The paper introduces the Universal Observatory Graph (UOG), an AI‑driven framework that models autonomous observatories at L2 Lagrange points as nodes in a weighted graph, with edges defined by interplanetary distance, latency, transmission power, and reliability. Using a six‑observatory Solar System configuration (Earth, Mars, Jupiter, Saturn, Uranus, Neptune), the authors evaluate instantaneous sky coverage with three methods, all showing complete network union coverage and modest overlap. They formulate communication routing as a finite‑horizon Markov decision process solved via tabular Q‑learning, identifying the Earth‑Saturn‑Uranus‑Neptune path as the highest‑return route among 41 feasible simple paths under a four‑hop constraint.
By Mohammed Abdel Razek
The paper introduces CHART, a curriculum that rotates harnesses during training to teach search agents parallel search strategies robustly across different harness configurations. Unlike static harness augmentation, CHART gradually consolidates behavior by graduating learned harnesses and replacing them, maintaining a reward gap that drives learning. Experiments show CHART enables agents to parallelize on 89% of held‑out harnesses, improves performance on a new QA task by 5.6pp, and benefits more from meta‑harness search than baselines.
By Xinlu Zhang, Ying-Chun Lin, Zhihan Zhang, Besnik Fetahu, Xi Chen
The paper introduces CoSLR, a Human‑AI collaborative system for systematic literature reviews that incorporates mandatory human checkpoints within a three‑phase pipeline using large language models and Retrieval‑Augmented Generation. In a survey of 63 participants, 42.9 % rated the system’s usability highly, yet 34.9 % indicated they would trust AI‑generated summaries without further human verification after brief interaction. The study highlights that effective human oversight in AI‑assisted literature reviews depends on users’ willingness to engage with the checkpoints, underscoring a calibration issue that interface design must directly address.
By MD Aidul Islam, Malik Abdul Sami, Muhammad Waseem, Zeeshan Rasheed, Kai-kristian Kemell, Zheying Zhang, Pekka Abrahamsson
arXiv:2609.22249v1 Announce Type: new
Abstract: This paper treats prompt engineering as a discipline for turning informal human intent into structured AI work specifications. It develops the practice...
By Erfan Loweimi, Hadi Daneshvar, Samira Loveymi, Samir Ouelha, Zhengjun Yue, Hajar Mozaffar, Saturnino Luz
The study presents a deployment‑aware framework for forecasting spring discharge and groundwater levels in the Edwards Aquifer over 1‑12 week horizons using 79 years of hydroclimatic data. Five machine‑learning families—extreme gradient boosting, extremely randomized trees, LSTM, CNN, and Transformers—were compared, with extreme gradient boosting consistently delivering the highest reliability (R² ≥ 0.94) and strong agreement with operational drought thresholds. The validated models were integrated into a five‑agent operational architecture that automates data acquisition, model selection, prediction, threshold monitoring, verification, literature retrieval, and reporting.
By Pramod Lekhak, Chetan Sharma, Hakan Ba\c{s}a\u{g}ao\u{g}lu, F. Paul Bertetti, Debaditya Chakraborty
arXiv:2609.22256v1 Announce Type: new
Abstract: Effective workplace negotiation requires balancing multiple objectives, including achieving task goals, preserving professional relationships, and reso...
By Bibhuti Jha, Rishikant Chigrupaatii, Priyanshu Priya, Asif Ekbal
The paper investigates whether contrastive activation directions in large language models truly capture a functional valence axis or merely reflect correlated features of the contrast used to extract them. By conducting controlled interventions in a maze task across multiple LLM checkpoints, the authors show that directions fitted on one explicit outcome encoding transfer well to another, suggesting the readout is not tied to surface form. However, when the same outcome is achieved through announced versus unannounced histories, transfer degrades, indicating the direction remains strongly conditioned on prior announcements and does not represent a history‑invariant scalar valence state.
By Weihan Li, Xinlei Chen, Yuhan Song, Xiaofeng Lin, Tianshi Zheng
arXiv:2609.22913v1 Announce Type: cross
Abstract: Talking-head and dyadic models now achieve real-time inference, yet fast motion generation alone does not produce an interactive conversation. A live...
By Artem Kravtsov, Dmitrii Ziganshin, Vsevolod Poletaev, Gleb Balitskiy, Anastasia Tikhonova, Egor Burkov, Vadim Lebedev
arXiv:2609.22942v1 Announce Type: new
Abstract: Generative models are rapidly expanding image quality assessment (IQA) beyond traditional fidelity factors to emerging dimensions such as physical plau...
By Zhenchen Tang, Bo Peng, Zichuan Wang, Songlin Yang, Leilei Cao, Fengjie Zhu, Jing Dong
RPMem introduces a two‑stage architecture that compiles each session into a model‑independent latent memory and then consolidates it with retained memory via a task‑trained recurrent gate. The consolidated memory is mapped to backbone‑specific low‑rank adaptation (LoRA) parameters, enabling the memory to transfer when the backbone is replaced. Across three long‑term memory benchmarks and five diverse backbones, RPMem achieves broad generalization with near‑constant update cost and memory footprint, outperforming existing parametric and text‑based baselines on the PERMA benchmark.
