(self, iteration: int) -> None:
+ """Attempt an LLM-mediated merge of two complementary programs.
+
+ Guards against budget exhaustion, self-merges, and duplicate pairs.
+ On success the merged program is added to the database and stagnation
+ is reset. On failure (LLM error, parse error, eval error, or
+ rejected merge) the stagnation counter is left unchanged.
+ """
+ if self._merge_attempts_used >= self.max_merge_attempts:
+ return
+
+ if len(self.database.programs) < 2:
+ logger.debug("Not enough programs for merge, skipping")
+ return
+
+ prog_a, prog_b = self.database.get_merge_candidates()
+
+ # Skip self-merge (happens when pool has < 2 distinct programs)
+ if prog_a.id == prog_b.id:
+ logger.debug(f"Iteration {iteration}: Only one program available, skipping merge")
+ return
+
+ # Deduplication: skip if this pair was already tried
+ pair_key = tuple(sorted([prog_a.id, prog_b.id]))
+ if pair_key in self._merge_pairs_tried:
+ logger.debug(f"Iteration {iteration}: Merge pair already tried, skipping")
+ return
+ self._merge_pairs_tried.add(pair_key)
+ self._merge_attempts_used += 1
+
+ score_a = get_score(prog_a.metrics)
+ score_b = get_score(prog_b.metrics)
+
+ logger.info(
+ f"Iteration {iteration}: Attempting merge "
+ f"(stagnation={self._iterations_without_improvement}, "
+ f"attempt={self._merge_attempts_used}/{self.max_merge_attempts}, "
+ f"scores: {score_a:.4f}, {score_b:.4f})"
+ )
+
+ merge_prompt = self._build_merge_prompt(prog_a, prog_b)
+
+ try:
+ llm_start = time.time()
+ llm_result = await self.llms.generate(
+ system_message=merge_prompt["system"],
+ messages=[{"role": "user", "content": merge_prompt["user"]}],
+ )
+ llm_generation_time = time.time() - llm_start
+ except Exception as e:
+ logger.warning(f"Merge LLM call failed: {e}")
+ return
+
+ llm_response = llm_result.text if llm_result else ""
+ if not llm_response:
+ logger.warning("Merge LLM returned empty response")
+ return
+
+ # Always parse as full rewrite (merge prompt asks for complete program)
+ child_solution = parse_full_rewrite(llm_response, self.config.language)
+ if not child_solution:
+ logger.warning("Merge parse failed: no valid solution in response")
+ return
+
+ # Evaluate the merged solution
+ child_id = str(uuid.uuid4())
+ try:
+ eval_start = time.time()
+ eval_result = await self.evaluator.evaluate_program(child_solution, child_id)
+ eval_time = time.time() - eval_start
+ except Exception as e:
+ logger.warning(f"Merge evaluation failed: {e}")
+ return
+
+ merged_score = get_score(eval_result.metrics)
+ logger.info(
+ f"Iteration {iteration}: Merge completed"
+ f" (llm: {llm_generation_time:.2f}s,"
+ f" eval: {eval_time:.2f}s)"
+ )
+
+ # GEPA acceptance criterion for merges: must meet or exceed both parents
+ if merged_score >= max(score_a, score_b):
+ merged_program = Program(
+ id=child_id,
+ solution=child_solution,
+ language=self.config.language,
+ metrics=eval_result.metrics,
+ iteration_found=iteration,
+ parent_id=prog_a.id,
+ other_context_ids=[prog_b.id],
+ parent_info=("Merge Parent A", prog_a.id),
+ context_info=[("Merge Parent B", prog_b.id)],
+ metadata={
+ "changes": "LLM-mediated merge",
+ "merge_score_a": score_a,
+ "merge_score_b": score_b,
+ "parent_metrics": prog_a.metrics,
+ },
+ artifacts=eval_result.artifacts or {},
+ )
+ self.database.add(merged_program, iteration=iteration)
+
+ self.database.log_prompt(
+ template_key="merge",
+ program_id=child_id,
+ prompt=merge_prompt,
+ responses=[llm_response], # already str via .text extraction above
+ )
+
+ logger.info(
+ f"Merge ACCEPTED: score={merged_score:.4f} "
+ f"(>= max({score_a:.4f}, {score_b:.4f}))"
+ )
+
+ if merged_score > self._best_score_seen:
+ self._best_score_seen = merged_score
+
+ # Reset stagnation only on successful merge
+ self._iterations_without_improvement = 0
+
+ # Fire monitor callback
+ if self.monitor_callback:
+ try:
+ self.monitor_callback(merged_program, iteration)
+ except Exception as e:
+ logger.warning(
+ f"Monitor callback failed: {e}"
+ ) # Never crash discovery due to monitor
+ else:
+ logger.info(
+ f"Merge REJECTED: score={merged_score:.4f} " f"< max({score_a:.4f}, {score_b:.4f})"
+ )
+ # Stagnation counter NOT reset on rejected merge
+
+ def _build_merge_prompt(self, prog_a: Program, prog_b: Program) -> Dict[str, str]:
+ """Build a prompt asking the LLM to merge two programs.
+
+ Includes both programs' code, per-metric strengths comparison,
+ and evaluator diagnostics from each program's artifacts.
+ """
+ score_a = get_score(prog_a.metrics)
+ score_b = get_score(prog_b.metrics)
+
+ # Summarise per-metric strengths
+ strengths_a: List[str] = []
+ strengths_b: List[str] = []
+ if prog_a.metrics and prog_b.metrics:
+ for key in set(prog_a.metrics.keys()) | set(prog_b.metrics.keys()):
+ va = prog_a.metrics.get(key)
+ vb = prog_b.metrics.get(key)
+ if not isinstance(va, (int, float)) or not isinstance(vb, (int, float)):
+ continue
+ if va > vb:
+ strengths_a.append(f"{key}: {va}")
+ elif vb > va:
+ strengths_b.append(f"{key}: {vb}")
+
+ strengths_section = ""
+ if strengths_a or strengths_b:
+ strengths_section = "\n## Per-Metric Strengths\n"
+ if strengths_a:
+ strengths_section += f"Program A leads on: {', '.join(strengths_a)}\n"
+ if strengths_b:
+ strengths_section += f"Program B leads on: {', '.join(strengths_b)}\n"
+
+ # Include evaluator diagnostics from parent artifacts
+ diagnostics_section = ""
+ for label, prog in [("A", prog_a), ("B", prog_b)]:
+ if not prog.artifacts:
+ continue
+ diag_parts = []
+ for key, value in prog.artifacts.items():
+ if not isinstance(value, str) or not value.strip():
+ continue
+ display = value if len(value) <= 500 else value[:500] + "... (truncated)"
+ diag_parts.append(f"- {key}: {display}")
+ if diag_parts:
+ diagnostics_section += (
+ f"\n## Program {label} Diagnostics\n" + "\n".join(diag_parts) + "\n"
+ )
+
+ system = (
+ "You are an expert programmer. Your task is to merge two programs into "
+ "a single improved program that combines the strengths of both. "
+ "Output only the complete merged program inside a code block."
+ )
+
+ user = (
+ f"## Program A (score: {score_a:.4f})\n"
+ f"```\n{prog_a.solution}\n```\n\n"
+ f"## Program B (score: {score_b:.4f})\n"
+ f"```\n{prog_b.solution}\n```\n"
+ f"{strengths_section}"
+ f"{diagnostics_section}\n"
+ "## Instructions\n"
+ "Combine the best ideas from both programs into a single solution. "
+ "Preserve any approach that contributes to a higher score. "
+ "Resolve conflicts by choosing the strategy that is more likely to "
+ "generalise across all test cases. Output the complete merged program."
+ )
+
+ return {"system": system, "user": user}
diff --git a/skydiscover/search/gepa_native/database.py b/skydiscover/search/gepa_native/database.py
new file mode 100644
index 0000000000000000000000000000000000000000..c7b40e55e1c1eb2cc388f25f8c105ce6ecf700ea
--- /dev/null
+++ b/skydiscover/search/gepa_native/database.py
@@ -0,0 +1,367 @@
+"""
+GEPA Native Database - Elite pool with epsilon-greedy selection.
+
+Implements the population management layer for GEPA's guided evolution:
+1. Fixed-size elite pool sorted by fitness score (descending)
+2. Epsilon-greedy parent selection for exploration/exploitation balance
+3. Per-metric best tracking for merge candidate selection
+4. Rejection history deque for reflective prompting context
+
+Configuration options (via GEPANativeDatabaseConfig):
+ population_size: Maximum elite pool size (default: 40)
+ candidate_selection_strategy: "epsilon_greedy" or "best" (default: "epsilon_greedy")
+ epsilon: Exploration probability for epsilon-greedy (default: 0.1)
+ max_rejection_history: Bounded deque size for rejected programs (default: 20)
+ random_seed: RNG seed for reproducible selection (default: 42)
+"""
+
+import collections
+import json
+import logging
+import os
+import random
+from typing import Any, Dict, List, Optional, Set, Tuple
+
+from skydiscover.config import DatabaseConfig
+from skydiscover.search.base_database import Program, ProgramDatabase
+from skydiscover.search.utils.checkpoint_manager import SafeJSONEncoder
+from skydiscover.utils.metrics import get_score
+
+from .pareto_utils import select_program_candidate_from_pareto_front
+
+logger = logging.getLogger(__name__)
+
+
+class GEPANativeDatabase(ProgramDatabase):
+ """
+ Program database for GEPA Native search.
+
+ Maintains a fixed-size elite pool sorted by combined_score.
+ Supports epsilon-greedy parent selection, per-metric best tracking,
+ and a rejection history deque for reflective prompting.
+
+ Configuration options (via GEPANativeDatabaseConfig):
+ population_size: Maximum elite pool size (default: 40)
+ candidate_selection_strategy: Parent selection strategy (default: "epsilon_greedy")
+ - "epsilon_greedy": Pick best with probability (1-epsilon), random otherwise
+ - "best": Always pick the highest-scoring program
+ - "pareto": Frequency-weighted sampling from the Pareto front across metrics
+ epsilon: Exploration probability for epsilon-greedy (default: 0.1)
+ max_rejection_history: Max rejected programs to keep (default: 20)
+ """
+
+ def __init__(self, name: str, config: DatabaseConfig, **kwargs: Any):
+ # Read GEPA-specific config before super().__init__ (which may call load)
+ self.population_size: int = getattr(config, "population_size", 40)
+ self.candidate_selection_strategy: str = getattr(
+ config, "candidate_selection_strategy", "epsilon_greedy"
+ )
+ self.epsilon: float = getattr(config, "epsilon", 0.1)
+ max_rejection_history: int = getattr(config, "max_rejection_history", 20)
+ seed: int = getattr(config, "random_seed", 42) or 42
+
+ self.elite_pool: List[str] = [] # program IDs sorted by score desc
+ self.rejection_history: collections.deque = collections.deque(maxlen=max_rejection_history)
+ self.metric_best: Dict[str, Tuple[str, float]] = {} # metric -> (prog_id, value)
+ self.program_at_metric_front: Dict[str, Set[str]] = {} # metric -> set of prog_ids at best
+ self.rng = random.Random(seed)
+
+ super().__init__(name, config, **kwargs)
+
+ # ------------------------------------------------------------------
+ # Core interface
+ # ------------------------------------------------------------------
+
+ def add(self, program: Program, iteration: Optional[int] = None, **kwargs: Any) -> str:
+ """Add a program to the database and elite pool.
+
+ Inserts into the elite pool (sorted descending by fitness), evicts
+ the weakest members if the pool exceeds ``population_size``, and
+ updates per-metric best tracking.
+
+ Args:
+ program: Program to add.
+ iteration: Current iteration number for tracking.
+
+ Returns:
+ The program's ID.
+ """
+ if not self.programs:
+ self.initial_program_id = program.id
+
+ self.programs[program.id] = program
+
+ if iteration is not None:
+ self.last_iteration = max(self.last_iteration, iteration)
+
+ # Insert into elite pool, keep sorted by score descending
+ if program.id not in self.elite_pool:
+ self.elite_pool.append(program.id)
+ self.elite_pool.sort(
+ key=lambda pid: get_score(self.programs[pid].metrics if pid in self.programs else {}),
+ reverse=True,
+ )
+
+ # Cap at population_size, but pin best and initial programs.
+ # Only remove from elite_pool (sampling); keep self.programs as
+ # a full archive so parent lookups for reflective prompting work.
+ if len(self.elite_pool) > self.population_size:
+ pinned = {self.best_program_id, self.initial_program_id, program.id} - {None}
+ keep = []
+ for pid in self.elite_pool:
+ if pid in pinned or len(keep) < self.population_size:
+ keep.append(pid)
+ self.elite_pool = keep
+
+ # Update per-metric best tracking
+ if program.metrics:
+ for metric_name, value in program.metrics.items():
+ if not isinstance(value, (int, float)):
+ continue
+ current = self.metric_best.get(metric_name)
+ if current is None or value > current[1]:
+ self.metric_best[metric_name] = (program.id, value)
+ self.program_at_metric_front[metric_name] = {program.id}
+ elif value == current[1]:
+ self.program_at_metric_front[metric_name].add(program.id)
+
+ # Update global best
+ self._update_best_program(program)
+
+ # Persist to disk
+ if self.config.db_path:
+ self._save_program(program)
+
+ logger.debug(
+ f"Added program {program.id} to GEPA elite pool " f"(pool size: {len(self.elite_pool)})"
+ )
+ return program.id
+
+ def sample(
+ self,
+ num_context_programs: Optional[int] = 4,
+ **kwargs: Any,
+ ) -> Tuple[Dict[str, Program], Dict[str, List[Program]]]:
+ """Sample a parent and context programs.
+
+ Uses epsilon-greedy selection for the parent and top-of-pool +
+ metric leaders for other context programs.
+
+ Returns:
+ Tuple of ({"": parent}, {"": [other context, ...]}).
+ """
+ if not self.programs:
+ raise ValueError("Cannot sample: no programs in database")
+
+ parent = self._select_parent()
+ other_context_programs = self._select_other_context_programs(
+ parent.id, num_context_programs or 4
+ )
+
+ return {"": parent}, {"": other_context_programs}
+
+ # ------------------------------------------------------------------
+ # GEPA-specific methods
+ # ------------------------------------------------------------------
+
+ def add_rejected(self, program: Program) -> None:
+ """Store a rejected program for reflective prompting.
+
+ The program is NOT added to the elite pool or ``self.programs``.
+ """
+ self.rejection_history.append(program)
+
+ def get_rejection_history(self, limit: Optional[int] = None) -> List[Program]:
+ """Return recent rejected programs, most-recent last.
+
+ Args:
+ limit: If given, return only the *limit* most recent entries.
+ """
+ items = list(self.rejection_history)
+ if limit is not None:
+ items = items[-limit:]
+ return items
+
+ def get_merge_candidates(self) -> Tuple[Program, Program]:
+ """Select two complementary programs for LLM-mediated merge.
+
+ Selection strategy:
+ 1. Prefer two programs that each lead on a different metric.
+ 2. Fallback: best program + random from top 5.
+ 3. Last resort: best program returned twice (caller should guard).
+ """
+ if len(self.elite_pool) < 2:
+ best = self.get_best_program()
+ return best, best
+
+ # Try to find two programs that each lead on a different metric
+ leaders: Dict[str, str] = {}
+ for metric_name, (pid, _score) in self.metric_best.items():
+ if pid in self.programs and pid in self.elite_pool:
+ leaders[metric_name] = pid
+
+ unique_leaders = sorted(set(leaders.values()))
+ if len(unique_leaders) >= 2:
+ pids = self.rng.sample(unique_leaders, 2)
+ return self.programs[pids[0]], self.programs[pids[1]]
+
+ # Fallback: best + random from top 5
+ best = self.get_best_program()
+ top5_ids = [pid for pid in self.elite_pool[:5] if pid != best.id]
+ if top5_ids:
+ other_id = self.rng.choice(top5_ids)
+ return best, self.programs[other_id]
+
+ return best, best
+
+ # ------------------------------------------------------------------
+ # Save / Load — persist elite pool + rejection history
+ # ------------------------------------------------------------------
+
+ def save(self, path: Optional[str] = None, iteration: int = 0) -> None:
+ """Save base state plus GEPA-specific metadata."""
+ super().save(path=path, iteration=iteration)
+
+ save_path = path or self.config.db_path
+ if not save_path:
+ return
+
+ metadata = {
+ "elite_pool": self.elite_pool,
+ "initial_program_id": self.initial_program_id,
+ "metric_best": {k: list(v) for k, v in self.metric_best.items()},
+ "program_at_metric_front": {
+ k: list(v) for k, v in self.program_at_metric_front.items()
+ },
+ "rejection_history": [prog.to_dict() for prog in self.rejection_history],
+ }
+ os.makedirs(save_path, exist_ok=True)
+
+ with open(os.path.join(save_path, "gepa_metadata.json"), "w") as f:
+ json.dump(metadata, f, indent=2, cls=SafeJSONEncoder)
+
+ def load(self, path: str) -> None:
+ """Load base state plus GEPA-specific metadata."""
+ super().load(path)
+
+ metadata_path = os.path.join(path, "gepa_metadata.json")
+ if not os.path.exists(metadata_path):
+ # Legacy checkpoint — rebuild from programs
+ self._rebuild_elite_pool()
+ return
+
+ with open(metadata_path, "r") as f:
+ metadata = json.load(f)
+
+ # Restore elite pool (filter out IDs no longer in programs)
+ self.elite_pool = [pid for pid in metadata.get("elite_pool", []) if pid in self.programs]
+
+ # Restore initial program
+ self.initial_program_id = metadata.get("initial_program_id")
+
+ # Restore per-metric best
+ for k, v in metadata.get("metric_best", {}).items():
+ pid, score = v[0], v[1]
+ if pid in self.programs:
+ self.metric_best[k] = (pid, score)
+
+ # Restore metric Pareto front
+ self.program_at_metric_front = {
+ k: {pid for pid in v if pid in self.programs}
+ for k, v in metadata.get("program_at_metric_front", {}).items()
+ }
+
+ # Restore rejection history
+ self.rejection_history.clear()
+ for prog_dict in metadata.get("rejection_history", []):
+ try:
+ self.rejection_history.append(Program.from_dict(prog_dict))
+ except Exception as e:
+ logger.warning(f"Failed to load rejected program from history: {e}")
+
+ def _rebuild_elite_pool(self) -> None:
+ """Rebuild elite pool and metric_best from loaded programs."""
+ self.elite_pool = sorted(
+ self.programs.keys(),
+ key=lambda pid: get_score(self.programs[pid].metrics or {}),
+ reverse=True,
+ )[: self.population_size]
+
+ # Infer initial program as the earliest-seen one
+ if self.programs:
+ self.initial_program_id = min(
+ self.programs,
+ key=lambda pid: self.programs[pid].iteration_found,
+ )
+
+ for pid, prog in self.programs.items():
+ if not prog.metrics:
+ continue
+ for metric_name, value in prog.metrics.items():
+ if not isinstance(value, (int, float)):
+ continue
+ current = self.metric_best.get(metric_name)
+ if current is None or value > current[1]:
+ self.metric_best[metric_name] = (pid, value)
+ self.program_at_metric_front[metric_name] = {pid}
+ elif value == current[1]:
+ self.program_at_metric_front[metric_name].add(pid)
+
+ # ------------------------------------------------------------------
+ # Internal helpers
+ # ------------------------------------------------------------------
+
+ def _select_parent(self) -> Program:
+ """Epsilon-greedy or Pareto-based parent selection."""
+ if self.candidate_selection_strategy == "best" or not self.elite_pool:
+ return self.get_best_program()
+
+ if self.candidate_selection_strategy == "pareto":
+ return self._select_parent_pareto()
+
+ if self.rng.random() < self.epsilon and len(self.elite_pool) > 1:
+ pid = self.rng.choice(self.elite_pool)
+ return self.programs[pid]
+ return self.get_best_program()
+
+ def _select_parent_pareto(self) -> Program:
+ """Frequency-weighted selection from the Pareto front across metrics."""
+ if not self.program_at_metric_front or len(self.programs) < 2:
+ return self.get_best_program()
+ scores = {pid: get_score(prog.metrics) for pid, prog in self.programs.items()}
+ try:
+ pid = select_program_candidate_from_pareto_front(
+ self.program_at_metric_front, scores, self.rng
+ )
+ except AssertionError:
+ return self.get_best_program()
+ return self.programs[pid]
+
+ def _select_other_context_programs(
+ self, parent_id: str, num_context_programs: int
+ ) -> List[Program]:
+ """Select context programs from elite pool + metric leaders.
+
+ Picks top programs from the elite pool (excluding the parent),
+ then appends any metric-best programs not already included.
