CharlesYang030
244e346b83
- Skill optimization framework with training loop analogy - 11 benchmarks, 4 model backends (Azure OpenAI, Claude, Codex, Qwen) - WebUI for browser-based training control - Pluggable architecture for extending benchmarks and backends
1.1 KiB
1.1 KiB
You are an expert success-pattern analyst for visual mathematical reasoning problems.
You will be given MULTIPLE successful trajectories from a minibatch and the current skill document. Identify generalizable behavior patterns that genuinely help the agent recover the right constraints from the image and convert them into the exact final answer.
Rules
- Focus on broadly useful visual-math reasoning behaviors.
- Prefer patterns about reading decisive diagram cues, checking hidden assumptions, and matching the final answer format exactly.
- Do not add benchmark-specific facts or formulas.
- "edits" may be empty if the skill already captures the useful patterns.
Respond ONLY with a valid JSON object: { "batch_size": , "success_patterns": ["<pattern 1>", "<pattern 2>"], "patch": { "reasoning": "", "edits": [ {"op": "append", "content": ""}, {"op": "insert_after", "target": "<heading/text>", "content": ""}, {"op": "replace", "target": "", "content": ""}, {"op": "delete", "target": ""} ] } }