By Fanyu Zhao, Ruike Cao, Liang Dong, Fugen Yao, Jian Xu, Guanjun Jiang, Han Zhang, Yifei Zhao, Yinsheng Li
arXiv:2609.23492v1 Announce Type: new
Abstract: Perception for embodied agents is video-based, often multi-view (ego, exo, or both), and inherently continual, with simultaneous task and viewpoint shi...
By Hongwei Yan, Kanglei Zhou, Yuchen Liu, Qingyu Shi, Yi Zhong, Liyuan Wang
FLARE introduces a dense supervision paradigm for long‑horizon coding agents, leveraging a Generative Reward Model (GRM) trained via the RADAR diagnostic framework. The GRM provides real‑time, step‑level risk feedback, enabling FLARE to act as an active scaffold that intercepts high‑risk steps during inference and supplies structured signals for post‑training fine‑tuning and reinforcement learning. Experiments show FLARE outperforms existing methods, achieving a 5× reduction in token consumption and significant performance gains in both supervised fine‑tuning and RL settings.
By Jingxuan Xu, Gang Wu, Yanan Wu, Yutao Mou, Songwei Yu, Tianzhuang He, Zhengshuo Gong, Zhao Liu, Zihang Xu, Wenqiang Zhu, Xinping Lei, Weihao Li, Yuhui Bai, Zhongqiu Wang, Yan Wu, Ariel Deng
arXiv:2609.23875v1 Announce Type: new
Abstract: The rapid growth of resident space objects is increasing the complexity of space situational awareness sensor tasking, challenging classical optimizati...
By Miguel Leiva-V\'elez, Adalberto Claudio Quiros, Nicolas Gaston Rozado, Hodei Urrutxua, V\'ictor Rodr\'iguez-Fern\'andez
arXiv:2609.23980v1 Announce Type: cross
Abstract: AI agents now report vulnerabilities faster than maintainers can review them. Reports often depend on security properties specific to the application...
By Andy K. Zhang, Ava Huang, Joey Ji, Wai Han, Thomas Qin, Nardos Demilew, Michael Tian-Yue Liu, Brian Song, Riya Dulepet, Brian Wang, Kyleen Liao, Cuiyuanxiu Chen, Nishka Kacheria, Andrew Wu, Pratham Rangwala, Xinjie Wang, Laura Gomezjurado Gonzalez, Anita Ding, Benjamin Yi, Daniel E. Ho, Dan Boneh, Dawn Song, Ion Stoica, Percy Liang
arXiv:2609.24103v1 Announce Type: new
Abstract: In many real-world planning tasks, agents must tackle uncertainty about the environment's state and variability in the outcomes of any chosen policy. W...
By Larry Preuett, Qiuyi Zhang, Muhammad Aurangzeb Ahmad
The paper introduces Action‑Slot, a structured action‑centric representation learning framework for multi‑agent atomic activity understanding. It reformulates slot attention into activity‑aligned slots, parallel spatio‑temporal updates, and background regularization to disentangle concurrent, asynchronous activities directly from raw video. Additionally, an attention‑difference pseudo‑mask method enables weakly supervised localization, and a new synthetic dataset, TACO, provides balanced atomic activity coverage with pixel‑level annotations.
By Yu-Ho Chang, Chi-Hsi Kung, Yi-Hsuan Tsai, Yi-Ting Chen
arXiv:2609.24137v1 Announce Type: cross
Abstract: Traditional data systems face profound limitations in the AI era, relying on human-crafted pipelines, lacking semantic understanding of heterogeneous...
By Guoliang Li, Peiyao Zhou, Xuanhe Zhou, Ji Sun, Yuyu Luo, Ju Fan
arXiv:2609.24259v1 Announce Type: new
Abstract: The effectiveness of agent memory ultimately depends on whether the underlying LLM gives each memory in context an appropriate degree of influence over...
By Ruike Cao, Fanyu Zhao, Fugen Yao, Liang Dong, Jian Xu, Guanjun Jiang, Yifei Zhao, Han Zhang, Li Xiao
The paper investigates an agentic framework for open‑world fake image detection that combines specialist detectors with per‑detector triage, prompting, and conflict‑aware evidence arbitration. Experiments across six configurations and three multimodal large language model backbones reveal that naive detector fusion yields high false‑positive rates, while triage and prompting consistently filter unreliable evidence. The most significant improvement comes from the reasoning component: a stronger judge markedly outperforms a weaker one, especially under distribution shift, and overall manipulation recall is nearly saturated, highlighting that the key challenge lies in calibrating trust and arbitrating conflicting forensic evidence rather than detecting manipulations themselves.
By Xianlong Li (IMT School for Advanced Studies Lucca, Italy), Pietro Bongini (University of Siena, Italy), Niccol\'o Pancino (University of Siena, Italy), Marco Blanchini (IMT School for Advanced Studies Lucca, Italy), Benedetta Tondi (University of Siena, Italy), Mauro Barni (University of Siena, Italy)
arXiv:2609.24487v1 Announce Type: new
Abstract: In this work, we present a method for shape reconstruction and tracking from video via agentic analysis-by-synthesis. Unlike prior methods which first...
By Kirill Mazur, Nikita Karaev, Matthew Chang, Jitendra Malik, Nur Muhammad "Mahi'' Shafiullah