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Germany","points":160,"comments":126,"url":"https://ua.news/en/world/kiberataka-zupinila-budivnitstvo-tuneliu-mizh-daniieiu-ta-nimechchinoiu-rmf24","discussion":"https://news.ycombinator.com/item?id=50074289"}],"research":[{"title":"ChunkAct: Action Chunking for Agentic Robotics","abstract":"Agentic robotics places a large language model (LLM) agent in the robot control loop, where it composes perception, motion, and vision-language-action (VLA) tools under the guidance of a memory and thereby generalizes to new tasks without retraining. This generality comes at a steep price in efficiency. Every primitive requires a round of LLM reasoning over the full interaction history, so the cost of an episode grow","authors":["Bingxin Xu","Wenrui Bao","Xinxin Liu","Jiawei Zhou","Yuzhang Shang","Emilio Ferrara"],"url":"https://arxiv.org/abs/2610.18874v1","arxiv_id":"2610.18874v1","published":"2026-10-13T17:59:57Z"},{"title":"What Matters in Quality-Aware VLA Pretraining: A Benchmark and Empirical Study","abstract":"How should demonstration quality be used during vision-language-action (VLA) pretraining to improve performance after target-task fine-tuning? We compare weighting, filtering, and conditioning across reward definitions and label granularities using 5,000 naturally suboptimal human demonstrations and 24 target tasks. Within each VLA, we match demonstrations and training budgets. Additional comparisons examine pretrain","authors":["Mingxuan Yan","Litian Gong","Dihong Huang","Zhixu Li","Tianyu Zhang","Wenqian Zhang"],"url":"https://arxiv.org/abs/2610.18869v1","arxiv_id":"2610.18869v1","published":"2026-10-13T17:59:52Z"},{"title":"Tev: Scaling Long-Horizon Agentic Environments with Dense Progress Rewards","abstract":"Long-horizon agents have drawn growing attention for their ability to work autonomously -- from hours to days -- on economically valuable tasks, a capability essential to automated discovery and recursive self-improvement (RSI). Yet open-source models still struggle on long-horizon tasks, largely for lack of both training data and a working recipe, and scaling both is hard: long, complex tasks are difficult to curate","authors":["Wenbo Hu","Shraman Pramanick","Krishna Kumar Singh","Nanyun Peng","Kai-Wei Chang","Yong Jae Lee"],"url":"https://arxiv.org/abs/2610.18868v1","arxiv_id":"2610.18868v1","published":"2026-10-13T17:59:51Z"},{"title":"DoubtLess: Training-Free Contrastive Decoding for Efficient Reasoning","abstract":"Reasoning models achieve strong performance through long chains of thought (CoT), but often spend more tokens than needed to solve a problem. Existing ways to shorten the CoT require additional training for each model, add a fixed steering vector to the model's hidden states, or use decoding heuristics such as stopping early or banning words like \"wait\", which can reduce accuracy. We propose DoubtLess, a training-fre","authors":["Minghao Luo","Liang Chen"],"url":"https://arxiv.org/abs/2610.18862v1","arxiv_id":"2610.18862v1","published":"2026-10-13T17:59:32Z"},{"title":"AesCode: Aesthetic Code Generation with Decoupled Cross-Modal Rewards","abstract":"Generating information-rich visual artifacts such as slides, posters, and dashboards as code is appealing because the outputs are structured, editable, and verifiable. Yet code models cannot see how their choices of layout and color come together on the canvas, often limiting spatial planning and aesthetic quality. Image generators excel at visual design but can misrender requested content, making their outputs unrel","authors":["Xin Gao","Yifan Yang","Xin Li","Pan Wang","Yan Li","Muzhao Tian"],"url":"https://arxiv.org/abs/2610.18860v1","arxiv_id":"2610.18860v1","published":"2026-10-13T17:59:31Z"},{"title":"Hybrid Reasoning Agents","abstract":"How can we build an agent that thinks beyond human language, yet still acts through it in the external environment? Modern LLM agents still reason and interact through text, while latent reasoning offers an efficient internal medium but lacks native tool access