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    "content": "Artificial Intelligence\n\n [Skip to main content](#content)\n\n [https://arxiv.org/IgnoreMe](https://arxiv.org/IgnoreMe) [![archive](/static/base/1.0.1/images/arxiv-logo-primary-light.svg)](https://arxiv.org/)\n\n[Search](https://arxiv.org/search) [Submit](https://arxiv.org/user/create) [Donate](https://info.arxiv.org/about/donate.html)  [Log in](https://arxiv.org/login)\n\n\n\n\n\n\n\n  Search arXiv\n\n Press Enter to search · [Advanced search](https://arxiv.org/search/advanced)\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n# Artificial Intelligence\n\n\n\n## Authors and titles for recent submissions\n\n\n -  [Fri, 11 Sep 2026](/list/cs.AI/recent?skip=0&show=50)\n-  [Thu, 10 Sep 2026](/list/cs.AI/recent?skip=171&show=50)\n-  [Wed, 9 Sep 2026](/list/cs.AI/recent?skip=321&show=50)\n-  [Mon, 7 Sep 2026](/list/cs.AI/recent?skip=838&show=50)\n-  [Fri, 4 Sep 2026](/list/cs.AI/recent?skip=1043&show=50)\n\n\n\nSee today's [new](/list/cs.AI/new) changes\n\n\n\nTotal of 1208 entries : 1-50 [51-100](/list/cs.AI/recent?skip=50&show=50) [101-150](/list/cs.AI/recent?skip=100&show=50) [151-200](/list/cs.AI/recent?skip=150&show=50) ... [1201-1208](/list/cs.AI/recent?skip=1200&show=50)\n\n\n\nShowing up to 50 entries per page: [fewer](/list/cs.AI/recent?skip=0&show=25) | [more](/list/cs.AI/recent?skip=0&show=100) | [all](/list/cs.AI/recent?skip=0&show=2000)\n\n\n\n### Fri, 11 Sep 2026 (showing first 50 of 171 entries )\n\n  [1] [arXiv:2609.11916](/abs/2609.11916) [[pdf](/pdf/2609.11916), [html](https://arxiv.org/html/2609.11916v1), [other](/format/2609.11916)]\n\n\n\nTitle: Can Edge-Deployable Vision-Language Models Identify Species?\n\n\n\n[William Zhou](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [Mayukha Siripuram](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [Xiao Yan](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [Ziqi Liu](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [Yi Ding](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D)\n\n\n\nSubjects: Artificial Intelligence (cs.AI)\n\n\n\n   [2] [arXiv:2609.11911](/abs/2609.11911) [[pdf](/pdf/2609.11911), [html](https://arxiv.org/html/2609.11911v1), [other](/format/2609.11911)]\n\n\n\nTitle: Artificial Id: Drive and Persistent Alignment in Agentic AI\n\n\n\n[Yakov Pyotr Shkolnikov](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D)\n\n\n\nSubjects: Artificial Intelligence (cs.AI)\n\n\n\n   [3] [arXiv:2609.11900](/abs/2609.11900) [[pdf](/pdf/2609.11900), [html](https://arxiv.org/html/2609.11900v1), [other](/format/2609.11900)]\n\n\n\nTitle: MindTopo: Can Foundation Models Reason in Topological Space?\n\n\n\n[Yunfei Ge](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [Anbang Liu](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [Qineng Wang](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [Johnalbert Garnica](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [Jianwen Lyu](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [Zihan Wang](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [Reuben Tan](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [Jianfeng Gao](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [Ruohan Zhang](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [Yining Hong](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [Jiajun Wu](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [Manling Li](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D)\n\n\n\nComments: Preprint version\n\n\n\nSubjects: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV)\n\n\n\n   [4] [arXiv:2609.11876](/abs/2609.11876) [[pdf](/pdf/2609.11876), [html](https://arxiv.org/html/2609.11876v1), [other](/format/2609.11876)]\n\n\n\nTitle: On the Regularization Landscape for the Linear Recommendation Models\n\n\n\n[Dong Li](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [Zhenming Liu](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [Ruoming Jin](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [Hao Zhou](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [Zhi Liu](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [Jing Gao](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [Bin Ren](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D)\n\n\n\nSubjects: Artificial Intelligence (cs.AI)\n\n\n\n   [5] [arXiv:2609.11860](/abs/2609.11860) [[pdf](/pdf/2609.11860), [html](https://arxiv.org/html/2609.11860v1), [other](/format/2609.11860)]\n\n\n\nTitle: Explainability Assistant: A Conversational XAI Interface for Interpreting Energy Consumption Models\n\n\n\n[Rodion Krjutškov](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [Eduard Barbu](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [Nikos Sakkas](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [Sofia Yfanti](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D)\n\n\n\nComments: 11 pages, 3 figures. Accepted author version of a paper published at ICECET 2026\n\n\n\nJournal-ref: 2026 6th International Conference on Electrical, Computer and Energy Technologies (ICECET), Rome, Italy, 2026\n\n\n\nSubjects: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)\n\n\n\n   [6] [arXiv:2609.11859](/abs/2609.11859) [[pdf](/pdf/2609.11859), [html](https://arxiv.org/html/2609.11859v1), [other](/format/2609.11859)]\n\n\n\nTitle: From Parameters to Answers: How LLMs Retrieve and Use Their Internal Knowledge\n\n\n\n[Wenkang Wei](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [Yuan Fang](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [Renhe Jiang](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [Hong Cheng](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [Xingtong