Looking Again: Measuring Sycophancy in the Reasoning Chains of Multimodal Models Under Pressure
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arXiv:2608.28623v2 Announce Type: replace-cross Abstract: Large multimodal reasoning models (LMRMs) are increasingly capable, largely through generating explicit chain-of-thought reasoning before ans...
arXiv:2511. 17731v2 Announce Type: replace-cross Abstract: Chain-of-Thought (CoT) prompting has proven remarkably effective for eliciting complex reasoning in large language models (LLMs).
arXiv:2608. 10954v1 Announce Type: cross Abstract: While Multimodal Large Language Models (MLLMs) demonstrate impressive performance in benign scenarios, their cognitive reliability deteriorates significantly in complex scenes under adverse conditions.
ANCHOR is a new benchmark for audio‑visually grounded chain‑of‑thought reasoning, containing 2,319 videos and 8,272 question‑answer pairs with structured reasoning traces, spatial bounding boxes, temporal intervals, and audio cues. It evaluates not only answer correctness but also hallucination, logical divergence, and conciseness, providing direct spatial and temporal grounding measurements on a focused subset. The benchmark reveals a substantial alignment gap among contemporary multimodal large language models, showing that similar answer accuracy can mask differences in evidence‑supported explanations.
arXiv:2506.09557v2 Announce Type: replace-cross Abstract: While Multimodal Large Language Models (MLLMs) demonstrate impressive performance in benign scenarios, their cognitive reliability deteriorat...
arXiv:2604. 14888v3 Announce Type: replace-cross Abstract: Recent advances in vision language models (VLMs) offer reasoning capabilities, yet how these unfold and integrate visual and textual information remains unclear.