AI agents

Tool use, function calling, orchestration and the protocols that let models act rather than only answer.

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arXiv Machine Learning

Sep 10

Spatial Feature-wise Linear Modulation (SpFiLM) for Contrast Agent-Aware Brain Parcellation

arXiv:2609.07718v1 Announce Type: cross Abstract: Most automated brain parcellation tools are developed and validated on T1-weighted (T1w) MRI. Yet, some clinical workflows for which parcellation is...

By Pushpendra Singh (School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK), Joshua R. Astley (School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK), Roman Rodionov (Department of Epilepsy, Queen Square Institute of Neurology, University College London, London, UK), John Duncan (Department of Epilepsy, Queen Square Institute of Neurology, University College London, London, UK), Tom Vercauteren (School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK), Rachel Sparks (School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK)

agents

arXiv Machine Learning

Sep 10

The OCUDU dApp Platform: An Open Runtime and E3 Interface for Real-Time AI-RAN

arXiv:2609.07843v1 Announce Type: cross Abstract: Machine learning has shown its largest gains in the band below 10 ms inside a 3GPP new radio (NR) 5G distributed unit (DU): link adaptation, per-slot...

By Timothy O'Shea, Matthew Pennybacker, Andriy Kharchenko

llms agents

arXiv Machine Learning

Sep 10

VEX-Bench: Benchmarking LLM Agents for Assessing Exploitability of Software Supply Chain Vulnerabilities

arXiv:2609.08040v1 Announce Type: cross Abstract: The software supply chain has become an increasingly exposed attack surface because of its reliance on intricate yet fragile dependencies. Existing d...

By Jiahao Shi, Edward Tsien, Yifeng Di, Hongjiao Zhang, Yuan Tang, Ronit Dey, Ilona Shishov, Gal Netanel, Zvi Grinberg, Vladimir Belousov, Bat-Zion Rotman, Ilan Pinto, Tianyi Zhang

llms agents benchmarks

arXiv Machine Learning

Sep 10

Apriel-Reasoner: RL Post-Training for General-Purpose and Efficient Reasoning

arXiv:2604.02007v3 Announce Type: replace Abstract: Building general-purpose reasoning models using reinforcement learning with verifiable rewards (RLVR) across diverse domains has been widely adopte...

By Rafael Pardinas, Ehsan Kamalloo, David Vazquez, Alexandre Drouin

llms agents reinforcement-learning benchmarks

arXiv Machine Learning

Sep 10

Marginal-Contribution Policy Gradients under Filtered Feedback for Multi-Agent LLMs

arXiv:2604.22785v2 Announce Type: replace Abstract: We develop a unified treatment of credit assignment for RL training in multi-agent LLM systems. We show that observed reward alone cannot distingui...

By Elai Ben-Gal, Stela Tong

llms agents reinforcement-learning

arXiv Machine Learning

Sep 10

DAGLFNet: Deep Feature Attention Guided Global and Local Feature Fusion for Pseudo-Image Point Cloud Segmentation

arXiv:2510.10471v3 Announce Type: replace-cross Abstract: Environmental perception systems are crucial for high-precision mapping and autonomous navigation, with LiDAR serving as a core sensor provid...

By Chuang Chen, Yi Lin, Bo Wang, Jing Hu, Xi Wu, Wenyi Ge

agents computer-vision

arXiv Machine Learning

Sep 10

SWE-Tester: Training Open-Source LLMs for Issue Reproduction in Real-World Repositories

arXiv:2601.13713v2 Announce Type: replace-cross Abstract: Software testing is crucial for ensuring the correctness and reliability of software systems. Automated generation of issue reproduction test...

By Aditya Bharat Soni, Rajat Ghosh, Vaishnavi Bhargava, Valerie Chen, Debojyoti Dutta

llms agents fine-tuning

arXiv Machine Learning

Sep 10

Environment Parameter Gradient Theorem for Co-Design in Reinforcement Learning

arXiv:2607.12590v2 Announce Type: replace-cross Abstract: Reinforcement learning (RL) is traditionally concerned with learning a control policy for a fixed environment. In many engineering systems, h...

By Amber Srivastava

agents reinforcement-learning