SEAM learns to generate utility-optimized structured experiences via rollouts to boost frozen LLM performance on mathematical reasoning benchmarks with low overhead.
Mirac Suzgun, Mert Yuksekgonul, Federico Bianchi, Dan Jurafsky, and James Zou
13 Pith papers cite this work. Polarity classification is still indexing.
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2026 13representative citing papers
RWGBench measures related-work generation by citation choices, and shows citation-focused metrics expose failures that text-similarity and LLM-judge scores miss.
A 4B deep research agent trained on 10K open data outperforms prior agents under 9B parameters and narrows the gap to 30B-class systems on research benchmarks.
The paper introduces the KDR task, HKA multi-agent framework, and KDR-Bench to enable LLM agents to integrate structured knowledge into deep research reports, with experiments showing outperformance over prior agents.
Multi-agent deep research systems self-optimize prompts through self-play to match or outperform expert-crafted versions.
Retrievers trained on agent trajectories via the LRAT framework improve evidence recall, task success, and efficiency in agentic search benchmarks.
Scaling the delegation backbone in hierarchical search agents improves EM by ~11 points while scaling the executor moves EM by only ~2.6 points, and a 1.7B SFT executor matches a frontier sub-agent at 37% fewer tokens.
MetaResearcher is a proposed multi-component framework for scaling deep research agent training via adversarial virtual worlds, discovery tasks, meta-rewards, and multi-agent collaboration.
DuMate-DeepResearch introduces a multi-agent deep research system with graph-based planning, recursive execution, and rubric optimization that reports new state-of-the-art scores of 58.03% and 61.95% on two benchmarks.
A 22M-parameter hyperbolic model answers structured EHR questions with accuracy close to LLM-based systems (EHRXQA 89.5%, MIMIC-Instr 76.0%).
Empirical study finds diversity collapse in multi-agent LLM ideation arises from structural coupling in interactions, not model limitations.
Pi-Serini shows a tuned BM25 lexical retriever with adequate depth, used inside an LLM agentic loop, reaches 83.1% accuracy and 94.7% evidence recall on BrowseComp-Plus while beating released dense-retriever agents.
Argues for a denoising-first paradigm in LLM-oriented information retrieval, framing challenges via a four-stage progression and providing a taxonomy of signal-to-noise optimization techniques across the pipeline.
citing papers explorer
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Beyond Experience Retrieval: Learning to Generate Utility-Optimized Structured Experience for Frozen LLMs
SEAM learns to generate utility-optimized structured experiences via rollouts to boost frozen LLM performance on mathematical reasoning benchmarks with low overhead.
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RWGBench: Evaluating Scholarly Positioning in Related Work Generation
RWGBench measures related-work generation by citation choices, and shows citation-focused metrics expose failures that text-similarity and LLM-judge scores miss.
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DR-Venus: Towards Frontier Edge-Scale Deep Research Agents with Only 10K Open Data
A 4B deep research agent trained on 10K open data outperforms prior agents under 9B parameters and narrows the gap to 30B-class systems on research benchmarks.
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Towards Knowledgeable Deep Research: Framework and Benchmark
The paper introduces the KDR task, HKA multi-agent framework, and KDR-Bench to enable LLM agents to integrate structured knowledge into deep research reports, with experiments showing outperformance over prior agents.
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Self-Optimizing Multi-Agent Systems for Deep Research
Multi-agent deep research systems self-optimize prompts through self-play to match or outperform expert-crafted versions.
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Learning to Retrieve from Agent Trajectories
Retrievers trained on agent trajectories via the LRAT framework improve evidence recall, task success, and efficiency in agentic search benchmarks.
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Think Big, Search Small: Where Capacity Matters in Hierarchical Search Agents?
Scaling the delegation backbone in hierarchical search agents improves EM by ~11 points while scaling the executor moves EM by only ~2.6 points, and a 1.7B SFT executor matches a frontier sub-agent at 37% fewer tokens.
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MetaResearcher: Scaling Deep Research via Self-Reflective Reinforcement Learning in Adversarial Virtual Environments
MetaResearcher is a proposed multi-component framework for scaling deep research agent training via adversarial virtual worlds, discovery tasks, meta-rewards, and multi-agent collaboration.
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DuMate-DeepResearch: An Auditable Multi-Agent System with Recursive Search and Rubric-Grounded Reasoning
DuMate-DeepResearch introduces a multi-agent deep research system with graph-based planning, recursive execution, and rubric optimization that reports new state-of-the-art scores of 58.03% and 61.95% on two benchmarks.
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HypEHR: Hyperbolic Modeling of Electronic Health Records for Efficient Question Answering
A 22M-parameter hyperbolic model answers structured EHR questions with accuracy close to LLM-based systems (EHRXQA 89.5%, MIMIC-Instr 76.0%).
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Diversity Collapse in Multi-Agent LLM Systems: Structural Coupling and Collective Failure in Open-Ended Idea Generation
Empirical study finds diversity collapse in multi-agent LLM ideation arises from structural coupling in interactions, not model limitations.
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Rethinking Agentic Search with Pi-Serini: Is Lexical Retrieval Sufficient?
Pi-Serini shows a tuned BM25 lexical retriever with adequate depth, used inside an LLM agentic loop, reaches 83.1% accuracy and 94.7% evidence recall on BrowseComp-Plus while beating released dense-retriever agents.
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LLM-Oriented Information Retrieval: A Denoising-First Perspective
Argues for a denoising-first paradigm in LLM-oriented information retrieval, framing challenges via a four-stage progression and providing a taxonomy of signal-to-noise optimization techniques across the pipeline.