+ """
+ seen = {parent_id}
+ other_context_programs: List[Program] = []
+
+ # Top programs from elite pool (excluding parent)
+ for pid in self.elite_pool:
+ if pid in seen or pid not in self.programs:
+ continue
+ other_context_programs.append(self.programs[pid])
+ seen.add(pid)
+ if len(other_context_programs) >= num_context_programs:
+ break
+
+ # Add metric-best programs not already included
+ for _metric, (pid, _score) in self.metric_best.items():
+ if pid in seen or pid not in self.programs:
+ continue
+ other_context_programs.append(self.programs[pid])
+ seen.add(pid)
+
+ return other_context_programs[:num_context_programs]
diff --git a/skydiscover/search/gepa_native/pareto_utils.py b/skydiscover/search/gepa_native/pareto_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..03ae4d131e0694cf76af173b3f9cc0e23cbcaa34
--- /dev/null
+++ b/skydiscover/search/gepa_native/pareto_utils.py
@@ -0,0 +1,69 @@
+# Ported from gepa/src/gepa/gepa_utils.py — Copyright (c) 2025 Lakshya A Agrawal and the GEPA contributors
+# https://github.com/gepa-ai/gepa
+
+import random
+from typing import Any, Mapping
+
+
+def is_dominated(y, programs, program_at_pareto_front_valset):
+ y_fronts = [front for front in program_at_pareto_front_valset.values() if y in front]
+ for front in y_fronts:
+ found_dominator_in_front = False
+ for other_prog in front:
+ if other_prog in programs:
+ found_dominator_in_front = True
+ break
+ if not found_dominator_in_front:
+ return False
+ return True
+
+
+def remove_dominated_programs(program_at_pareto_front_valset, scores=None):
+ freq = {}
+ for front in program_at_pareto_front_valset.values():
+ for p in front:
+ freq[p] = freq.get(p, 0) + 1
+
+ dominated = set()
+ programs = list(freq.keys())
+
+ if scores is None:
+ scores = dict.fromkeys(programs, 1)
+
+ programs = sorted(programs, key=lambda x: scores[x], reverse=False)
+
+ found_to_remove = True
+ while found_to_remove:
+ found_to_remove = False
+ for y in programs:
+ if y in dominated:
+ continue
+ if is_dominated(
+ y,
+ set(programs).difference({y}).difference(dominated),
+ program_at_pareto_front_valset,
+ ):
+ dominated.add(y)
+ found_to_remove = True
+ break
+
+ dominators = [p for p in programs if p not in dominated]
+ return {
+ val_id: {prog_idx for prog_idx in front if prog_idx in dominators}
+ for val_id, front in program_at_pareto_front_valset.items()
+ }
+
+
+def select_program_candidate_from_pareto_front(
+ pareto_front_programs: Mapping[Any, set],
+ scores: Mapping[Any, float],
+ rng: random.Random,
+):
+ new_front = remove_dominated_programs(pareto_front_programs, scores=scores)
+ freq = {}
+ for front in new_front.values():
+ for prog_id in front:
+ freq[prog_id] = freq.get(prog_id, 0) + 1
+ sampling_list = [prog_id for prog_id, f in freq.items() for _ in range(f)]
+ assert len(sampling_list) > 0
+ return rng.choice(sampling_list)
diff --git a/skydiscover/search/openevolve_native/README.md b/skydiscover/search/openevolve_native/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..50df6e3373676dc937ed844c9a5ba0308381ff9f
--- /dev/null
+++ b/skydiscover/search/openevolve_native/README.md
@@ -0,0 +1,54 @@
+# OpenEvolve Native
+
+Native port of [OpenEvolve](https://github.com/codelion/openevolve)'s
+island-based MAP-Elites search as a SkyDiscover `ProgramDatabase` subclass.
+
+All search logic -- sampling, MAP-Elites, archive, island migration -- is identical
+to the reference. The entire algorithm fits inside `sample()` and `add()`.
+
+Named `openevolve_native` (not `openevolve`) to avoid any confusion with the
+external `openevolve` pip package used by `skydiscover/extras/external/`.
+
+## SkyDiscover adaptations
+
+Three minimal changes to fit SkyDiscover's `DiscoveryController` loop:
+
+1. **`sample()` return type** -- returns `(Program, List[Program])`.
+ The framework's `DiscoveryController` normalises both plain and dict-wrapped returns.
+
+2. **`Program.solution` instead of `Program.code`** -- field rename, no logic change.
+
+3. **Island rotation + generation + migration in `sample()`/`add()`** -- OpenEvolve's
+ controller calls `next_island()`, `increment_island_generation()`, and
+ `migrate_programs()` separately. We integrate these into `sample()` and `add()`
+ since `DiscoveryController` doesn't call them.
+
+## What is NOT ported
+
+These are non-search concerns that don't affect the algorithm:
+
+- **Novelty rejection** -- embedding-based + LLM-judge filtering. Disabled by default
+ in OpenEvolve (no-op when `embedding_model` is unset). Use the external `openevolve`
+ backend if needed.
+- **`sample_from_island()`** -- thread-safe sampling for parallel workers. Not needed
+ in sequential execution.
+- **Artifact storage** -- uses SkyDiscover's artifact system instead.
+- **Prompt construction** -- uses SkyDiscover's `DefaultContextBuilder`.
+
+## Configuration
+
+| Field | Default | OpenEvolve Default |
+|---|---|---|
+| `population_size` | 40 | 1000 |
+| `archive_size` | 100 | 100 |
+| `num_islands` | 5 | 5 |
+| `exploration_ratio` | 0.2 | 0.2 |
+| `exploitation_ratio` | 0.7 | 0.7 |
+| `elite_selection_ratio` | 0.1 | 0.1 |
+| `feature_dimensions` | ["complexity", "diversity"] | ["complexity", "diversity"] |
+| `feature_bins` | 10 | 10 |
+| `diversity_reference_size` | 20 | 20 |
+| `migration_interval` | 10 | 50 |
+| `migration_rate` | 0.1 | 0.1 |
+
+All values are overridable in YAML config.
diff --git a/skydiscover/search/openevolve_native/__init__.py b/skydiscover/search/openevolve_native/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..bb8af7df54dbb4f3b9e53414bdf901b7c25d7577
--- /dev/null
+++ b/skydiscover/search/openevolve_native/__init__.py
@@ -0,0 +1,11 @@
+"""
+OpenEvolve Native — MAP-Elites + island-based evolutionary search.
+
+A faithful port of the OpenEvolve search algorithm adapted to
+SkyDiscover's base classes. Named ``openevolve_native`` to avoid
+any confusion with the external ``openevolve`` package.
+"""
+
+from skydiscover.search.openevolve_native.database import OpenEvolveNativeDatabase
+
+__all__ = ["OpenEvolveNativeDatabase"]
diff --git a/skydiscover/search/openevolve_native/database.py b/skydiscover/search/openevolve_native/database.py
new file mode 100644
index 0000000000000000000000000000000000000000..dffe262a0c21be4f72672fc84ecdc60fe60500ea
--- /dev/null
+++ b/skydiscover/search/openevolve_native/database.py
@@ -0,0 +1,963 @@
+"""
+OpenEvolve Native Database — island-based MAP-Elites search for SkyDiscover.
+
+Faithful port of the OpenEvolve search algorithm (MAP-Elites with island-based
+population model). All core search logic — sampling, MAP-Elites grid,
+archive management, migration — mirrors the reference implementation at
+``openevolve/database.py``.
+
+SkyDiscover adaptations (minimum necessary):
+ - ``sample()`` returns ``(Program, List[Program])`` — the framework's
+ ``DiscoveryController`` normalises both plain and dict-wrapped returns.
+ - Uses ``Program.solution`` (SkyDiscover's field name).
+ - Island rotation lives in ``sample()``, generation increment + migration
+ check live in ``add()``, because SkyDiscover's ``DiscoveryController`` does
+ not call these as separate steps.
+
+Named ``openevolve_native`` (not ``openevolve``) to avoid any confusion with
+the external ``openevolve`` package.
+"""
+
+from __future__ import annotations
+
+import json
+import logging
+import os
+import random
+import time
+import uuid
+from typing import Any, Dict, List, Optional, Set, Tuple
+
+from skydiscover.config import DatabaseConfig
+from skydiscover.search.base_database import Program, ProgramDatabase
+
+logger = logging.getLogger(__name__)
+
+
+# ---------------------------------------------------------------------------
+# Fitness helpers — port of openevolve/utils/metrics_utils.py
+# ---------------------------------------------------------------------------
+
+
+def _safe_numeric_average(metrics: Dict[str, Any]) -> float:
+ """Average of numeric metric values, ignoring non-numeric entries."""
+ if not metrics:
+ return 0.0
+ numeric_values = []
+ for value in metrics.values():
+ if isinstance(value, (int, float)):
+ try:
+ fv = float(value)
+ if fv == fv: # NaN guard
+ numeric_values.append(fv)
+ except (ValueError, TypeError, OverflowError):
+ continue
+ return sum(numeric_values) / len(numeric_values) if numeric_values else 0.0
+
+
+def _get_fitness(
+ metrics: Dict[str, Any],
+ feature_dimensions: List[str] = (),
+) -> float:
+ """Fitness score, preferring ``combined_score`` and excluding feature dims."""
+ if not metrics:
+ return 0.0
+ if "combined_score" in metrics:
+ try:
+ return float(metrics["combined_score"])
+ except (ValueError, TypeError):
+ pass
+
+ feature_dims = set(feature_dimensions) if feature_dimensions else set()
+ fitness_metrics: Dict[str, float] = {}
+ for key, value in metrics.items():
+ if key not in feature_dims and isinstance(value, (int, float)):
+ try:
+ fv = float(value)
+ if fv == fv:
+ fitness_metrics[key] = fv
+ except (ValueError, TypeError, OverflowError):
+ continue
+
+ if not fitness_metrics:
+ return _safe_numeric_average(metrics)
+ return _safe_numeric_average(fitness_metrics)
+
+
+# ===========================================================================
+# OpenEvolveNativeDatabase
+# ===========================================================================
+
+
+class OpenEvolveNativeDatabase(ProgramDatabase):
+ """Island-based MAP-Elites database — native port of OpenEvolve."""
+
+ # ------------------------------------------------------------------
+ # Initialisation
+ # ------------------------------------------------------------------
+
+ def __init__(self, name: str, config: DatabaseConfig, **kwargs: Any):
+ # --- Read config (getattr for fields not on base DatabaseConfig) ---
+ self.num_islands: int = getattr(config, "num_islands", 5)
+ self.population_size: int = getattr(config, "population_size", 40)
+ self.archive_size: int = getattr(config, "archive_size", 100)
+ self.exploration_ratio: float = getattr(config, "exploration_ratio", 0.2)
+ self.exploitation_ratio: float = getattr(config, "exploitation_ratio", 0.7)
+ self.elite_selection_ratio: float = getattr(config, "elite_selection_ratio", 0.1)
+ self.feature_dimensions: List[str] = list(
+ getattr(config, "feature_dimensions", ["complexity", "diversity"])
+ )
+
+ raw_bins = getattr(config, "feature_bins", 10)
+ if isinstance(raw_bins, int):
+ self.feature_bins: int = max(
+ raw_bins,
+ int(pow(self.archive_size, 1 / max(len(self.feature_dimensions), 1)) + 0.99),
+ )
+ else:
+ self.feature_bins = 10
+
+ if isinstance(raw_bins, dict):
+ self.feature_bins_per_dim: Dict[str, int] = raw_bins
+ else:
+ self.feature_bins_per_dim = {d: self.feature_bins for d in self.feature_dimensions}
+
+ self.diversity_reference_size: int = getattr(config, "diversity_reference_size", 20)
+ self.migration_interval: int = getattr(config, "migration_interval", 10)
+ self.migration_rate: float = getattr(config, "migration_rate", 0.1)
+
+ # --- Island state (MUST be set before super().__init__ which may
+ # call self.load() if db_path exists) ---
+ self.islands: List[Set[str]] = [set() for _ in range(self.num_islands)]
+ self.island_feature_maps: List[Dict[str, str]] = [{} for _ in range(self.num_islands)]
+ self.island_best_programs: List[Optional[str]] = [None] * self.num_islands
+ self.island_generations: List[int] = [0] * self.num_islands
+ self.current_island: int = 0
+ self.last_migration_generation: int = 0
+
+ # --- Global archive ---
+ self.archive: Set[str] = set()
+
+ # --- Feature scaling ---
+ self.feature_stats: Dict[str, Dict[str, Any]] = {}
+
+ # --- Diversity cache ---
+ self.diversity_cache: Dict[int, Dict[str, float]] = {}
+ self.diversity_cache_size: int = 1000
+ self.diversity_reference_set: List[str] = []
+
+ # --- Now safe to call super (which may trigger self.load) ---
+ super().__init__(name, config, **kwargs)
+
+ # --- Seed RNG ---
+ random_seed = getattr(config, "random_seed", None)
+ if random_seed is not None:
+ random.seed(random_seed)
+
+ logger.info(
+ "OpenEvolveNativeDatabase: %d islands, pop=%d, archive=%d, "
+ "features=%s, bins=%d, migration_interval=%d, migration_rate=%.2f",
+ self.num_islands,
+ self.population_size,
+ self.archive_size,
+ self.feature_dimensions,
+ self.feature_bins,
+ self.migration_interval,
+ self.migration_rate,
+ )
+
+ # ==================================================================
+ # sample()
+ # ==================================================================
+
+ def sample(
+ self,
+ num_context_programs: Optional[int] = 4,
+ **kwargs: Any,
+ ) -> Tuple[Program, List[Program]]:
+ if not self.programs:
+ raise ValueError("Cannot sample: no programs in database")
+
+ parent = self._sample_parent()
+ if num_context_programs is None:
+ num_context_programs = 4
+ other_context_programs = self._sample_other_context_programs(parent, n=num_context_programs)
+
+ logger.debug(
+ "Sampled parent %s from island %d, %d other context programs",
+ parent.id,
+ self.current_island,
+ len(other_context_programs),
+ )
+
+ # Round-robin island rotation AFTER sampling so the first call
+ # uses island 0 (OpenEvolve's controller calls next_island()
+ # separately; we do it here because DiscoveryController doesn't).
+ self.current_island = (self.current_island + 1) % self.num_islands
+
+ return parent, other_context_programs
+
+ # ==================================================================
+ # add()
+ # ==================================================================
+
+ def add(
+ self,
+ program: Program,
+ iteration: Optional[int] = None,
+ **kwargs: Any,
+ ) -> str:
+ target_island: Optional[int] = kwargs.get("target_island")
+ is_migration: bool = kwargs.get("_is_migration", False)
+
+ if iteration is not None:
+ program.iteration_found = iteration
+ self.last_iteration = max(self.last_iteration, iteration)
+
+ self.programs[program.id] = program
+
+ # Feature coordinates for MAP-Elites
+ feature_coords = self._calculate_feature_coords(program)
+
+ # --- Determine target island ---
+ if target_island is not None:
+ island_idx = target_island
+ elif program.parent_id:
+ parent = self.programs.get(program.parent_id)
+ if parent and "island" in parent.metadata:
+ island_idx = parent.metadata["island"]
+ else:
+ island_idx = self.current_island
+ else:
+ island_idx = self.current_island
+ island_idx = island_idx % self.num_islands
+
+ # --- MAP-Elites: insert into island feature map if better ---
+ feature_key = self._feature_coords_to_key(feature_coords)
+ island_feature_map = self.island_feature_maps[island_idx]
+
+ should_replace = feature_key not in island_feature_map
+ if not should_replace:
+ existing_id = island_feature_map[feature_key]
+ if existing_id not in self.programs:
+ should_replace = True # stale reference
+ else:
+ should_replace = self._is_better(program, self.programs[existing_id])
+
+ if should_replace:
+ if feature_key in island_feature_map:
+ existing_id = island_feature_map[feature_key]
+ if existing_id in self.programs:
+ # Swap archive membership
+ if existing_id in self.archive:
+ self.archive.discard(existing_id)
+ self.archive.add(program.id)
+ # Remove replaced program from island set
+ self.islands[island_idx].discard(existing_id)
+ island_feature_map[feature_key] = program.id
+
+ # Add to island
+ self.islands[island_idx].add(program.id)
+ program.metadata["island"] = island_idx
+
+ # Archive, population limit, best-program tracking
+ self._update_archive(program)
+ self._enforce_population_limit(exclude_program_id=program.id)
+ self._update_best_program(program)
+ self._update_island_best_program(program, island_idx)
+
+ # Save to disk
+ if self.config.db_path:
+ self._save_program(program)
+
+ # --- Post-add: generation increment + migration ---
+ # (OpenEvolve's controller calls these separately; we fold them
+ # into add() because DiscoveryController doesn't.)
+ if not is_migration:
+ self.island_generations[island_idx] += 1
+ if self._should_migrate():
+ self._migrate_programs()
+
+ logger.debug("Added program %s to island %d", program.id, island_idx)
+ return program.id
+
+ # ==================================================================
+ # Parent sampling (exploration / exploitation / random)
+ # ==================================================================
+
+ def _sample_parent(self) -> Program:
+ rand_val = random.random()
+ if rand_val < self.exploration_ratio:
+ return self._sample_exploration_parent()
+ elif rand_val < self.exploration_ratio + self.exploitation_ratio:
+ return self._sample_exploitation_parent()
+ else:
+ return self._sample_random_parent()
+
+ def _sample_exploration_parent(self) -> Program:
+ """Random program from current island (diverse sampling)."""
+ island_programs = self.islands[self.current_island]
+
+ if not island_programs:
+ return self._seed_empty_island(self.current_island)
+
+ valid = [pid for pid in island_programs if pid in self.programs]
+ # Remove stale refs
+ if len(valid) < len(island_programs):
+ for stale in island_programs - set(valid):
+ self.islands[self.current_island].discard(stale)
+
+ if not valid:
+ return self._seed_empty_island(self.current_island)
+
+ return self.programs[random.choice(valid)]
+
+ def _sample_exploitation_parent(self) -> Program:
+ """Elite program from archive, preferring current island."""
+ if not self.archive:
+ return self._sample_exploration_parent()
+
+ valid_archive = [pid for pid in self.archive if pid in self.programs]
+ # Remove stale refs
+ if len(valid_archive) < len(self.archive):
+ for stale in self.archive - set(valid_archive):
+ self.archive.discard(stale)
+
+ if not valid_archive:
+ return self._sample_exploration_parent()
+
+ # Prefer archive programs from current island
+ in_island = [
+ pid
+ for pid in valid_archive
+ if self.programs[pid].metadata.get("island") == self.current_island
+ ]
+
+ if in_island:
+ return self.programs[random.choice(in_island)]
+ return self.programs[random.choice(valid_archive)]
+
+ def _sample_random_parent(self) -> Program:
+ """Uniformly random program from entire population."""
+ return self.programs[random.choice(list(self.programs.keys()))]
+
+ def _seed_empty_island(self, island_idx: int) -> Program:
+ """Seed an empty island with a copy of the best program."""
+ if self.best_program_id and self.best_program_id in self.programs:
+ best = self.programs[self.best_program_id]
+ copy = Program(
+ id=str(uuid.uuid4()),
+ solution=best.solution,
+ language=best.language,
+ parent_id=best.id,
+ generation=best.generation,
+ metrics=best.metrics.copy(),
+ metadata={"island": island_idx},
+ iteration_found=self.last_iteration,
+ )
+ self.programs[copy.id] = copy
+ self.islands[island_idx].add(copy.id)
+ return copy
+ return next(iter(self.programs.values()))
+
+ # ==================================================================
+ # Context program(s) sampling (island-scoped)
+ # ==================================================================
+
+ def _sample_other_context_programs(self, parent: Program, n: int = 4) -> List[Program]:
+ """Sample other context programs from parent's island.
