A revised arXiv paper examines a failure mode in which large language models lose reasoning ability on out-of-distribution tasks as model depth grows. The authors report that giving models access to tools mitigates this depth-related collapse, and they note that OOD generalization remains hard to measure reliably.
A new arXiv preprint in computational linguistics studies how arguments are structured when a direct proof of a claim is difficult, and how an alternative but related statement can be used instead. The authors apply large language models to compare argumentation and proof patterns across different text genres. The work falls within NLP research on reasoning and argument mining.
A new arXiv preprint introduces CWM, a controllable white-box meta-prompting approach aimed at making retrieval-augmented generation and reasoning more adaptive in large language models. The work targets the problem of deciding how and when models should draw on external knowledge alongside their own reasoning. The abstract is brief, so full evaluation details remain unclear.
A newly posted arXiv paper proposes an operational architecture for cognitive digital twins, which extend traditional digital twins by adding reasoning and knowledge-driven behavior. Rather than simply mirroring the state of a physical system, the framework is designed to let twins pursue tasks and improve their own cognitive capabilities over time. The work positions this shift as the next stage in the evolution of digital twin systems.
A newly released arXiv paper examines a weakness in how multimodal large language models use in-context learning, noting that current methods tend to copy superficial patterns from examples rather than the underlying reasoning process. The authors present a contrastive modeling technique designed to align a model's reasoning path with the logic demonstrated in-context. The approach is intended to improve performance across a range of multimodal tasks by fostering genuine reasoning instead of imitation.