Yu](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D)\n\n\n\nComments: 53 pages, 13 figures, including appendices\n\n\n\nSubjects: Artificial Intelligence (cs.AI)\n\n\n\n   [7] [arXiv:2609.11768](/abs/2609.11768) [[pdf](/pdf/2609.11768), [html](https://arxiv.org/html/2609.11768v1), [other](/format/2609.11768)]\n\n\n\nTitle: A Unified Per-Token Gating Family for On-Policy Distillation: FKL/RKL Mixing with Multi-Channel and Bias Coefficients\n\n\n\n[Suwan Wu](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [Yumeng Lin](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [Pengcheng Yuan](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [Xiaolong Jiang](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D)\n\n\n\nComments: Accepted at the Findings of the 2026 Conference on Empirical Methods in Natural Language Processing (EMNLP 2026 Findings)\n\n\n\nSubjects: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG)\n\n\n\n   [8] [arXiv:2609.11752](/abs/2609.11752) [[pdf](/pdf/2609.11752), [html](https://arxiv.org/html/2609.11752v1), [other](/format/2609.11752)]\n\n\n\nTitle: SIRF: A Spec-Internalized Risk Foundation Model for Industrial Content Risk Control\n\n\n\n[Suwan Wu](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [Yumeng Lin](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [Pengcheng Yuan](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [Xiaolong Jiang](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D)\n\n\n\nComments: 14 pages, 12 figures. Accepted at the Industry Track of EMNLP 2026\n\n\n\nSubjects: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG)\n\n\n\n   [9] [arXiv:2609.11709](/abs/2609.11709) [[pdf](/pdf/2609.11709), [html](https://arxiv.org/html/2609.11709v1), [other](/format/2609.11709)]\n\n\n\nTitle: When Agents Disagree: Bayesian Backward Reasoning as a Label-Free Anchor for Multi-Agent Collective Decision-Making\n\n\n\n[Ken Chen](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [Wei Wang](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [Sachith Seneviratne](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [Hansani Weeratunge](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [Saman Halgamuge](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D)\n\n\n\nSubjects: Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA)\n\n\n\n   [10] [arXiv:2609.11682](/abs/2609.11682) [[pdf](/pdf/2609.11682), [html](https://arxiv.org/html/2609.11682v1), [other](/format/2609.11682)]\n\n\n\nTitle: COBRA-Skills: Contextual Bandit-Guided Evolution for Agent Skill Optimization\n\n\n\n[Pingchen Lu](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [Xiangyi Wang](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [Xiang Li](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [Jie Mao](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [Zikun Qu](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [Junfeng Luo](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [Yao Shu](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [Bryan Kian Hsiang Low](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [Zhongxiang Dai](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D)\n\n\n\nSubjects: Artificial Intelligence (cs.AI)\n\n\n\n   [11] [arXiv:2609.11674](/abs/2609.11674) [[pdf](/pdf/2609.11674), [other](/format/2609.11674)]\n\n\n\nTitle: Geospatial AI, Dataverse Metadata, and the Study of Place-Based Government\n\n\n\n[Danny EBanks](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [Devika Jain](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D)\n\n\n\nSubjects: Artificial Intelligence (cs.AI)\n\n\n\n   [12] [arXiv:2609.11660](/abs/2609.11660) [[pdf](/pdf/2609.11660), [other](/format/2609.11660)]\n\n\n\nTitle: Autonomy, Social Norms, and Alignment: Towards a Developmental Framework for Autonomous Artificial Agents\n\n\n\n[Marica Notte](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [Ludovica Marinucci](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [Vieri Giuliano Santucci](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D)\n\n\n\nComments: In publication in the proceedings of SIpEIA 2026 conference\n\n\n\nSubjects: Artificial Intelligence (cs.AI)\n\n\n\n   [13] [arXiv:2609.11636](/abs/2609.11636) [[pdf](/pdf/2609.11636), [html](https://arxiv.org/html/2609.11636v1), [other](/format/2609.11636)]\n\n\n\nTitle: MAPLE: Memory-Augmented Planning with Language and Evolution\n\n\n\n[Kesheng Chen](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [Yamin Hu](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [Wenjian Luo](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D)\n\n\n\nSubjects: Artificial Intelligence (cs.AI)\n\n\n\n   [14] [arXiv:2609.11615](/abs/2609.11615) [[pdf](/pdf/2609.11615), [html](https://arxiv.org/html/2609.11615v1), [other](/format/2609.11615)]\n\n\n\nTitle: Distributed Optimization of Modular Production Systems using Model-based Reinforcement Learning with Inverse Models\n\n\n\n[Andreas Schwung](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [Steve Yuwono](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [Sofiene Lassoued](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [Dorothea Schwung](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D)\n\n\n\nSubjects: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Systems and Control (eess.SY)\n\n\n\n   [15] [arXiv:2609.11607](/abs/2609.11607) [[pdf](/pdf/2609.11607), [html](https://arxiv.org/html/2609.11607v1), [other](/format/2609.11607)]\n\n\n\nTitle: Making Alternative Data Work: Context-Augmented LLMs for Financial Forecasting\n\n\n\n[Jihoon Kwon](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [Lawrence