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cs.CL2026

Interlock: From Local Correctness to Task Completion in Long-Horizon Agents

Linji Hao, Yanji He, Zihan Wang +6

Large language model agents increasingly undertake long-horizon tasks in complex environments characterized by fragmented evidence, partially completed states, and dependencies acr…

cs.CL2026

CAST: Game Solvers as Turn-Level Teachers for LLM Agents

Yu Wang, Yi-Kai Zhang, Wentao Shi +8

Training large language models (LLMs) to act in long-horizon games is a promising step toward generalist decision-making, yet reinforcement learning with verifiable rewards (RLVR)…

cs.CL2026

GUI-CIDER: Mid-training GUI Agents via Causal Internalization and Density-aware Exemplar Reselection

Zheng Wu, Chengcheng Han, Zhengxi Lu +5

Despite the rapid progress of multimodal large language models in building Graphical User Interface (GUI) agents, their real-world task completion is fundamentally bottlenecked by…

cs.CL2026

MineExplorer: Evaluating Open-World Exploration of MLLM Agents in Minecraft

Tianjie Ju, Yueqing Sun, Zheng Wu +7

Multimodal large language models (MLLMs) have shown strong capabilities in perception, reasoning, and action generation. However, their ability to sustain exploration in dynamic op…

cs.CL2025

VitaBench: Benchmarking LLM Agents with Versatile Interactive Tasks in Real-world Applications

Wei He, Yueqing Sun, Hongyan Hao +13

As LLM-based agents are increasingly deployed in real-life scenarios, existing benchmarks fail to capture their inherent complexity of handling extensive information, leveraging di…

cs.CL2025

Look Back to Reason Forward: Revisitable Memory for Long-Context LLM Agents

Yaorui Shi, Yuxin Chen, Siyuan Wang +5

Large language models face challenges in long-context question answering, where key evidence of a query may be dispersed across millions of tokens. Existing works equip large langu…