Upload reports/final_status.md

reports/final_status.md

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+# OCC Stack: Final Status
+
+## What Ships
+
+### ✅ Done (production quality)
+1. **Impact Oracle** (`oracle/oracle.py`) — Rule-based scoring for code/QA/debate. Detects hidden-test gaming, rewards abstention, Brier-score calibration, compute-cost penalty.
+2. **Credit Ledger** (`ledger/ledger.py`) — Non-transferable, decaying, capability-scoped credits with full provenance.
+3. **Resource Broker** (`broker/broker.py`) — Capability-based gating with 6 decision types and risk classes.
+4. **GRPO/RL Hook** (`rl/reward.py`, `rl/grpo_hook.py`) — TRL-compatible reward function + offline comparator.
+5. **Literature Review** (`reports/literature_review.md` + RS-OS paper comparison in `reports/report.md`).
+6. **Blog Post** (`reports/blog_post.md`).
+
+### ✅ Done (simulated benchmarks)
+1. **Code Compute Allocation** (`benchmarks/benchmark_code.py`) — 52.3% compute savings at iso-accuracy.
+2. **Retrieval QA** (`benchmarks/benchmark_retrieval_qa.py`, `_nli.py`) — OCC underperforms, honest negative result.
+3. **Debate v2** (`benchmarks/benchmark_debate_v2.py`) — 43.2% savings at iso-accuracy, adversarial containment.
+4. **Anti-Gaming** (`eval_runner.py`) — 100% hidden-test detection, credit exhaustion for spam.
+5. **Ablations** — 10 ablations measuring each mechanism's contribution.
+
+### ✅ Done (external validation)
+- Debate v2 job `69fa273ab745af80fb373135`: **COMPLETED**. Results at `reports/debate_v2_results.json`.
+  - OCC: 0.930 accuracy, 2,890 mean compute → **43.2% savings** vs equal turns (5,087)
+  - Confidence-weighted voting with adversarial agents: dangerous (amplifies overconfident wrong answers)
+
+### ⚠️ Blocked (real LLM)
+- 4 GPU jobs attempted, 4 failed due to model capability or infrastructure:
+  1. Qwen-Coder-0.5B: chat template mismatch → all answers wrong
+  2. Qwen-Coder-0.5B v2: chat template fixed, model generates code but 0% pass rate (0.5B too weak for HumanEval)
+  3. Qwen-Coder-0.5B v3: robust extraction, same 0% pass rate (model capability floor)
+  4. StarCoder2-3B: model loading timed out before generation (3B download too slow on provisioned T4)
+
+### ❌ Not Done
+- GRPO training (needs GPU + TRL, not attempted due to sandbox rate-limiting)
+- Retrieval QA with domain-tuned NLI
+- Real LLM results for code benchmark
+
+## Key Numbers
+
+| Benchmark | OCC Result | Best Baseline | Savings |
+|-----------|-----------|---------------|---------|
+| Code allocation (sim) | 0.780 acc, 8,350 tokens | 0.780 acc, 17,500 tokens | 52.3% |
+| Debate v2 (40% adversarial) | 0.930 acc, 2,890 tokens | 0.930 acc, 5,087 tokens | 43.2% |
+| Anti-gaming detection | 100% | — | — |
+| Retrieval QA | 0.710 acc | 0.790 (RAG+verifier) | OCC loses |
+
+## Honest Bottom Line
+
+OCC works for code allocation and debate — the mechanisms (tiered escalation, credit-based turn allocation) are sound and backed by published literature. The real-LLM validation is the missing piece, blocked by model choice (0.5B too weak) and infrastructure (3B download timing). The system design, anti-gaming properties, and literature positioning are solid enough for a workshop paper.
+
+## Repository
+
+https://huggingface.co/narcolepticchicken/occ-stack