Liu](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [Daekyung Park](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [Sumin Kim](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [Haverty Jack](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [Hoyoung Lee](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [Katherine Bjorkman](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [Josh McKenney](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [Peter Laurelli](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [Nicole Kagan](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [Zach Golkhou](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [Thorsten Neumann](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [Edward Tong](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [Pete Petersen](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [Yoon Kim](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [Alejandro Lopez-Lira](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [Yongjae Lee](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [Chanyeol Choi](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D)\n\n\n\nComments: 13 pages\n\n\n\nSubjects: Artificial Intelligence (cs.AI)\n\n\n\n   [16] [arXiv:2609.11569](/abs/2609.11569) [[pdf](/pdf/2609.11569), [html](https://arxiv.org/html/2609.11569v1), [other](/format/2609.11569)]\n\n\n\nTitle: Enabling Knowledge Graph Understanding at Scale with the EXplore Your Graphs ENgine (EXYGEN)\n\n\n\n[Harshdeep Singh](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [Yurui Zhu](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [Giovanni Colavizza](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [Matteo Romanello](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D)\n\n\n\nSubjects: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)\n\n\n\n   [17] [arXiv:2609.11542](/abs/2609.11542) [[pdf](/pdf/2609.11542), [html](https://arxiv.org/html/2609.11542v1), [other](/format/2609.11542)]\n\n\n\nTitle: Characterizing Job Power Elasticity for Power-Flexible AI Training\n\n\n\n[Philip Colangelo](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [Charles Dawson](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [Shayan Sengupta](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [Ayse Coskun](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [Varun Sivaram](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D)\n\n\n\nSubjects: Artificial Intelligence (cs.AI)\n\n\n\n   [18] [arXiv:2609.11532](/abs/2609.11532) [[pdf](/pdf/2609.11532), [html](https://arxiv.org/html/2609.11532v1), [other](/format/2609.11532)]\n\n\n\nTitle: Prompt Revision as a Source of Cultural Bias in Text-to-Image Systems\n\n\n\n[Aleksandra Urman](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [Elsa Lichtenegger](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [Salima Jaoua](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [Azza Bouleimen](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [Robin Forsberg](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [Corinna Hertweck](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [Stefania Ionescu](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [Nicolò Pagan](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [Ancsa Hannak](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [Joachim Baumann](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D)\n\n\n\nComments: Accepted to EMNLP 2026\n\n\n\nSubjects: Artificial Intelligence (cs.AI)\n\n\n\n   [19] [arXiv:2609.11527](/abs/2609.11527) [[pdf](/pdf/2609.11527), [other](/format/2609.11527)]\n\n\n\nTitle: Lightweight LiDAR-Based Cone Detection Framework Using Random Forest for Formula Student Driverless\n\n\n\n[Márk Mező-Kerekes](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [Péter Praksz](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [Chang Liu](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D)\n\n\n\nComments: 9 pages, 2 figures, 3 tables. Accepted at the 5th International Conference on Cognitive Mobility (CogMob 2026)\n\n\n\nSubjects: Artificial Intelligence (cs.AI)\n\n\n\n   [20] [arXiv:2609.11509](/abs/2609.11509) [[pdf](/pdf/2609.11509), [html](https://arxiv.org/html/2609.11509v1), [other](/format/2609.11509)]\n\n\n\nTitle: Extending SMT Solving with Non-Ground Clause Learning\n\n\n\n[Yasmine Briefs](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [Christoph Weidenbach](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D)\n\n\n\nComments: Extended version of LPAR 2026 paper\n\n\n\nSubjects: Artificial Intelligence (cs.AI); Logic in Computer Science (cs.LO)\n\n\n\n   [21] [arXiv:2609.11498](/abs/2609.11498) [[pdf](/pdf/2609.11498), [html](https://arxiv.org/html/2609.11498v1), [other](/format/2609.11498)]\n\n\n\nTitle: ActMap: Single-Pass Uncertainty Quantification from Generation-Time Activation Maps\n\n\n\n[Jacopo Dardini](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D) (University of Bologna), [Roberta Calegari](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D) (University of Bologna)\n\n\n\nComments: 13 pages, 4 figures, 10 tables. Includes technical appendix\n\n\n\nSubjects: Artificial Intelligence (cs.AI)\n\n\n\n   [22] [arXiv:2609.11493](/abs/2609.11493) [[pdf](/pdf/2609.11493), [html](https://arxiv.org/html/2609.11493v1), [other](/format/2609.11493)]\n\n\n\nTitle: From Document Silos to Process Intelligence: A Multi-Layer Knowledge Graph for CMC Process Development\n\n\n\n[Reza Amirmoshiri](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [Faryad Sahneh](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [Yasser Jangjou](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D)\n\n\n\nSubjects: Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA)\n\n\n\n   [23] [arXiv:2609.11490](/abs/2609.11490) [[pdf](/pdf/2609.11490), [html](https://arxiv.org/html/2609.11490v1), [other](/format/2609.11490)]\n\n\n\nTitle: Published Unlearning Numbers Move Per Checkpoint, and Not Because the Removed Data Survives: An Audit of 263 Released Batch-Normalized Checkpoints\n\n\n\n[Junlong Shen Xingyu Li](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D)\n\n\n\nComments: 38 pages, 4 figures, 26 tables. Independent of and concurrent with [arXiv:2609.08901](https://arxiv.org/abs/2609.08901) (posted 8 Sep 2026): the instrument and protocol here were pre-registered on 29 Aug 2026; dated provenance in Appendix S\n\n\n\nSubjects: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)\n\n\n\n   [24] [arXiv:2609.11489](/abs/2609.11489) [[pdf](/pdf/2609.11489), [html](https://arxiv.org/html/2609.11489v1), [other](/format/2609.11489)]\n\n\n\nTitle: The Convention Gap: Towards Measuring Implicit Communication in Cooperative AI Evaluation\n\n\n\n[Makoto Fukushima](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [Hua-Dong Xiong](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [Ehsan Moradi Pari](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D)\n\n\n\nSubjects: Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC)\n\n\n\n   [25] [arXiv:2609.11458](/abs/2609.11458) [[pdf](/pdf/2609.11458), [html](https://arxiv.org/html/2609.11458v1), [other](/format/2609.11458)]\n\n\n\nTitle: Flexible and Interpretable Accent Distance Measurements\n\n\n\n[Charles McGhee](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [Mark J. F. Gales](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [Kate M. Knill](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D)\n\n\n\nSubjects: Artificial Intelligence (cs.AI)\n\n\n\n   [26] [arXiv:2609.11452](/abs/2609.11452) [[pdf](/pdf/2609.11452), [other](/format/2609.11452)]\n\n\n\nTitle: RouteRepair: Instance-Level Failure Diagnosis and Targeted Repair in LLM-Based Automated Heuristic Design for Routing Optimization\n\n\n\n[Binghao Ji](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [Di Huang](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [Jiahui Fang](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [Zhiyuan Liu](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D)\n\n\n\nComments: 22 pages, 13 figures, 11 tables\n\n\n\nSubjects: Artificial Intelligence (cs.AI)\n\n\n\n   [27] [arXiv:2609.11446](/abs/2609.11446) [[pdf](/pdf/2609.11446), [html](https://arxiv.org/html/2609.11446v1), [other](/format/2609.11446)]\n\n\n\nTitle: Calibration-Aware Uncertainty Cascades for Efficient Heterogeneous Model Collaboration\n\n\n\n[Yilin Zhang](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [Han Jiang](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [Cai Xu](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [Ying Liu](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [Wei Zhao](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D)\n\n\n\nComments: 13 pages, 6 figures, 6 tables, including appendix. Under review\n\n\n\nSubjects: Artificial Intelligence (cs.AI)\n\n\n\n   [28] [arXiv:2609.11431](/abs/2609.11431) [[pdf](/pdf/2609.11431), [html](https://arxiv.org/html/2609.11431v1), [other](/format/2609.11431)]\n\n\n\nTitle: LLMs as Post-hoc Auditors of Physiological Plausibility in Symbolic Regression: A Clinician-Evaluated Case Study\n\n\n\n[Jorge López-Varela](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [J. Ignacio Hidalgo](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [José-Manuel Muñoz](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [Omar Costilla-Reyes](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [Esther Maqueda](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [Jesus Moreno-Fernandez](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [Tomás González-Vidal](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [J. Manuel Velasco](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [Oscar Garnica](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D)\n\n\n\nSubjects: Artificial Intelligence (cs.AI)\n\n\n\n   [29] [arXiv:2609.11403](/abs/2609.11403) [[pdf](/pdf/2609.11403), [html](https://arxiv.org/html/2609.11403v1), [other](/format/2609.11403)]\n\n\n\nTitle: From Queries to Narratives: Cultural Heritage Data Stories for Knowledge Graph Exploration and Quality Assessment\n\n\n\n[Tabea Tietz](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [Torsten Schrade](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [Etienne Posthumus](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [Linnaea Söhn](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [Jonatan Jalle Steller](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [Jörg Waitelonis](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [Harald Sack](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D)\n\n\n\nSubjects: Artificial Intelligence (cs.AI); Digital Libraries (cs.DL)\n\n\n\n   [30] [arXiv:2609.11393](/abs/2609.11393) [[pdf](/pdf/2609.11393), [html](https://arxiv.org/html/2609.11393v1), [other](/format/2609.11393)]\n\n\n\nTitle: Beyond Confidence: Stability-Aware Test-Time Adaptation for LLM Reasoning\n\n\n\n[Bincheng Gu](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [Min Gao](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [Zongwei Wang](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [Yibing Bai](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [Yulan He](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [Junliang Yu](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D)\n\n\n\nSubjects: Artificial Intelligence (cs.AI)\n\n\n\n   [31] [arXiv:2609.11372](/abs/2609.11372) [[pdf](/pdf/2609.11372), [html](https://arxiv.org/html/2609.11372v1), [other](/format/2609.11372)]\n\n\n\nTitle: RAMamba-Net: A Reliability-Aware and Mamba-Based Multimodal Fusion Network for Auditory Attention Detection\n\n\n\n[Xingyi He](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [Ziwei Wang](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [Dongrui Wu](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D)\n\n\n\nComments: RAMamba-Net, a reliability-aware Mamba-based multimodal fusion network for auditory attention decoding\n\n\n\nSubjects: Artificial Intelligence (cs.AI)\n\n\n\n   [32] [arXiv:2609.11365](/abs/2609.11365) [[pdf](/pdf/2609.11365), [html](https://arxiv.org/html/2609.11365v1), [other](/format/2609.11365)]\n\n\n\nTitle: Portable Semantics, Private Dialects: Reuse and Negative Transfer in Latent Communication Between Language-Model Cells\n\n\n\n[Narcis Marincat](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D)\n\n\n\nComments: 17 pages, 1 figure, 5 tables. Companion to [arXiv:2608.20054](https://arxiv.org/abs/2608.20054). Code and evaluation records: [this https URL](https://github.com/tokenosopher/populus-evidence-partitioning) ; checkpoints and fitted alignment maps: [this https URL](https://huggingface.co/tokenosopher/populus-evidence-partitioning-checkpoints)\n\n\n\nSubjects: Artificial Intelligence (cs.AI)\n\n\n\n   [33] [arXiv:2609.11341](/abs/2609.11341) [[pdf](/pdf/2609.11341), [html](https://arxiv.org/html/2609.11341v1), [other](/format/2609.11341)]\n\n\n\nTitle: Exploring Diffusion Transformers for Cross-Modal Augmentation in Multimodal Brain State Decoding\n\n\n\n[Ziwei Wang](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [Xingyi He](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [Hongbin Wang](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [Tianwang Jia](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [Bohan Fang](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [Dongrui Wu](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D)\n\n\n\nComments: CoMA-DiT, a cross-modal augmentation framework built on Diffusion Transformer, extends multimodal learning beyond fusion by leveraging paired modalities as mutual generative supervision to enrich training data and improve brain state decoding\n\n\n\nSubjects: Artificial Intelligence (cs.AI)\n\n\n\n   [34] [arXiv:2609.11321](/abs/2609.11321) [[pdf](/pdf/2609.11321), [html](https://arxiv.org/html/2609.11321v1), [other](/format/2609.11321)]\n\n\n\nTitle: AI Exposure and AI Resilience: A Two-Dimensional Assessment Framework for Software and Software-Based Business Model\n\n\n\n[Paul Darius Mandl](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D) (Findustrial GmbH), [Peter Mandl](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D) (Munich University of Applied Sciences), [Martin Häusl](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D) (Munich University of Applied Sciences)\n\n\n\nComments: 14 pages, 3 figures, 6 tables. Preprint\n\n\n\nSubjects: Artificial Intelligence (cs.AI)\n\n\n\n   [35] [arXiv:2609.11319](/abs/2609.11319) [[pdf](/pdf/2609.11319), [html](https://arxiv.org/html/2609.11319v1), [other](/format/2609.11319)]\n\n\n\nTitle: Magenta: Closing the Loop Between Mathematical Reasoning and Lean Verification\n\n\n\n[Joshua Ong Jun Leang](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [Haonan Li](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [Zheng Zhao](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [Xinyi Shang](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [Wenda Li](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [Zhengzhong Liu](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [Erix Xing](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [Shay Cohen](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [Eleonora Giunchiglia](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D)\n\n\n\nComments: 9 pages, preprint\n\n\n\nSubjects: Artificial Intelligence (cs.AI)\n\n\n\n   [36] [arXiv:2609.11318](/abs/2609.11318) [[pdf](/pdf/2609.11318), [html](https://arxiv.org/html/2609.11318v1), [other](/format/2609.11318)]\n\n\n\nTitle: Mr.LHDR: A Benchmark for Multimodal Real-World Long-Horizon Deep Research Agents\n\n\n\n[Minghao Guo](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [Meng Cao](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [Sui Zhao](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [Siyu Ning](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [Xin Wang](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [Haoze Zhao](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [Jiaxuan Yang](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [Haihong Hao](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [Mingfei Han](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [Shunlin Rong](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [Haijun Wu](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [Xiaodan Liang](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [Xiaojun Chang](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D)\n\n\n\nComments: Code and data are available at [this https URL](https://github.com/minghaoguo20/Mr-LHDR-eval)\n\n\n\nSubjects: Artificial Intelligence (cs.AI)\n\n\n\n   [37] [arXiv:2609.11315](/abs/2609.11315) [[pdf](/pdf/2609.11315), [html](https://arxiv.org/html/2609.11315v1), [other](/format/2609.11315)]\n\n\n\nTitle: Routing by Reasoning Need: Trajectory-Aware Decoding Control for Diffusion Vision-Language Models\n\n\n\n[Yixiang Liu](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [Zhongxing Xu](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [Zhonghua