feat:Claude_Agent

[allierc]

Implements an iterative exploration framework where an LLM proposes parameter mutations guided by UCB tree search to map GNN training landscapes.

Architecture

External (Python)          LLM
─────────────────          ───
UCB computation     →      Reads ucb_scores.txt
Metrics evaluation  →      Reads analysis results  
                    ←      Appends analysis.log
                    ←      Rewrites memory.md
                    ←      Proposes mutation
                    ←      (Optional) Edits protocol

Components

• experiment.md: Protocol definition with self-modifiable exploration rules
• analysis.log: Full iteration history (append-only)
• memory.md: LLM-maintained compressed knowledge
• ucb_scores.txt: External tree structure with node selection

Exploration Strategies

Strategy	Trigger	Action
exploit	default	Mutate highest UCB node
failure-probe	3+ successes	Find failure boundary
explore	plateau	Branch to distant node
robustness-test	good config	Verify reproducibility
recombine	2+ distant successes	Merge best parameters

Key Features

• Self-modifying protocol: LLM can edit exploration rules mid-search
• Two-tier memory: Raw log + compressed working memory
• Block structure: 24 iterations per simulation regime