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cs.CL2026
SFT-as-Context Mitigates Forgetting in Supervised Fine-Tuning
Kenan Tang, Andong Hua, Chengxuan Qian +2
Supervised fine-tuning (SFT) equips large language models (LLMs) with specialized capabilities, but often comes at the cost of forgetting the general capabilities of their parent m…
cs.CL2026
ProgressCompass: Embodied Progress Reward Models Are Lost Without the Right Context
Jianshu Zhang, Keliang Wu, Chengxuan Qian +6
Embodied agents now take on ever longer tasks. For long tasks, knowing only whether a task finally succeeds or fails says little; the steps along the way matter. Progress Reward Mo…
cs.CL2025★ 1 cited
fMRI-LM: Towards a Universal Foundation Model for Language-Aligned fMRI Understanding
Yuxiang Wei, Yanteng Zhang, Xi Xiao +3
Recent advances in multimodal large language models (LLMs) have enabled unified reasoning across images, audio, and video, but extending such capability to brain imaging remains la…