and reasoning transparency. We bridge these two directions with Hybrid Reasoning Agents (HybridAgents), which dynamically navigate between latent reasoning an","authors":["Jiaru Zou","Yunzhe Qi","Sirui Chen","Federico Bianchi","Shang Zhu","Pan Lu"],"url":"https://arxiv.org/abs/2610.18861v1","arxiv_id":"2610.18861v1","published":"2026-10-13T17:59:31Z"},{"title":"Two Clocks: Why Larger Rollout Batches Learn Slower but Fail on Schedule in Reinforcement Learning with Verifiable Rewards","abstract":"Reinforcement learning with verifiable rewards (RLVR) relies on costly model-generated responses, motivating larger rollout batches and square-root learning-rate scaling. Reported to approximately preserve progress per sample with one optimizer step per rollout, this rule is less understood in practical multi-step training. We develop a two-clock framework that separates the samples needed to make progress from those","authors":["Yuante Li","Junwei Quan","Kai Qiu","Kate Zhang","Yicheng Tao"],"url":"https://arxiv.org/abs/2610.18853v1","arxiv_id":"2610.18853v1","published":"2026-10-13T17:58:57Z"},{"title":"MFG: Multi-turn Feedback Guided Molecular Optimization","abstract":"Multi-objective molecular optimization is a critical process in drug discovery that aims to modify a given molecule to simultaneously improve multiple properties while maintaining a similar overall structure. This process often requires iterative modifications guided by computational or experimental feedback. Recent large language model (LLM)-based methods have begun to explore this iterative approach; however, these","authors":["Trieu Nguyen","Frazier N. Baker","Ruoxi Gao","Daniel Adu-Ampratwum","Xia Ning"],"url":"https://arxiv.org/abs/2610.18851v1","arxiv_id":"2610.18851v1","published":"2026-10-13T17:58:41Z"},{"title":"LIBERO-RIG: A Hardware-Setup Robustness Benchmark for Vision-Language-Action Models","abstract":"Robot policies are expected to generalize across deployment environments, including variations in the robot itself: cameras are bumped and remounted, calibrations drift, controllers run at different rates, and end-effector pads wear down. We present LIBERO-RIG, a benchmark that perturbs only the robot's hardware and setup while holding every LIBERO task fixed. The benchmark spans eleven perturbation axes, including c","authors":["Andrea Yaoyun Cui","Hanyu Wang","Xinyi Wang","Bangzheng Li","Lin Zhang","Zhenggang Tang"],"url":"https://arxiv.org/abs/2610.18843v1","arxiv_id":"2610.18843v1","published":"2026-10-13T17:57:18Z"},{"title":"Who Improves the Robot? Toward Physical Recursive Self-Improvement","abstract":"Robots are increasingly autonomous in task execution, yet their improvement remains largely human-driven. This study investigates Physical Recursive Self-Improvement (Physical RSI), in which physical experience guides lasting updates to both a robot's capabilities and its improvement procedure, making subsequent improvement more effective across tasks. Physical RSI faces challenges from noisy observations, continuous","authors":["Hai Zhou","Fan Yang","Zeyi Chen","Lejun Zhang","Chenyang Fu","Jianping Li"],"url":"https://arxiv.org/abs/2610.18842v1","arxiv_id":"2610.18842v1","published":"2026-10-13T17:57:04Z"},{"title":"Fast Rates for Offline Q-Learning with KL Regularization","abstract":"This paper investigates offline Q-learning for finite-horizon Markov decision processes with Kullback-Leibler (KL) regularization. Recent studies have shown that KL regularization can yield fast rates in offline decision-making problems. However, it remains unclear whether Q-learning, which adopts incremental stochastic approximation updates, can achieve such fast rates in the offline setting. We first propose KL-reg","authors":["Yize Hao","Guangxin Jiang","Shuang Qiu"],"url":"https://arxiv.org/abs/2610.18839v1","arxiv_id":"2610.18839v1","published":"2026-10-13T17:56:27Z"},{"title":"RoboSimStudio: An Interactive Toolkit for Rapid Real2Sim of Robot Manipulation Tasks","abstract":"It is difficult to accurately model the physics of the interaction between a robot and