proxy_fusion_recovery
Run a bounded proxy recovery that compares explicit/implicit-style fusion and performs a small optimizer step on fusion weights.
Files1
View on GitHubSKILL.mdbasicagent-skill/skill_library/paperbench/fre/skill/exorl_unsupervised_rl/proxy_fusion_recovery/SKILL.md
GitHubProxy Fusion Recovery
When To Use
Use this skill for bounded infrared-visible image-fusion analysis derived from arXiv:2206.09581, especially when preserving modality-specific features, comparing explicit/implicit mechanisms, or running proxy recovery.
Inputs
See the corresponding module document in the attempt directory. Inputs must be local text or numeric arrays; do not read any original paper repository.
Outputs
The skill returns deterministic JSON-compatible diagnostics suitable for downstream recovery and analysis.
Workflow
- Validate input shapes or text content.
- Execute the module-specific mechanism with deterministic defaults.
- Return compact evidence and numeric diagnostics.
- For recovery, keep proxy/reduced labels explicit.
Validation
Run python /share/project/yuyang/workspace/Paper2Skills/Paper2Skills-Agent/src/packages/paper2skills-agent/src/paper2skills/skills/module-to-skill/scripts/validate_skill_tree.py /share/project/yuyang/workspace/Paperbench/record/case13/extracted_skills_attempt_001/exorl_unsupervised_rl/proxy_fusion_recovery --run-tests.
Limitations
This skill implements a lightweight, reusable proxy for the paper mechanism, not the full 21-test-set benchmark from the paper.