clean: remove stale reports and results files"

LAUNCH_READINESS.md

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-# LAUNCH READINESS REPORT — Purpose Agent v2.0.0
-
-**Date:** 2025-04-30
-**Package:** [pypi.org/project/purpose-agent/2.0.0](https://pypi.org/project/purpose-agent/2.0.0/)
-**Repository:** [huggingface.co/Rohan03/purpose-agent](https://huggingface.co/Rohan03/purpose-agent)
-
----
-
-## VERDICT: ✅ READY FOR LAUNCH
-
-**119 tests. 0 failures. 100% pass rate.**
-
----
-
-## Test Results by Section
-
-### Section 1: Smoke Tests (52/52 ✓)
-All 33 modules import cleanly. All 19 core classes instantiate without errors.
-
-### Section 2: Functional Tests (14/14 ✓)
-| Test | Result |
-|------|--------|
-| Full orchestrator loop completes | ✓ |
-| Trajectory has steps | ✓ |
-| Φ_before in [0,10] | ✓ |
-| Φ_after in [0,10] | ✓ |
-| Confidence in [0,1] | ✓ |
-| Optimizer produces heuristics | ✓ |
-| Experience Replay store/retrieve/clear | ✓ ✓ ✓ |
-| Strip `<think>` tags (4 variants) | ✓ ✓ ✓ ✓ |
-| Multi-provider routing (ollama:, auto-detect) | ✓ ✓ |
-
-### Section 3: Tools Security (5/5 ✓)
-| Test | Result |
-|------|--------|
-| Calculator: 2+3*4=14 | ✓ |
-| Calculator: sqrt(16)=4.0 | ✓ |
-| Calculator blocks `__import__` | ✓ |
-| ReadFile blocks /etc/passwd | ✓ |
-| WriteFile blocks /tmp/evil | ✓ |
-
-### Section 4: V2 Kernel (16/16 ✓)
-| Test | Result |
-|------|--------|
-| RunMode: TRAIN allows write | ✓ |
-| RunMode: EVAL blocks write | ✓ |
-| RunMode: EVAL is_eval | ✓ |
-| Trace: events recorded + JSONL roundtrip | ✓ ✓ |
-| Memory: 7 kinds, 5 statuses, scoped retrieve | ✓ ✓ ✓ |
-| Compiler: respects budget, returns memory IDs | ✓ ✓ |
-| Immune: safe passes, injection/hack/leak/misuse blocked | ✓ ✓ ✓ ✓ ✓ |
-| Memory CI: quarantine, promote, reject | ✓ ✓ ✓ |
-
-### Section 5: Unified Capabilities (10/10 ✓)
-| Capability | Source Framework | Test | Result |
-|-----------|-----------------|------|--------|
-| Agent (plug-and-play) | OpenAI Agents SDK | run() completes | ✓ |
-| Graph (control flow) | LangGraph | Conditional routing works | ✓ |
-| Parallel (speed) | CrewAI | 3 parallel tasks complete | ✓ |
-| Conversation (talking) | AutoGen | Messages produced | ✓ |
-| KnowledgeStore (RAG) | LlamaIndex | store + query + as_tool | ✓ ✓ ✓ |
-| Easy API | — | purpose() auto-detects teams | ✓ ✓ ✓ |
-
-### Section 6: Research Papers (8/8 ✓)
-All 5 research modules import. PromptOptimizer compiles prompts. LLMCompiler plans and executes parallel tool calls.
-
-### Section 7: Breakthroughs (8/8 ✓)
-| Breakthrough | Test | Result |
-|-------------|------|--------|
-| B2: MoH | 2 shared + 3 routed = 5 active | ✓ |
-| B6: Adversarial | 95% catch rate, 0% false positive | ✓ |
-| Robust Parser | TOML + JSON + code extraction | ✓ ✓ ✓ ✓ |
-
-### Section 8: Benchmark (2/2 ✓)
-| Metric | Value |
-|--------|-------|
-| Improvement curve | Φ: 1.0 → 10.0 → 10.0 |
-| Heuristics learned | 6 |
-
----
-
-## Real Model Validation
-
-Tested with **Llama-3.3-70B-Instruct** via **OpenRouter**:
-
-| Task | Run 1 | Run 2 | Run 3 | Heuristics |
-|------|-------|-------|-------|------------|
-| fibonacci | ✓ ALL PASS | ✓ ALL PASS | ✓ ALL PASS | 0→5→11→20 |
-| fizzbuzz | ✓ ALL PASS | ✓ ALL PASS | ✓ ALL PASS | 0→3→9→18 |
-
-Self-improving critic (B1) produced 2 calibration examples in 2 runs.
-
----
-
-## Framework Statistics
-
-| Metric | Value |
-|--------|-------|
-| Total modules | 34 Python files |
-| Total size | ~500KB |
-| PyPI package | 142KB wheel |
-| Exports | 103 public symbols |
-| External dependencies (core) | 0 (stdlib only) |
-| Research papers implemented | 13 |
-| Breakthroughs | 6 |
-| Providers supported | 8+ (OpenRouter, Groq, OpenAI, Ollama, HF, Together, Fireworks, etc.) |
-| Tests | 119 pass, 0 fail |
-| Immune catch rate | 95% adversarial, 0% false positive |
-
----
-
-## What the Open Source Community Can Use It For
-
-### Immediate Use Cases (works today)
-1. **Build self-improving coding assistants** — agents that get better at writing code with each task
-2. **Create knowledge-aware chatbots** — RAG-as-a-tool with automatic learning
-3. **Run multi-agent teams** — researcher + coder + reviewer that share learned knowledge
-4. **Local-first AI** — runs entirely on laptop with Ollama, zero cloud cost
-
-### For Researchers
-1. **Implement and test agent self-improvement hypotheses** — the Purpose-MDP formalism with proven convergence
-2. **Benchmark the Φ improvement curve** — cold/warm/ablation/transfer tests built in
-3. **Test memory safety** — immune system with 95% adversarial catch rate
-
-### For Production
-1. **Evidence-gated learning** — memories only promoted after immune scan + replay test
-2. **Honest evaluation** — RunMode.EVAL_TEST guarantees zero memory writes during benchmarking
-3. **8+ provider support** — switch between local/cloud models with one string change
-
----
-
-## Install
-
-```bash
-pip install purpose-agent
-```
-
-```python
-import purpose_agent as pa
-
-team = pa.purpose("Help me write Python code")
-result = team.run("Write a fibonacci function")
-print(result)
-```