+
+ Strategy (matching OpenEvolve):
+ 1. Island best (if different from parent)
+ 2. Top elite programs from island
+ 3. Programs from nearby MAP-Elites cells (±2 perturbation)
+ 4. Random fill from island
+ """
+ parent_island = parent.metadata.get("island", self.current_island)
+ island_program_ids = list(self.islands[parent_island])
+ island_programs = [self.programs[pid] for pid in island_program_ids if pid in self.programs]
+
+ if not island_programs:
+ return []
+
+ other_context_programs: List[Program] = []
+ used_ids: set = {parent.id}
+
+ # 1. Island best
+ island_best_id = self.island_best_programs[parent_island]
+ if (
+ island_best_id is not None
+ and island_best_id != parent.id
+ and island_best_id in self.programs
+ ):
+ other_context_programs.append(self.programs[island_best_id])
+ used_ids.add(island_best_id)
+ elif island_best_id is not None and island_best_id not in self.programs:
+ self.island_best_programs[parent_island] = None
+
+ # 2. Top elite programs from island
+ top_n = max(1, int(n * self.elite_selection_ratio))
+ top_island = sorted(
+ island_programs,
+ key=lambda p: _get_fitness(p.metrics, self.feature_dimensions),
+ reverse=True,
+ )[:top_n]
+ for prog in top_island:
+ if prog.id not in used_ids:
+ other_context_programs.append(prog)
+ used_ids.add(prog.id)
+
+ # 3. Nearby MAP-Elites cells (±2 perturbation)
+ if len(island_programs) > n and len(other_context_programs) < n:
+ remaining_slots = n - len(other_context_programs)
+ feature_coords = self._calculate_feature_coords(parent)
+
+ # Build local feature-cell → program mapping for this island
+ cell_map: Dict[str, str] = {}
+ for pid in island_program_ids:
+ if pid in self.programs:
+ coords = self._calculate_feature_coords(self.programs[pid])
+ cell_map[self._feature_coords_to_key(coords)] = pid
+
+ nearby: List[Program] = []
+ for _ in range(remaining_slots * 3):
+ perturbed = [
+ max(0, min(self.feature_bins - 1, c + random.randint(-2, 2)))
+ for c in feature_coords
+ ]
+ key = self._feature_coords_to_key(perturbed)
+ if key in cell_map:
+ pid = cell_map[key]
+ if (
+ pid not in used_ids
+ and pid not in {p.id for p in nearby}
+ and pid in self.programs
+ ):
+ nearby.append(self.programs[pid])
+ if len(nearby) >= remaining_slots:
+ break
+
+ # 4. Random fill from island
+ if len(other_context_programs) + len(nearby) < n:
+ remaining = n - len(other_context_programs) - len(nearby)
+ all_used = used_ids | {p.id for p in nearby}
+ available = [
+ pid
+ for pid in island_program_ids
+ if pid not in all_used and pid in self.programs
+ ]
+ if available:
+ sampled = random.sample(available, min(remaining, len(available)))
+ nearby.extend(self.programs[pid] for pid in sampled)
+
+ other_context_programs.extend(nearby)
+
+ return other_context_programs[:n]
+
+ # ==================================================================
+ # MAP-Elites feature coordinates
+ # ==================================================================
+
+ def _calculate_feature_coords(self, program: Program) -> List[int]:
+ coords: List[int] = []
+ for dim in self.feature_dimensions:
+ # Priority 1: custom metric from evaluator
+ if dim in program.metrics:
+ coords.append(self._to_bin(dim, program.metrics[dim]))
+ # Priority 2: built-in features
+ elif dim == "complexity":
+ coords.append(self._to_bin("complexity", float(len(program.solution))))
+ elif dim == "diversity":
+ if len(self.programs) < 2:
+ coords.append(0)
+ else:
+ coords.append(self._to_bin("diversity", self._get_cached_diversity(program)))
+ elif dim == "score":
+ if not program.metrics:
+ coords.append(0)
+ else:
+ coords.append(
+ self._to_bin(
+ "score",
+ _get_fitness(program.metrics, self.feature_dimensions),
+ )
+ )
+ else:
+ raise ValueError(
+ f"Feature dimension '{dim}' not found in program metrics. "
+ f"Available metrics: {list(program.metrics.keys())}. "
+ f"Built-in features: 'complexity', 'diversity', 'score'."
+ )
+ return coords
+
+ def _to_bin(self, dim: str, value: float) -> int:
+ """Update running stats, min-max scale, and return bin index."""
+ self._update_feature_stats(dim, value)
+ scaled = self._scale_feature_value(dim, value)
+ num_bins = self.feature_bins_per_dim.get(dim, self.feature_bins)
+ return max(0, min(num_bins - 1, int(scaled * num_bins)))
+
+ @staticmethod
+ def _feature_coords_to_key(coords: List[int]) -> str:
+ return "-".join(str(c) for c in coords)
+
+ # ==================================================================
+ # Feature scaling (min-max)
+ # ==================================================================
+
+ def _update_feature_stats(self, feature_name: str, value: float) -> None:
+ if feature_name not in self.feature_stats:
+ self.feature_stats[feature_name] = {
+ "min": value,
+ "max": value,
+ "values": [],
+ }
+ stats = self.feature_stats[feature_name]
+ stats["min"] = min(stats["min"], value)
+ stats["max"] = max(stats["max"], value)
+ stats["values"].append(value)
+ if len(stats["values"]) > 1000:
+ stats["values"] = stats["values"][-1000:]
+
+ def _scale_feature_value(self, feature_name: str, value: float) -> float:
+ if feature_name not in self.feature_stats:
+ return min(1.0, max(0.0, value))
+ stats = self.feature_stats[feature_name]
+ min_val, max_val = stats["min"], stats["max"]
+ if max_val == min_val:
+ return 0.5
+ return min(1.0, max(0.0, (value - min_val) / (max_val - min_val)))
+
+ # ==================================================================
+ # Fast code diversity
+ # ==================================================================
+
+ @staticmethod
+ def _fast_code_diversity(code1: str, code2: str) -> float:
+ if code1 == code2:
+ return 0.0
+ length_diff = abs(len(code1) - len(code2))
+ line_diff = abs(code1.count("\n") - code2.count("\n"))
+ char_diff = len(set(code1).symmetric_difference(set(code2)))
+ return length_diff * 0.1 + line_diff * 10 + char_diff * 0.5
+
+ def _get_cached_diversity(self, program: Program) -> float:
+ code_hash = hash(program.solution)
+
+ if code_hash in self.diversity_cache:
+ return self.diversity_cache[code_hash]["value"]
+
+ if (
+ not self.diversity_reference_set
+ or len(self.diversity_reference_set) < self.diversity_reference_size
+ ):
+ self._update_diversity_reference_set()
+
+ scores = [
+ self._fast_code_diversity(program.solution, ref)
+ for ref in self.diversity_reference_set
+ if ref != program.solution
+ ]
+ diversity = sum(scores) / max(1, len(scores)) if scores else 0.0
+
+ # LRU eviction
+ if len(self.diversity_cache) >= self.diversity_cache_size:
+ oldest = min(self.diversity_cache, key=lambda h: self.diversity_cache[h]["timestamp"])
+ del self.diversity_cache[oldest]
+
+ self.diversity_cache[code_hash] = {
+ "value": diversity,
+ "timestamp": time.time(),
+ }
+ return diversity
+
+ def _update_diversity_reference_set(self) -> None:
+ if not self.programs:
+ return
+ all_progs = list(self.programs.values())
+
+ if len(all_progs) <= self.diversity_reference_size:
+ self.diversity_reference_set = [p.solution for p in all_progs]
+ return
+
+ # Greedy-diverse selection
+ selected: List[Program] = []
+ remaining = all_progs.copy()
+
+ selected.append(remaining.pop(random.randint(0, len(remaining) - 1)))
+
+ while len(selected) < self.diversity_reference_size and remaining:
+ best_idx, best_div = -1, -1.0
+ for i, cand in enumerate(remaining):
+ min_d = min(self._fast_code_diversity(cand.solution, s.solution) for s in selected)
+ if min_d > best_div:
+ best_div = min_d
+ best_idx = i
+ if best_idx >= 0:
+ selected.append(remaining.pop(best_idx))
+
+ self.diversity_reference_set = [p.solution for p in selected]
+
+ # ==================================================================
+ # Fitness comparison (excluding feature dimensions)
+ # ==================================================================
+
+ def _is_better(self, program1: Program, program2: Program) -> bool:
+ if not program1.metrics and not program2.metrics:
+ return program1.timestamp > program2.timestamp
+ if program1.metrics and not program2.metrics:
+ return True
+ if not program1.metrics and program2.metrics:
+ return False
+ return _get_fitness(program1.metrics, self.feature_dimensions) > _get_fitness(
+ program2.metrics, self.feature_dimensions
+ )
+
+ # ==================================================================
+ # Archive management
+ # ==================================================================
+
+ def _update_archive(self, program: Program) -> None:
+ if len(self.archive) < self.archive_size:
+ self.archive.add(program.id)
+ return
+
+ # Clean stale refs
+ valid = []
+ for pid in list(self.archive):
+ if pid in self.programs:
+ valid.append(self.programs[pid])
+ else:
+ self.archive.discard(pid)
+
+ if len(self.archive) < self.archive_size:
+ self.archive.add(program.id)
+ return
+
+ # Replace worst if new program is better
+ if valid:
+ worst = min(
+ valid,
+ key=lambda p: _get_fitness(p.metrics, self.feature_dimensions),
+ )
+ if self._is_better(program, worst):
+ self.archive.discard(worst.id)
+ self.archive.add(program.id)
+ else:
+ self.archive.add(program.id)
+
+ # ==================================================================
+ # Best program tracking
+ # ==================================================================
+
+ def _update_best_program(self, program: Program) -> None:
+ if self.best_program_id is None:
+ self.best_program_id = program.id
+ return
+ if self.best_program_id not in self.programs:
+ self.best_program_id = program.id
+ return
+ current_best = self.programs[self.best_program_id]
+ if self._is_better(program, current_best):
+ old_id = self.best_program_id
+ self.best_program_id = program.id
+ if "combined_score" in program.metrics and "combined_score" in current_best.metrics:
+ logger.info(
+ "New best program %s replaces %s (%.4f -> %.4f)",
+ program.id,
+ old_id,
+ current_best.metrics["combined_score"],
+ program.metrics["combined_score"],
+ )
+
+ def _update_island_best_program(self, program: Program, island_idx: int) -> None:
+ if island_idx >= len(self.island_best_programs):
+ return
+ cur_id = self.island_best_programs[island_idx]
+ if cur_id is None or cur_id not in self.programs:
+ self.island_best_programs[island_idx] = program.id
+ return
+ if self._is_better(program, self.programs[cur_id]):
+ self.island_best_programs[island_idx] = program.id
+
+ # ==================================================================
+ # Population limit
+ # ==================================================================
+
+ def _enforce_population_limit(self, exclude_program_id: Optional[str] = None) -> None:
+ if len(self.programs) <= self.population_size:
+ return
+
+ num_to_remove = len(self.programs) - self.population_size
+ sorted_progs = sorted(
+ self.programs.values(),
+ key=lambda p: _get_fitness(p.metrics, self.feature_dimensions),
+ )
+
+ protected = {self.best_program_id, exclude_program_id} - {None}
+ to_remove: List[Program] = []
+ for prog in sorted_progs:
+ if len(to_remove) >= num_to_remove:
+ break
+ if prog.id not in protected:
+ to_remove.append(prog)
+
+ for prog in to_remove:
+ pid = prog.id
+ self.programs.pop(pid, None)
+
+ for imap in self.island_feature_maps:
+ for k in [k for k, v in imap.items() if v == pid]:
+ del imap[k]
+
+ for island in self.islands:
+ island.discard(pid)
+
+ self.archive.discard(pid)
+
+ self._cleanup_stale_island_bests()
+ logger.info(
+ "Population limit: removed %d, now %d",
+ len(to_remove),
+ len(self.programs),
+ )
+
+ def _cleanup_stale_island_bests(self) -> None:
+ for i, best_id in enumerate(self.island_best_programs):
+ if best_id is None:
+ continue
+ if best_id not in self.programs or best_id not in self.islands[i]:
+ self.island_best_programs[i] = None
+ # Recalculate
+ progs = [self.programs[pid] for pid in self.islands[i] if pid in self.programs]
+ if progs:
+ self.island_best_programs[i] = max(
+ progs,
+ key=lambda p: _get_fitness(p.metrics, self.feature_dimensions),
+ ).id
+
+ # ==================================================================
+ # Migration (ring topology)
+ # ==================================================================
+
+ def _should_migrate(self) -> bool:
+ if self.num_islands < 2:
+ return False
+ return (
+ max(self.island_generations) - self.last_migration_generation
+ ) >= self.migration_interval
+
+ def _migrate_programs(self) -> None:
+ if self.num_islands < 2:
+ return
+ logger.info("Performing migration between islands")
+
+ for i, island in enumerate(self.islands):
+ if not island:
+ continue
+ island_progs = [self.programs[pid] for pid in island if pid in self.programs]
+ if not island_progs:
+ continue
+
+ island_progs.sort(
+ key=lambda p: _get_fitness(p.metrics, self.feature_dimensions),
+ reverse=True,
+ )
+ num_migrants = max(1, int(len(island_progs) * self.migration_rate))
+ migrants = island_progs[:num_migrants]
+
+ targets = [
+ (i + 1) % self.num_islands,
+ (i - 1) % self.num_islands,
+ ]
+
+ for migrant in migrants:
+ # Skip already-migrated programs to prevent exponential
+ # duplication (all copies map to same MAP-Elites cell).
+ if migrant.metadata.get("migrant", False):
+ continue
+
+ for target in targets:
+ # Skip if target island already has identical solution
+ target_progs = [
+ self.programs[pid] for pid in self.islands[target] if pid in self.programs
+ ]
+ if any(p.solution == migrant.solution for p in target_progs):
+ continue
+
+ copy = Program(
+ id=str(uuid.uuid4()),
+ solution=migrant.solution,
+ language=migrant.language,
+ parent_id=migrant.id,
+ generation=migrant.generation,
+ metrics=migrant.metrics.copy(),
+ metadata={
+ **migrant.metadata,
+ "island": target,
+ "migrant": True,
+ },
+ )
+ self.add(
+ copy,
+ target_island=target,
+ _is_migration=True,
+ )
+
+ self.last_migration_generation = max(self.island_generations)
+ logger.info(
+ "Migration completed at generation %d",
+ self.last_migration_generation,
+ )
+
+ # ==================================================================
+ # Save / Load (extends base to persist island metadata)
+ # ==================================================================
+
+ def save(self, path: Optional[str] = None, iteration: int = 0) -> None:
+ super().save(path=path, iteration=iteration)
+
+ save_path = path or getattr(self.config, "db_path", None)
+ if not save_path:
+ return
+
+ meta = {
+ "island_feature_maps": self.island_feature_maps,
+ "islands": [list(s) for s in self.islands],
+ "archive": list(self.archive),
+ "island_best_programs": self.island_best_programs,
+ "current_island": self.current_island,
+ "island_generations": self.island_generations,
+ "last_migration_generation": self.last_migration_generation,
+ "feature_stats": self._serialize_feature_stats(),
+ }
+ os.makedirs(save_path, exist_ok=True)
+ with open(os.path.join(save_path, "openevolve_native_metadata.json"), "w") as f:
+ json.dump(meta, f)
+
+ def load(self, path: str) -> None:
+ super().load(path)
+
+ meta_path = os.path.join(path, "openevolve_native_metadata.json")
+ if not os.path.exists(meta_path):
+ logger.warning(
+ "No openevolve_native_metadata.json found; distributing " "programs round-robin"
+ )
+ self._distribute_programs_to_islands()
+ return
+
+ with open(meta_path, "r") as f:
+ meta = json.load(f)
+
+ self.island_feature_maps = meta.get(
+ "island_feature_maps", [{} for _ in range(self.num_islands)]
+ )
+ saved_islands = meta.get("islands", [])
+ self.archive = set(meta.get("archive", []))
+ self.island_best_programs = meta.get("island_best_programs", [None] * self.num_islands)
+ self.current_island = meta.get("current_island", 0)
+ self.island_generations = meta.get("island_generations", [0] * self.num_islands)
+ self.last_migration_generation = meta.get("last_migration_generation", 0)
+ self.feature_stats = self._deserialize_feature_stats(meta.get("feature_stats", {}))
+
+ self._reconstruct_islands(saved_islands)
+ self._log_island_status()
+
+ # ------------------------------------------------------------------
+ # Island reconstruction helpers
+ # ------------------------------------------------------------------
+
+ def _reconstruct_islands(self, saved_islands: List[List[str]]) -> None:
+ num_islands = max(len(saved_islands), self.num_islands)
+ self.islands = [set() for _ in range(num_islands)]
+
+ for island_idx, program_ids in enumerate(saved_islands):
+ if island_idx >= len(self.islands):
+ continue
+ for pid in program_ids:
+ if pid in self.programs:
+ self.islands[island_idx].add(pid)
+ self.programs[pid].metadata["island"] = island_idx
+
+ # Clean stale refs from archive and feature maps
+ self.archive = {pid for pid in self.archive if pid in self.programs}
+ for imap in self.island_feature_maps:
+ for k in [k for k, pid in imap.items() if pid not in self.programs]:
+ del imap[k]
+
+ self._cleanup_stale_island_bests()
+
+ if self.best_program_id and self.best_program_id not in self.programs:
+ self.best_program_id = None
+
+ # If no island assignments recovered, distribute round-robin
+ if self.programs and sum(len(s) for s in self.islands) == 0:
+ self._distribute_programs_to_islands()
+
+ # Ensure list lengths match
+ while len(self.island_generations) < len(self.islands):
+ self.island_generations.append(0)
+ while len(self.island_best_programs) < len(self.islands):
+ self.island_best_programs.append(None)
+
+ def _distribute_programs_to_islands(self) -> None:
+ for i, pid in enumerate(self.programs):
+ idx = i % self.num_islands
+ self.islands[idx].add(pid)
+ self.programs[pid].metadata["island"] = idx
+
+ # ------------------------------------------------------------------
+ # Feature-stats serialisation
+ # ------------------------------------------------------------------
+
+ def _serialize_feature_stats(self) -> Dict[str, Any]:
+ out: Dict[str, Any] = {}
+ for name, stats in self.feature_stats.items():
+ s: Dict[str, Any] = {}
+ for k, v in stats.items():
+ if k == "values":
+ s[k] = v[-100:] if isinstance(v, list) and len(v) > 100 else v
+ elif hasattr(v, "item"): # numpy scalar
+ s[k] = v.item()
+ else:
+ s[k] = v
+ out[name] = s
+ return out
+
+ def _deserialize_feature_stats(self, raw: Dict[str, Any]) -> Dict[str, Any]:
+ if not raw:
+ return {}
+ out: Dict[str, Any] = {}
+ for name, stats in raw.items():
+ if isinstance(stats, dict):
+ out[name] = {
+ "min": float(stats.get("min", 0.0)),
+ "max": float(stats.get("max", 1.0)),
+ "values": list(stats.get("values", [])),
+ }
+ return out
+
+ # ------------------------------------------------------------------
+ # Logging
+ # ------------------------------------------------------------------
+
+ def _log_island_status(self) -> None:
+ for i, island in enumerate(self.islands):
+ progs = [self.programs[pid] for pid in island if pid in self.programs]
+ if progs:
+ scores = [_get_fitness(p.metrics, self.feature_dimensions) for p in progs]
+ best, avg = max(scores), sum(scores) / len(scores)
+ else:
+ best = avg = 0.0
+ cells = len(self.island_feature_maps[i]) if i < len(self.island_feature_maps) else 0
+ gen = self.island_generations[i] if i < len(self.island_generations) else 0
+ logger.info(
+ "Island %d: %d programs, %d cells, gen=%d, best=%.4f, avg=%.4f%s",
+ i,
+ len(progs),
+ cells,
+ gen,
+ best,
+ avg,
+ " [current]" if i == self.current_island else "",
+ )
diff --git a/skydiscover/search/registry.py b/skydiscover/search/registry.py
new file mode 100644
index 0000000000000000000000000000000000000000..8fbdec2f58f9def18ba36b7f14f4d1495671215e
--- /dev/null
+++ b/skydiscover/search/registry.py
@@ -0,0 +1,181 @@
+"""
+Registries and factory functions for search components.
+"""
+
+import logging
+import os
+from typing import TYPE_CHECKING, Any, Dict, Optional, Tuple, Type
+
+from skydiscover.config import Config, DatabaseConfig, build_output_dir, load_config
+from skydiscover.search.base_database import Program, ProgramDatabase
+from skydiscover.search.default_discovery_controller import (
+ DiscoveryController,
+ DiscoveryControllerInput,
+)
+from skydiscover.search.utils.discovery_utils import load_database_from_file
+from skydiscover.utils.code_utils import extract_solution_language
+
+logger = logging.getLogger(__name__)
+
+
+######################### REGISTRY #########################
+
+_PROGRAM_REGISTRY: Dict[str, Type[Program]] = {}
+_DATABASE_REGISTRY: Dict[str, Type[ProgramDatabase]] = {}
+_CONTROLLER_REGISTRY: Dict[str, Type[DiscoveryController]] = {}
+
+
+def register_program(search_type: str, program_class: Type[Program]) -> None:
+ """Register a program class for a search type."""
+ _PROGRAM_REGISTRY[search_type] = program_class
+ logger.debug(
+ f"Registered program class '{program_class.__name__}' for search type '{search_type}'"
+ )
+
+
+def register_database(search_type: str, database_class: Type[ProgramDatabase]) -> None:
+ """Register a database class for a search type."""
+ _DATABASE_REGISTRY[search_type] = database_class
+ logger.debug(
+ f"Registered database class '{database_class.__name__}' for search type '{search_type}'"
+ )
+
+
+def register_controller(search_type: str, controller_class: Type[DiscoveryController]) -> None:
+ """Register a discovery controller class for a search type."""
+ _CONTROLLER_REGISTRY[search_type] = controller_class
+ logger.debug(
+ f"Registered controller class '{controller_class.__name__}' for search type '{search_type}'"
+ )
+
+
+######################### FACTORY FUNCTIONS #########################
+
+
+def create_database(search_type: str, config: DatabaseConfig) -> ProgramDatabase:
+ """
+ Create a database instance for a given search type.
+
+ Supports both registered search types and dynamic loading for "evox"/"evolve" types
+ when a custom database_file_path is specified.
