#neural-search News & Analysis

3 articles tagged with #neural-search. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.

3 articles

AIBullisharXiv – CS AI · May 277/10

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ICICLE: Expanding Retrieval with In-Context Documents

Researchers introduce ICICLE, a generative retrieval framework that addresses the inefficiency of traditional corpus expansion by treating new documents as in-context evidence rather than requiring model retraining. The approach uses a copy-based routing mechanism to distinguish between parametric memory and context-provided document associations, achieving better scalability without catastrophic forgetting.

AINeutralarXiv – CS AI · Jun 16/10

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LinTree: Improving LLM Reasoning with Explicitly Structured Search Histories

Researchers demonstrate that Large Language Models improve their reasoning performance when search histories are explicitly structured with parent pointers (LinTree), rather than implicitly represented. The finding suggests that LLMs benefit from tree-aware representations during problem-solving, outperforming both implicit trace-based reasoning and traditional heuristic-guided search across multiple domains.

AINeutralarXiv – CS AI · May 126/10

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Budget-Efficient Automatic Algorithm Design via Code Graph

Researchers propose a budget-efficient automatic algorithm design framework using large language models that operates on code graphs rather than full algorithms. The approach uses LLMs to generate compact corrections—code modifications that add, replace, or remove blocks—which compose into new algorithms, reducing computational waste and improving fitness outcomes on combinatorial optimization problems.