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cs.CL2026
Context-Aware RL for Agentic and Multimodal LLMs
Peiyang Xu, Bangzheng Li, Sijia Liu +4
Large language models (LLMs) often fail when answering requires identifying a small but decisive piece of evidence within a long or complex context, such as a single line in a tool…
cs.CL2026
Correct Answers from Sound Reasoning: Verifiable Process Supervision for Language Models
Kyuyoung Kim, Kevin Wang, Yunfei Xie +7
Training language models to produce both correct answers and sound reasoning remains an open challenge. Reinforcement learning with verifiable rewards typically optimizes only fina…
cs.CL2025
MMDT: Decoding the Trustworthiness and Safety of Multimodal Foundation Models
Chejian Xu, Jiawei Zhang, Zhaorun Chen +22
Multimodal foundation models (MMFMs) play a crucial role in various applications, including autonomous driving, healthcare, and virtual assistants. However, several studies have re…