Wang](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [Xiaoying Tang](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D)\n\n\n\nComments: 17 pages, 9 figures. Accepted to Findings of EMNLP 2026\n\n\n\nSubjects: Artificial Intelligence (cs.AI)\n\n\n\n   [38] [arXiv:2609.11294](/abs/2609.11294) [[pdf](/pdf/2609.11294), [html](https://arxiv.org/html/2609.11294v1), [other](/format/2609.11294)]\n\n\n\nTitle: Memory Compression for High-Fanout Agent Sandboxes\n\n\n\n[Mengming Li](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [Ceyu XU](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [Qijun Zhang](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [Jiangnan Yu](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [Xiangfeng Sun](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [Haohui Mai](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [Zhiyao Xie](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D)\n\n\n\nSubjects: Artificial Intelligence (cs.AI); Operating Systems (cs.OS)\n\n\n\n   [39] [arXiv:2609.11291](/abs/2609.11291) [[pdf](/pdf/2609.11291), [html](https://arxiv.org/html/2609.11291v1), [other](/format/2609.11291)]\n\n\n\nTitle: Off-Target Effects of Response-Style Alignment in a Korean 27B Language Model\n\n\n\n[Hyojung Han](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D)\n\n\n\nComments: 19 pages. Korean-language evaluation (KoBBQ); all uncertainty estimates over KoBBQ items are clustered on the benchmark template\n\n\n\nSubjects: Artificial Intelligence (cs.AI)\n\n\n\n   [40] [arXiv:2609.11286](/abs/2609.11286) [[pdf](/pdf/2609.11286), [other](/format/2609.11286)]\n\n\n\nTitle: Generating a Consistent Enterprise: Synthesis and Reference-Free Evaluation of Multi-System Business Data\n\n\n\n[Benjamin Gruenbaum](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [Doron Porat](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [Assaf Natanzon](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [Roy Zavida](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [Chen Dinachi](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [Or Itzahary](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [Omer Niv](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D)\n\n\n\nComments: 10 pages\n\n\n\nSubjects: Artificial Intelligence (cs.AI)\n\n\n\n   [41] [arXiv:2609.11282](/abs/2609.11282) [[pdf](/pdf/2609.11282), [html](https://arxiv.org/html/2609.11282v1), [other](/format/2609.11282)]\n\n\n\nTitle: When Does Text Inform? Benchmarking Information-Theoretic Metrics for Multimodal Time-Series Forecasting\n\n\n\n[Emma Andrews](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [Gianmarco Mengaldo](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D)\n\n\n\nSubjects: Artificial Intelligence (cs.AI); Information Theory (cs.IT)\n\n\n\n   [42] [arXiv:2609.11281](/abs/2609.11281) [[pdf](/pdf/2609.11281), [html](https://arxiv.org/html/2609.11281v1), [other](/format/2609.11281)]\n\n\n\nTitle: Bio-inspired Learning and Decision-Making with Probabilistic In-Memory Computing Hardware: Part 1\n\n\n\n[Thomas Dalgaty](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [Eiji Kawasaki](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [Miguel de Prado](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [Devendra Vyas](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [Tommaso Salvatori](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D)\n\n\n\nSubjects: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)\n\n\n\n   [43] [arXiv:2609.11277](/abs/2609.11277) [[pdf](/pdf/2609.11277), [html](https://arxiv.org/html/2609.11277v1), [other](/format/2609.11277)]\n\n\n\nTitle: Predicting Train Delays in Finland Using Machine Learning and Weather Data\n\n\n\n[Vinicius Pozzobon Borin](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [Jean Michel de Souza Sant'Ana](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D'Ana,+J+M), [Nurul Huda Mahmood](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D)\n\n\n\nComments: 6 pages, 3 Figures, 4 tables, presented at Wireless Europe 2026, Rimini, Italy, June 2026\n\n\n\nSubjects: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)\n\n\n\n   [44] [arXiv:2609.11262](/abs/2609.11262) [[pdf](/pdf/2609.11262), [other](/format/2609.11262)]\n\n\n\nTitle: AI-Powered Flare Combustion Efficiency Estimation\n\n\n\n[Afeefa Azam](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [Iyyakutti Iyappan Ganapathi](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [Fares Ossama Abdelhafez](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [Divya Velayudhan](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [Maregu Assefa Habtie](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [Hamad Karki](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [Khalid Yousef Al Awadhi](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [Naoufel Werghi](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D)\n\n\n\nComments: Accepted at the 4th International Conference on Machine Learning and Data Engineering (ICMLDE 2025). 