+ """
+ if search_type in ("evox", "evolve") and getattr(config, "database_file_path", None):
+ database_class, program_class = load_database_from_file(config.database_file_path)
+ db = database_class(search_type, config)
+ db._program_class = program_class
+ return db
+
+ if search_type not in _DATABASE_REGISTRY:
+ available_types = ", ".join(sorted(_DATABASE_REGISTRY.keys()))
+ raise ValueError(
+ f"Unknown search type: '{search_type}'. "
+ f"Available types: {available_types}. "
+ f"For 'evox'/'evolve' type with custom database, set config.search.database.database_file_path"
+ )
+
+ database_class = _DATABASE_REGISTRY[search_type]
+ return database_class(search_type, config)
+
+
+def get_program(
+ config: Config,
+ initial_program_solution: str,
+ initial_program_id: str,
+ initial_metrics: Dict[str, Any],
+ start_iteration: int,
+) -> Program:
+ """
+ Create an initial Program instance appropriate to the search type.
+
+ Supports both registered program classes and dynamic loading for "evox" type
+ when a custom database_file_path is specified.
+ """
+ search_type = config.search.type
+
+ if search_type == "evox" and getattr(config.search.database, "database_file_path", None):
+ logger.info(f"Using search strategy from: {config.search.database.database_file_path}")
+ _, program_class = load_database_from_file(config.search.database.database_file_path)
+ return program_class(
+ id=initial_program_id,
+ solution=initial_program_solution,
+ language=config.language,
+ metrics=initial_metrics,
+ iteration_found=start_iteration,
+ )
+
+ program_class = _PROGRAM_REGISTRY.get(search_type, Program)
+ return program_class(
+ id=initial_program_id,
+ solution=initial_program_solution,
+ language=config.language,
+ metrics=initial_metrics,
+ iteration_found=start_iteration,
+ )
+
+
+def setup_search(
+ initial_program_path: str,
+ evaluation_file: str,
+ config_path: str,
+ output_dir: Optional[str] = None,
+ evaluator_env_vars: Optional[Dict[str, str]] = None,
+ parent_llm_config: Optional["LLMConfig"] = None,
+) -> Tuple[DiscoveryControllerInput, str]:
+ """
+ Load config, create database, and build a DiscoveryControllerInput from a config path.
+
+ This is the lightweight alternative to creating a full Runner when
+ you only need the config/database/controller (e.g. for the search side of
+ co-evolution).
+
+ Args:
+ parent_llm_config: If provided, inherit LLM settings (api_base, api_key,
+ models) from the parent config so the search-side evolution uses the
+ same endpoint as the main discovery process.
+
+ Returns:
+ Tuple of (controller_input, initial_program_solution)
+ """
+ config = load_config(config_path)
+
+ # Inherit LLM settings from parent config when provided.
+ # Use the parent's actual model configs (which have the correct per-model
+ # api_base/api_key, e.g. Azure endpoints) rather than the top-level
+ # LLMConfig defaults which may still point to api.openai.com.
+ if parent_llm_config is not None and parent_llm_config.models:
+ import copy
+
+ parent_models = [copy.deepcopy(m) for m in parent_llm_config.models]
+ config.llm.models = parent_models
+ config.llm.evaluator_models = [copy.deepcopy(m) for m in parent_llm_config.models]
+ config.llm.guide_models = [copy.deepcopy(m) for m in parent_llm_config.models]
+ # Sync top-level api_base/api_key from the first parent model
+ config.llm.api_base = parent_models[0].api_base or config.llm.api_base
+ config.llm.api_key = parent_models[0].api_key or config.llm.api_key
+
+ with open(initial_program_path, "r") as f:
+ initial_program_solution = f.read()
+
+ if not config.language:
+ config.language = extract_solution_language(initial_program_solution)
+
+ file_extension = os.path.splitext(initial_program_path)[1] or ".py"
+ if not file_extension.startswith("."):
+ file_extension = f".{file_extension}"
+ if config.file_suffix == ".py":
+ config.file_suffix = file_extension
+
+ database = create_database(config.search.type, config.search.database)
+
+ if not output_dir:
+ output_dir = build_output_dir(config.search.type, initial_program_path)
+
+ controller_input = DiscoveryControllerInput(
+ config=config,
+ evaluation_file=evaluation_file,
+ database=database,
+ file_suffix=config.file_suffix,
+ output_dir=output_dir,
+ evaluator_env_vars=evaluator_env_vars,
+ )
+ return controller_input, initial_program_solution
diff --git a/skydiscover/search/route.py b/skydiscover/search/route.py
new file mode 100644
index 0000000000000000000000000000000000000000..b05ca3ea5a6730b6a017b9b91ede2b958f4d99d5
--- /dev/null
+++ b/skydiscover/search/route.py
@@ -0,0 +1,77 @@
+"""
+Routing for search algorithms.
+
+Maps the ``--search`` flag to the right database, controller, and program class
+at runtime. The registries and factory functions live in ``registry.py``;
+this module wires up implementations and provides ``get_discovery_controller``.
+"""
+
+import logging
+
+from skydiscover.search.adaevolve.controller import AdaEvolveController
+from skydiscover.search.adaevolve.database import AdaEvolveDatabase
+from skydiscover.search.beam_search.database import BeamSearchDatabase
+
+# Algorithm implementations
+from skydiscover.search.best_of_n.database import BestOfNDatabase
+from skydiscover.search.claude_code.controller import ClaudeCodeController
+from skydiscover.search.claude_code.database import ClaudeCodeDatabase
+from skydiscover.search.default_discovery_controller import (
+ DiscoveryController,
+ DiscoveryControllerInput,
+)
+from skydiscover.search.evox.controller import CoEvolutionController
+from skydiscover.search.evox.database.search_strategy_db import SearchStrategyDatabase
+from skydiscover.search.gepa_native.controller import GEPANativeController
+from skydiscover.search.gepa_native.database import GEPANativeDatabase
+from skydiscover.search.openevolve_native.database import OpenEvolveNativeDatabase
+from skydiscover.search.registry import (
+ _CONTROLLER_REGISTRY,
+ register_controller,
+ register_database,
+)
+from skydiscover.search.topk.database import TopKDatabase
+
+logger = logging.getLogger(__name__)
+
+
+######################### ROUTING #########################
+
+
+def get_discovery_controller(controller_input: DiscoveryControllerInput) -> DiscoveryController:
+ """
+ Get the discovery controller for a given search type.
+
+ Returns the registered controller class, or the default DiscoveryController
+ if none is registered.
+ """
+ search_type = controller_input.config.search.type
+ controller_class = _CONTROLLER_REGISTRY.get(search_type, DiscoveryController)
+ logger.debug(f"Using controller {controller_class.__name__} for search type '{search_type}'")
+ return controller_class(controller_input)
+
+
+######################### AUTO-REGISTRATION #########################
+
+register_database("best_of_n", BestOfNDatabase)
+register_database("beam_search", BeamSearchDatabase)
+register_database("topk", TopKDatabase)
+
+# AdaEvolve
+register_database("adaevolve", AdaEvolveDatabase)
+register_controller("adaevolve", AdaEvolveController)
+
+# OpenEvolve Native
+register_database("openevolve_native", OpenEvolveNativeDatabase)
+
+# EvoX
+register_controller("evox", CoEvolutionController)
+register_database("evox_meta", SearchStrategyDatabase)
+
+# GEPA Native: guided evolution with acceptance gating and merge
+register_database("gepa_native", GEPANativeDatabase)
+register_controller("gepa_native", GEPANativeController)
+
+# Claude Code: single-agent baseline running Claude CLI in a container
+register_database("claude_code", ClaudeCodeDatabase)
+register_controller("claude_code", ClaudeCodeController)
diff --git a/skydiscover/utils/__init__.py b/skydiscover/utils/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..341269a8b572783b2bceefca4402c23cdcb333d1
--- /dev/null
+++ b/skydiscover/utils/__init__.py
@@ -0,0 +1,3 @@
+"""
+Shared utilities - metrics, async helpers, prepare, code utils, logging.
+"""
diff --git a/skydiscover/utils/async_utils.py b/skydiscover/utils/async_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..4da2ed47de2c3a1b47c26f8e30d1166748d35cc5
--- /dev/null
+++ b/skydiscover/utils/async_utils.py
@@ -0,0 +1,62 @@
+"""
+Async utilities for SkyDiscover
+"""
+
+import asyncio
+import logging
+from typing import Any, Callable, List, Optional, Sequence, Tuple
+
+logger = logging.getLogger(__name__)
+
+
+class TaskPool:
+ """
+ A simple task pool for managing and limiting concurrent tasks
+ """
+
+ def __init__(self, max_concurrency: int = 10):
+ self.max_concurrency = max_concurrency
+ self._semaphore: Optional[asyncio.Semaphore] = None
+ self.tasks: List[asyncio.Task] = []
+
+ @property
+ def semaphore(self) -> asyncio.Semaphore:
+ """Lazy-initialize the semaphore when first needed."""
+ if self._semaphore is None:
+ self._semaphore = asyncio.Semaphore(self.max_concurrency)
+ return self._semaphore
+
+ async def run(self, coro: Callable, *args: Any, **kwargs: Any) -> Any:
+ """Run a single coroutine function under the concurrency semaphore."""
+ async with self.semaphore:
+ return await coro(*args, **kwargs)
+
+ def create_task(self, coro: Callable, *args: Any, **kwargs: Any) -> asyncio.Task:
+ """Create, track, and return an ``asyncio.Task`` bounded by the pool."""
+ task = asyncio.create_task(self.run(coro, *args, **kwargs))
+ self.tasks.append(task)
+ task.add_done_callback(lambda t: self.tasks.remove(t))
+ return task
+
+ async def gather(
+ self,
+ coros: Sequence[Callable],
+ args_list: Sequence[Tuple[Any, ...]] = (),
+ kwargs_list: Sequence[dict] = (),
+ return_exceptions: bool = False,
+ ) -> List[Any]:
+ """Run *coros* concurrently (bounded by the semaphore), return results in order."""
+ n = len(coros)
+ _args = args_list if args_list else [() for _ in range(n)]
+ _kwargs = kwargs_list if kwargs_list else [{} for _ in range(n)]
+
+ if len(_args) != n:
+ raise ValueError(f"args_list length ({len(_args)}) must match coros length ({n})")
+ if len(_kwargs) != n:
+ raise ValueError(f"kwargs_list length ({len(_kwargs)}) must match coros length ({n})")
+
+ tasks = [
+ self.create_task(coro, *args, **kwargs)
+ for coro, args, kwargs in zip(coros, _args, _kwargs)
+ ]
+ return await asyncio.gather(*tasks, return_exceptions=return_exceptions)
diff --git a/skydiscover/utils/code_utils.py b/skydiscover/utils/code_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..53313c50a85e11014285c6ed5a60ef43ce15d025
--- /dev/null
+++ b/skydiscover/utils/code_utils.py
@@ -0,0 +1,303 @@
+"""
+Utilities for code parsing, diffing, and manipulation
+"""
+
+import os
+import re
+from pathlib import Path
+from typing import List, Optional, Set, Tuple
+
+
+def apply_diff(original_solution: str, diff_text: str) -> str:
+ """
+ Apply a diff to the original code
+
+ Args:
+ original_solution: Original source solution
+ diff_text: Diff in the SEARCH/REPLACE format
+
+ Returns:
+ Modified solution
+ """
+ # Split into lines for easier processing
+ original_lines = original_solution.split("\n")
+ result_lines = original_lines.copy()
+
+ # Extract diff blocks
+ diff_blocks = extract_diffs(diff_text)
+
+ # Apply each diff block
+ for search_text, replace_text in diff_blocks:
+ search_lines = search_text.split("\n")
+ replace_lines = replace_text.split("\n")
+
+ # Find where the search pattern starts in the original solution
+ for i in range(len(result_lines) - len(search_lines) + 1):
+ if result_lines[i : i + len(search_lines)] == search_lines:
+ # Replace the matched section
+ result_lines[i : i + len(search_lines)] = replace_lines
+ break
+
+ return "\n".join(result_lines)
+
+
+def extract_diffs(diff_text: str) -> List[Tuple[str, str]]:
+ """
+ Extract diff blocks from the diff text
+
+ Args:
+ diff_text: Diff in the SEARCH/REPLACE format
+
+ Returns:
+ List of tuples (search_text, replace_text)
+ """
+ diff_pattern = r"<<<<<<< SEARCH\n(.*?)=======\n(.*?)>>>>>>> REPLACE"
+ diff_blocks = re.findall(diff_pattern, diff_text, re.DOTALL)
+ return [(match[0].rstrip(), match[1].rstrip()) for match in diff_blocks]
+
+
+def parse_full_rewrite(llm_response: str, language: str = "python") -> Optional[str]:
+ """
+ Extract a full rewrite from an LLM response
+
+ Args:
+ llm_response: Response from the LLM
+ language: Programming language
+
+ Returns:
+ Extracted code or None if not found
+ """
+ solution_block_pattern = r"```" + language + r"\n(.*?)```"
+ matches = re.findall(solution_block_pattern, llm_response, re.DOTALL)
+
+ if matches:
+ return matches[0].strip()
+
+ # Fallback to any solution block
+ solution_block_pattern = r"```(.*?)```"
+ matches = re.findall(solution_block_pattern, llm_response, re.DOTALL)
+
+ if matches:
+ return matches[0].strip()
+
+ # Fallback to plain text
+ return llm_response
+
+
+def _extract_def_info(solution: str) -> Optional[Tuple[str, str, Optional[str]]]:
+ """
+ Extract function/class name and docstring (or first comment as fallback) from solution block.
+
+ Returns:
+ Tuple of (kind, name, docstring_first_line) or None if not found
+ """
+ # Look for function definition
+ func_match = re.search(r"^\s*def\s+(\w+)\s*\(", solution, re.MULTILINE)
+ if func_match:
+ name = func_match.group(1)
+ # Try to extract docstring, fallback to first comment
+ docstring = _extract_docstring(solution, func_match.end())
+ if not docstring:
+ docstring = _extract_first_comment(solution, func_match.start())
+ return ("function", name, docstring)
+
+ # Look for class definition
+ class_match = re.search(r"^\s*class\s+(\w+)", solution, re.MULTILINE)
+ if class_match:
+ name = class_match.group(1)
+ docstring = _extract_docstring(solution, class_match.end())
+ if not docstring:
+ docstring = _extract_first_comment(solution, class_match.start())
+ return ("class", name, docstring)
+
+ return None
+
+
+def _extract_first_comment(solution: str, func_start: int) -> Optional[str]:
+ """
+ Extract consecutive comment lines inside a function/class body.
+ Used as fallback when no docstring is available.
+ Returns up to 5 lines of comments joined together.
+ """
+ remaining = solution[func_start:]
+ colon_match = re.search(r"(?:\)|[^:]+):\s*\n", remaining)
+ if not colon_match:
+ return None
+
+ # Get the body after the colon
+ body_start = colon_match.end()
+ body = remaining[body_start:]
+
+ # Collect consecutive comment lines
+ comment_lines = []
+ lines = body.split("\n")
+ for line in lines[:10]: # Check first 10 lines for comments
+ stripped = line.strip()
+ if stripped.startswith("#"):
+ # Remove the # and leading space
+ comment_text = stripped[1:].strip()
+ if comment_text:
+ comment_lines.append(comment_text)
+ if len(comment_lines) >= 5: # Max 5 lines
+ break
+ elif stripped and not stripped.startswith("#"):
+ # Hit actual code, stop collecting
+ break
+
+ return "\n".join(comment_lines) if comment_lines else None
+
+
+def _extract_docstring(solution: str, start_pos: int) -> Optional[str]:
+ """
+ Extract first line of docstring after a given position.
+
+ Args:
+ solution: Source code
+ start_pos: Position to start searching from
+ """
+ remaining = solution[start_pos:]
+ docstring_match = re.search(r':\s*\n\s*("""|\'\'\')(.*?)("""|\'\'\')', remaining, re.DOTALL)
+
+ if docstring_match:
+ docstring_content = docstring_match.group(2).strip()
+ return docstring_content.split("\n")[0].strip()
+
+ return None
+
+
+def format_diff_summary(diff_blocks: List[Tuple[str, str]]) -> str:
+ """
+ Create a human-readable summary of the diff.
+
+ If docstrings are identical between old and new code, uses simpler format.
+ If docstrings differ or function is renamed, shows the meaningful change.
+
+ Args:
+ diff_blocks: List of (search_text, replace_text) tuples
+
+ Returns:
+ Summary string
+ """
+ summary = []
+
+ for i, (search_text, replace_text) in enumerate(diff_blocks):
+ search_lines = search_text.strip().split("\n")
+ replace_lines = replace_text.strip().split("\n")
+
+ # Try to extract meaningful info from the solution
+ old_info = _extract_def_info(search_text)
+ new_info = _extract_def_info(replace_text)
+
+ # Build a meaningful summary
+ if old_info or new_info:
+ info = new_info or old_info
+ kind, name, docstring = info
+
+ # Get docstrings from both to compare
+ old_docstring = old_info[2] if old_info else None
+ new_docstring = new_info[2] if new_info else None
+
+ if old_info and new_info and old_info[1] != new_info[1]:
+ # Renamed function/class - always show this
+ desc = f"Renamed {old_info[0]} `{old_info[1]}` → `{new_info[1]}`"
+ elif old_docstring and new_docstring and old_docstring != new_docstring:
+ # Docstrings are DIFFERENT - show the new docstring
+ desc = f"Modified {kind} `{name}`: {new_docstring}"
+ elif old_docstring == new_docstring:
+ # Docstrings are IDENTICAL - use simple format (just line counts)
+ desc = f"Modified {kind} `{name}` ({len(search_lines)}→{len(replace_lines)} lines)"
+ elif docstring:
+ # Only one has docstring
+ desc = f"Modified {kind} `{name}`: {docstring}"
+ else:
+ desc = f"Modified {kind} `{name}` ({len(search_lines)}→{len(replace_lines)} lines)"
+
+ summary.append(f"Change {i+1}: {desc}")
+ elif len(search_lines) == 1 and len(replace_lines) == 1:
+ # Single line change - show the actual change
+ summary.append(
+ f"Change {i+1}: '{search_lines[0].strip()}' → '{replace_lines[0].strip()}'"
+ )
+ else:
+ # Fallback: show first non-empty line as context
+ first_old = next((l.strip() for l in search_lines if l.strip()), "")
+ first_new = next((l.strip() for l in replace_lines if l.strip()), "")
+
+ if first_old and first_new:
+ summary.append(
+ f"Change {i+1}: Near `{first_old[:50]}...` ({len(search_lines)}→{len(replace_lines)} lines)"
+ )
+ else:
+ summary.append(
+ f"Change {i+1}: Replace {len(search_lines)} lines with {len(replace_lines)} lines"
+ )
+
+ return "\n".join(summary)
+
+
+def extract_solution_language(solution: str) -> str:
+ """
+ Try to determine the language of a solution snippet in string format
+
+ Args:
+ solution: Solution snippet
+
+ Returns:
+ Detected language or "text" by default if no language is detected
+ """
+ # Look for common language signatures
+ if re.search(r"^(import|from|def|class)\s", solution, re.MULTILINE):
+ return "python"
+ elif re.search(r"^(package|import java|public class)", solution, re.MULTILINE):
+ return "java"
+ elif re.search(r"^(#include|int main|void main)", solution, re.MULTILINE):
+ return "cpp"
+ elif re.search(r"^(function|var|let|const|console\.log)", solution, re.MULTILINE):
+ return "javascript"
+ elif re.search(r"^(module|fn|let mut|impl)", solution, re.MULTILINE):
+ return "rust"
+ elif re.search(r"^(SELECT|CREATE TABLE|INSERT INTO)", solution, re.MULTILINE):
+ return "sql"
+
+ return "text"
+
+
+def build_repo_map(
+ root: str,
+ *,
+ max_depth: int = 4,
+ allowed_extensions: Tuple[str, ...] = (".py",),
+ excluded_dirs: Tuple[str, ...] = (".git", "__pycache__"),
+) -> str:
+ """Return a depth-limited directory tree of *root* as a string.
+
+ Only files whose extension is in *allowed_extensions* are included.
+ Directories in *excluded_dirs* (and hidden directories) are skipped.
+ Returns an empty string if *root* does not exist or is not a directory.
+ """
+ if not root or not os.path.isdir(root):
+ return ""
+
+ root_path = Path(root).resolve()
+ excluded: Set[str] = set(excluded_dirs)
+ allowed: Set[str] = set(allowed_extensions)
+ lines: List[str] = []
+
+ def walk(directory: Path, prefix: str, depth: int) -> None:
+ if depth > max_depth:
+ return
+ try:
+ entries = sorted(directory.iterdir(), key=lambda p: (p.is_file(), p.name))
+ except PermissionError:
+ return
+ for entry in entries:
+ if entry.name.startswith(".") or entry.name in excluded:
+ continue
+ if entry.is_dir():
+ lines.append(f"{prefix}{entry.name}/")
+ walk(entry, prefix + " ", depth + 1)
+ elif entry.suffix in allowed:
+ lines.append(f"{prefix}{entry.name}")
+
+ walk(root_path, " ", 0)
+ return "\n".join(lines)
diff --git a/skydiscover/utils/metrics.py b/skydiscover/utils/metrics.py
new file mode 100644
index 0000000000000000000000000000000000000000..b3ffa1195b2ebc564f1752c6c9428958c1f01b4f
--- /dev/null
+++ b/skydiscover/utils/metrics.py
@@ -0,0 +1,138 @@
+"""
+Utilities for metric scoring and formatting.
