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artificial intelligence

13,701 papers

#artificial intelligence Preprint Open access Sep 2026

Kairos: Grounded Forecasting of Presence and Directional Flow in 4D Scene Graphs

Long-term autonomy in human-populated environments requires anticipating whether and how people will move at times a robot has not yet observed. Existing representations of pedestrian motion face a tradeoff: they either forecast future activity, reducing each location to a scalar rate, or model the full directional dis...

Iacopo Catalano, Julio A. Placed, Javier Civera et al. · 0 citations
#artificial intelligence Preprint Sep 2026

Issuer-Sovereign Agentic Payments

AI agents are beginning to make real payments. Current approaches let an agent pay by relying on a credential provider that, in the approaches deployed today, typically sits outside the cardholder's bank. The spending rules are then enforced by the card network or that provider, and not by the bank itself. This leaves...

Disha Sharma, R. Kaushal, Ashu Kanaujia · 0 citations
#artificial intelligence Preprint Sep 2026

BEE: Intervention-Adaptive Real-World Reinforcement Learning with Vision-Language-Action Models

Vision-language-action (VLA) models handle long-horizon manipulation, yet success hinges on a few precision-critical phases where millimeter-scale errors undo all prior progress. Online reinforcement learning (RL) can optimize exactly these actions, but free exploration is far too costly on real robots, which makes hum...

Wei-Hui Zhao, Xiao Yan, Zu-Nian Wan et al. · 0 citations
#artificial intelligence Preprint Sep 2026

Forget who you Forgot: Speaker Unlearning to Prevent Re-Identification in Zero-Shot Text-to-Speech

Recent zero-shot text-to-speech (ZS-TTS) systems can reproduce a speaker's voice with high fidelity from only a few seconds of reference speech, raising concerns over unauthorized voice cloning and impersonation. Speaker identity unlearning has recently emerged as an approach to selectively suppress this capability for...

Hyoeun Kim, Y. Lee, Kyuhong Shim · 0 citations
#artificial intelligence Preprint Sep 2026

Automotive mmWave Spinning Radar Place Recognition with Spatially Gated Feature-Correlation Representation

Automotive spinning FMCW radar provides dense, $360^\circ$ sensing and remains reliable under poor illumination and adverse weather, making it well-suited to autonomous navigation. Place recognition uses these observations to identify previously visited locations for re-localization and long-term navigation. However, h...

Saimunur Rahman, Sagun Shrestha, Abdelwahed Khamis et al. · 0 citations
#artificial intelligence Preprint Sep 2026

Geometry-Conditioned Visual Place Recognition in Natural Environments

Visual Place Recognition (VPR) in natural environments remains challenging due to repetitive vegetation, sparse distinctive landmarks, and substantial appearance and viewpoint variation across traversals. While visual observations of the same place can change considerably, their underlying spatial structure is often mo...

Walter Nedov, Saimunur Rahman, Kavindie Katuwandeniya et al. · 0 citations
#artificial intelligence Preprint Sep 2026

Constraint-Driven Context Engineering: Designing Domain Interfaces for AI Systems

Constraint-Driven Context Engineering (CDCE), a design approach for engineering domain interfaces for AI systems that identifies and characterises constraints, determines the required context assets, and designs representations through which these assets are made available to AI systems, is proposed.

Xi-Wei Xu, Chen Wang, Meng-Meng Yang et al. · 0 citations
#artificial intelligence Preprint Open access Sep 2026

Breaking Weather-Content Coupling: Type-Severity Guided Progressive Disentanglement for All-in-One Infrared Restoration

Infrared (IR) imaging is crucial for autonomous driving, remote sensing, and other perception tasks. However, adverse weather may introduce fake structural responses that are entangled with real thermal structures. Existing IR restoration methods are typically designed for a single degradation type or directly reconstr...

Xinyao Wang, Lijun He, Zhihan Ren et al. · 0 citations
#artificial intelligence Preprint Sep 2026

Multi-View Fusion for Encrypted C2 Detection: A Leakage-Controlled Measurement Study of Evaluation Pitfalls

This work reports three findings that matter more than the fusion result itself, and concludes that for encrypted C2 detection, the evaluation design is not a preliminary step and fusion beats the best single view by only 0.022 in F1.

H. Nguyen-Huu, V. Phan, Khuong Nguyen-An · 0 citations
#artificial intelligence Preprint Open access Sep 2026

Teach-to-Crash: A Closed-Loop Student-Teacher LLM Framework for Collision-Inducing Test Scenario Generation

Validating Autonomous Driving Systems (ADS) in simulation requires testing architectures that can discover rare, safety-critical failures while generating scenarios that are executable, diverse, and useful for downstream failure analysis. We introduce Teach-to-Crash, a closed-loop testing framework that combines a cons...

Zaid Ghazal, Khouloud Gaaloul, Bruce Maxim · 0 citations
#artificial intelligence Preprint Sep 2026

The Risk-Sensitive Schr\"odinger Bridge: Is Not a KL Projection

The Schr\"odinger bridge owes its computational power to a single structural fact: by Girsanov's theorem the controlled problem is a Kullback--Leibler (KL) projection onto a fixed reference measure, solvable by alternating projections. This letter shows that the fact does not survive risk sensitivity. When the expected...

Hamidreza Behjoo · 0 citations
#artificial intelligence Preprint Open access Sep 2026

Combining LLMs and Genetic Search for ARC-AGI-2

LLMs can generate programs for ARC-AGI-2 tasks, but the provided compute only allows a small number of attempts to generate, debug and validate solutions. Genetic algorithms can search and test many more programs, but random search rarely starts in a useful neighborhood of the solution space. We combine the two methods...

Val Dyachenko · 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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