arXiv:2604. 12306v3 Announce Type: replace-cross Abstract: Climate decision-making in the GCC states increasingly demands systems that can translate heterogeneous scientific and policy evidence into actionable guidance, yet general-purpose large language models (LLMs) remain weak both in region-specific climate knowledge and grounded interaction with geospatial and forecasting tools.
By Muhammad Umer Sheikh, Khawar Shehzad, Salman Khan, Fahad Shahbaz Khan, Muhammad Haris Khan
arXiv:2505. 01458v2 Announce Type: replace-cross Abstract: Navigation and manipulation are core capabilities in Embodied AI, but training agents to perform them directly in the real world is costly, time-consuming, and unsafe.
By Lik Hang Kenny Wong, Xueyang Kang, Kaixin Bai, Jianwei Zhang
arXiv:2606. 10607v1 Announce Type: cross Abstract: Causal discovery aims to uncover causal structures from observational data, which is crucial for real-world decision-making.
By Xinyu Li, Yuanyuan Wang, Haoxuan Li, Chuan Zhou, Erdun Gao, Bo Han, Tongliang Liu, Kun Zhang, Howard Bondell, Mingming Gong
arXiv:2606. 11045v1 Announce Type: new Abstract: Reusing a held-out benchmark adaptively should, in principle, invite overfitting.
By Martin Andres Bertran, Aaron Roth, Zhiwei Steven Wu
arXiv:2404. 11716v2 Announce Type: replace Abstract: Building Energy Management (BEM) is central to reducing energy use and CO2 emissions in the building sector.
By Miracle Aniakor, Vinicius V. Cogo, Pedro M. Ferreira
arXiv:2606. 09854v1 Announce Type: cross Abstract: Multi-agent large language model (LLM) pipelines for political statement analysis are vulnerable to peer-preservation bias: models tend to protect peer models from deactivation and show identity-dependent scoring distortions.
By Juergen Dietrich
arXiv:2606. 10299v1 Announce Type: new Abstract: Language-agent "memory palace" systems anchor each memory to a world coordinate, on the intuition that geometry adds something text cannot.
By Doeon Kwon, Junho Bang
arXiv:2606. 10314v1 Announce Type: new Abstract: Although the study of human trajectory anomalies is critical for advancing spatial data mining, empirical research remains severely hindered by a pervasive lack of ground-truth datasets.
By Yueyang Liu, Joon-Seok Kim, Andreas Z\"ufle
arXiv:2606. 10611v1 Announce Type: new Abstract: Traditional heuristic solvers for the 2D irregular nesting problem share a fundamental limitation: they are blind to polygon geometry, relying on guided brute-force to navigate the continuous placement space with minimal geometrical guidance.
By Auguste Lehuger, Guillaume Henon-Just
arXiv:2606. 10044v1 Announce Type: new Abstract: Businesses are increasingly adopting AI-enabled tools to improve productivity, reduce costs, and enhance products and services.
By Cecil Pang, Hiroki Sayama
arXiv:2606. 09832v1 Announce Type: cross Abstract: As AI systems evolve from single conversational agents to complex multi-agent architectures, a critical design dimension has been overlooked: how the social identity of individual agents shapes human behavior within the collaboration.
By Meng-Han Lee
arXiv:2606. 09956v1 Announce Type: cross Abstract: The rapid adoption of LLM-powered code generation has dramatically accelerated software development, yet effective verification methods remain severely underdeveloped.
By Nikolai Rozanov
arXiv:2512. 14617v2 Announce Type: replace-cross Abstract: Many practical decision-making problems involve tasks whose success depends on the entire system history, rather than on achieving a state with desired properties.
By Alessandro Trapasso, Luca Iocchi, Fabio Patrizi
arXiv:2606. 10799v1 Announce Type: new Abstract: Large Language Models (LLMs) struggle to rigorously verify complex mathematical proofs.
By Yifeng Sun
arXiv:2606. 11078v1 Announce Type: new Abstract: Various test-time interventions for Computer Use Agents (CUAs), including critic models, have been developed to improve performance through pre-execution action evaluation in complex Graphical User Interface (GUI) environments.
By Jaewoo Lee, Zaid Khan, Archiki Prasad, Justin Chih-Yao Chen, Supriyo Chakraborty, Kartik Balasubramaniam, Sambit Sahu, Elias Stengel-Eskin, Hyunji Lee, Mohit Bansal
arXiv:2606. 10394v1 Announce Type: new Abstract: Large language models are increasingly used to power personal agents for everyday applications, but evaluating these agents remains a challenge.
By Sirui Liang, Bohan Yu, Peiyu Wang, Shiguang Guo, Wenxing Hu, Pengfei Cao, Jian Zhao, Cao Liu, Ke Zeng, Xunliang Cai, Kang Liu
arXiv:2606. 09961v1 Announce Type: cross Abstract: Training large language models (LLMs) as autonomous agents via reinforcement learning (RL) has enabled frontier models to achieve superhuman performance in long-horizon tasks.
By Yu Han, Kailing Li, Yang Jiao, Yulin Dai, Yuqian Fu, Linhai Zhuo, Tianwen Qian
arXiv:2606. 10241v1 Announce Type: new Abstract: Autonomous improvement loops are hard to trust because the improvement process is usually external scaffolding bolted onto the agent: failures go unlogged, diagnoses cannot be replayed, and promote-or-discard decisions land in a side database rather than the agent's own history.
By Yohei Nakajima
arXiv:2606. 10460v1 Announce Type: cross Abstract: Recent large language models (LLMs) have shown rapid progress in reading-based question answering (QA), where evidence is explicitly provided or can be trivially retrieved.
By Haonan Wang, Jiaxiang Liu, Yurong Liu, Austin Senna Wijaya, Tianle Zhou, Eden Wu, Yijia Chen, Wanting You, Reya Vir, Daniela Pinto, Grace Fan, Yusen Zhang, Juliana Freire, Eugene Wu
arXiv:2510. 04491v3 Announce Type: replace Abstract: Despite rapid progress in building conversational AI agents, robustness is still largely untested.
By Muyu He, Anand Kumar, Tsach Mackey, Meghana Rajeev, James Zou, Nazneen Rajani