#cross-model-transfer News & Analysis

2 articles tagged with #cross-model-transfer. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.

2 articles

AIBullisharXiv – CS AI · May 297/10

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Less is Enough: Synthesizing Diverse Data in LLM Feature Space with Sparse Autoencoders

Researchers propose Feature Activation Coverage (FAC), a new metric for measuring data diversity in large language models using sparse autoencoders instead of traditional text-based metrics. The FAC Synthesis framework generates synthetic training data to fill feature gaps, demonstrating consistent improvements across multiple tasks and revealing transferable feature spaces across different model families.

AIBullisharXiv – CS AI · Jun 26/10

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SkillRevise: Improving LLM-Authored Agent Skills via Trace-Conditioned Skill Revision

Researchers introduce SkillRevise, a framework that automatically refines LLM agent skills through execution-grounded iteration, improving task success rates from 36% to 62% on benchmarks. The approach addresses the cold-start problem in agent development by diagnosing defects from execution traces and applying targeted repairs, while demonstrating strong cross-model transferability.