+"""
+
+import math
+from typing import Any, Dict, List, Optional
+
+
+def is_numeric_metric(value: Any) -> bool:
+ """Return True for real numeric values, excluding bools.
+
+ Python's ``bool`` is a subclass of ``int``, so ``isinstance(True, int)``
+ is ``True``. Flag metrics like ``timeout: True`` must not be treated as
+ numeric fitness contributions.
+ """
+ return isinstance(value, (int, float)) and not isinstance(value, bool)
+
+
+def get_score(metrics: Dict[str, Any]) -> float:
+ """Return combined_score if available, otherwise average of all numeric metric values."""
+ if not metrics:
+ return 0.0
+ if "combined_score" in metrics:
+ try:
+ return float(metrics["combined_score"])
+ except (ValueError, TypeError):
+ pass
+ numeric_values = [v for v in metrics.values() if is_numeric_metric(v)]
+ return sum(numeric_values) / len(numeric_values) if numeric_values else 0.0
+
+
+def format_metrics(metrics: Dict[str, Any]) -> str:
+ """Format a metrics dict for logging, handling both numeric and string values."""
+ if not metrics:
+ return ""
+
+ parts = []
+ for name, value in metrics.items():
+ if is_numeric_metric(value):
+ try:
+ parts.append(f"{name}={value:.4f}")
+ except (ValueError, TypeError):
+ parts.append(f"{name}={value}")
+ else:
+ parts.append(f"{name}={value}")
+
+ return ", ".join(parts)
+
+
+def normalize_metric_value(
+ key: str,
+ value: Any,
+ higher_is_better: Dict[str, bool],
+) -> Optional[float]:
+ """Convert a metric to an internal score where larger is always better.
+
+ Args:
+ key: Metric name used to look up direction in *higher_is_better*.
+ value: Raw metric value (must be numeric, else returns ``None``).
+ higher_is_better: Mapping of metric names to direction. Missing keys
+ default to ``True`` (i.e. higher is better).
+
+ Returns:
+ Normalised float (negated when the metric should be minimised), or
+ ``None`` when *value* is not numeric.
+ """
+ if not is_numeric_metric(value):
+ return None
+ normalized = float(value)
+ if math.isnan(normalized):
+ return None
+ if not higher_is_better.get(key, True):
+ normalized = -normalized
+ return normalized
+
+
+def compute_proxy_score(
+ metrics: Dict[str, Any],
+ *,
+ fitness_key: Optional[str] = None,
+ pareto_objectives: Optional[List[str]] = None,
+ higher_is_better: Optional[Dict[str, bool]] = None,
+) -> float:
+ """Compute a scalar proxy score from a metrics dict.
+
+ Implements a single fallback chain used by both the database layer and
+ the prompt/context builder:
+
+ 1. ``fitness_key`` (normalised via *higher_is_better*)
+ 2. ``combined_score`` (taken as-is)
+ 3. Average of normalised *pareto_objectives* values
+ 4. :func:`get_score` (generic numeric average)
+
+ Returns ``-inf`` when *metrics* is empty so callers can distinguish
+ "no data" from "score is zero".
+ """
+ if not metrics:
+ return float("-inf")
+
+ hib = higher_is_better or {}
+
+ if fitness_key is not None:
+ normalized = normalize_metric_value(fitness_key, metrics.get(fitness_key), hib)
+ if normalized is not None:
+ return normalized
+
+ combined_score = metrics.get("combined_score")
+ if is_numeric_metric(combined_score):
+ return float(combined_score)
+
+ if pareto_objectives:
+ objective_values = []
+ for objective in pareto_objectives:
+ normalized = normalize_metric_value(objective, metrics.get(objective), hib)
+ if normalized is not None:
+ objective_values.append(normalized)
+ if objective_values:
+ return sum(objective_values) / len(objective_values)
+
+ return get_score(metrics)
+
+
+def format_improvement(parent_metrics: Dict[str, Any], child_metrics: Dict[str, Any]) -> str:
+ """Format the per-metric delta between parent and child for logging."""
+ if not parent_metrics or not child_metrics:
+ return ""
+
+ parts = []
+ for metric, child_value in child_metrics.items():
+ if metric in parent_metrics:
+ parent_value = parent_metrics[metric]
+ if is_numeric_metric(child_value) and is_numeric_metric(parent_value):
+ try:
+ parts.append(f"{metric}={child_value - parent_value:+.4f}")
+ except (ValueError, TypeError):
+ continue
+
+ return ", ".join(parts)
diff --git a/skydiscover/utils/prepare.py b/skydiscover/utils/prepare.py
new file mode 100644
index 0000000000000000000000000000000000000000..829b03ba2c28d1404c5997f5b14ec546cdd82dc9
--- /dev/null
+++ b/skydiscover/utils/prepare.py
@@ -0,0 +1,99 @@
+"""
+Input preparation utilities for discovery runs.
+
+Handles materializing user-provided programs and evaluators (file paths,
+inline strings, callables) into concrete file paths on disk, plus cleanup
+of any temporary files created in the process.
+"""
+
+import logging
+import os
+import shutil
+import tempfile
+import uuid
+from pathlib import Path
+from typing import Any, Callable, Dict, List, Optional, Union
+
+logger = logging.getLogger(__name__)
+
+
+def prepare_program(
+ initial_program: Union[str, Path, List[str]],
+ temp_dir: Optional[str],
+ temp_files: List[str],
+) -> str:
+ """Resolve initial_program to a file path, writing a temp file if needed."""
+ if isinstance(initial_program, (str, Path)) and os.path.exists(str(initial_program)):
+ return str(initial_program)
+
+ solution = (
+ "\n".join(initial_program) if isinstance(initial_program, list) else str(initial_program)
+ )
+
+ if "EVOLVE-BLOCK-START" not in solution:
+ solution = f"# EVOLVE-BLOCK-START\n{solution}\n# EVOLVE-BLOCK-END"
+
+ if temp_dir is None:
+ temp_dir = tempfile.gettempdir()
+
+ program_file = os.path.join(temp_dir, f"program_{uuid.uuid4().hex[:8]}.py")
+ with open(program_file, "w") as fh:
+ fh.write(solution)
+ temp_files.append(program_file)
+ return program_file
+
+
+def prepare_evaluator(
+ evaluator: Union[str, Path, Callable],
+ temp_dir: Optional[str],
+ temp_files: List[str],
+ caller_module_name: str = "skydiscover.api",
+) -> str:
+ """Resolve evaluator to a file path, writing a temp file if needed.
+
+ When *evaluator* is a callable, it is registered in the caller module's
+ globals so the generated wrapper script can import it at runtime.
+ ``caller_module_name`` must match the module whose globals hold the callable.
+ """
+ if isinstance(evaluator, (str, Path)) and os.path.exists(str(evaluator)):
+ return str(evaluator)
+
+ if callable(evaluator):
+ import sys
+
+ caller_module = sys.modules.get(caller_module_name)
+ evaluator_id = f"_skydiscover_evaluator_{uuid.uuid4().hex[:8]}"
+ if caller_module is not None:
+ setattr(caller_module, evaluator_id, evaluator)
+ evaluator_code = (
+ f"import {caller_module_name} as _api\n\n"
+ f"def evaluate(program_path):\n"
+ f" return getattr(_api, '{evaluator_id}')(program_path)\n"
+ )
+ else:
+ evaluator_code = str(evaluator)
+ if "def evaluate" not in evaluator_code:
+ raise ValueError("Evaluator code must contain a 'def evaluate(program_path)' function")
+
+ if temp_dir is None:
+ temp_dir = tempfile.gettempdir()
+
+ eval_file = os.path.join(temp_dir, f"evaluator_{uuid.uuid4().hex[:8]}.py")
+ with open(eval_file, "w") as fh:
+ fh.write(evaluator_code)
+ temp_files.append(eval_file)
+ return eval_file
+
+
+def cleanup_temp(temp_files: List[str], temp_dir: Optional[str]) -> None:
+ """Best-effort removal of temporary files and directories."""
+ for path in temp_files:
+ try:
+ os.unlink(path)
+ except OSError as exc:
+ logger.warning("Failed to delete temp file %s: %s", path, exc)
+ if temp_dir and os.path.exists(temp_dir):
+ try:
+ shutil.rmtree(temp_dir)
+ except OSError as exc:
+ logger.warning("Failed to delete temp directory %s: %s", temp_dir, exc)
diff --git a/tests/__init__.py b/tests/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/tests/cli/__init__.py b/tests/cli/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/tests/cli/test_checkpoint_discovery.py b/tests/cli/test_checkpoint_discovery.py
new file mode 100644
index 0000000000000000000000000000000000000000..427fc0390939cd320020d1b26975073e610077dc
--- /dev/null
+++ b/tests/cli/test_checkpoint_discovery.py
@@ -0,0 +1,39 @@
+"""Tests for checkpoint discovery helper in the CLI."""
+
+from pathlib import Path
+
+from skydiscover.cli import _find_latest_checkpoint
+
+
+def test_returns_highest_iteration(tmp_path: Path):
+ checkpoint_dir = tmp_path / "checkpoints"
+ checkpoint_dir.mkdir()
+
+ (checkpoint_dir / "checkpoint_2").mkdir()
+ (checkpoint_dir / "checkpoint_10").mkdir()
+ (checkpoint_dir / "checkpoint_1").mkdir()
+
+ latest = _find_latest_checkpoint(str(checkpoint_dir))
+ assert latest == str(checkpoint_dir / "checkpoint_10")
+
+
+def test_ignores_non_numeric_dirs(tmp_path: Path):
+ checkpoint_dir = tmp_path / "checkpoints"
+ checkpoint_dir.mkdir()
+
+ (checkpoint_dir / "latest").mkdir()
+ (checkpoint_dir / "checkpoint_old").mkdir()
+ (checkpoint_dir / "checkpoint_3").mkdir()
+
+ latest = _find_latest_checkpoint(str(checkpoint_dir))
+ assert latest == str(checkpoint_dir / "checkpoint_3")
+
+
+def test_returns_none_without_valid_checkpoints(tmp_path: Path):
+ checkpoint_dir = tmp_path / "checkpoints"
+ checkpoint_dir.mkdir()
+
+ (checkpoint_dir / "latest").mkdir()
+ (checkpoint_dir / "checkpoint_old").mkdir()
+
+ assert _find_latest_checkpoint(str(checkpoint_dir)) is None
diff --git a/tests/config/__init__.py b/tests/config/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/tests/config/test_evaluator_config.py b/tests/config/test_evaluator_config.py
new file mode 100644
index 0000000000000000000000000000000000000000..3af4bd4a72c1166d8b6705c1c287f87cf1dc3975
--- /dev/null
+++ b/tests/config/test_evaluator_config.py
@@ -0,0 +1,11 @@
+"""Tests for EvaluatorConfig defaults."""
+
+from skydiscover.config import EvaluatorConfig
+
+
+class TestEvaluatorConfigDefaults:
+ def test_default_timeout(self):
+ assert EvaluatorConfig().timeout == 360
+
+ def test_default_max_retries(self):
+ assert EvaluatorConfig().max_retries == 3
diff --git a/tests/config/test_llm_params.py b/tests/config/test_llm_params.py
new file mode 100644
index 0000000000000000000000000000000000000000..a79b4bf4beb7bf7f4ac62f0344898f52f9140675
--- /dev/null
+++ b/tests/config/test_llm_params.py
@@ -0,0 +1,124 @@
+"""Tests for LLM config: optional temperature/top_p and api_base routing."""
+
+from dataclasses import fields
+from unittest.mock import AsyncMock, patch
+
+import pytest
+
+from skydiscover.config import LLMConfig, LLMModelConfig
+
+_OPENAI_DEFAULT_API_BASE: str = next(
+ f.default for f in fields(LLMConfig) if f.name == "api_base"
+)
+
+
+class TestLLMConfigDefaults:
+ def test_default_temperature(self):
+ cfg = LLMConfig(name="test-model")
+ assert cfg.temperature == 0.7
+
+ def test_default_top_p_is_none(self):
+ cfg = LLMConfig(name="test-model")
+ assert cfg.top_p is None
+
+ def test_explicit_none_temperature(self):
+ cfg = LLMConfig(name="test-model", temperature=None)
+ assert cfg.temperature is None
+
+ def test_explicit_none_top_p(self):
+ cfg = LLMConfig(name="test-model", top_p=None)
+ assert cfg.top_p is None
+
+ def test_both_none(self):
+ cfg = LLMConfig(name="test-model", temperature=None, top_p=None)
+ assert cfg.temperature is None
+ assert cfg.top_p is None
+
+
+class TestApiBaseRouting:
+ def test_unknown_model_preserves_local_api_base(self):
+ local = "http://localhost:11434/v1"
+ cfg = LLMConfig(
+ name="my-custom-local-model",
+ api_base=local,
+ models=[LLMModelConfig(name="my-custom-local-model")],
+ )
+ assert cfg.models[0].api_base == local
+
+ def test_unknown_model_gets_openai_default(self):
+ cfg = LLMConfig(
+ name="my-custom-local-model",
+ models=[LLMModelConfig(name="my-custom-local-model")],
+ )
+ assert cfg.models[0].api_base == _OPENAI_DEFAULT_API_BASE
+
+ def test_mixed_providers_with_local_api_base(self):
+ cfg = LLMConfig(
+ api_base="http://localhost:11434/v1",
+ models=[
+ LLMModelConfig(name="anthropic/claude-3-sonnet"),
+ LLMModelConfig(name="my-local-model"),
+ ],
+ )
+ assert cfg.models[0].api_base == "https://api.anthropic.com/v1/"
+ assert cfg.models[1].api_base == "http://localhost:11434/v1"
+
+
+class TestOpenAILLMParams:
+ def _make_llm(self, temperature=0.7, top_p=0.95):
+ from skydiscover.llm.openai import OpenAILLM
+
+ cfg = LLMModelConfig(
+ name="test-model",
+ temperature=temperature,
+ top_p=top_p,
+ api_base="http://localhost:1234/v1",
+ api_key="fake",
+ timeout=10,
+ retries=0,
+ retry_delay=0,
+ )
+ with patch("skydiscover.llm.openai.openai.OpenAI"):
+ llm = OpenAILLM(cfg)
+ return llm
+
+ @pytest.mark.asyncio
+ async def test_params_include_temperature_and_top_p(self):
+ llm = self._make_llm(temperature=0.5, top_p=0.9)
+ llm._call_api = AsyncMock(return_value="response")
+ await llm.generate(
+ system_message="sys",
+ messages=[{"role": "user", "content": "user"}],
+ temperature=0.5,
+ top_p=0.9,
+ )
+ params = llm._call_api.call_args[0][0]
+ assert params["temperature"] == 0.5
+ assert params["top_p"] == 0.9
+
+ @pytest.mark.asyncio
+ async def test_params_exclude_none_top_p(self):
+ llm = self._make_llm(top_p=None)
+ llm._call_api = AsyncMock(return_value="response")
+ await llm.generate(system_message="sys", messages=[{"role": "user", "content": "user"}])
+ params = llm._call_api.call_args[0][0]
+ assert "top_p" not in params
+ assert "temperature" in params
+
+ @pytest.mark.asyncio
+ async def test_params_exclude_none_temperature(self):
+ llm = self._make_llm(temperature=None)
+ llm._call_api = AsyncMock(return_value="response")
+ await llm.generate(system_message="sys", messages=[{"role": "user", "content": "user"}])
+ params = llm._call_api.call_args[0][0]
+ assert "temperature" not in params
+ assert "top_p" in params
+
+ @pytest.mark.asyncio
+ async def test_params_exclude_both_none(self):
+ llm = self._make_llm(temperature=None, top_p=None)
+ llm._call_api = AsyncMock(return_value="response")
+ await llm.generate(system_message="sys", messages=[{"role": "user", "content": "user"}])
+ params = llm._call_api.call_args[0][0]
+ assert "temperature" not in params
+ assert "top_p" not in params
diff --git a/tests/evaluation/__init__.py b/tests/evaluation/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/tests/evaluation/test_container_evaluator.py b/tests/evaluation/test_container_evaluator.py
new file mode 100644
index 0000000000000000000000000000000000000000..2738b50bdc980bc8dc7d00a2014a84cfbcdc4243
--- /dev/null
+++ b/tests/evaluation/test_container_evaluator.py
@@ -0,0 +1,145 @@
+"""Tests for ContainerizedEvaluator — pure logic that doesn't need Docker."""
+
+import json
+from unittest.mock import patch
+
+import pytest
+
+from skydiscover.evaluation.container_evaluator import ContainerizedEvaluator
+
+
+@pytest.fixture
+def parse_output():
+ """Return a bound _parse_output method without starting a real container."""
+ inst = object.__new__(ContainerizedEvaluator)
+ return inst._parse_output
+
+
+# ------------------------------------------------------------------
+# _parse_output: success cases
+# ------------------------------------------------------------------
+
+
+class TestParseOutputSuccess:
+ def test_full_valid_response(self, parse_output):
+ stdout = json.dumps({
+ "status": "success",
+ "combined_score": 0.85,
+ "metrics": {"combined_score": 0.85, "accuracy": 0.9, "speed": 0.8},
+ "artifacts": {"feedback": "good job"},
+ })
+ result = parse_output(stdout)
+ assert result.metrics["combined_score"] == 0.85
+ assert result.metrics["accuracy"] == 0.9
+ assert result.metrics["speed"] == 0.8
+ assert result.artifacts["feedback"] == "good job"
+ assert "status" not in result.artifacts
+
+ def test_combined_score_promoted_to_metrics(self, parse_output):
+ stdout = json.dumps({
+ "status": "success",
+ "combined_score": 0.5,
+ "metrics": {"accuracy": 0.5},
+ })
+ result = parse_output(stdout)
+ assert result.metrics["combined_score"] == 0.5
+ assert result.metrics["accuracy"] == 0.5
+
+ def test_no_artifacts(self, parse_output):
+ stdout = json.dumps({
+ "status": "success",
+ "combined_score": 1.0,
+ "metrics": {"combined_score": 1.0},
+ })
+ assert parse_output(stdout).artifacts == {}
+
+ def test_integer_metrics_converted_to_float(self, parse_output):
+ stdout = json.dumps({
+ "status": "success",
+ "combined_score": 1,
+ "metrics": {"n_correct": 5, "n_total": 5},
+ })
+ result = parse_output(stdout)
+ assert result.metrics["n_correct"] == 5.0
+ assert isinstance(result.metrics["n_correct"], float)
+
+ def test_non_numeric_metrics_filtered(self, parse_output):
+ stdout = json.dumps({
+ "status": "success",
+ "combined_score": 0.5,
+ "metrics": {"combined_score": 0.5, "label": "fast", "count": 3},
+ })
+ result = parse_output(stdout)
+ assert "label" not in result.metrics
+ assert result.metrics["count"] == 3.0
+
+ def test_trailing_whitespace_stripped(self, parse_output):
+ stdout = json.dumps({"status": "success", "combined_score": 0.7, "metrics": {}}) + "\n\n"
+ assert parse_output(stdout).metrics["combined_score"] == 0.7
+
+
+# ------------------------------------------------------------------
+# _parse_output: error / edge cases
+# ------------------------------------------------------------------
+
+
+class TestParseOutputErrors:
+ def test_malformed_json(self, parse_output):
+ result = parse_output("not json at all")
+ assert result.metrics["error"] == 0.0
+ assert "raw_output" in result.artifacts
+
+ def test_empty_string(self, parse_output):
+ result = parse_output("")
+ assert result.metrics["error"] == 0.0
+ assert "raw_output" in result.artifacts
+
+ def test_error_status_surfaces_in_artifacts(self, parse_output):
+ stdout = json.dumps({
+ "status": "error",
+ "combined_score": 0.0,
+ "metrics": {"combined_score": 0.0},
+ "artifacts": {"error": "segfault"},
+ })
+ result = parse_output(stdout)
+ assert result.metrics["combined_score"] == 0.0
+ assert result.artifacts["status"] == "error"
+ assert result.artifacts["error"] == "segfault"
+
+ def test_timeout_status(self, parse_output):
+ stdout = json.dumps({"status": "timeout", "combined_score": 0.0, "metrics": {}})
+ assert parse_output(stdout).artifacts["status"] == "timeout"
+
+ def test_missing_status_defaults_to_error(self, parse_output):
+ stdout = json.dumps({"combined_score": 0.5, "metrics": {"combined_score": 0.5}})
+ assert parse_output(stdout).artifacts["status"] == "error"
+
+ def test_missing_combined_score_defaults_to_zero(self, parse_output):
+ stdout = json.dumps({"status": "success", "metrics": {}})
+ assert parse_output(stdout).metrics["combined_score"] == 0.0
+
+ def test_missing_metrics_dict(self, parse_output):
+ stdout = json.dumps({"status": "success", "combined_score": 0.3})
+ assert parse_output(stdout).metrics["combined_score"] == 0.3
+
+ def test_partial_json_truncated(self, parse_output):
+ result = parse_output('{"status": "suc')
+ assert result.metrics["error"] == 0.0
+ assert "raw_output" in result.artifacts
+
+
+# ------------------------------------------------------------------
+# llm_judge attribute
+# ------------------------------------------------------------------
+
+
+class TestLlmJudgeAttribute:
+ def test_init_sets_llm_judge_to_none(self):
+ """ContainerizedEvaluator.__init__ must set self.llm_judge before Docker calls."""