the objects it manipulates, including contact mechanics, coupled contact dynamics, deformations, stiffness and friction. We present RoboSimStudio (RSS), an interactive toolkit that makes choices of solver, contact model, and physical parameters explicit and editable, allowing both humans and coding agents to iteratively refine simu","authors":["Kaiyuan Chen","Shuangyu Xie","Uksang Yoo","Xinyue Zhu","Cael Magner","Alvin Zhu"],"url":"https://arxiv.org/abs/2610.18835v1","arxiv_id":"2610.18835v1","published":"2026-10-13T17:55:14Z"},{"title":"Read the Room Before Your Model Generates","abstract":"Large language models (LLMs) generally rely on sampling temperature to regulate the amount of randomness in their outputs. Mainstream methods for selecting temperature typically involve fixed, heuristic global settings and context or token-level calibration, rather than a geometrically interpretable representation of the communicative scenario. In this work, we introduce a simple and interpretable temperature selecti","authors":["Muyao Kong","Zhihao Lin"],"url":"https://arxiv.org/abs/2610.18820v1","arxiv_id":"2610.18820v1","published":"2026-10-13T17:51:18Z"},{"title":"Back-Translation Still Matters: Leveraging Monolingual Data for Low-Resource Machine Translation with LLMs","abstract":"Pre-trained Large Language Model (LLMs) are effective on many tasks in zero-shot and few-shot settings. For machine translation of low-resource languages, the obvious options are few-shot prompting and fine-tuning on a small parallel set. We ask: what if moderate amounts of low-resource monolingual data are also available? We argue that, rather than continued pretraining, a more effective use of this data is the pre-","authors":["Divyanshu Bathla","Parag Singla","Jitin Singla"],"url":"https://arxiv.org/abs/2610.18818v1","arxiv_id":"2610.18818v1","published":"2026-10-13T17:50:18Z"},{"title":"Self-Evolving Agent Harness","abstract":"Harness self-evolution revises an agent's software scaffolding, including prompts, memory, workflows, and executable components, while keeping model weights fixed. We formalize a feedback-driven process in which accepted revisions persist across runs. The framework separates update targets from proposal and admission mechanisms. It distinguishes changes to interpreted state from revisions to tools and code, repositor","authors":["Chuyang Wei","Haoxiang Guan","Minghang Zhu","Kexin Zhou","Jiyan He","Xiren Zhou"],"url":"https://arxiv.org/abs/2610.18813v1","arxiv_id":"2610.18813v1","published":"2026-10-13T17:48:49Z"},{"title":"Optimizing Team Behavior via Extremum-seeking Control in Multi-agent Reinforcement Learning","abstract":"In cooperative Multi-Agent Reinforcement Learning (MARL), agents must learn to coordinate their actions to achieve shared objectives. A key indicator of effective coordination is behavioral diversity, i.e., the agents' potential to enact distinct yet complementary control policies given the same set of observations. Although behavioral diversity can enable role specialization and richer joint behaviors, its appropria","authors":["Svar Rajankumar Patel","Kshitij Jerath"],"url":"https://arxiv.org/abs/2610.18808v1","arxiv_id":"2610.18808v1","published":"2026-10-13T17:46:27Z"},{"title":"SEAR: Self-Evolving Agentic Robotics","abstract":"Frontier language-model agents can now improve robots on their own: they write control code, test it, and keep what works as skills and memories for the next task. We introduce SEAR (Self-Evolving Agentic Robotics), a framework and benchmark built around two questions. Is the agent really learning, or just trying more often? And does what it learns still help on new tasks, in a new simulator, and on a real robot? The","authors":["Xinyi Wang","Bangzheng Li","Hanyu Wang","Lin Zhang","Andrea Yaoyun Cui","Zhenggang Tang"],"url":"https://arxiv.org/abs/2610.18792v1","arxiv_id":"2610.18792v1","published":"2026-10-13T17:40:20Z"},{"title":"Bytes to Threats: Byte-Sequence Transformer For Malware Detection","abstract":"Existing malware detection approaches heavily rely on handcrafted features