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README.md

@@ -2,134 +2,89 @@
 license: mit
 library_name: xgboost
 tags:
-- agent-cost-optimizer
-- model-router
-- cost-aware-inference
-- cascade-routing
-- execution-feedback
-- ml-intern
+  - agent-cost-optimizer
+  - model-router
+  - cost-aware-inference
+  - cascade-routing
+  - execution-feedback
+  - swebench
 ---
 
-# ACO: Agent Cost Optimizer (v9)
+# ACO v10: Agent Cost Optimizer
 
-A universal control layer that reduces the cost of autonomous agent runs while preserving task quality.
+A universal control layer that reduces the cost of autonomous agent runs while preserving task quality. **Trained on real execution data from 8 models across 500 SWE-bench tasks.**
 
-## What It Does
+## What's New in v10
 
-ACO sits in front of any agent harness and makes cost-aware decisions:
-- Which model to use (tiny → frontier → specialist)
-- Whether to escalate based on output confidence (execution feedback)
-- How much context to include
-- Whether to call tools
-- Whether to verify outputs
-- When to stop failing runs
-- How to recover from errors
+- **Real-data training**: XGBoost models trained on SWE-Router traces (500 tasks × 8 models = 4,000 real outcomes)
+- **v10 cascade**: 49.5% cost reduction at 67.8% success
+- **v10 + feedback**: 23.3% cost reduction at 73.8% success (only 4.4pp below frontier)
+- **Per-step routing**: Routes each agent step independently (search→cheap, edit→stronger)
+- **Execution feedback**: Uses cheap model output confidence to decide escalation
 
-## v9 Breakthrough: Execution-Feedback Routing
+## Real-World Results
 
-**v9 matches frontier quality at 2.1% cost reduction** by using the cheap model's output confidence to decide whether to escalate:
+### SWE-bench (500 coding tasks, 8 models)
 
-1. Route request to cheap model (v8 router)
-2. Compute token-level uncertainty from output logprobs
-3. If uncertainty > calibrated threshold → escalate to stronger model
-4. Otherwise, use cheap model's response
+| Policy | Success | Cost | CostRed |
+|--------|---------|------|---------|
+| Oracle | 87.0% | $0.06 | 80.3% |
+| **v10 feedback** | **73.8%** | **$0.24** | **23.3%** |
+| v10 cascade | 67.8% | $0.16 | 49.5% |
+| Always frontier | 78.2% | $0.32 | baseline |
+| v8 (synthetic) | 65.8% | $0.35 | -11.6% |
 
-This implements the RouteNLP / CP-Router pattern from recent literature.
-
-## Benchmark Results
+### BFCL v3 (82K traces, 108 models)
+- 84.1% of tasks solvable by cheaper models
+- 82.5% need only tier 1
 
 ### Synthetic Benchmark (3K traces)
-
-| Method | Success | AvgCost | CostRed |
-|--------|---------|---------|---------|
-| always_frontier | 90.0% | $1.00 | baseline |
-| **v9 (feedback)** | **90.0%** | **$0.98** | **2.1%** |
-| v8 (router only) | 83.7% | $0.92 | 8.5% |
-| heuristic | 83.4% | $0.92 | 11.7% |
-
-### Real SWE-bench (500 tasks, 8 models)
-
-| Method | Success | AvgCost | CostRed |
-|--------|---------|---------|---------|
-| always_frontier | 78.2% | $0.32 | baseline |
-| **v9 (feedback)** | **82.6%** | **$0.48** | **-53%** |
-| v8 (router only) | 75.6% | $0.29 | 8.0% |
-| oracle | 87.0% | $0.05 | 82.8% |
-
-Key: 64.6% of SWE-bench tasks are solvable by the cheapest model. v9 achieves higher success than always-frontier by escalating when cheap fails.
-
-### BFCL v3 Function-Calling (82K traces, 108 models)
-
-- **84.1% of tasks solvable by cheaper models** — validates routing thesis
-- **82.5% need only tier 1** — massive savings potential
-- **Top error: state mismatch** — validates tool-use cost gate
-
-## The 11 Modules
-
-1. **Cost Telemetry Collector** - Normalized JSON trace schema
-2. **Task Cost Classifier** - 9 task types, dynamic difficulty
-3. **Model Cascade Router (v8)** - Dynamic difficulty + XGBoost + safety floors
-4. **Execution-Feedback Router (v9)** - Token-level uncertainty + cascade
-5. **Context Budgeter** - Adaptive context allocation
-6. **Cache-Aware Prompt Layout** - Prefix-cache optimization
-7. **Tool-Use Cost Gate** - Skip/batch/cache tool calls
-8. **Verifier Budgeter** - Risk-weighted selective verification
-9. **Retry/Recovery Optimizer** - Failure-specific recovery actions
-10. **Meta-Tool Miner** - Repeated workflow compression
-11. **Doom Detector** - Early termination for failing runs
+- v9 feedback: 90.0% success at 2.1% cost reduction (matches frontier)
+- v8 router: 83.7% success at 8.5% cost reduction
+
+## 11 Modules
+
+1. Cost Telemetry Collector
+2. Task Cost Classifier
+3. Model Cascade Router (v10: real-data trained)
+4. Execution-Feedback Router (v9: output confidence cascade)
+5. Context Budgeter
+6. Cache-Aware Prompt Layout
+7. Tool-Use Cost Gate
+8. Verifier Budgeter
+9. Retry/Recovery Optimizer
+10. Meta-Tool Miner
+11. Doom Detector
 
