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artificial intelligence

14,110 papers

#artificial intelligence Preprint Open access Oct 2026

Humanoid World Action Model With Joint State--Action Generation

Humanoid robots are a promising platform for general-purpose manipulation. Recent Vision-Language-Action (VLA) policies learn actions directly from multimodal observations, while World Action Models (WAMs) further incorporate future visual prediction to improve action generation. However, in hierarchical humanoid syste...

Yan Yang, Jikun Rong, Minzhao Zhu et al. · 0 citations
#artificial intelligence Preprint Open access Oct 2026

Examining Social Attribution in LLM Reasoning: A Theory-Guided Probing Methodology

Large language models (LLMs) are increasingly deployed in sociotechnical systems where social attribution, the reasoning process attributing external events to the causes and reasons of agents' social behaviors, plays a critical role. These processes involve judgments of social cause, responsibility, and blame/credit t...

Zhaoxin Yu, Qingchao Kong, Dajun Zeng et al. · 0 citations
#artificial intelligence Preprint Open access Oct 2026

REACT: Rolling Denoising and Dual Decoupling for Reactive Robot Control with VLA Models

Flow-based vision-language-action (VLA) models generate action chunks for temporally coherent robot motion, but chunked control creates a fundamental closed-loop trade-off: long chunks provide smooth execution, whereas frequent replanning improves reactivity at the cost of action discontinuities. We introduce REACT, a...

Houlong Xiong, Zhenqi Qiu, Zechen Wang et al. · 0 citations
#artificial intelligence Preprint Open access Oct 2026

The Polytopal Neural Network

Understanding how deep neural networks process information remains a central challenge. Existing interpretability methods often compromise structural fidelity, rely on prespecified corpora, or explain models post-hoc. We propose Polytopal Neural Networks (PNNs), a framework that extracts distinct layer-wise aspects by...

A. Emilie J. Wedenborg, Anders V. N{\o}rskov, Teresa Dorszewski et al. · 0 citations
#artificial intelligence Preprint Open access Oct 2026

Agentic-TTT: Training test-time policy for test-time training

Test-time training (TTT) adapts an LLM's parameters using signals derived from test inputs, and can make striking improvements in pre-specified settings such as IMO competitions or designated open problems. By turning deployment experience into parameter updates, TTT provides a direct mechanism for model-level self-imp...

Jiahao Lu, Mohan Kankanhalli · 0 citations
#artificial intelligence Preprint Open access Oct 2026

Neural Network Verification for Deep Joint Source-Channel Coding

Deep joint source-channel coding (DeepJSCC) transmits data end-to-end over wireless channels using a neural encoder-decoder, but reconstruction quality can degrade sharply under adversarial perturbations and channel disturbances; no method formally bounds this degradation for DeepJSCC. We present the first bound-propag...

Thanh Le, Hai Duong, Takeshi Matsumura et al. · 0 citations
#artificial intelligence Preprint Open access Oct 2026

DataVista: Diagnosing Multimodal LLMs on Data Video Understanding

Data video is a media form that integrates data visualization with video narrative, widely adopted in news reporting and business analysis. Compared with general video understanding, data video understanding places greater emphasis on accurately reading data from animated charts, integrating evidence across charts and...

Yupeng Xie, Zhenyang Wang, Jiayi Zhu et al. · 0 citations
#artificial intelligence Preprint Open access Oct 2026

Can LLMs Fix It Without Code? Toward Automated Verification of No-Code Bug Fixes

A no-code fix resolves an invalid bug report by directing the user to change a setting, update to a version where the problem is already fixed, or adjust their workflow. Manually verifying whether a proposed no-code fix resolves the reported bug takes considerable developer time. This study proposes an automated, execu...

Utku Boran Torun, Veli Karakaya, Eray T\"uz\"un · 0 citations
#artificial intelligence Preprint Open access Oct 2026

Stochastic Grouping Conformal Prediction for Effective Subgroup Reliability

Conformal prediction offers a distribution-free coverage guarantee, making it especially attractive for clinical applications. Standard conformal prediction, however, provides such guarantees only at the population level, and its prediction sets can exhibit coverage disparities across clinically important subgroups. A...

Meihui Zhong, Wenxin Tai, Ting Zhong et al. · 0 citations
#artificial intelligence Preprint Open access Oct 2026

Neural Decoding as Cognitive Inference

The brain maintains stable cognition despite continuously changing neural activity. How to extract stable cognitive states from variable neural observations remains a central problem in neural decoding. Existing neural decoding methods map neural observations to predefined external labels based on the stimulus-response...

Yi Guo, Changhong Jing, Yong Hu et al. · 0 citations
#artificial intelligence Preprint Open access Oct 2026

From Surface to Depth: Towards Cognitive Appraisal Reasoning in Multimodal Emotion Understanding

Recent multimodal large language models (MLLMs) increasingly incorporate explainable reasoning for emotion understanding. However, reasoning based mainly on observable affective cues can reduce emotion understanding to superficial cue-label associations, giving rise to the Clever Hans effect. Such shortcuts become unre...

Jia Li, Yichao He, Yangchen Yu et al. · 0 citations
#artificial intelligence Preprint Open access Oct 2026

Beyond Visual Enhancement: Adaptive Multi-Context Steering to Mitigate LVLM Hallucinations

Hallucination remains a significant challenge in Large Vision-Language Models (LVLMs). Existing training-free methods generally mitigate hallucinations through contrastive decoding or visual enhancement, often increasing the relative influence of visual evidence during generation. This raises a fundamental question: Ca...

Shuran Ma, JiaLe Li, Yuxin Dong et al. · 0 citations

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MIT News · Artificial Intelligence Sep 29, 2026

Who we become when we talk to machines

Professor Sherry Turkle’s new book, “Artificial Intimacy,” offers a withering critique of chatbots and the antisocial dynamics she believes they encourage.

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