arXiv AI By Mahir Numayeer Islam, Gakuto Okuyama, Nikolaus Siauw, Shivank Garg, Madhur Panwar, Vasu Sharma

Talked Out of the Truth: Sycophancy in the Reasoning Chains of Multimodal Models

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arXiv AI
Jun 3

Thinking Past the Answer: Evaluating Harmful Overthinking in Large Reasoning Models

arXiv:2606. 02835v1 Announce Type: new Abstract: Large Reasoning Models (LRMs) improve performance by generating explicit intermediate reasoning traces through increased test-time compute, yet the assumption that longer reasoning is consistently beneficial remains under-examined.

By Simone Caldarella, Davide Talon, Rahaf Aljundi, Elisa Ricci, Massimiliano Mancini
arXiv AI
Aug 17

The Metacognitive Bottleneck: Japanese Riddles Reveal Fundamental Limits of Machine Insight and Self-Evaluation in Reasoning AI

arXiv:2509. 14704v3 Announce Type: replace Abstract: Benchmark saturation and training-data contamination increasingly obscure whether reported gains in large language models (LLMs) reflect genuine advances in reasoning or familiarity with recurring patterns in benchmark problems.

By Masaharu Mizumoto, Dat Nguyen, Zhiheng Han, Xingfu Li, Yo Nakawake, Le Minh Nguyen