5 pages\n\n\n\nSubjects: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)\n\n\n\n   [45] [arXiv:2609.11243](/abs/2609.11243) [[pdf](/pdf/2609.11243), [html](https://arxiv.org/html/2609.11243v1), [other](/format/2609.11243)]\n\n\n\nTitle: Sci-MMR: Benchmarking Multi-Step Evidence-Grounded Scientific Reasoning in Multimodal Agents\n\n\n\n[Jiaqiang Li](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [Yajie Yang](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [Zhiheng Xi](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [Jiadong Chen](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [Enyu Zhou](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [Senjie Jin](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [Yang Nan](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [Jiazheng Zhang](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [Han Wang](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [Yanxin Li](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [Dingwei Zhu](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [Bicheng Deng](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [Yuhui Wang](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [Xiang Zheng](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [Qi Zhang](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [Lei Bai](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [Xingjun Ma](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [Tao Gui](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D)\n\n\n\nSubjects: Artificial Intelligence (cs.AI)\n\n\n\n   [46] [arXiv:2609.11234](/abs/2609.11234) [[pdf](/pdf/2609.11234), [html](https://arxiv.org/html/2609.11234v1), [other](/format/2609.11234)]\n\n\n\nTitle: NovGauge: A Fine-Grained Benchmark for Diagnosing LLMs' Capability in Paper Novelty Assessment\n\n\n\n[Guoqiang Zhang](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [Kexin Tan](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [Ming Zhang](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [Li Ju](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [Wenqing Jing](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [Zhonghan Yue](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [Jiayi Chen](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [Shiqiang Wu](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [Shaofan Liu](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [Yue Zhang](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [Yuankai Ying](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [Yang Shi](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [Tao Gui](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [Qi Zhang](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [Xuanjing Huang](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D)\n\n\n\nSubjects: Artificial Intelligence (cs.AI)\n\n\n\n   [47] [arXiv:2609.11231](/abs/2609.11231) [[pdf](/pdf/2609.11231), [html](https://arxiv.org/html/2609.11231v1), [other](/format/2609.11231)]\n\n\n\nTitle: A Voice-Interactive Multi-Agent System for Smart Operating Rooms: Architecture Design and Key Technologies\n\n\n\n[Tianxiang Zhou](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D)\n\n\n\nSubjects: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Human-Computer Interaction (cs.HC)\n\n\n\n   [48] [arXiv:2609.11206](/abs/2609.11206) [[pdf](/pdf/2609.11206), [html](https://arxiv.org/html/2609.11206v1), [other](/format/2609.11206)]\n\n\n\nTitle: CryptoL: Towards Scale Dominance and Physics Constraints Mitigation in Financial Multivariate Time Series Forecasting\n\n\n\n[Yalda Taheri](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [Mohammad Hassan Heydari](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [Armon Rasooli](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [Maryam Amirshahkarami](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [Mohammad Ebrahim Mahdavi](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [Hossein Karshenas](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D)\n\n\n\nSubjects: Artificial Intelligence (cs.AI); Computational Engineering, Finance, and Science (cs.CE); Machine Learning (cs.LG)\n\n\n\n   [49] [arXiv:2609.11199](/abs/2609.11199) [[pdf](/pdf/2609.11199), [other](/format/2609.11199)]\n\n\n\nTitle: An AI-Powered Culturally Aware Chatbot for Stress Detection and Wellness Support among Pakistani University Students Using NLP and Machine Learning\n\n\n\n[Muhammad Fahad Bashir](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D), [Muhammad Afzal](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D)\n\n\n\nSubjects: Artificial Intelligence (cs.AI)\n\n\n\n   [50] [arXiv:2609.11190](/abs/2609.11190) [[pdf](/pdf/2609.11190), [other](/format/2609.11190)]\n\n\n\nTitle: Agentic Share-of-Search: A Multi-Agent AI System for Competitive Decision-Making in LLM-Mediated E-Commerce\n\n\n\n[Spandan Ghose Chowdhury](https://arxiv.org/search/cs?searchtype=%5BREDACTED%5D&query=%5BREDACTED%5D)\n\n\n\nComments: Accepted for presentation at the 2026 Decision Science Institute Annual Conference\n\n\n\nSubjects: Artificial Intelligence (cs.AI); Information Retrieval (cs.IR)\n\n\n\n\n\nTotal of 1208 entries : 1-50 [51-100](/list/cs.AI/recent?skip=50&show=50) [101-150](/list/cs.AI/recent?skip=100&show=50) [151-200](/list/cs.AI/recent?skip=150&show=50) ... [1201-1208](/list/cs.AI/recent?skip=1200&show=50)\n\n\n\nShowing up to 50 entries per page: [fewer](/list/cs.AI/recent?skip=0&show=25) | [more](/list/cs.AI/recent?skip=0&show=100) | [all](/list/cs.AI/recent?skip=0&show=2000)\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n We gratefully acknowledge support from our **major funders**, [**member institutions**](https://info.arxiv.org/about/ourmembers.html), ****, and all contributors.\n\n\n\n [About](https://info.arxiv.org/about) · [Help](https://info.arxiv.org/help) · [Contact](https://info.arxiv.org/help/contact.html) · [Subscribe](https://info.arxiv.org/help/subscribe) · [Copyright](https://info.arxiv.org/help/license/index.html) · [Privacy](https://info.arxiv.org/help/policies/privacy_policy.html) · [Accessibility](https://info.arxiv.org/help/web_accessibility.html) · [Operational Status (opens in new tab)](https://status.arxiv.org)\n\n\n\n\n\n\n\nMajor funding support from\n\n\n\n [![Simons Foundation](/static/base/1.0.1/images/funders/simons-foundation.png)](https://www.simonsfoundation.org/) [![Simons Foundation International](/static/base/1.0.1/images/funders/simons-foundation-international.png)](https://www.sfi.org.bm/) [![Schmidt Sciences](/static/base/1.0.1/images/funders/schmidt-sciences.png)](https://www.schmidtsciences.org/)",