tests/results/REAL_WORLD_RESULTS.md

@@ -1,32 +0,0 @@
-# Real-World Regression Test Results
-
-## Models Tested
-- **Llama-3.3-70B-Instruct** via OpenRouter
-- **Gemma-4-26B** via OpenRouter
-
-## Results: 10/10 PASS across both models
-
-### Llama-3.3-70B (6/6 ✓)
-| Domain | Test | Result | Time |
-|--------|------|--------|------|
-| Coding | fibonacci (4 tests) | ✓ 100% | 7s |
-| Coding | fizzbuzz (4 tests) | ✓ 100% | 8s |
-| Coding | factorial (3 tests) | ✓ 100% | 8s |
-| Learning | heuristic_growth | ✓ [3→9→18] | - |
-| Security | catch_rate | ✓ 93% | - |
-| Security | false_positive | ✓ 0% | - |
-
-### Gemma-4-26B (4/4 ✓)
-| Domain | Test | Result | Time |
-|--------|------|--------|------|
-| Coding | fibonacci (4 tests) | ✓ 100% | 9s |
-| Coding | fizzbuzz (4 tests) | ✓ 100% | 7s |
-| Coding | factorial (3 tests) | ✓ 100% | 10s |
-| Learning | heuristic_growth | ✓ [3→6→11] | - |
-
-## Key Findings
-1. **Both models produce correct code** on first attempt for all coding tasks
-2. **Heuristic library grows** across runs: Llama 3→9→18, Gemma 3→6→11
-3. **Universal parser works** — no JSON requirement, handles both models' output formats
-4. **Immune system** catches 93% of adversarial content with 0% false positives
-5. **Average response time** ~8 seconds per task (includes actor + critic + optimizer calls)