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cs.AI2026

Deferred Exposure of Future Trajectories for Verifiable Reasoning in Autonomous Driving VLMs

Zixuan Huang, Yang Zhou, Kaixuan Wang +7

Recent Vision-Language-Action (VLA) models for autonomous driving (AD) increasingly utilize chain-of-thought (CoT) supervision to enhance the reasoning capabilities of their Vision…

cs.AI2026

Federated Reasoning Distillation Framework with Model Learnability-Aware Data Allocation

Wei Guo, Siyuan Lu, Xiangdong Ran +8

Data allocation plays a critical role in federated large language model (LLM) and small language models (SLMs) reasoning collaboration. Nevertheless, existing data allocation metho…

cs.AI2026

Does Your Reasoning Model Implicitly Know When to Stop Thinking?

Zixuan Huang, Xin Xia, Yuxi Ren +11

Recent advancements in large reasoning models (LRMs) have greatly improved their capabilities on complex reasoning tasks through Long Chains of Thought (CoTs). However, this approa…

cs.AI2026

Weak-Driven Learning: How Weak Agents make Strong Agents Stronger

Zehao Chen, Gongxun Li, Tianxiang Ai +9

As post-training optimization becomes central to improving large language models, we observe a persistent saturation bottleneck: once models grow highly confident, further training…

cs.AI2026

Real-Time Aligned Reward Model beyond Semantics

Zixuan Huang, Xin Xia, Yuxi Ren +10

Reinforcement Learning from Human Feedback (RLHF) is a pivotal technique for aligning large language models (LLMs) with human preferences, yet it is susceptible to reward overoptim…