arXiv AI August 19, 2026 By Ruth Cohen, Lu Feng, Ayala Bloch, Sarit Kraus

Supporting Calibrated Reliance in Human-AI Collaboration: Different Strategies for Different Tasks

The study investigates how different AI support formats influence human decision-making across two tasks: abstract visual reasoning with RAVEN matrices and deductive logical reasoning with LSAT problems. Findings reveal that in visual reasoning, predictions alone and predicted probabilities best support accuracy and error recovery, while in logical reasoning, LLM explanations outperform other supports. The results suggest that effective human–AI collaboration requires task‑specific support strategies rather than a one‑size‑fits‑all approach.

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arXiv Machine Learning

Sep 10

Do Reasoning Representations Help Humans Evaluate LLM Outputs?

The paper investigates whether reasoning representations—explanations for large language model outputs—aid humans in evaluating those outputs. A controlled human study tested six reasoning formats across tasks of varying complexity, measuring structural understanding, error detection, and trust calibration. Results revealed a mismatch: participants favored planning- and decomposition-based representations, yet simpler chain-of-thought traces better supported verification, trust, and interpretability, while preferred formats increased calibration risks.

By Jaewoo Lim, Sungbok Shin, Sanghyun Hong

llms safety

Hugging Face Trending Papers

Aug 25

EgoErrorVQA: Assess Egocentric Comprehension Capabilities through Procedural Errors for Ego-Agentic AI

The majority of our everyday activities are procedural and consist of sequences of interdependent steps. However, existing benchmarks for Visual Agents and Visual Language Models (VLMs) overlook the e...

llms agents benchmarks

arXiv AI

Sep 16

Available but Unclaimed: An Empirical Study of Human-AI Synergy

arXiv:2609.16793v1 Announce Type: cross Abstract: People increasingly reason with large language models (LLMs), yet complementary capabilities do not guarantee outperforming both components. In a bet...

By Robin Welsch, Michelle Rausch, Pascal Knierim, Thomas Kosch, Jochen Kuhn, Albrecht Schmidt, Daniela Fernandes

llms

arXiv Machine Learning

Jul 14

The Cost of Reasoning: Chain-of-Thought Induces Overconfidence in Vision-Language Models

arXiv:2603. 16728v2 Announce Type: replace Abstract: Vision-language models (VLMs) are increasingly deployed in high-stakes settings where reliable uncertainty quantification (UQ) is as important as predictive accuracy.

By Robert Welch, Emir Konuk, Kevin Smith

llms multimodal

arXiv AI

2d ago

When Reasoning Helps Action: Monitoring and Steering Chain-of-Thought in Vision-Language-Action Policies

arXiv:2610.00601v1 Announce Type: cross Abstract: Reasoning-enabled VLA policies expose chain-of-thought (CoT) traces that appear to explain and guide their actions, creating a potential interface fo...

By Sathwik Karnik, Joseph JR. Lee, Aryaman Gupta, Somil Bansal

robotics multimodal safety

arXiv Computer Vision

4d ago

Beacon: Knowing When and How to Perform Agentic Visual Reasoning

Beacon is a new agentic visual reasoning model that improves multimodal large language models (MLLMs) by better deciding when to use tools and how to use them. It introduces two key concepts—Mode Adaptiveness, which ensures tools are invoked only when necessary, and Tool Effect, which measures the net benefit of tool use— and trains the model with supervised fine‑tuning and reinforcement learning that rewards necessity-aware decisions and expands capability through expert hints. Across 13 benchmarks, Beacon outperforms other open‑source models, achieving the highest average score and the largest net tool‑gain on diagnostic tests.

By Qixun Wang, Yang Shi, Letian Cheng, Zhuoran Zhang, Yan He, Yuqi Tang, Qi Zhang, Xinlei Yu, Ruizhe Chen, Tianrun Xu, Yuanxing Zhang, Pengfei Wan, Haotian Wang, Xianghua Ying

llms agents reinforcement-learning fine-tuning multimodal benchmarks