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cs.CL2026

SPIEval: Evaluating Large Language Models as Mobile Assistants over Scattered Personal Information

Junjie Ye, Zhuohui Sheng, Shaofan Liu +12

Large language models (LLMs) are increasingly deployed as mobile assistants, where a key challenge is leveraging personal information scattered across multiple applications (apps)…

cs.CL2026

IACM-RL: Intent-Aware Context Management and Reinforcement Learning for Complex Tool Invocation under Dynamic Intent Fluctuations

Dingwei Zhu, Jiahan Li, Chengjun Pan +22

Executing long-horizon tool invocations in real-world environments is severely challenged by dynamic user intent noise. Existing methods attempt robustness via implicit history sca…

cs.CL2026

AgentV-RL: Scaling Reward Modeling with Agentic Verifier

Jiazheng Zhang, Ziche Fu, Zhiheng Xi +13

Verifiers have been demonstrated to enhance LLM reasoning via test-time scaling (TTS). Yet, they face significant challenges in complex domains. Error propagation from incorrect in…

cs.CL2026

MM-Doc-R1: Training Agents for Long Document Visual Question Answering through Multi-turn Reinforcement Learning

Jiahang Lin, Kai Hu, Binghai Wang +12

Conventional Retrieval-Augmented Generation (RAG) systems often struggle with complex multi-hop queries over long documents due to their single-pass retrieval. We introduce MM-Doc-…

cs.CL2026

Which Reasoning Trajectories Teach Students to Reason Better? A Simple Metric of Informative Alignment

Yuming Yang, Mingyoung Lai, Wanxu Zhao +13

Long chain-of-thought (CoT) trajectories provide rich supervision signals for distilling reasoning from teacher to student LLMs. However, both prior work and our experiments show t…

cs.CL2026

Can Deep Research Agents Retrieve and Organize? Evaluating the Synthesis Gap with Expert Taxonomies

Ming Zhang, Jiabao Zhuang, Wenqing Jing +18

Deep Research Agents increasingly automate survey writing, yet existing benchmarks do not jointly test whether they retrieve the papers experts consider essential and organize thos…