2 articles tagged with #decomposition. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.
Researchers introduce Reasoning Primitive Induction, a method that extracts reusable reasoning patterns from ReAct-style LLM agent traces and converts them into a compact library of pseudo-tools. The induced libraries consistently outperform the original agents by 22-44 percentage points across multiple reasoning tasks, suggesting a systematic path to improve LLM reasoning through learned decomposition.
Researchers propose Core-Halo decomposition, a novel approach to solving large-scale fixed-point problems in decentralized systems that separates write ownership from read-only evaluation context. Unlike standard strict decomposition methods that create structural bias by truncating dependencies, Core-Halo aligns with block-dependence structures to enable faithful implementation of the original fixed-point problem across distributed multi-agent systems while maintaining parallelism benefits.
