arXiv Computation and Language October 26, 2026 By Masoud Monajatipoor, Fazlolah Mohaghegh, Hamid Reza Hassanzadeh, Akash Uttamrao Bhoite, Robert E. Tillman

From Noisy Retrieval to Decision Evidence: Silver Evidence Demonstrations for In-Context Learning

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arXiv Machine Learning

Sep 22

EAVer: Long-Form Factuality Verification as an End-to-End Agentic Policy

arXiv:2609.22223v1 Announce Type: cross Abstract: Long-form factuality verification is commonly implemented as a static decompose-search-verify pipeline, with separately prompted modules processing c...

By Kening Zheng, Aoying Zheng, Zhigang Chang, Yazhi Guo, Miaotian Guo, Qingwei Zong, Xianhai Xie, Weiqiang Jin, Chengze Li, Hanrong Zhang, Jie Yang, Wei-Chieh Huang, Lingzhe Zhang, Liancheng Fang, Xin Zou, Hanqian Li, Jiahao Huo, Yibo Yan, Zizhuang Deng, Lei Miao, Wei Guo, Haihong Tang, Bo Zheng, Philip S. Yu

llms agents reinforcement-learning benchmarks safety

arXiv AI

Jun 4

Smart Picks in the Dark: Towards Efficient RLVR for Reasoning via Tracing Metacognitive Pivots

arXiv:2606. 04503v1 Announce Type: cross Abstract: Reinforcement learning with verifiable rewards (RLVR) has greatly advanced large reasoning models (LRMs), but it requires timely training on a huge fully-annotated dataset.

By Guangcheng Zhu, Shenzhi Yang, Haobo Wang, Xing Zheng, Yingfan MA, Xuening Feng, Zhongqi Chen, Bowen Song, Weiqiang Wang, Gang Chen

reinforcement-learning

arXiv AI

Sep 30

TAEC: Trajectory-Aware Evidence Coordination for Multi-Step Visual RAG

arXiv:2609.37349v1 Announce Type: cross Abstract: Multi-step visual retrieval-augmented generation (RAG) answers complex questions by repeatedly retrieving visual evidence, updating an intermediate s...

By Yalun Wu, Bingzhou Wang, Boyang Wang, Peiying Wang, Shaojie He, Yunhan Wang, Shaozu Yuan, Jiawei Wang

llms rag multimodal

arXiv AI

Aug 11

Bounding Hallucinations: Merlin-Arthur Protocols for Mutual-Information Bounds in Language Models

arXiv:2512. 11614v3 Announce Type: replace-cross Abstract: Retrieval-augmented generation (RAG) relies on retrieved context to guide large language models (LLM), yet treats the retrieval as a heuristic rather than verifiable evidence -- leading to unsupported answers, hallucinations, and reliance on spurious context.

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llms rag agents fine-tuning benchmarks safety

arXiv AI

Oct 15

VisExMEM: Visual Evidence-Based Explainable Multimodal Evaluation Metric

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By Subhadeep Roy, Christoph Leiter, Steffen Eger

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arXiv AI

Aug 7

Contextual Information Policy Optimization for Search Agents

arXiv:2608. 06128v1 Announce Type: new Abstract: Search agents extend large language models beyond static parametric memory by enabling them to acquire and use ex ternal evidence during multi-step reasoning.

By Xingyu Guo, Wei Chen, Linlin Yang, Baochang Zhang

llms agents reinforcement-learning benchmarks safety

One email a morning, machine-written

Related stories

EAVer: Long-Form Factuality Verification as an End-to-End Agentic Policy

Smart Picks in the Dark: Towards Efficient RLVR for Reasoning via Tracing Metacognitive Pivots

TAEC: Trajectory-Aware Evidence Coordination for Multi-Step Visual RAG

Bounding Hallucinations: Merlin-Arthur Protocols for Mutual-Information Bounds in Language Models

VisExMEM: Visual Evidence-Based Explainable Multimodal Evaluation Metric

Contextual Information Policy Optimization for Search Agents