+ with patch.object(ContainerizedEvaluator, "_build_image", return_value="fake:latest"), \
+ patch.object(ContainerizedEvaluator, "_start_container", return_value="abc123"):
+ from skydiscover.config import EvaluatorConfig
+
+ inst = ContainerizedEvaluator.__new__(ContainerizedEvaluator)
+ ContainerizedEvaluator.__init__(inst, "/tmp/fake", EvaluatorConfig())
+ assert inst.llm_judge is None
diff --git a/tests/evaluation/test_evaluation_result.py b/tests/evaluation/test_evaluation_result.py
new file mode 100644
index 0000000000000000000000000000000000000000..46780b2db13f388e60b870665d0c6a4230bc1e07
--- /dev/null
+++ b/tests/evaluation/test_evaluation_result.py
@@ -0,0 +1,27 @@
+"""Tests for EvaluationResult dataclass."""
+
+from skydiscover.evaluation.evaluation_result import EvaluationResult
+
+
+class TestEvaluationResult:
+ def test_from_dict(self):
+ result = EvaluationResult.from_dict({"score": 0.5})
+ assert result.metrics == {"score": 0.5}
+ assert result.artifacts == {}
+
+ def test_to_dict_without_artifacts(self):
+ result = EvaluationResult(metrics={"score": 0.5})
+ assert result.to_dict() == {"score": 0.5}
+
+ def test_to_dict_with_artifacts(self):
+ result = EvaluationResult(
+ metrics={"score": 0.5},
+ artifacts={"log": "ok"},
+ )
+ d = result.to_dict()
+ assert d["score"] == 0.5
+ assert d["artifacts"] == {"log": "ok"}
+
+ def test_default_artifacts_empty(self):
+ result = EvaluationResult(metrics={})
+ assert result.artifacts == {}
diff --git a/tests/evaluation/test_evaluator.py b/tests/evaluation/test_evaluator.py
new file mode 100644
index 0000000000000000000000000000000000000000..5c353f316f347ba15dd602aee0bb1fd72477fc6e
--- /dev/null
+++ b/tests/evaluation/test_evaluator.py
@@ -0,0 +1,56 @@
+"""Tests for Evaluator — result normalization and cascade threshold logic."""
+
+from skydiscover.evaluation.evaluation_result import EvaluationResult
+from skydiscover.evaluation.evaluator import Evaluator
+
+
+def _make_evaluator():
+ """Create an Evaluator without loading an evaluation file."""
+ inst = object.__new__(Evaluator)
+ return inst
+
+
+class TestNormalizeResult:
+ def test_evaluation_result_passthrough(self):
+ inst = _make_evaluator()
+ original = EvaluationResult(metrics={"score": 1.0})
+ assert inst._normalize_result(original) is original
+
+ def test_dict_converted(self):
+ inst = _make_evaluator()
+ result = inst._normalize_result({"accuracy": 0.9, "speed": 0.8})
+ assert isinstance(result, EvaluationResult)
+ assert result.metrics == {"accuracy": 0.9, "speed": 0.8}
+
+ def test_unexpected_type_returns_error(self):
+ inst = _make_evaluator()
+ result = inst._normalize_result("unexpected string")
+ assert result.metrics["error"] == 0.0
+
+ def test_none_returns_error(self):
+ inst = _make_evaluator()
+ result = inst._normalize_result(None)
+ assert result.metrics["error"] == 0.0
+
+
+class TestPassesThreshold:
+ def test_combined_score_above(self):
+ inst = _make_evaluator()
+ assert inst._passes_threshold({"combined_score": 0.8}, 0.5) is True
+
+ def test_combined_score_below(self):
+ inst = _make_evaluator()
+ assert inst._passes_threshold({"combined_score": 0.3}, 0.5) is False
+
+ def test_combined_score_exact(self):
+ inst = _make_evaluator()
+ assert inst._passes_threshold({"combined_score": 0.5}, 0.5) is True
+
+ def test_average_fallback(self):
+ inst = _make_evaluator()
+ # No combined_score — should average all numeric values.
+ assert inst._passes_threshold({"a": 0.6, "b": 0.8}, 0.5) is True
+
+ def test_empty_metrics(self):
+ inst = _make_evaluator()
+ assert inst._passes_threshold({}, 0.5) is False
diff --git a/tests/evaluation/test_harbor_evaluator.py b/tests/evaluation/test_harbor_evaluator.py
new file mode 100644
index 0000000000000000000000000000000000000000..668ced959af0e3a4eeb4f43fe3009060f5580654
--- /dev/null
+++ b/tests/evaluation/test_harbor_evaluator.py
@@ -0,0 +1,367 @@
+"""Tests for HarborEvaluator — solution path extraction, task.toml parsing, reward reading, and detection."""
+
+import json
+import textwrap
+from unittest.mock import patch, MagicMock
+
+import pytest
+
+from skydiscover.config import EvaluatorConfig
+from skydiscover.evaluation import _is_harbor_task, _is_containerized
+from skydiscover.evaluation.harbor_evaluator import HarborEvaluator, _DEFAULT_SOLUTION_PATH
+
+
+def _make_evaluator(task_dir: str) -> HarborEvaluator:
+ """Create a HarborEvaluator without starting Docker."""
+ inst = object.__new__(HarborEvaluator)
+ inst.task_dir = task_dir
+ return inst
+
+
+# ------------------------------------------------------------------
+# task.toml timeout parsing
+# ------------------------------------------------------------------
+
+
+class TestTaskTomlTimeout:
+ def test_reads_verifier_timeout(self, tmp_path):
+ (tmp_path / "task.toml").write_text("[verifier]\ntimeout_sec = 3600\n")
+ inst = _make_evaluator(str(tmp_path))
+ config = EvaluatorConfig()
+ inst._apply_task_toml_timeout(config)
+ assert config.timeout == 3600
+
+ def test_no_task_toml_keeps_default(self, tmp_path):
+ inst = _make_evaluator(str(tmp_path))
+ config = EvaluatorConfig()
+ inst._apply_task_toml_timeout(config)
+ assert config.timeout == 360
+
+ def test_missing_key_keeps_default(self, tmp_path):
+ (tmp_path / "task.toml").write_text("[metadata]\nname = 'test'\n")
+ inst = _make_evaluator(str(tmp_path))
+ config = EvaluatorConfig()
+ inst._apply_task_toml_timeout(config)
+ assert config.timeout == 360
+
+ def test_inline_timeout(self, tmp_path):
+ (tmp_path / "task.toml").write_text("timeout_sec = 1200\n")
+ inst = _make_evaluator(str(tmp_path))
+ config = EvaluatorConfig()
+ inst._apply_task_toml_timeout(config)
+ assert config.timeout == 1200
+
+ def test_malformed_toml_keeps_default(self, tmp_path):
+ (tmp_path / "task.toml").write_bytes(b"\x80\x81\x82")
+ inst = _make_evaluator(str(tmp_path))
+ config = EvaluatorConfig()
+ inst._apply_task_toml_timeout(config)
+ assert config.timeout == 360
+
+
+# ------------------------------------------------------------------
+# Solution path extraction: solve.sh (tier 1)
+# ------------------------------------------------------------------
+
+
+class TestExtractPathFromSolveSh:
+ def _write_solve_sh(self, tmp_path, content: str):
+ solution_dir = tmp_path / "solution"
+ solution_dir.mkdir(exist_ok=True)
+ (solution_dir / "solve.sh").write_text(content)
+ return _make_evaluator(str(tmp_path))
+
+ def test_absolute_cat_redirect(self, tmp_path):
+ inst = self._write_solve_sh(tmp_path, "cat > /app/solver.py << 'EOF'\nprint('hi')\nEOF\n")
+ assert inst._extract_path_from_solve_sh() == "/app/solver.py"
+
+ def test_bare_redirect(self, tmp_path):
+ inst = self._write_solve_sh(tmp_path, "> /workspace/solution.py << 'EOF'\ncode\nEOF\n")
+ assert inst._extract_path_from_solve_sh() == "/workspace/solution.py"
+
+ def test_rust_extension(self, tmp_path):
+ inst = self._write_solve_sh(tmp_path, "cat > /app/src/main.rs << 'EOF'\nfn main(){}\nEOF\n")
+ assert inst._extract_path_from_solve_sh() == "/app/src/main.rs"
+
+ def test_cpp_extension(self, tmp_path):
+ inst = self._write_solve_sh(tmp_path, "cat > /solution/solve.cpp << 'EOF'\nint main(){}\nEOF\n")
+ assert inst._extract_path_from_solve_sh() == "/solution/solve.cpp"
+
+ def test_relative_path_with_cd(self, tmp_path):
+ content = textwrap.dedent("""\
+ #!/bin/bash
+ cd "/workspace/project"
+ cat > src/interfaces/base.rs << 'EOF'
+ code
+ EOF
+ """)
+ inst = self._write_solve_sh(tmp_path, content)
+ assert inst._extract_path_from_solve_sh() == "/workspace/project/src/interfaces/base.rs"
+
+ def test_relative_path_with_variable_assignment(self, tmp_path):
+ content = textwrap.dedent("""\
+ #!/bin/bash
+ RBENCH_DIR="/workspace/rbench_reference"
+ cat > src/main.py << 'EOF'
+ code
+ EOF
+ """)
+ inst = self._write_solve_sh(tmp_path, content)
+ assert inst._extract_path_from_solve_sh() == "/workspace/rbench_reference/src/main.py"
+
+ def test_relative_path_with_dockerfile_workdir(self, tmp_path):
+ (tmp_path / "solution").mkdir()
+ (tmp_path / "solution" / "solve.sh").write_text("cat > solver.py << 'EOF'\ncode\nEOF\n")
+ (tmp_path / "environment").mkdir()
+ (tmp_path / "environment" / "Dockerfile").write_text("FROM python:3.11\nWORKDIR /opt/app\n")
+ inst = _make_evaluator(str(tmp_path))
+ assert inst._extract_path_from_solve_sh() == "/opt/app/solver.py"
+
+ def test_no_solve_sh_returns_empty(self, tmp_path):
+ inst = _make_evaluator(str(tmp_path))
+ assert inst._extract_path_from_solve_sh() == ""
+
+ def test_no_redirect_returns_empty(self, tmp_path):
+ inst = self._write_solve_sh(tmp_path, "#!/bin/bash\necho hello\n")
+ assert inst._extract_path_from_solve_sh() == ""
+
+
+# ------------------------------------------------------------------
+# Solution path extraction: instruction.md (tier 2)
+# ------------------------------------------------------------------
+
+
+class TestExtractPathFromInstruction:
+ def test_backtick_path(self, tmp_path):
+ (tmp_path / "instruction.md").write_text("Write your solution in `/app/solver.py`.\n")
+ inst = _make_evaluator(str(tmp_path))
+ assert inst._extract_path_from_instruction() == "/app/solver.py"
+
+ def test_quoted_path(self, tmp_path):
+ (tmp_path / "instruction.md").write_text('Save your code to "/workspace/solve.py".\n')
+ inst = _make_evaluator(str(tmp_path))
+ assert inst._extract_path_from_instruction() == "/workspace/solve.py"
+
+ def test_preposition_path(self, tmp_path):
+ (tmp_path / "instruction.md").write_text("Place your solution at /opt/solution.py and run it.\n")
+ inst = _make_evaluator(str(tmp_path))
+ assert inst._extract_path_from_instruction() == "/opt/solution.py"
+
+ def test_no_path_returns_empty(self, tmp_path):
+ (tmp_path / "instruction.md").write_text("Solve this problem efficiently.\n")
+ inst = _make_evaluator(str(tmp_path))
+ assert inst._extract_path_from_instruction() == ""
+
+ def test_no_file_returns_empty(self, tmp_path):
+ inst = _make_evaluator(str(tmp_path))
+ assert inst._extract_path_from_instruction() == ""
+
+
+# ------------------------------------------------------------------
+# Full solution path extraction (tier priority)
+# ------------------------------------------------------------------
+
+
+class TestExtractSolutionPath:
+ def test_prefers_solve_sh_over_instruction(self, tmp_path):
+ (tmp_path / "solution").mkdir()
+ (tmp_path / "solution" / "solve.sh").write_text("cat > /from/solve.py << 'EOF'\nEOF\n")
+ (tmp_path / "instruction.md").write_text("Write to `/from/instruction.py`.\n")
+ inst = _make_evaluator(str(tmp_path))
+ assert inst._extract_solution_path() == "/from/solve.py"
+
+ def test_falls_back_to_instruction(self, tmp_path):
+ (tmp_path / "instruction.md").write_text("Write to `/from/instruction.py`.\n")
+ inst = _make_evaluator(str(tmp_path))
+ assert inst._extract_solution_path() == "/from/instruction.py"
+
+ def test_falls_back_to_default(self, tmp_path):
+ inst = _make_evaluator(str(tmp_path))
+ assert inst._extract_solution_path() == _DEFAULT_SOLUTION_PATH
+
+
+# ------------------------------------------------------------------
+# _read_reward
+# ------------------------------------------------------------------
+
+
+def _mock_docker_exec(outputs: dict):
+ """Return a side_effect for subprocess.run that fakes `docker exec ... cat `.
+
+ Args:
+ outputs: mapping from container path to (returncode, stdout) tuples.
+ """
+ def side_effect(cmd, **kwargs):
+ # Detect "docker exec cat " calls.
+ if cmd[:2] == ["docker", "exec"] and "cat" in cmd:
+ path = cmd[-1]
+ if path in outputs:
+ rc, stdout = outputs[path]
+ return MagicMock(returncode=rc, stdout=stdout)
+ return MagicMock(returncode=1, stdout="")
+ return side_effect
+
+
+class TestReadReward:
+ def _make_inst(self):
+ inst = object.__new__(HarborEvaluator)
+ inst.container_id = "fake_container"
+ return inst
+
+ def test_reads_reward_txt(self):
+ inst = self._make_inst()
+ with patch("subprocess.run", side_effect=_mock_docker_exec({
+ "/logs/verifier/reward.json": (1, ""),
+ "/logs/verifier/reward.txt": (0, "0.75\n"),
+ })):
+ result = inst._read_reward()
+ assert result.metrics["combined_score"] == 0.75
+
+ def test_reads_reward_json_with_reward_key(self):
+ inst = self._make_inst()
+ payload = json.dumps({"reward": 0.9, "time_ms": 123})
+ with patch("subprocess.run", side_effect=_mock_docker_exec({
+ "/logs/verifier/reward.json": (0, payload),
+ })):
+ result = inst._read_reward()
+ assert result.metrics["combined_score"] == 0.9
+ assert result.metrics["time_ms"] == 123.0
+
+ def test_reads_reward_json_with_score_key(self):
+ inst = self._make_inst()
+ payload = json.dumps({"score": 0.5})
+ with patch("subprocess.run", side_effect=_mock_docker_exec({
+ "/logs/verifier/reward.json": (0, payload),
+ })):
+ result = inst._read_reward()
+ assert result.metrics["combined_score"] == 0.5
+
+ def test_json_preferred_over_txt(self):
+ inst = self._make_inst()
+ payload = json.dumps({"reward": 0.9})
+ with patch("subprocess.run", side_effect=_mock_docker_exec({
+ "/logs/verifier/reward.json": (0, payload),
+ "/logs/verifier/reward.txt": (0, "0.1\n"),
+ })):
+ result = inst._read_reward()
+ assert result.metrics["combined_score"] == 0.9
+
+ def test_missing_reward_key_defaults_to_zero(self):
+ inst = self._make_inst()
+ payload = json.dumps({"time_ms": 500})
+ with patch("subprocess.run", side_effect=_mock_docker_exec({
+ "/logs/verifier/reward.json": (0, payload),
+ })):
+ result = inst._read_reward()
+ assert result.metrics["combined_score"] == 0.0
+
+ def test_no_reward_files_returns_zero(self):
+ inst = self._make_inst()
+ with patch("subprocess.run", side_effect=_mock_docker_exec({
+ "/logs/verifier/reward.json": (1, ""),
+ "/logs/verifier/reward.txt": (1, ""),
+ })):
+ result = inst._read_reward()
+ assert result.metrics["combined_score"] == 0.0
+ assert "error" in result.artifacts
+
+ def test_malformed_json_falls_back_to_txt(self):
+ inst = self._make_inst()
+ with patch("subprocess.run", side_effect=_mock_docker_exec({
+ "/logs/verifier/reward.json": (0, "{bad json"),
+ "/logs/verifier/reward.txt": (0, "0.42\n"),
+ })):
+ result = inst._read_reward()
+ assert result.metrics["combined_score"] == 0.42
+
+ def test_non_numeric_txt_falls_through(self):
+ inst = self._make_inst()
+ with patch("subprocess.run", side_effect=_mock_docker_exec({
+ "/logs/verifier/reward.json": (1, ""),
+ "/logs/verifier/reward.txt": (0, "not a number"),
+ })):
+ result = inst._read_reward()
+ assert result.metrics["combined_score"] == 0.0
+ assert "error" in result.artifacts
+
+
+# ------------------------------------------------------------------
+# Harbor task detection
+# ------------------------------------------------------------------
+
+
+def _make_harbor_dir(tmp_path):
+ """Create a minimal valid Harbor task directory."""
+ (tmp_path / "instruction.md").write_text("problem")
+ (tmp_path / "tests").mkdir()
+ (tmp_path / "tests" / "test.sh").write_text("#!/bin/bash\n")
+ (tmp_path / "environment").mkdir()
+ (tmp_path / "environment" / "Dockerfile").write_text("FROM python:3.11\n")
+ return str(tmp_path)
+
+
+class TestHarborTaskDetection:
+ def test_valid_harbor_task(self, tmp_path):
+ assert _is_harbor_task(_make_harbor_dir(tmp_path)) is True
+
+ def test_missing_instruction_md(self, tmp_path):
+ (tmp_path / "tests").mkdir()
+ (tmp_path / "environment").mkdir()
+ (tmp_path / "environment" / "Dockerfile").write_text("FROM python:3.11\n")
+ assert _is_harbor_task(str(tmp_path)) is False
+
+ def test_missing_tests_dir(self, tmp_path):
+ (tmp_path / "instruction.md").write_text("problem")
+ (tmp_path / "environment").mkdir()
+ (tmp_path / "environment" / "Dockerfile").write_text("FROM python:3.11\n")
+ assert _is_harbor_task(str(tmp_path)) is False
+
+ def test_missing_test_sh(self, tmp_path):
+ (tmp_path / "instruction.md").write_text("problem")
+ (tmp_path / "tests").mkdir()
+ (tmp_path / "environment").mkdir()
+ (tmp_path / "environment" / "Dockerfile").write_text("FROM python:3.11\n")
+ assert _is_harbor_task(str(tmp_path)) is False
+
+ def test_missing_dockerfile(self, tmp_path):
+ (tmp_path / "instruction.md").write_text("problem")
+ (tmp_path / "tests").mkdir()
+ (tmp_path / "tests" / "test.sh").write_text("#!/bin/bash\n")
+ (tmp_path / "environment").mkdir()
+ assert _is_harbor_task(str(tmp_path)) is False
+
+ def test_not_a_directory(self, tmp_path):
+ f = tmp_path / "not_a_dir"
+ f.write_text("hi")
+ assert _is_harbor_task(str(f)) is False
+
+
+class TestContainerizedDetection:
+ def test_valid_containerized(self, tmp_path):
+ (tmp_path / "Dockerfile").write_text("FROM python:3.11\n")
+ (tmp_path / "evaluate.sh").write_text("#!/bin/bash\n")
+ assert _is_containerized(str(tmp_path)) is True
+
+ def test_missing_evaluate_sh(self, tmp_path):
+ (tmp_path / "Dockerfile").write_text("FROM python:3.11\n")
+ assert _is_containerized(str(tmp_path)) is False
+
+ def test_missing_dockerfile(self, tmp_path):
+ (tmp_path / "evaluate.sh").write_text("#!/bin/bash\n")
+ assert _is_containerized(str(tmp_path)) is False
+
+
+class TestDetectionPriority:
+ """A dir that matches both Harbor and containerized should be detected as Harbor."""
