arXiv Computation and Language August 31, 2026 By Eunkyu Park, Wesley Hanwen Deng, Gunhee Kim, Motahhare Eslami, Maarten Sap

Cognitive Chain-of-Thought (CoCoT): Structured Multimodal Reasoning about Social Situations

Read the original on arXiv Computation and Language →

The paper introduces Cognitive Chain-of-Thought (CoCoT), a structured reasoning framework for vision‑language models that divides multimodal social reasoning into three cognitively inspired stages: Perception, Situation, and Norm. CoCoT improves performance across diverse tasks—multimodal intent disambiguation, theory of mind, social commonsense reasoning, and safety instruction following—by 5.9% to 4.6% on average. Fine‑tuning on CoCoT‑structured traces further boosts accuracy by 5–6% without explicit prompting, indicating that models internalize the structured reasoning pattern and that the approach enhances interpretability and social alignment in multimodal systems.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computation and Language.

arXiv AI

Aug 12

Evidence-Grounded Trustworthy Multimodal Reasoning and Evaluation Benchmark in Complex Urban Scenes

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.

By Zhaoyang Wei, Bowen Jiang, Xumeng Han, Jiashu Li, Xuehui Yu, Yuling Liu, Guorong Li, Zhenjun Han, Jianbin Jiao

llms reinforcement-learning multimodal benchmarks safety

arXiv AI

Sep 1

Evidence-Grounded Trustworthy Multimodal Reasoning and Evaluation Benchmark in Complex Urban Scenes

arXiv:2506.09557v2 Announce Type: replace-cross Abstract: While Multimodal Large Language Models (MLLMs) demonstrate impressive performance in benign scenarios, their cognitive reliability deteriorat...

By Zhaoyang Wei, Bowen Jiang, Xumeng Han, Jiashu Li, Xuehui Yu, Yuling Liu, Guorong Li, Zhenjun Han, Jianbin Jiao

llms reinforcement-learning multimodal benchmarks safety

arXiv AI

Oct 1

Reinforcing Multimodal Reasoning via Token-Level Perception-Grounded Advantage Estimation

arXiv:2609.39168v1 Announce Type: new Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) has improved the reasoning capabilities of Multimodal Large Language Models (MLLMs), yet existing...

By Zhihan Zhang, Lizi Liao

llms reinforcement-learning multimodal benchmarks

arXiv Computation and Language

2d ago

MagiC: Evaluating Multimodal Cognition Toward Grounded Visual Reasoning

arXiv:2507.07297v2 Announce Type: cross Abstract: Recent advances in large vision-language models have led to impressive performance in visual question answering and multimodal reasoning. However, it...

By Chengfei Wu, Ronald Seoh, Bingxuan Li, Liqiang Zhang, Fengrong Han, Dan Goldwasser

llms nlp multimodal benchmarks safety

arXiv Computer Vision

3d ago

CASHEW: Stabilizing Multimodal Reasoning via Iterative Trajectory Aggregation

arXiv:2601.08010v4 Announce Type: replace Abstract: Vision-language models achieve strong performance across a wide range of multimodal understanding and reasoning tasks, yet their multi-step reasoni...

By Chaoyu Li, Fei Tao, Pooyan Fazli

llms reinforcement-learning multimodal benchmarks

arXiv AI

Oct 9

SLVR: Structured Latent Visual Reasoning via Human-like Reasoning Flows

SLVR (Structured Latent Visual Reasoning) is a training framework that blends explicit chain-of-thought with latent reasoning for multimodal large language models. It organizes reasoning into typed latent stages—planning, grounding, evidence selection, and integration—each supervised by corresponding signals such as plans, bounding boxes, visual evidence, and final rationales. Built on Qwen2.5‑VL‑7B, SLVR consistently improves performance on multimodal reasoning benchmarks, achieving significant gains on MMVP, BLINK Relation, V*, MathVista, and ChartQA.

By Albert Gao, Bing Xue, Andrea Zanette

llms multimodal benchmarks

One email a morning, machine-written

Related stories

Evidence-Grounded Trustworthy Multimodal Reasoning and Evaluation Benchmark in Complex Urban Scenes

Evidence-Grounded Trustworthy Multimodal Reasoning and Evaluation Benchmark in Complex Urban Scenes

Reinforcing Multimodal Reasoning via Token-Level Perception-Grounded Advantage Estimation

MagiC: Evaluating Multimodal Cognition Toward Grounded Visual Reasoning

CASHEW: Stabilizing Multimodal Reasoning via Iterative Trajectory Aggregation

SLVR: Structured Latent Visual Reasoning via Human-like Reasoning Flows