derived from static or dynamic analysis of the executable files. These methods require specialized domain expertise, dedicated analysis tools, and time-consuming analysis, which limits their scalability and robustness in the face of the increasing complexity and diversity of modern malware. Consequently, there is a growing interest in approach","authors":["Abdelrahman Yassin Eljamal","Abdeltawab Hendawi"],"url":"https://arxiv.org/abs/2610.18787v1","arxiv_id":"2610.18787v1","published":"2026-10-13T17:37:17Z"},{"title":"Where Do LLM Ensemble Gains Come From? Decomposing Candidate Value, Diversity, and Aggregation","abstract":"LLM ensemble gains are often measured against a single baseline, but the value attributed to additional candidates can change with the comparison used. We study an LLM aggregator under four matched conditions: Direct (no candidates), Single-slot (one candidate in a multi-slot interface), Duplicate (one candidate repeated), and Multi-candidate (separately generated candidates). Under either non-diverse control, overal","authors":["Donghwan Kim"],"url":"https://arxiv.org/abs/2610.18786v1","arxiv_id":"2610.18786v1","published":"2026-10-13T17:36:51Z"},{"title":"Pack Light, CarryOn: Building Object-Oriented Context for Long-Horizon Agents","abstract":"Long-horizon agents accumulate progress in persistent task state, yet repeatedly process interaction histories describing how that progress was achieved. This raises a natural question: can long-horizon context instead be organized around reusable progress rather than the history that produced it? In this paper, we introduce CarryOn, an object-oriented context runtime that organizes continuation around reusable task ","authors":["Yuchen Lu","Julian McAuley","Lei Zhang"],"url":"https://arxiv.org/abs/2610.18784v1","arxiv_id":"2610.18784v1","published":"2026-10-13T17:35:27Z"},{"title":"BLADE: Auditing Information Use for Failure Attribution in LLM Multi-Agent Systems","abstract":"Large language model multi-agent systems extend individual agents' capabilities through collaboration across specialized roles, tools, and workflows. Yet collaboration makes failures harder to diagnose: one agent may misinterpret a correct result while others carry the mistake forward. Failure attribution therefore seeks to identify the responsible agent and decisive error step. Recent methods trace dependencies, but","authors":["Xiaoguang Guo","Yaohao Long","Chuxu Zhang"],"url":"https://arxiv.org/abs/2610.18780v1","arxiv_id":"2610.18780v1","published":"2026-10-13T17:34:34Z"},{"title":"Jeva: System One Models from First Principles","abstract":"System one models enable direct, low-latency decision-making. Yet recent practice of predicting an answer from serialized text does not, by itself, respect the structure of a decision problem. We argue that current designs underemphasize four important structural requirements. (i) null-aware: Models should distinguish answer absence from uncertainty among candidates; (ii) permutation-invariant: Reordering alternative","authors":["Qi Li","Xinchao Wang"],"url":"https://arxiv.org/abs/2610.18773v1","arxiv_id":"2610.18773v1","published":"2026-10-13T17:31:09Z"},{"title":"Beyond the Verdict: Agentic Code Judging with Verifiable Feedback","abstract":"Test-time scaling for large language models (LLMs) can involve generating multiple candidate solutions for selection or revising an earlier attempt. To guide selection and revision, we need a verifier that can estimate candidate correctness and provide evidence of failure. In competitive programming, however, official judging systems provide authoritative verdicts under finite submission budgets, without revealing fa","authors":["Zheye Deng","Sha Li","Changlong Yu","Xin Liu","Qin Lu","Priyanka Nigam"],"url":"https://arxiv.org/abs/2610.18760v1","arxiv_id":"2610.18760v1","published":"2026-10-13T17:22:58Z"},{"title":"PreciseCoder: Faithful and Generalizable Debugging Beyond Unit-Test Rewards","abstract":"Reinforcement learning (RL) from unit tests is the common recipe for post-training code models, but a unit