# E16A: Active Observability - HumanEval Baseline

**Date:** 2025-12-04  
**Status:** ✅ Completed (Inconclusive)  
**Dataset:** HumanEval (Hard problems)

## Hypothesis

**H₀:** Progressive trace summarization (after every 3 tool calls) does not improve ReAct agent performance on hard coding problems.  
**H₁:** Providing compressed trace summaries mid-execution helps the agent maintain context awareness and improves success rate.

## Results Summary

**2025-12-04 Run:**
- **Tasks:** HumanEval/5 (LCS), HumanEval/6 (Sudoku)
- **Model:** gpt-5-mini
- **Outcome:** Both Baseline and Treatment achieved 100% success.
- **Observation:** The model solved the problems in 1-2 tool calls. The progressive summarization trigger (every 3 tool calls) was rarely reached.
- **Conclusion:** HumanEval tasks, even "hard" ones, may be too short/atomic to test the benefit of progressive summarization, which targets long-context drift.

## Next Steps

Moved to **E16B** to test on a more complex, noisy task:
- **Task:** GitHub ↔ Asana Integration (sync PRs to tasks)
- **Why:** Requires multiple steps (Search → Read → Update → Close), involving verbose API responses (JSON noise).
- **Hypothesis:** Summarization will shine by compressing 100-line API responses into actionable state updates ("Found task 123"), preventing context overflow/distraction.
