Dimensions overall score 4.0

GitHub Code Pulse

No public code linked for this paper yet.

• Access to experimental feedback increases discoveries per feature by 53.4% on average compared to a zero-shot baseline.result 95%
• The performance gain from experimental feedback disappears when hit/miss labels are randomly permuted, indicating genuine feedback-driven learning.result 95%
• Upgrading the LLM from Claude Sonnet 4.5 to 4.6 reduces gene hallucination rates from approximately 33%-45% to approximately 3%-9%.technical 95%
• A higher capability LLM (Claude Sonnet 4.6) converts a non-significant in-context learning effect into a large and highly significant improvement.result 95%
• Effective in-context learning from experimental feedback emerges only when LLMs reach a sufficient capability threshold.result 90%
• The feedback-enabled LLMs outperform the GP-UCB baseline, achieving 19.5 hits on average with statistical significance (p=0.003).result 90%
• The ICL-EF agent is highly efficient at exploitation, allowing it to discover more genes once initial hits in major protein complexes are found.result 85%

Competitive landscape

Competitor map is still being generated for this paper. Enable generation or check back soon.

Startup potential card