test only asks whether the final program passes. We analyze what this unit-test-only reward teaches a model during debugging and find that, while it raises pass rates, it also teaches the model to edit more and to hallucinate bugs in code that is already correct. We introduce PreciseCoder, a training algorithm t","authors":["Yunkai Zhan","Miaosen Chai","Shangshang Wang","Jike Zhong","Yuqing Yang","Deqing Fu"],"url":"https://arxiv.org/abs/2610.18755v1","arxiv_id":"2610.18755v1","published":"2026-10-13T17:19:10Z"},{"title":"SolBehave: Evaluating the Behavioural Correctness of LLM-Generated Smart Contracts","abstract":"Large language models increasingly generate Solidity smart contracts that, once deployed, can never be patched, making a single latent fault permanent and exploitable. Yet current evaluations mostly ask only whether generated code compiles. We introduce SolBehave, a benchmark for evaluating the correctness of model-generated contracts at scale, moving beyond ad hoc unit tests. Across 250 real, reproducible Ethereum c","authors":["Giovanni Pio Delvecchio","Stefano Fantazzini","Giacomo Frisoni","Gianluca Moro","Lorenzo Bacchiani","Mario Bravetti"],"url":"https://arxiv.org/abs/2610.18745v1","arxiv_id":"2610.18745v1","published":"2026-10-13T17:12:59Z"}],"history":{"events":[{"year":2019,"text":"At the Chicago Marathon, Kenyan runner Brigid Kosgei (pictured) set the marathon world record for women running in a mixed-sex race.","url":"https://en.wikipedia.org/wiki/2019_Chicago_Marathon"},{"year":2016,"text":"The King of Thailand, Bhumibol Adulyadej, died after 70 years on the throne, at which point was the longest-reigning current monarch.","url":"https://en.wikipedia.org/wiki/Monarchy_of_Thailand"},{"year":2013,"text":"During the Hindu festival of Navaratri at a temple in Madhya Pradesh, India, rumours about an impending bridge collapse caused a stampede that resulted in 115 deaths.","url":"https://en.wikipedia.org/wiki/Navaratri"},{"year":2011,"text":"The Dragon King of Bhutan, Jigme Khesar Namgyel Wangchuck (pictured) married Jetsun Pema at the Punakha Dzong.","url":"https://en.wikipedia.org/wiki/King_of_Bhutan"},{"year":1994,"text":"The Troubles: In a meeting at Fernhill House, Belfast, loyalist leader Gusty Spence announced that the Combined Loyalist Military Command would observe a ceasefire.","url":"https://en.wikipedia.org/wiki/The_Troubles"},{"year":1972,"text":"Uruguayan Air Force Flight 571 crashed into a remote area in the Andes mountains near the border of Chile and Argentina; the 16 remaining survivors were not rescued until more than two months later.","url":"https://en.wikipedia.org/wiki/Uruguayan_Air_Force_Flight_571"},{"year":1961,"text":"Newly elected Burundian prime minister Louis Rwagasore was assassinated by his political rivals.","url":"https://en.wikipedia.org/wiki/Louis_Rwagasore"},{"year":1921,"text":"The Soviet republics of Russia, Armenia, Azerbaijan, and Georgia signed the Treaty of Kars with the Grand National Assembly of Turkey, establishing the current borders between Turkey and the Caucasian states.","url":"https://en.wikipedia.org/wiki/Russian_Soviet_Federative_Socialist_Republic"},{"year":1917,"text":"At least 30,000 people witnessed the Miracle of the Sun in the fields of Cova da Iria near Fátima, Portugal.","url":"https://en.wikipedia.org/wiki/Miracle_of_the_Sun"},{"year":1911,"text":"Prince Arthur, a son of Queen Victoria, became the first Governor General of Canada of royal descent, as well as the first Prince of Great Britain and Ireland to hold that position.","url":"https://en.wikipedia.org/wiki/Prince_Arthur%2C_Duke_of_Connaught_and_Strathearn"},{"year":1908,"text":"British suffragette Margaret Travers Symons became the first woman to speak in the House of Commons when she escaped from her escort into the chamber and shouted at the assembly.","url":"https://en.wikipedia.org/wiki/Suffragette"},{"year":1814,"text":"War of 1812: After three days of fighting, the beached U.S. Revenue Cutter Service vessel Eagle was captured by the Royal Navy.","url":"https://en.wikipedia.org/wiki/War_of_1812"},{"year":1812,"text":"War