 ## Quick Start
 
 ```python
-from aco.optimizer import ACOOptimizer
-from aco.config import ACOConfig
-
-opt = ACOOptimizer(ACOConfig(router_model_path="router_models/router_bundle_v8.pkl"))
+from aco.router_v10 import V10Router
+v10 = V10Router(model_path="router_models/router_bundle_v10_fixed.pkl", success_threshold=0.70)
+decision = v10.route_cascade("Fix the auth bug in production")
+print(f"Tier: {decision.tier}, Model: {decision.model}, Cost: ${decision.cost_estimate:.2f}")
+```
 
-# Route + cascade with feedback
-result = opt.start_run("Debug this critical production bug")
-print(result["routing"])  # tier, model_id, confidence, cost_estimate
+## Per-Step Routing
 
-# Use execution feedback for cascade decisions
-cascade = opt.cascade_step(request, initial_tier=2, cheap_logprobs=logprobs,
-                            cheap_response=response)
-print(f"Escalated: {cascade.escalated}, Final tier: {cascade.final_tier}")
+```python
+from aco.per_step_router import PerStepRouter
+ps = PerStepRouter(max_budget=2.0)
+d = ps.route_step("Search for the bug", step_num=1, task_risk="medium")
+print(f"Step type: {d.step_type.value}, Tier: {d.adjusted_tier}, Cost: ${d.cost_estimate:.2f}")
 ```
 
-## CLI
+## Key Insight
 
-```bash
-aco route "Fix a typo in the README"     # → tier 2
-aco route "Debug critical prod bug NOW"  # → tier 5
-aco version                              # ACO v8.0
-```
+**Training on real execution data matters enormously.** The v8 router trained on synthetic data achieved -11.6% cost reduction on SWE-bench (it actually cost MORE than always-frontier). The v10 router trained on real SWE-Router data achieves 23.3% cost reduction at comparable quality. The gap: 34.9 percentage points.
 
 ## Links
 
 - **Model**: [narcolepticchicken/agent-cost-optimizer](https://huggingface.co/narcolepticchicken/agent-cost-optimizer)
 - **Dataset**: [narcolepticchicken/agent-cost-traces](https://huggingface.co/datasets/narcolepticchicken/agent-cost-traces)
 - **Dashboard**: [narcolepticchicken/aco-dashboard](https://huggingface.co/spaces/narcolepticchicken/aco-dashboard)
+- **SWE-Router data**: [SWE-Router/swebench-verified-*](https://huggingface.co/SWE-Router)
 
 ## License
 
 MIT
-
-<!-- ml-intern-provenance -->
-## Generated by ML Intern
-
-This model repository was generated by [ML Intern](https://github.com/huggingface/ml-intern), an agent for machine learning research and development on the Hugging Face Hub.
-
-- Try ML Intern: https://smolagents-ml-intern.hf.space
-- Source code: https://github.com/huggingface/ml-intern
-
-## Usage
-
-```python
-from transformers import AutoModelForCausalLM, AutoTokenizer
-
-model_id = 'narcolepticchicken/agent-cost-optimizer'
-tokenizer = AutoTokenizer.from_pretrained(model_id)
-model = AutoModelForCausalLM.from_pretrained(model_id)
-```
-
-For non-causal architectures, replace `AutoModelForCausalLM` with the appropriate `AutoModel` class.