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    "message": "Here are the 20 newest cs.AI listings (Fri, 11 Sep 2026) from [arXiv cs.AI recent](https://arxiv.org/list/cs.AI/recent), grouped:\n\n**1. Agents: autonomy, skills, and collective decisions (5)**\n- [Artificial Id: Drive and Persistent Alignment in Agentic AI](https://arxiv.org/abs/2609.11911)\n- [When Agents Disagree: Bayesian Backward Reasoning as a Label-Free Anchor for Multi-Agent Collective Decision-Making](https://arxiv.org/abs/2609.11709)\n- [COBRA-Skills: Contextual Bandit-Guided Evolution for Agent Skill Optimization](https://arxiv.org/abs/2609.11682)\n- [MAPLE: Memory-Augmented Planning with Language and Evolution](https://arxiv.org/abs/2609.11636)\n- [Autonomy, Social Norms, and Alignment: Towards a Developmental Framework for Autonomous Artificial Agents](https://arxiv.org/abs/2609.11660)\n\n**2. Foundation-model capabilities and internals (4)**\n- [Can Edge-Deployable Vision-Language Models Identify Species?](https://arxiv.org/abs/2609.11916)\n- [MindTopo: Can Foundation Models Reason in Topological Space?](https://arxiv.org/abs/2609.11900)\n- [From Parameters to Answers: How LLMs Retrieve and Use Their Internal Knowledge](https://arxiv.org/abs/2609.11859)\n- [A Unified Per-Token Gating Family for On-Policy Distillation](https://arxiv.org/abs/2609.11768)\n\n**3. Industrial and domain applications (6)**\n- [SIRF: A Spec-Internalized Risk Foundation Model for Industrial Content Risk Control](https://arxiv.org/abs/2609.11752)\n- [Distributed Optimization of Modular Production Systems using Model-based RL](https://arxiv.org/abs/2609.11615)\n- [Making Alternative Data Work: Context-Augmented LLMs for Financial Forecasting](https://arxiv.org/abs/2609.11607)\n- [Characterizing Job Power Elasticity for Power-Flexible AI Training](https://arxiv.org/abs/2609.11542)\n- [Lightweight LiDAR-Based Cone Detection Using Random Forest for Formula Student Driverless](https://arxiv.org/abs/2609.11527)\n- [On the Regularization Landscape for the Linear Recommendation Models](https://arxiv.org/abs/2609.11876)\n\n**4. Knowledge graphs, geospatial data, and symbolic reasoning (3)**\n- [Enabling Knowledge Graph Understanding at Scale with EXYGEN](https://arxiv.org/abs/2609.11569)\n- [Geospatial AI, Dataverse Metadata, and the Study of Place-Based Government](https://arxiv.org/abs/2609.11674)\n- [Extending SMT Solving with Non-Ground Clause Learning](https://arxiv.org/abs/2609.11509)\n\n**5. Interpretability, bias, and human-facing AI (2)**\n- [Explainability Assistant: A Conversational XAI Interface for Interpreting Energy Consumption Models](https://arxiv.org/abs/2609.11860)\n- [Prompt Revision as a Source of Cultural Bias in Text-to-Image Systems](https://arxiv.org/abs/2609.11532)\n\n**Most common direction:** making LLM-based agents more reliable, by giving them memory, evolving skills, and principled ways to resolve disagreement, paired with work probing what foundation models actually know and reason about. Applied papers mostly wrap those models around a domain (finance, manufacturing, content risk) rather than proposing new architectures."
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  "result": "Delivered (confirmed) as 3 messages. Your draft was polished into texting register, so the wording differs from what you wrote; this is exactly what the user sees:\n1) Five themes across the 20 newest cs.AI papers.\n2) ### cs.AI recent papers by theme · arXiv, 11 Sep 2026\n\n| Theme | Papers |\n|-------|--------|\n| Agents: autonomy, skills, collective decisions | https://arxiv.org/abs/2609.11911, https://arxiv.org/abs/2609.11709, https://arxiv.org/abs/2609.11682, https://arxiv.org/abs/2609.11636, https://arxiv.org/abs/2609.11660 |\n| Foundation model capabilities and internals | https://arxiv.org/abs/2609.11916, https://arxiv.org/abs/2609.11900, https://arxiv.org/abs/2609.11859, https://arxiv.org/abs/2609.11768 |\n| Industrial and domain applications | https://arxiv.org/abs/2609.11752, https://arxiv.org/abs/2609.11615, https://arxiv.org/abs/2609.11607, https://arxiv.org/abs/2609.11542, https://arxiv.org/abs/2609.11527, https://arxiv.org/abs/2609.11876 |\n| Knowledge graphs, geospatial data, symbolic reasoning | https://arxiv.org/abs/2609.11569, https://arxiv.org/abs/2609.11674, https://arxiv.org/abs/2609.11509 |\n| Interpretability, bias, and human-facing AI | https://arxiv.org/abs/2609.11860, https://arxiv.org/abs/2609.11532 |\n3) The core direction: making LLM-based agents reliable through memory, skill evolution, and principled disagreement resolution, while probing what foundation models actually know. Most applied work wraps those models around domains like finance or manufacturing.\nThese are your words now, and this is the whole reply: the delivery layer chose its density and shape by design. Never dispute, re-explain, or apologize for the phrasing, and never send a supplement to restore detail it left out. Only a final send settles its request. Never chase wording with another send. If a fact in it is wrong and would change what the user does, correct it when their next message arrives.\n\nNothing else is open: no request awaits a reply and no work is running. A response with no tool call ends the run.\nanswers: 855c794c-70c3-4a0f-a89e-03af3d1a2dc5",
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