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cs.LG2026
Remember Less, Adapt Longer: Bounded-State In-Context Reinforcement Learning with Recurrent Summary Memory
Jiawen Wang, Cheng Xue, Peng Zhang +1
In-context reinforcement learning (ICRL) enables agents to adapt to new tasks without parameter updates, relying on memory to retain what they learn from experience. For agents ope…
cs.AI2026
Can Reasoning Models Detect Changes to their Chains of Thought?
Sathvik Napa, Utkarsh Singh, Chengyuan Xue +2
There are many reasons one may want to edit a model's chain of thought (CoT) -- e.g., to prefill it with reasoning from a stronger model or to remove steps that may yield unsafe ou…