of 1812: British troops and Mohawk warriors repelled an American invasion from across the Niagara River at the Battle of Queenston Heights near Queenston, Ontario.","url":"https://en.wikipedia.org/wiki/War_of_1812"},{"year":1773,"text":"French astronomer Charles Messier discovered the Whirlpool Galaxy (pictured), an interacting, grand design spiral galaxy located an estimated 31 million light-years away.","url":"https://en.wikipedia.org/wiki/Charles_Messier"},{"year":1710,"text":"Queen Anne's War: French and Wabanaki forces surrendered to end the Siege of Port Royal, giving the British permanent possession of Nova Scotia.","url":"https://en.wikipedia.org/wiki/Queen_Anne's_War"},{"year":1307,"text":"Agents of King Philip IV of France launched a raid on the Knights Templar at dawn, arresting many members, subsequently torturing them into giving false confessions and burning them at the stake.","url":"https://en.wikipedia.org/wiki/Philip_IV_of_France"},{"year":645,"text":"Goguryeo–Tang War: Led by Emperor Taizong, the Tang army was forced to abandon a siege of Ansi Fortress.","url":"https://en.wikipedia.org/wiki/Goguryeo%E2%80%93Tang_War"}],"births":[{"year":2001,"text":"Caleb McLaughlin, American actor","url":"https://en.wikipedia.org/wiki/Caleb_McLaughlin"},{"year":2001,"text":"Cam Thomas, American basketball player","url":"https://en.wikipedia.org/wiki/Cam_Thomas"},{"year":2001,"text":"De'Von Achane, American football player","url":"https://en.wikipedia.org/wiki/De'Von_Achane"},{"year":1999,"text":"Andrew Capobianco. American diver","url":"https://en.wikipedia.org/wiki/Andrew_Capobianco"},{"year":1996,"text":"Joshua Wong, Hong Kong pro-democracy activist","url":"https://en.wikipedia.org/wiki/Joshua_Wong"},{"year":1995,"text":"Jimin, South Korean singer","url":"https://en.wikipedia.org/wiki/Jimin"},{"year":1994,"text":"Ryan Matterson, Australian rugby league player","url":"https://en.wikipedia.org/wiki/Ryan_Matterson"},{"year":1994,"text":"Yuta Watanabe, Japanese basketball player","url":"https://en.wikipedia.org/wiki/Yuta_Watanabe"}],"deaths":[{"year":2026,"text":"Ken Friar, English businessman and life president of Arsenal Football Club (born 1934)","url":"https://en.wikipedia.org/wiki/Ken_Friar"},{"year":2024,"text":"Mayra Gómez Kemp, Cuban-Spanish television host and actress (born 1948)","url":"https://en.wikipedia.org/wiki/Mayra_G%C3%B3mez_Kemp"},{"year":2024,"text":"Donal Murray, Irish Catholic bishop (born 1940)","url":"https://en.wikipedia.org/wiki/Donal_Murray_(bishop)"},{"year":2023,"text":"Louise Glück, American poet and essayist (born 1943)","url":"https://en.wikipedia.org/wiki/Louise_Gl%C3%BCck"},{"year":2018,"text":"Annapurna Devi, Indian surbahar (bass sitar) player (born 1927)","url":"https://en.wikipedia.org/wiki/Annapurna_Devi"},{"year":2017,"text":"Albert Zafy, Malagasy politician (born 1927)","url":"https://en.wikipedia.org/wiki/Albert_Zafy"},{"year":2016,"text":"Bhumibol Adulyadej (Rama IX), King of Thailand (born 1927)","url":"https://en.wikipedia.org/wiki/Bhumibol_Adulyadej"},{"year":2016,"text":"Dario Fo, Italian playwright, actor, director, and composer, Nobel Prize laureate (born 1926)","url":"https://en.wikipedia.org/wiki/Dario_Fo"}]},"census":{"panel_version":2,"probed":14770,"error_rate":22.13,"degraded":false,"rates":{"head":12.8,"tail":5.6,"ai_native":74.55}},"sources":[{"id":"attention","name":"Wikipedia most-read","url":"https://api.wikimedia.org","license":"CC BY-SA 4.0","attribution":"Wikipedia contributors"},{"id":"frontier","name":"Hacker News","url":"https://news.ycombinator.com","license":"Public API — titles and links, credited","attribution":"Hacker News / Y Combinator"},{"id":"research","name":"arXiv cs.AI","url":"https://arxiv.org","license":"Open metadata, per arXiv API terms","attribution":"arXiv.org"},{"id":"history","name":"Wikipedia On this day","url":"https://api.wikimedia.org","license":"CC BY-SA 4.0","attribution":"Wikipedia contributors"}],"license":"Records are CC BY-SA 4.0, inheriting from Wikipedia. Every item links to its source."}