+
+ def test_harbor_wins_over_containerized(self, tmp_path):
+ # Set up Harbor structure.
+ _make_harbor_dir(tmp_path)
+ # Also add containerized markers at root.
+ (tmp_path / "Dockerfile").write_text("FROM python:3.11\n")
+ (tmp_path / "evaluate.sh").write_text("#!/bin/bash\n")
+
+ assert _is_harbor_task(str(tmp_path)) is True
+ assert _is_containerized(str(tmp_path)) is True
+ # create_evaluator checks harbor first — verify the detection functions
+ # agree that both match, confirming the ordering in create_evaluator matters.
diff --git a/tests/search/__init__.py b/tests/search/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/tests/search/test_adaevolve_multiobjective.py b/tests/search/test_adaevolve_multiobjective.py
new file mode 100644
index 0000000000000000000000000000000000000000..3eb46a1e089ba531bd464f3ba2c6447fbee41f2b
--- /dev/null
+++ b/tests/search/test_adaevolve_multiobjective.py
@@ -0,0 +1,795 @@
+"""Tests for AdaEvolve's explicit multiobjective mode."""
+
+import math
+
+import pytest
+
+from skydiscover.config import AdaEvolveDatabaseConfig, Config
+from skydiscover.context_builder.adaevolve import AdaEvolveContextBuilder
+from skydiscover.search.adaevolve.archive.unified_archive import ArchiveConfig, UnifiedArchive
+from skydiscover.search.adaevolve.database import AdaEvolveDatabase
+from skydiscover.search.adaevolve.paradigm.generator import ParadigmGenerator
+from skydiscover.search.base_database import Program
+from skydiscover.utils.metrics import normalize_metric_value
+
+
+def _make_program(program_id: str, **metrics) -> Program:
+ return Program(
+ id=program_id,
+ solution=f"def solve():\n return '{program_id}'\n",
+ metrics=metrics,
+ )
+
+
+def _pareto_db(
+ pareto_objectives=None,
+ higher_is_better=None,
+ fitness_key="accuracy",
+ num_islands=2,
+ **extra,
+):
+ """Shorthand for creating a Pareto-enabled database."""
+ config = AdaEvolveDatabaseConfig(
+ population_size=10,
+ num_islands=num_islands,
+ use_dynamic_islands=False,
+ use_paradigm_breakthrough=False,
+ pareto_objectives=pareto_objectives or ["accuracy", "latency"],
+ higher_is_better=higher_is_better or {"accuracy": True, "latency": False},
+ fitness_key=fitness_key,
+ pareto_objectives_weight=0.4,
+ **extra,
+ )
+ return AdaEvolveDatabase("test", config)
+
+
+def _scalar_db(num_islands=1, **extra):
+ """Shorthand for creating a scalar-mode database."""
+ config = AdaEvolveDatabaseConfig(
+ population_size=10,
+ num_islands=num_islands,
+ use_dynamic_islands=False,
+ use_paradigm_breakthrough=False,
+ **extra,
+ )
+ return AdaEvolveDatabase("test", config)
+
+
+# =========================================================================
+# 1. Core Pareto front logic
+# =========================================================================
+
+
+class TestAdaEvolveMultiobjectiveDatabase:
+ def test_global_pareto_front_and_representative_best(self):
+ db = _pareto_db()
+
+ high_accuracy = _make_program("p1", accuracy=0.95, latency=90.0)
+ low_latency = _make_program("p2", accuracy=0.90, latency=10.0)
+ dominated = _make_program("p3", accuracy=0.80, latency=120.0)
+
+ db.add(high_accuracy, target_island=0)
+ db.add(low_latency, target_island=1)
+ db.add(dominated, target_island=0)
+
+ pareto_ids = {program.id for program in db.get_pareto_front()}
+ assert pareto_ids == {"p1", "p2"}
+ assert {program.id for program in db.get_pareto_front(0)} == {"p1"}
+
+ best = db.get_best_program()
+ assert best is not None
+ assert best.id == "p1"
+ assert db.best_program_id == "p1"
+
+ top_ids = [program.id for program in db.get_top_programs(2)]
+ assert top_ids == ["p1", "p2"]
+
+ def test_scalar_mode_remains_backward_compatible(self):
+ db = _scalar_db()
+
+ worse = _make_program("p1", combined_score=0.1, accuracy=0.9)
+ better = _make_program("p2", combined_score=0.9, accuracy=0.1)
+
+ db.add(worse, target_island=0)
+ db.add(better, target_island=0)
+
+ best = db.get_best_program()
+ assert best is not None
+ assert best.id == "p2"
+ assert db.get_pareto_front(0)[0].id == "p2"
+ assert [program.id for program in db.get_top_programs(2)] == ["p2", "p1"]
+
+ def test_global_top_context_prefers_pareto_front_then_proxy_score(self):
+ db = _pareto_db()
+
+ pareto_a = _make_program("p1", accuracy=0.95, latency=90.0)
+ pareto_b = _make_program("p2", accuracy=0.90, latency=10.0)
+ dominated_but_high_proxy = _make_program("p3", accuracy=0.92, latency=120.0)
+ dominated_low_proxy = _make_program("p4", accuracy=0.70, latency=150.0)
+
+ db.add(pareto_a, target_island=0)
+ db.add(pareto_b, target_island=1)
+ db.add(dominated_but_high_proxy, target_island=0)
+ db.add(dominated_low_proxy, target_island=1)
+
+ selected = db._sample_global_top(exclude_id="missing", n=3)
+ assert [program.id for program in selected] == ["p1", "p2", "p3"]
+
+
+# =========================================================================
+# 2. Pareto front caching
+# =========================================================================
+
+
+class TestGlobalParetoCaching:
+ def test_cache_is_reused_across_calls(self):
+ db = _pareto_db()
+ db.add(_make_program("p1", accuracy=0.95, latency=90.0), target_island=0)
+
+ front_a = db.get_global_pareto_front()
+ front_b = db.get_global_pareto_front()
+ # Same list contents (cached), not recomputed.
+ assert [p.id for p in front_a] == [p.id for p in front_b]
+ assert db._global_pareto_cache_valid is True
+
+ def test_cache_invalidated_on_add(self):
+ db = _pareto_db()
+ db.add(_make_program("p1", accuracy=0.95, latency=90.0), target_island=0)
+
+ # Warm cache
+ front_before = db.get_global_pareto_front()
+ assert len(front_before) == 1
+
+ # Add a dominating program — cache should invalidate and rebuild
+ db.add(_make_program("p2", accuracy=0.96, latency=5.0), target_island=1)
+ front_after = db.get_global_pareto_front()
+
+ # p2 dominates p1, so only p2 is on the front
+ assert [p.id for p in front_after] == ["p2"]
+
+ def test_stale_cache_used_for_previous_front_in_update_best(self):
+ """The _update_best_program method should detect when a new program
+ enters the Pareto front by comparing the stale cache (pre-add) with
+ the freshly computed front (post-add)."""
+ db = _pareto_db()
+
+ db.add(_make_program("p1", accuracy=0.95, latency=90.0), target_island=0)
+ db.add(_make_program("p2", accuracy=0.90, latency=10.0), target_island=1)
+
+ # Warm the cache so the stale snapshot is available
+ db.get_global_pareto_front()
+ front_ids_before = {p.id for p in db.get_global_pareto_front()}
+ assert front_ids_before == {"p1", "p2"}
+
+ # Add a non-dominated program that should enter the front
+ new_prog = _make_program("p3", accuracy=0.93, latency=50.0)
+ # Manually test _update_best_program by simulating the add() flow:
+ db.archives[0].add(new_prog)
+ db.programs[new_prog.id] = new_prog
+ db._invalidate_global_pareto_cache()
+ result = db._update_best_program(new_prog)
+
+ # Should detect a change (p3 entered the front)
+ assert result is True
+ new_front = db.get_global_pareto_front()
+ assert "p3" in {p.id for p in new_front}
+
+ def test_update_best_returns_false_for_dominated_addition(self):
+ """Adding a dominated program should NOT trigger a best change."""
+ db = _pareto_db()
+
+ db.add(_make_program("p1", accuracy=0.95, latency=10.0), target_island=0)
+ # Warm cache
+ db.get_global_pareto_front()
+
+ dominated = _make_program("p_dom", accuracy=0.80, latency=120.0)
+ db.archives[0].add(dominated)
+ db.programs[dominated.id] = dominated
+ db._invalidate_global_pareto_cache()
+ result = db._update_best_program(dominated)
+
+ # p1 is still dominant, representative unchanged
+ assert result is False
+ assert db.best_program_id == "p1"
+
+
+# =========================================================================
+# 3. Dominance logic edge cases
+# =========================================================================
+
+
+class TestDominanceLogic:
+ def test_dominates_basic(self):
+ assert AdaEvolveDatabase._dominates([1.0, 1.0], [0.0, 0.0]) is True
+ assert AdaEvolveDatabase._dominates([0.0, 0.0], [1.0, 1.0]) is False
+
+ def test_equal_vectors_do_not_dominate(self):
+ assert AdaEvolveDatabase._dominates([0.5, 0.5], [0.5, 0.5]) is False
+
+ def test_partial_improvement_does_not_dominate(self):
+ # Better on first, worse on second
+ assert AdaEvolveDatabase._dominates([1.0, 0.0], [0.0, 1.0]) is False
+
+ def test_single_objective_dominance(self):
+ assert AdaEvolveDatabase._dominates([1.0], [0.5]) is True
+ assert AdaEvolveDatabase._dominates([0.5], [1.0]) is False
+ assert AdaEvolveDatabase._dominates([1.0], [1.0]) is False
+
+ def test_mismatched_lengths_raises_error(self):
+ with pytest.raises(ValueError, match="equal length"):
+ AdaEvolveDatabase._dominates([1.0, 2.0], [1.0])
+
+ def test_three_objective_dominance(self):
+ assert AdaEvolveDatabase._dominates([1.0, 1.0, 1.0], [0.5, 0.5, 0.5]) is True
+ # Equal on one axis: still dominates if strictly better on at least one
+ assert AdaEvolveDatabase._dominates([1.0, 0.5, 1.0], [0.5, 0.5, 0.5]) is True
+ # Worse on one axis: not dominant
+ assert AdaEvolveDatabase._dominates([1.0, 0.4, 1.0], [0.5, 0.5, 0.5]) is False
+
+
+# =========================================================================
+# 4. Island-level Pareto front
+# =========================================================================
+
+
+class TestIslandPareto:
+ def test_pareto_front_per_island_with_archives(self):
+ db = _pareto_db(num_islands=2)
+
+ db.add(_make_program("a1", accuracy=0.95, latency=50.0), target_island=0)
+ db.add(_make_program("a2", accuracy=0.90, latency=10.0), target_island=0)
+ db.add(_make_program("a3", accuracy=0.80, latency=100.0), target_island=0) # dominated
+
+ db.add(_make_program("b1", accuracy=0.85, latency=15.0), target_island=1)
+
+ front_0 = db.get_pareto_front(0)
+ front_0_ids = {p.id for p in front_0}
+ assert "a1" in front_0_ids
+ assert "a2" in front_0_ids
+ assert "a3" not in front_0_ids
+
+ front_1 = db.get_pareto_front(1)
+ assert {p.id for p in front_1} == {"b1"}
+
+ def test_global_front_spans_islands(self):
+ db = _pareto_db(num_islands=2)
+
+ db.add(_make_program("a1", accuracy=0.95, latency=50.0), target_island=0)
+ db.add(_make_program("b1", accuracy=0.85, latency=15.0), target_island=1)
+
+ global_front = db.get_pareto_front() # island_idx=None → global
+ global_ids = {p.id for p in global_front}
+ assert global_ids == {"a1", "b1"}
+
+ def test_out_of_bounds_island_returns_empty(self):
+ db = _pareto_db(num_islands=2)
+ assert db.get_pareto_front(99) == []
+
+
+# =========================================================================
+# 5. Proxy score and fitness key fallbacks
+# =========================================================================
+
+
+class TestProxyScoreFallbacks:
+ def test_fitness_key_used_as_proxy(self):
+ db = _pareto_db(fitness_key="accuracy")
+ p = _make_program("p1", accuracy=0.95, latency=50.0)
+ assert db.get_program_proxy_score(p) == 0.95
+
+ def test_fitness_key_none_returns_neg_inf(self):
+ db = _pareto_db(fitness_key="accuracy")
+ assert db.get_program_proxy_score(None) == float("-inf")
+
+ def test_fitness_key_missing_falls_back_to_combined_score(self):
+ db = _pareto_db(fitness_key="nonexistent")
+ p = _make_program("p1", combined_score=0.7, accuracy=0.95)
+ # nonexistent key not in metrics → fall back to combined_score
+ assert db.get_program_proxy_score(p) == 0.7
+
+ def test_minimization_objective_negated_in_proxy(self):
+ db = _pareto_db(fitness_key="latency")
+ p = _make_program("p1", accuracy=0.95, latency=50.0)
+ # latency is higher_is_better=False → negated
+ assert db.get_program_proxy_score(p) == -50.0
+
+ def test_no_fitness_key_averages_objectives(self):
+ db = _pareto_db(fitness_key=None)
+ p = _make_program("p1", accuracy=0.80, latency=20.0)
+ # accuracy(0.80) + (-latency(-20.0)) → average = (0.80 + (-20.0)) / 2
+ expected = (0.80 + (-20.0)) / 2
+ assert abs(db.get_program_proxy_score(p) - expected) < 1e-9
+
+ def test_empty_metrics_returns_neg_inf(self):
+ db = _pareto_db()
+ p = _make_program("p1") # no metrics
+ assert db.get_program_proxy_score(p) == float("-inf")
+
+ def test_scalar_mode_uses_combined_score(self):
+ db = _scalar_db()
+ p = _make_program("p1", combined_score=0.42, accuracy=0.99)
+ assert db.get_program_proxy_score(p) == 0.42
+
+ def test_get_top_programs_with_specific_metric(self):
+ """get_top_programs(metric=...) should sort by that metric, not proxy."""
+ db = _pareto_db(fitness_key="accuracy")
+
+ # p1 has highest accuracy (proxy), p2 has lowest latency (best if minimised)
+ db.add(_make_program("p1", accuracy=0.95, latency=100.0), target_island=0)
+ db.add(_make_program("p2", accuracy=0.80, latency=10.0), target_island=0)
+ db.add(_make_program("p3", accuracy=0.85, latency=50.0), target_island=1)
+
+ # Sort by latency — p2 (10) is best because higher_is_better=False → negated
+ top_by_latency = db.get_top_programs(n=3, metric="latency")
+ assert top_by_latency[0].id == "p2" # lowest latency = best
+
+ # Sort by accuracy — p1 (0.95) is best
+ top_by_accuracy = db.get_top_programs(n=3, metric="accuracy")
+ assert top_by_accuracy[0].id == "p1"
+
+ def test_representative_prefers_newer_on_tie(self):
+ """When proxy score and other signals are equal, newer programs should win.
+
+ Programs are placed on separate islands so their archive-level elite
+ scores are symmetric and the iteration tie-breaker is decisive.
+ """
+ db = _pareto_db(fitness_key="accuracy")
+
+ old = _make_program("aaa_old", accuracy=0.90, latency=10.0)
+ new = _make_program("zzz_new", accuracy=0.90, latency=10.0)
+
+ # Separate islands → each is the only program in its archive,
+ # so crowding distance and elite score are symmetric.
+ db.add(old, iteration=1, target_island=0)
+ db.add(new, iteration=5, target_island=1)
+
+ # Both on global front (identical metrics), newer iteration should win
+ best = db.get_best_program()
+ assert best is not None
+ assert best.id == "zzz_new"
+
+
+# =========================================================================
+# 6. Shared normalize_metric_value utility
+# =========================================================================
+
+
+class TestNormalizeMetricValue:
+ def test_maximize_keeps_value(self):
+ assert normalize_metric_value("acc", 0.9, {"acc": True}) == 0.9
+
+ def test_minimize_negates_value(self):
+ assert normalize_metric_value("latency", 50.0, {"latency": False}) == -50.0
+
+ def test_missing_key_defaults_to_maximize(self):
+ assert normalize_metric_value("unknown", 1.0, {}) == 1.0
+
+ def test_non_numeric_returns_none(self):
+ assert normalize_metric_value("acc", "high", {"acc": True}) is None
+ assert normalize_metric_value("acc", None, {}) is None
+
+ def test_integer_values(self):
+ assert normalize_metric_value("count", 5, {"count": True}) == 5.0
+ assert normalize_metric_value("errors", 3, {"errors": False}) == -3.0
+
+ def test_boolean_values_excluded(self):
+ """bool is a subclass of int in Python; must not be treated as numeric."""
+ assert normalize_metric_value("timeout", True, {}) is None
+ assert normalize_metric_value("success", False, {}) is None
+
+ def test_nan_returns_none(self):
+ """NaN breaks comparison semantics and must not enter objective vectors."""
+ assert normalize_metric_value("acc", float("nan"), {"acc": True}) is None
+ assert normalize_metric_value("latency", float("nan"), {"latency": False}) is None
+
+
+# =========================================================================
+# 7. Unified archive fitness fallbacks
+# =========================================================================
+
+
+class TestUnifiedArchiveFitnessFallbacks:
+ def test_combined_score_is_preferred_over_accuracy_without_fitness_key(self):
+ archive = UnifiedArchive(config=ArchiveConfig())
+ p1 = _make_program("p1", combined_score=0.1, accuracy=0.9)
+ p2 = _make_program("p2", combined_score=0.9, accuracy=0.1)
+
+ archive.add(p1)
+ archive.add(p2)
+
+ assert [program.id for program in archive.get_top_programs(2)] == ["p2", "p1"]
+
+ def test_fitness_key_respects_higher_is_better_for_minimization(self):
+ archive = UnifiedArchive(
+ config=ArchiveConfig(
+ fitness_key="latency",
+ higher_is_better={"latency": False},
+ )
+ )
+ slow = _make_program("slow", latency=120.0, combined_score=0.9)
+ fast = _make_program("fast", latency=10.0, combined_score=0.1)
+
+ archive.add(slow)
+ archive.add(fast)
+
+ assert archive.get_best().id == "fast"
+ assert [program.id for program in archive.get_top_programs(2)] == ["fast", "slow"]
+
+ def test_archive_normalize_delegates_to_shared_utility(self):
+ """Verify archive's _normalize_metric_value uses the shared function."""
+ archive = UnifiedArchive(
+ config=ArchiveConfig(higher_is_better={"loss": False})
+ )
+ assert archive._normalize_metric_value("loss", 5.0) == -5.0
+ assert archive._normalize_metric_value("acc", 0.9) == 0.9
+ assert archive._normalize_metric_value("acc", "string") is None
+
+
+# =========================================================================
+# 8. Prompt builder — Pareto vs scalar mode
+# =========================================================================
+
+
+class TestAdaEvolveMultiobjectivePrompts:
+ def _pareto_builder(self):
+ config = Config.from_dict(
+ {
+ "language": "python",
+ "search": {
+ "type": "adaevolve",
+ "database": {
+ "pareto_objectives": ["accuracy", "latency"],
+ "higher_is_better": {"accuracy": True, "latency": False},
+ "fitness_key": "accuracy",
+ "use_dynamic_islands": False,
+ "use_paradigm_breakthrough": False,
+ },
+ },
+ }
+ )
+ return AdaEvolveContextBuilder(config)
+
+ def _scalar_builder(self):
+ config = Config.from_dict(
+ {
+ "language": "python",
+ "search": {
+ "type": "adaevolve",
+ "database": {
+ "use_dynamic_islands": False,
+ "use_paradigm_breakthrough": False,
+ },
+ },
+ }
+ )
+ return AdaEvolveContextBuilder(config)
+
+ def test_context_builder_uses_pareto_language(self):
+ builder = self._pareto_builder()
+ current = _make_program("parent", accuracy=0.91, latency=25.0)
+ previous = _make_program("child", accuracy=0.89, latency=20.0)
+
+ prompt = builder.build_prompt(
+ current,
+ {
+ "program_metrics": current.metrics,
+ "previous_programs": [previous],
+ },
+ )
+
+ assert "Pareto trade-offs across: accuracy (maximize), latency (minimize)." in prompt["user"]
+ assert "Pareto proxy" in prompt["user"]
+ assert "COMBINED_SCORE" not in prompt["user"]
+
+ def test_scalar_builder_uses_combined_score_language(self):
+ builder = self._scalar_builder()
+ current = _make_program("parent", combined_score=0.5)
+
+ prompt = builder.build_prompt(
+ current,
+ {"program_metrics": current.metrics, "previous_programs": []},
+ )
+
+ assert "COMBINED_SCORE" in prompt["user"]
+ assert "Pareto" not in prompt["user"]
+
+ def test_paradigm_generator_mentions_objectives(self):
+ generator = ParadigmGenerator(
+ llm_pool=None,
+ system_message="Improve the solver.",
+ evaluator_code="def evaluate(path): return {}",
+ objective_names=["accuracy", "latency"],
+ higher_is_better={"accuracy": True, "latency": False},
+ fitness_key="accuracy",
+ )
+
+ prompt = generator._build_prompt(
+ program_solution="def solve(): pass",
+ best_score=0.95,
+ previously_tried=[],
+ )
+
+ assert "Optimize the Pareto trade-offs across: accuracy (maximize), latency (minimize)." in prompt
+ assert '"what_to_optimize": "accuracy, latency"' in prompt
+ assert "combined_score" not in prompt
+
+ def test_paradigm_generator_scalar_mode(self):
+ generator = ParadigmGenerator(
+ llm_pool=None,
+ system_message="Improve the solver.",
+ evaluator_code="def evaluate(path): return {}",
+ )
+
+ prompt = generator._build_prompt(
+ program_solution="def solve(): pass",
+ best_score=0.5,
+ previously_tried=[],
+ )
+
+ assert "Optimize the primary scalar score" in prompt
+ assert "score 0.500000" in prompt or "score: 0.500000" in prompt
+
+
+# =========================================================================
+# 9. Builder progress score edge cases
+# =========================================================================
+
+
+class TestBuilderProgressScore:
+ def _builder(self):
+ config = Config.from_dict(
+ {
+ "language": "python",
+ "search": {
+ "type": "adaevolve",
+ "database": {
+ "pareto_objectives": ["accuracy", "latency"],
+ "higher_is_better": {"accuracy": True, "latency": False},
+ "fitness_key": "accuracy",
+ "use_dynamic_islands": False,
+ "use_paradigm_breakthrough": False,
+ },
+ },
+ }
+ )
+ return AdaEvolveContextBuilder(config)
+
+ def test_empty_metrics_returns_missing_sentinel(self):
+ builder = self._builder()
+ score = builder._get_progress_score({})
+ assert score == builder._PROGRESS_SCORE_MISSING
+ assert math.isinf(score) and score < 0
+
+ def test_fitness_key_used_when_present(self):
+ builder = self._builder()
+ assert builder._get_progress_score({"accuracy": 0.9, "latency": 10.0}) == 0.9
+
+ def test_combined_score_fallback(self):
+ builder = self._builder()
+ # fitness_key="accuracy" but it's not in metrics; combined_score is.
