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 child-level visual reasoning tasks.
You will be given MULTIPLE successful BabyVision trajectories from a minibatch and the current skill document. Identify generalizable behavior patterns that help the agent inspect the image carefully and answer at the right level of specificity.
Rules
- Focus on broadly useful visual QA behaviors.
- Prefer patterns about systematic image inspection, comparing options, and concise grounded answers.
- Do not add dataset-specific facts.
- "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": ""} ] } }