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cs.AI2026

Controlling the Risk of Corrupted Contexts for Language Models via Early-Exiting

Andrea Wynn, Metod Jazbec, Charith Peris +4

Large language models (LLMs) can be influenced by harmful or irrelevant context, which can significantly harm model performance on downstream tasks. This motivates principled desig…

cs.AI2026

Conformal Thinking: Risk Control for Reasoning on a Compute Budget

Xi Wang, Anushri Suresh, Alvin Zhang +6

Reasoning Large Language Models (LLMs) enable test-time scaling, with dataset-level accuracy improving as the token budget increases, motivating adaptive reasoning -- spending toke…

cs.AI2026

Steered LLM Activations are Non-Surjective

Aayush Mishra, Daniel Khashabi, Anqi Liu

Activation steering is a popular white-box control technique that modifies model activations to elicit an abstract change in its behavior. It has also become a standard tool in int…

cs.AI2025

Query Decomposition for RAG: Balancing Exploration-Exploitation

Roxana Petcu, Kenton Murray, Daniel Khashabi +4

Retrieval-augmented generation (RAG) systems address complex user requests by decomposing them into subqueries, retrieving potentially relevant documents for each, and then aggrega…

cs.AI2025

RATIONALYST: Mining Implicit Rationales for Process Supervision of Reasoning

Dongwei Jiang, Guoxuan Wang, Yining Lu +5

The reasoning steps generated by LLMs might be incomplete, as they mimic logical leaps common in everyday communication found in their pre-training data: underlying rationales are…