#retrieval-optimization News & Analysis

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

3 articles

AINeutralarXiv – CS AI · Jun 236/10

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CalVerT: Augmenting Agents with Calibrated Verifier Telemetry Improves Action and Learning in Knowledge-Intensive Tasks

CalVerT is a new framework that enhances LLM agents by providing calibrated confidence scores and grounding verification, helping agents distinguish between reliable and uncertain knowledge during question-answering tasks. The approach reduces both inaccurate confident answers and wasteful over-retrieval, improving performance across multiple QA benchmarks without requiring additional training.

AIBullisharXiv – CS AI · Jun 26/10

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Harness-1: Reinforcement Learning for Search Agents with State-Externalizing Harnesses

Researchers introduce Harness-1, a 20B parameter search agent that separates semantic decision-making from state management by externalizing working memory to a stateful harness environment. The system achieves 73% average curated recall across eight retrieval benchmarks, outperforming comparable open-source searchers by 11.4 points while generalizing well to held-out transfer tasks.

AINeutralarXiv – CS AI · Jun 26/10

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SkillPager: Query-Adaptive Intra-Skill Navigation via Semantic Node Retrieval

SkillPager is a novel retrieval framework that optimizes how large language model agents access long procedural documents by selecting minimal, execution-sufficient context from skill documents. The system achieves 78.89% sufficiency while reducing prompt tokens by 47.04% compared to full-document prompting, demonstrating that typed semantic granularity significantly improves efficiency in skill-based LLM agent systems.