| ---
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| language: en
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| license: apache-2.0
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| tags:
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| - reasoning
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| - agent
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| - world-model
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| - long-horizon
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| - code
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| - proof-of-concept
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| datasets:
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| - Qwen/AgentWorldBench
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| - datacurve/deep-swe
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| metrics:
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| - pass@1
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| model_name: OMEGA-Reasoner
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| ---
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|
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| # OMEGA-Reasoner (POC)
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|
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| ⚠️ **This is a proof-of-concept** — a small-scale demonstration of a
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| self-contained LLM-driven agent framework. It is **not** a frontier
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| model and is **not** intended to compete with production models on
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| public leaderboards such as DeepSWE.
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|
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| ## Honest positioning
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|
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| | Property | This repo | DeepSeek-V4.1-Flash (Top-1 DeepSWE) |
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| |---|---|---|
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| | Parameters | ≈ 100K (configurable) | 35B-class |
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| | Training data | none (synthetic / hand-coded) | trillions of tokens |
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| | Pass@1 on DeepSWE | **not measured** | 74.2 |
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| | Status | research POC | production model |
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| We do not submit to the official DeepSWE leaderboard. The integration
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| code (`deep_swe.py`) is provided for educational and self-evaluation
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| purposes only.
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|
|
| ## What this model is
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|
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| `OMEGA-Reasoner` is a self-contained **language world model** + **autonomous
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| agent** framework. It implements:
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| - HMoE-D reasoner: hierarchical mixture-of-experts with dynamic routing
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| - AGI_AgentWorld: full cognitive agent with episodic / semantic / procedural
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| memory, ReAct loop, Tree-of-Thoughts planning, Reflexion-style
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| self-correction, and constitutional guardrails
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| - AgentWorldBench adapter: 5-dim scoring (format, factuality, consistency,
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| realism, quality)
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| - DeepSWE adapter: Harbor-format task loader + verifier runner
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|
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| ## Why this repo exists
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|
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| To show that a complete agent framework — including memory, planning,
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| tool-use, self-correction, and benchmark adapters — can be implemented
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| from scratch without any external LLM API. It is meant as a research
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| artifact, not a competitive submission.
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|
|
| ## How to use
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|
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| ```python
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| from evo.agi_agent_world import AGIAgentWorld
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| from evo.agentworld_bench import load_all, WorldModelAgent, evaluate
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| from evo.deep_swe import load_all_tasks, run_benchmark
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|
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| # 1) Run a single goal
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| agi = AGIAgentWorld()
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| result = agi.run("Buy coffee by noon")
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|
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| # 2) Evaluate on AgentWorldBench (synthetic data)
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| records = load_all("data/agentworldbench_synth")
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| rep = evaluate(WorldModelAgent(), records)
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| print(rep.overall)
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|
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| # 3) Run on DeepSWE tasks (synthetic seed data)
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| tasks = load_all_tasks("data/deepswe_synth")
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| summary = run_benchmark(tasks)
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| print(summary.pass_at_1)
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| ```
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|
|
| ## Files
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|
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| - `agi_agent_world.py` — autonomous agent (memory, planning, tools)
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| - `omega_arch.py` — HMoE-D model definition
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| - `omega_nlu.py` — NLU engine (sentences, frames, FOL)
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| - `omega_math.py` — math reasoning primitives
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| - `omega_physics.py` — physics reasoner
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| - `omega_meta.py` — meta-cognition / audit
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| - `omega_quant.py` — quantization utilities
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| - `agentworld_bench.py` — AgentWorldBench loader + 5-dim scorer
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| - `deep_swe.py` — DeepSWE loader + verifier
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| - `tests/` — unit tests (78+ tests, all passing)
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|
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| ## Test results
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|
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| The repository ships with 78+ unit tests covering metrics, agents,
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| benchmarks, and the cognitive core. All tests pass on the included
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| synthetic fixtures.
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|
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| ## Limitations
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|
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| - The underlying HMoE-D model is a **demonstration** with no language
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| pretraining. Its `predict()` is template-based, not learned.
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| - `Pass@1` on DeepSWE is **not** claimed — the agent is heuristic.
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| - Numerical / symbolic reasoning is rule-based, not learned.
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|
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| ## Citation
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|
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| If you use this code in research, please cite the framework only
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| (no leaderboard claim):
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|
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| ```bibtex
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| @software{omega_reasoner_poc,
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| title = {OMEGA-Reasoner: a self-contained LLM-agent framework (POC)},
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| year = {2026},
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| url = {https://huggingface.co/<your-org>/omega-reasoner-poc},
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| }
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| ```
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|
|
| ## License
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|
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| Apache-2.0.
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|
|
