arXiv:2607. 28648v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly used for emotional support tasks, such as negative thought reframing.
By Hainiu Xu, Zhaoyue Sun, Hanqi Yan, Jinhua Du, Caroline Catmur, Yulan He
arXiv:2607. 28641v1 Announce Type: cross Abstract: We introduce the \textit{Agentic Formalism Trap} and the Evaluative Dissonance Index ($D_E$), quantifying how LLM-as-a-Judge systems conflate structural proceduralism with semantic truth under adversarial load.
By Dahlia Shehata, Ming Li
arXiv:2604. 02330v2 Announce Type: replace-cross Abstract: Recent advances in video diffusion have enabled the development of "world models" capable of simulating interactive environments.
By Alexander Pondaven, Ziyi Wu, Igor Gilitschenski, Philip Torr, Sergey Tulyakov, Fabio Pizzati, Aliaksandr Siarohin
arXiv:2606. 09037v2 Announce Type: replace Abstract: This study presents a large language model (LLM)-based multi-agent framework for interior permanent magnet synchronous motor (IPMSM) design optimization that mitigates limitations of conventional workflows: expertise-dependent problem setup and data preparation, the prohibitive computational cost of finite element analysis (FEA), and the unreliability of AI surrogates in unexplored regions.
By Jinseong Han, Sunwoong Yang, Namwoo Kang
arXiv:2607. 29055v1 Announce Type: cross Abstract: Multi-agent systems (MAS) are increasingly deployed to solve complex tasks.
By Hanxiao Lu, Tianyi Zhang
arXiv:2607. 29549v1 Announce Type: new Abstract: Large language models have demonstrated strong mathematical problem-solving capabilities, yet reliably verifying their candidate answers remains challenging.
By Rui Zou, Yutao Zhu, Mengqi Wei, Ji-Rong Wen
arXiv:2607. 29254v1 Announce Type: new Abstract: AI agents extend large language models (LLMs) with external tools, enabling them to perform complex tasks and translate model outputs into consequential real-world actions.
By Minghui Pan, Jiayuxuan Yang, Yuanyuan Yuan, Yu Jiang, Zhenpeng Chen
arXiv:2605. 07161v3 Announce Type: replace Abstract: AI agents are increasingly used to diagnose and mitigate failures in production systems, known as agentic Site Reliability Engineering (SRE).
By Jackson Clark, Yiming Su, Saad Mohammad Rafid Pial, Yifang Tian, Lily Gniedziejko, Hans-Arno Jacobsen, Yinfang Chen, Tianyin Xu
arXiv:2607. 29405v1 Announce Type: new Abstract: Agentic AI systems act through multi-step trajectories that combine planning, tool use, memory, interaction, and adaptation.
By Fabio Orazio Mirto, Luca D'Agati, Giuseppe Tricomi, Stefano Silvestri, Francesco Longo, Antonio Puliafito, Giovanni Merlino
arXiv:2607. 29167v1 Announce Type: cross Abstract: Long-term memory lets large language model(LLM) agents reuse prior preferences and work flows, but it also turns untrusted observations into persistent action context.
By Jinghan Xu, Yiyong Xiao, Wanru Shao, Hankai Liu, Xinjin Li
arXiv:2512. 05131v2 Announce Type: replace-cross Abstract: Active 3D reconstruction enables an agent to autonomously select viewpoints to efficiently obtain accurate and complete scene geometry, rather than passively reconstructing scenes from pre-collected images.
By Tianling Xu, Shengzhe Gan, Leslie Gu, Yuelei Li, Fangneng Zhan, Hanspeter Pfister
arXiv:2606. 05976v2 Announce Type: replace Abstract: Recent works show that LLM agents struggle to correct errors in their own reasoning traces, despite their ability to correct errors from external sources.
By Kuan-Yen Chen, Fang-Yi Su, Shih-Yen Lin, Bao Li, Jung-Hsien Chiang
arXiv:2607. 28802v1 Announce Type: new Abstract: Existing evaluations often reduce agent failures to system-level outcomes, obscuring where the fault originated and which intervention would improve the agent system.
By Harsh Raj, Vipul Gupta, Anas Mahmoud, Razvan-Gabriel Dumitru, Darvin Yi, Aakash Sabharwal, Yunzhong He
arXiv:2607. 29190v1 Announce Type: new Abstract: Tool-using LLM agents act on typed tool returns, records pairing provenance and categorical fields with numerical values.
By Blaise Delattre, Cong Wang, Yang Cao
arXiv:2509. 17192v3 Announce Type: replace Abstract: LLM-based social simulations can make a generated transcript look like a single behavioral signal, but the model behind that transcript may be doing several different jobs: choosing what an actor says or does, deciding what happens after an action, or both.
By Glenn Matlin, Isaac Song, Yixiong Hao, Parv Mahajan, Evan Montoya, Ryan Bard, Stuart R. Topp, Anthony Wen-Ming Zang, Mohammed Rehan Parwani, Soham Shetty, Mark Riedl
arXiv:2605. 18476v2 Announce Type: replace-cross Abstract: Coding and computation remain major bottlenecks in Markov chain Monte Carlo (MCMC) workflows, especially as modern sampling algorithms have become increasingly complex and existing probabilistic programming systems remain limited in model support, extensibility, and composability.
By Jungang Zou, Alex Ziyu Jiang, Qixuan Chen
arXiv:2607. 29002v1 Announce Type: new Abstract: Online shoppers increasingly turn to AI shopping assistants, using images and multi-turn dialogue to express and refine product needs that are difficult to articulate in text alone.
By Zeying Hao, Hao Guo, Mengtao Xu, Yimin Hu, Yuheng Song, Zesheng Zhou, Jinsong Lan, Xiaoyong Zhu
arXiv:2503. 15225v3 Announce Type: replace-cross Abstract: The deployment of autonomous virtual avatars (in extended reality) and robots in human group activities---such as rehabilitation therapy, sports, and manufacturing---is expected to increase as these technologies become more pervasive.
By Angelo Di Porzio, Marco Coraggio
arXiv:2607. 29440v1 Announce Type: new Abstract: Long-term multimodal memory must support not only retrieving relevant information but also computing over observations accumulated across interactions.
By Zhoujin Tian, Yao Tian, Hao Zhang, Cheng Chen, Yakun Li, Lei Zhang, Xiaofang Zhou
arXiv:2605. 22949v3 Announce Type: replace Abstract: Foundation-model pools are increasingly used as black-box responders in coordinated systems where a coordinator must decide which response to trust.
By Joss Armstrong