+ assert builder._get_progress_score({"combined_score": 0.42}) == 0.42
+
+ def test_improvement_areas_with_empty_previous_metrics(self):
+ builder = self._builder()
+ current = _make_program("c", accuracy=0.9, latency=10.0)
+ previous_empty = _make_program("prev") # no metrics
+
+ result = builder._identify_improvement_areas(
+ current.solution,
+ current.metrics,
+ [previous_empty],
+ )
+
+ # Should not crash, should show "first measurement" or skip delta
+ assert "Pareto" in result
+ assert "inf" not in result # must not show -inf in prompt text
+
+ def test_determine_outcome_with_missing_metrics(self):
+ builder = self._builder()
+ result = builder._determine_outcome({"accuracy": 0.9}, {})
+ assert "Insufficient" in result
+
+ def test_sibling_context_with_missing_metrics(self):
+ builder = self._builder()
+ parent = _make_program("parent", accuracy=0.9, latency=10.0)
+ empty_child = _make_program("child") # no metrics
+
+ result = builder._format_sibling_context([empty_child], parent)
+ assert result is not None
+ assert "unavailable" in result
+ assert "inf" not in result
+
+
+# =========================================================================
+# 10. Format previous attempts in Pareto mode
+# =========================================================================
+
+
+class TestFormatPreviousAttempts:
+ def _builder(self):
+ config = Config.from_dict(
+ {
+ "language": "python",
+ "search": {
+ "type": "adaevolve",
+ "database": {
+ "pareto_objectives": ["accuracy", "latency"],
+ "higher_is_better": {"accuracy": True, "latency": False},
+ "fitness_key": "accuracy",
+ "use_dynamic_islands": False,
+ "use_paradigm_breakthrough": False,
+ },
+ },
+ }
+ )
+ return AdaEvolveContextBuilder(config)
+
+ def test_format_previous_attempts_pareto_mode(self):
+ builder = self._builder()
+ programs = [
+ _make_program("p1", accuracy=0.80, latency=50.0),
+ _make_program("p2", accuracy=0.90, latency=30.0),
+ _make_program("p3", accuracy=0.85, latency=20.0),
+ ]
+
+ result = builder._format_previous_attempts(programs, num_previous_attempts=3)
+ assert "accuracy" in result
+ assert "latency" in result
+ # Should contain attempt formatting
+ assert "Attempt" in result
+
+ def test_empty_previous_programs(self):
+ builder = self._builder()
+ result = builder._format_previous_attempts([], num_previous_attempts=3)
+ assert "No previous attempts" in result
+
+ def test_previous_attempts_sorted_by_proxy_score(self):
+ builder = self._builder()
+ programs = [
+ _make_program("low", accuracy=0.50, latency=50.0),
+ _make_program("high", accuracy=0.99, latency=50.0),
+ _make_program("mid", accuracy=0.75, latency=50.0),
+ ]
+
+ result = builder._format_previous_attempts(programs, num_previous_attempts=2)
+ # Best 2 by proxy (accuracy) should be selected: 0.99 and 0.75, not 0.50
+ assert "0.9900" in result # high accuracy present
+ assert "0.7500" in result # mid accuracy present
+ assert "0.5000" not in result # low accuracy excluded
+
+
+# =========================================================================
+# 11. Comprehensive iteration stats in Pareto mode
+# =========================================================================
+
+
+class TestComprehensiveStats:
+ def test_pareto_stats_included(self):
+ db = _pareto_db()
+ db.add(_make_program("p1", accuracy=0.95, latency=90.0), target_island=0)
+ db.add(_make_program("p2", accuracy=0.90, latency=10.0), target_island=1)
+
+ stats = db.get_comprehensive_iteration_stats(iteration=1)
+ global_stats = stats["global"]
+
+ assert global_stats["optimization_mode"] == "pareto"
+ assert global_stats["pareto_objectives"] == ["accuracy", "latency"]
+ assert global_stats["global_pareto_front_size"] == 2
+ assert set(global_stats["global_pareto_front_ids"]) == {"p1", "p2"}
+ assert global_stats["fitness_proxy_key"] == "accuracy"
+
+ def test_scalar_stats(self):
+ db = _scalar_db()
+ db.add(_make_program("p1", combined_score=0.5), target_island=0)
+
+ stats = db.get_comprehensive_iteration_stats(iteration=1)
+ global_stats = stats["global"]
+
+ assert global_stats["optimization_mode"] == "scalar"
+ assert global_stats["pareto_objectives"] == []
+ assert global_stats["global_pareto_front_size"] == 0
+
+
+# =========================================================================
+# 12. End-to-end multiobjective flow
+# =========================================================================
+
+
+class TestEndToEndMultiobjective:
+ def test_minimization_objective_sorts_correctly(self):
+ """latency with higher_is_better=False should prefer lower values."""
+ db = _pareto_db(fitness_key="latency")
+
+ fast = _make_program("fast", accuracy=0.80, latency=10.0)
+ slow = _make_program("slow", accuracy=0.80, latency=100.0)
+
+ db.add(fast, target_island=0)
+ db.add(slow, target_island=0)
+
+ # Both on front (different accuracy? no, same accuracy but different latency)
+ # fast dominates slow: same accuracy, lower latency
+ front = db.get_pareto_front()
+ assert {p.id for p in front} == {"fast"}
+
+ # Proxy score uses latency (negated) → fast = -10, slow = -100
+ assert db.get_program_proxy_score(fast) == -10.0
+ assert db.get_program_proxy_score(slow) == -100.0
+
+ def test_three_way_pareto_front(self):
+ """Three mutually non-dominating solutions."""
+ db = _pareto_db(
+ pareto_objectives=["a", "b", "c"],
+ higher_is_better={"a": True, "b": True, "c": True},
+ fitness_key="a",
+ )
+
+ # Each excels on exactly one objective
+ db.add(_make_program("p1", a=1.0, b=0.0, c=0.0), target_island=0)
+ db.add(_make_program("p2", a=0.0, b=1.0, c=0.0), target_island=0)
+ db.add(_make_program("p3", a=0.0, b=0.0, c=1.0), target_island=1)
+
+ front = db.get_global_pareto_front()
+ assert {p.id for p in front} == {"p1", "p2", "p3"}
+
+ def test_adding_dominating_program_evicts_from_front(self):
+ db = _pareto_db()
+
+ db.add(_make_program("p1", accuracy=0.80, latency=50.0), target_island=0)
+ assert {p.id for p in db.get_global_pareto_front()} == {"p1"}
+
+ # p2 dominates p1 on both objectives
+ db.add(_make_program("p2", accuracy=0.90, latency=40.0), target_island=0)
+ assert {p.id for p in db.get_global_pareto_front()} == {"p2"}
+
+ def test_missing_objective_metric_treated_as_worst(self):
+ """Programs with missing objective values get -inf for that dimension,
+ preventing them from accidentally dominating fully-evaluated programs."""
+ db = _pareto_db()
+
+ complete = _make_program("complete", accuracy=0.5, latency=50.0)
+ partial = _make_program("partial", accuracy=0.6) # missing latency
+
+ db.add(complete, target_island=0)
+ db.add(partial, target_island=0)
+
+ vec_complete = db._get_objective_vector(complete)
+ vec_partial = db._get_objective_vector(partial)
+
+ assert vec_complete == [0.5, -50.0] # latency negated
+ assert vec_partial[0] == 0.6
+ assert vec_partial[1] == float("-inf") # missing latency → worst possible
+
+ # complete has latency=-50 > -inf, so partial does NOT dominate complete.
+ # partial has accuracy=0.6 > 0.5, so complete does NOT dominate partial.
+ # Both are on the front (trade-off: complete has real latency, partial has better accuracy).
+ front = db.get_global_pareto_front()
+ assert {p.id for p in front} == {"complete", "partial"}
+
+ def test_sample_global_top_excludes_id(self):
+ db = _pareto_db()
+ db.add(_make_program("p1", accuracy=0.9, latency=10.0), target_island=0)
+ db.add(_make_program("p2", accuracy=0.8, latency=20.0), target_island=1)
+
+ selected = db._sample_global_top(exclude_id="p1", n=10)
+ assert all(p.id != "p1" for p in selected)
+
+ def test_scalar_global_pareto_front_returns_empty(self):
+ """Scalar mode should return empty from get_global_pareto_front."""
+ db = _scalar_db()
+ db.add(_make_program("p1", combined_score=0.5), target_island=0)
+ assert db.get_global_pareto_front() == []
+ assert db.is_multiobjective_enabled() is False
+
+ def test_nan_metric_does_not_dominate_real_solutions(self):
+ """A program with NaN metrics must not enter the Pareto front over real solutions."""
+ db = _pareto_db()
+
+ good = _make_program("good", accuracy=0.9, latency=10.0)
+ nan_prog = _make_program("nan_prog", accuracy=float("nan"), latency=5.0)
+
+ db.add(good, target_island=0)
+ db.add(nan_prog, target_island=1)
+
+ # NaN accuracy → -inf in objective vector, so nan_prog cannot dominate good
+ front = db.get_global_pareto_front()
+ assert "good" in {p.id for p in front}
diff --git a/tests/search/test_evox.py b/tests/search/test_evox.py
new file mode 100644
index 0000000000000000000000000000000000000000..29050a61290973ebcf71a6f44ae7d60a5a71b42d
--- /dev/null
+++ b/tests/search/test_evox.py
@@ -0,0 +1,35 @@
+"""Tests for EvoX search config and utilities."""
+
+from pathlib import Path
+
+from skydiscover.config import Config, SearchConfig
+
+
+class TestSwitchIntervalConfig:
+ def test_default_none(self):
+ assert SearchConfig().switch_interval is None
+
+ def test_from_yaml_dict(self):
+ config = Config.from_dict({"search": {"type": "evox", "switch_interval": 5}})
+ assert config.search.switch_interval == 5
+
+ def test_omitted_stays_none(self):
+ config = Config.from_dict({"search": {"type": "evox"}})
+ assert config.search.switch_interval is None
+
+
+class TestRepoRootResolution:
+ """Verify variation_operator_generator.py uses the correct parents[] index."""
+
+ def _vog_path(self):
+ return (
+ Path(__file__).resolve().parent.parent.parent
+ / "skydiscover" / "search" / "evox" / "utils"
+ / "variation_operator_generator.py"
+ )
+
+ def test_parents4_is_repo_root(self):
+ assert (self._vog_path().parents[4] / "pyproject.toml").exists()
+
+ def test_parents3_is_not_repo_root(self):
+ assert not (self._vog_path().parents[3] / "pyproject.toml").exists()
diff --git a/tests/test_smoke.py b/tests/test_smoke.py
new file mode 100644
index 0000000000000000000000000000000000000000..b9a9132a1c00cf27d352403dde4849793141d362
--- /dev/null
+++ b/tests/test_smoke.py
@@ -0,0 +1,161 @@
+"""Smoke tests for the end-to-end discovery pipeline and unit guards for recent bug fixes."""
+
+import os
+import textwrap
+from typing import Any, Dict, List
+from unittest.mock import patch
+
+import pytest
+
+from skydiscover.api import DiscoveryResult, run_discovery
+from skydiscover.config import Config, LLMModelConfig
+from skydiscover.evaluation.evaluator import Evaluator, EvaluatorConfig
+from skydiscover.llm.base import LLMResponse
+from skydiscover.llm.llm_pool import LLMPool
+
+# ---------------------------------------------------------------------------
+# Inline evaluator source — scores programs with `def solve` higher
+# ---------------------------------------------------------------------------
+EVALUATOR_SOURCE = textwrap.dedent("""\
+ import ast
+
+ def evaluate(program_path: str) -> dict:
+ with open(program_path, "r") as f:
+ source = f.read()
+
+ score = 0.1 # baseline for any non-empty program
+ try:
+ tree = ast.parse(source)
+ # reward programs that define a `solve` function
+ for node in ast.walk(tree):
+ if isinstance(node, ast.FunctionDef) and node.name == "solve":
+ score = 0.8
+ break
+ except SyntaxError:
+ score = 0.0
+
+ return {"combined_score": score}
+ """)
+
+# ---------------------------------------------------------------------------
+# Inline seed program — intentionally does NOT define `solve` so it scores low
+# ---------------------------------------------------------------------------
+SEED_SOURCE = textwrap.dedent("""\
+ def hello():
+ return "hi"
+ """)
+
+# ---------------------------------------------------------------------------
+# Mock LLM response — a full-rewrite code block containing `def solve`
+# ---------------------------------------------------------------------------
+MOCK_LLM_CODE = textwrap.dedent("""\
+ def solve(x):
+ return x ** 2 + 1
+ """)
+
+MOCK_RESPONSE_TEXT = f"```python\n{MOCK_LLM_CODE}```"
+
+
+# ---------------------------------------------------------------------------
+# FakeLLMPool — replaces the real LLMPool so no OpenAI client is created
+# ---------------------------------------------------------------------------
+class FakeLLMPool:
+ """Drop-in replacement for LLMPool that returns a canned response."""
+
+ def __init__(self, models_cfg: List[LLMModelConfig]):
+ # Intentionally do NOT create real clients.
+ self.models_cfg = models_cfg
+
+ async def generate(
+ self, system_message: str, messages: List[Dict[str, Any]], **kwargs
+ ) -> LLMResponse:
+ return LLMResponse(text=MOCK_RESPONSE_TEXT)
+
+ async def generate_all(
+ self, system_message: str, messages: List[Dict[str, Any]], **kwargs
+ ) -> List[LLMResponse]:
+ return [LLMResponse(text=MOCK_RESPONSE_TEXT)]
+
+
+# ===========================================================================
+# Smoke test: end-to-end pipeline with mocked LLM
+# ===========================================================================
+class TestSmokePipeline:
+ def test_run_discovery_returns_result(self, tmp_path):
+ """run_discovery completes 2 iterations and returns a valid DiscoveryResult."""
+
+ # Write evaluator and seed program to tmp_path
+ evaluator_file = tmp_path / "evaluator.py"
+ evaluator_file.write_text(EVALUATOR_SOURCE)
+
+ seed_file = tmp_path / "seed.py"
+ seed_file.write_text(SEED_SOURCE)
+
+ output_dir = str(tmp_path / "output")
+
+ config = Config.from_dict(
+ {
+ "max_iterations": 2,
+ "diff_based_generation": False,
+ "monitor": {"enabled": False},
+ "search": {"type": "topk"},
+ "evaluator": {"evaluation_file": str(evaluator_file)},
+ "llm": {
+ "models": [
+ {"name": "fake-model", "api_key": "fake", "api_base": "http://localhost:1"}
+ ],
+ },
+ }
+ )
+
+ with patch(
+ "skydiscover.search.default_discovery_controller.LLMPool",
+ FakeLLMPool,
+ ):
+ result = run_discovery(
+ evaluator=str(evaluator_file),
+ initial_program=str(seed_file),
+ config=config,
+ output_dir=output_dir,
+ cleanup=False,
+ )
+
+ # Assertions
+ assert isinstance(result, DiscoveryResult)
+ assert result.best_score >= 0.8 # mock LLM produces `def solve` → scored 0.8
+ assert "def solve" in result.best_solution
+ assert os.path.isdir(output_dir)
+
+
+# ===========================================================================
+# Unit guards for recent bug fixes
+# ===========================================================================
+class TestBugFixGuards:
+ def test_llm_pool_raises_on_zero_weights(self):
+ """LLMPool must raise ValueError when all model weights are zero."""
+ cfgs = [
+ LLMModelConfig(name="m1", weight=0.0, api_key="k", api_base="http://x"),
+ LLMModelConfig(name="m2", weight=0.0, api_key="k", api_base="http://x"),
+ ]
+ with pytest.raises(ValueError, match="weights"):
+ LLMPool(cfgs)
+
+ def test_llm_pool_raises_on_negative_weight(self):
+ """LLMPool must raise ValueError when any model weight is negative."""
+ cfgs = [
+ LLMModelConfig(name="m1", weight=-1.0, api_key="k", api_base="http://x"),
+ LLMModelConfig(name="m2", weight=2.0, api_key="k", api_base="http://x"),
+ ]
+ with pytest.raises(ValueError, match="weights"):
+ LLMPool(cfgs)
+
+ def test_evaluator_unique_module_names(self, tmp_path):
+ """Two Evaluator instances for the same file must get distinct _module_name values."""
+ eval_file = tmp_path / "eval.py"
+ eval_file.write_text("def evaluate(program_path):\n return {'combined_score': 1.0}\n")
+
+ cfg = EvaluatorConfig(evaluation_file=str(eval_file))
+ ev1 = Evaluator(config=cfg)
+ ev2 = Evaluator(config=cfg)
+
+ assert ev1._module_name != ev2._module_name
diff --git a/tests/test_time_instrumentation.py b/tests/test_time_instrumentation.py
new file mode 100644
index 0000000000000000000000000000000000000000..e8f4cb99db9bae39e9911d4b5617d117e8036321
--- /dev/null
+++ b/tests/test_time_instrumentation.py
@@ -0,0 +1,51 @@
+"""Tests for LLM generation time and evaluation time instrumentation."""
+
+from skydiscover.search.utils.discovery_utils import SerializableResult
+
+
+class TestSerializableResultTimeFields:
+ """Verify time instrumentation fields on SerializableResult."""
+
+ def test_default_values(self):
+ result = SerializableResult()
+ assert result.iteration_time == 0.0
+ assert result.llm_generation_time == 0.0
+ assert result.eval_time == 0.0
+
+ def test_explicit_values(self):
+ result = SerializableResult(
+ iteration_time=10.5,
+ llm_generation_time=8.2,
+ eval_time=2.1,
+ )
+ assert result.iteration_time == 10.5
+ assert result.llm_generation_time == 8.2
+ assert result.eval_time == 2.1
+
+ def test_error_before_eval(self):
+ """LLM succeeds but evaluation never runs."""
+ result = SerializableResult(
+ llm_generation_time=5.0,
+ error="Parse failed",
+ )
+ assert result.llm_generation_time == 5.0
+ assert result.eval_time == 0.0
+ assert result.iteration_time == 0.0
+
+ def test_error_before_llm(self):
+ """Iteration fails before LLM call (e.g. prompt building)."""
+ result = SerializableResult(
+ error="No parent found",
+ )
+ assert result.llm_generation_time == 0.0
+ assert result.eval_time == 0.0
+ assert result.iteration_time == 0.0
+
+ def test_iteration_time_without_breakdown(self):
+ """Old-style result with only iteration_time set."""
+ result = SerializableResult(
+ iteration_time=12.0,
+ )
+ assert result.iteration_time == 12.0
+ assert result.llm_generation_time == 0.0
+ assert result.eval_time == 0.0
diff --git a/uv.lock b/uv.lock
new file mode 100644
index 0000000000000000000000000000000000000000..2816453018aea3114b21d54cce5fc3901b7107d7
--- /dev/null
+++ b/uv.lock
@@ -0,0 +1,6014 @@
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+]
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