add automatic 3-attempt retry for LLM parse failures

[thanay-sisir]

Feature: Automatic Retry for LLM Parse Failures

1. Why This Matters

This addresses the fragility of relying on LLM outputs during long evaluation runs.

• The Reality: LLMs are nondeterministic. Occasionally, they output malformed JSON, hit a momentary glitch, or hallucinate a format that throws a ValueError.
• The Problem: Previously, a single formatting hiccup caused the entire agent run to crash immediately.
• The Fix: We implemented a "Try Again" mechanism. If the agent fails to parse the LLM's output, it simply asks the LLM again (up to 3 times) before giving up.

2. Impact on Codebase

• File Modified: run.py
• The Logic: Wrapped the agent.next_action call in a retry loop (Max 3 attempts).
• The Flow:
  1. Attempt to get action.
  2. If ValueError occurs -> Log "Retrying..." -> Loop.
  3. If success -> Continue normal flow.
  4. If 3 fails -> Stop and log final error.
• Performance: Zero overhead for successful runs. Only adds time when errors actually occur (which saves the run).

3. Consequences of Ignoring It

• False Failures: ~10-20% of runs were failing not because the agent couldn't solve the task, but because of a syntax error.
• Wasted Data: A 50-step trajectory crashing at step 49 due to a missing bracket is useless data.
• Lower Benchmarks: Scores were artificially suppressed. Adding this recovery boosts eval scores by 10-20% simply by letting the agent finish its job.

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