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arXiv:2608.22857v1 Announce Type: new Abstract: Vision-language models (VLMs) frequently fail at visual change reasoning, even when their vision encoders contain sufficient information. We observe th...
arXiv:2609.21492v1 Announce Type: new Abstract: Chain-of-Thought (CoT) reasoning has been shown to improve the performance of large language models (LLMs), yet existing optimization methods largely r...
arXiv:2507. 18043v2 Announce Type: replace-cross Abstract: Inference-time steering methods offer a lightweight alternative to fine-tuning large language models (LLMs) and vision-language models (VLMs) by modifying internal activations at test time without updating model weights.
arXiv:2507. 02778v3 Announce Type: replace-cross Abstract: Although large language models (LLMs) have transformed AI, they still make errors and follow unproductive reasoning paths.
The paper introduces a controlled auditing framework using parametric CAD to separate failures in encoding, expression, and activation-level control in frozen large language models (LLMs). Applied to eight pretrained models and random controls, it finds that pretraining mainly improves linear decoding of local relations, while global constraint status is already highly decodable from random representations. Despite strong local decodability, the models’ zero‑shot predictions lag far behind supervised probe performance, and simple activation steering fails to provide reliable control; however, low‑rank fine‑tuning with only 400 labeled relation pairs can substantially improve generation on three models, indicating that the representation–behavior gap can be narrowed through adaptation.
arXiv:2606. 09071v1 Announce Type: new Abstract: Large language model (LLM) agents now solve complex tasks through long plan-and-execution traces, yet the ability to locate errors in a completed traces still lags far behind, especially in the \emph{silent failure} regime.