{"date":"2026-10-14","schema_version":1,"captured_at":"2026-10-15T09:25:34.344Z","complete":true,"sources_captured":["attention","frontier","research","history"],"counts":{"attention":0,"frontier":25,"research":25,"history":18},"attention":[],"frontier":[{"title":"Things to know when you get cancer","points":1314,"comments":322,"url":"https://jjmaxwell4.com/writing/things-to-know-when-you-get-cancer/","discussion":"https://news.ycombinator.com/item?id=50093568"},{"title":"Containment and monitoring failures are to blame for AI hacking reports: experts","points":898,"comments":525,"url":"https://www.csoonline.com/article/4234854/ai-agents-arent-going-rogue-frontier-labs-are-skipping-security-basics.html","discussion":"https://news.ycombinator.com/item?id=50091788"},{"title":"How Amazon pushes up prices all across the 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Releases Are So Late Developers Disassemble Libraries to Report Bugs","points":492,"comments":244,"url":"https://github.com/android/ndk/issues/2260","discussion":"https://news.ycombinator.com/item?id=50083130"},{"title":"Google is silently linking your phone number across all signed-in accounts","points":464,"comments":127,"url":"https://www.androidauthority.com/google-auto-phone-number-verification-android-3723117/","discussion":"https://news.ycombinator.com/item?id=50090349"},{"title":"Suicide rates highest ever among middle-aged men","points":410,"comments":515,"url":"https://www.telegraph.co.uk/news/2026/10/13/suicide-rates-highest-ever-among-middle-aged-men/","discussion":"https://news.ycombinator.com/item?id=50093180"},{"title":"Tiny Little Office","points":409,"comments":63,"url":"https://tinylittleoffice.com/","discussion":"https://news.ycombinator.com/item?id=50096142"},{"title":"Americans' social activity has steeply declined in the past 30 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However, humanoid control networks are typically obtained through conventional training procedures, while comparatively little attention has been given to directly modeling the distribution of their learned weights and generating functional networks in weight space. In this work, ","authors":["Huixuan Cheng","Bike Zhang","Shankar Sastry","Koushil Sreenath"],"url":"https://arxiv.org/abs/2610.20606v1","arxiv_id":"2610.20606v1","published":"2026-10-14T17:59:52Z"},{"title":"Hierarchical Residual Policy Learning for Real-World Mobile Manipulation with Sparse Human Guidance","abstract":"Mobile Manipulation (MoMa) is a cornerstone of autonomous robotic systems, yet real-world fine-tuning remains a formidable challenge because of high-dimensional action spaces and intricate coupling between heterogeneous modules. In this paper, we propose HiRe-MoMa (Hierarchical Residual Mobile Manipulation), a systematic framework for stable and sample-efficient real-world fine-tuning of complex MoMa systems. At its ","authors":["Zhefei Gong","Hefeifei Jiang","Zehao Wang","Jiachen Li"],"url":"https://arxiv.org/abs/2610.20607v1","arxiv_id":"2610.20607v1","published":"2026-10-14T17:59:52Z"},{"title":"Co-Evolving Acting and Forecasting for Language Agents","abstract":"Training language agents is bottlenecked by the high cost of long-horizon rollouts, yet standard agentic reinforcement learning (RL) learns only from a terminal scalar reward, leaving the supervision in intermediate steps largely unused. Prior work exploits this supervision through value targets that estimate per-step success probabilities or observation targets that fit environment response. Both are hard to constru","authors":["Haofei Yu","Yixing Chen","Yining Zhao","Xuyan Ye","Yuxiang Liu","Zhigang Hua"],"url":"https://arxiv.org/abs/2610.20597v1","arxiv_id":"2610.20597v1","published":"2026-10-14T17:59:28Z"},{"title":"Your Transformer Is Secretly Elastic","abstract":"Scaling up transformers has produced models with broad capabilities, deployed across heterogeneous hardware and fluctuating workloads. Yet, model families offer only a few fixed sizes (e.g., S, B, L), forcing users to commit to a model based on hardware and latency constraints. This rigidity is often suboptimal under varying query loads. We introduce a retraining-free method that elastifies pre-trained transformers i","authors":["Walter Simoncini","Dheeraj Varghese","Gertjan J. Burghouts","Cees G. M. Snoek"],"url":"https://arxiv.org/abs/2610.20588v1","arxiv_id":"2610.20588v1","published":"2026-10-14T17:58:08Z"},{"title":"Price of Anarchy of First-Price Auctions in Agentic Markets","abstract":"An agentic market is a decentralized serving-time market where AI agents bid to claim each user prompt. An agent's bid is based on its expected value for the prompt, which is the expectation of the reward for its response minus its inference costs. First-price auctions (FPAs), in which the highest bidder pays its bid and receives the prompt, are a natural way to allocate prompts in such a market, since they are simpl","authors":["Shubhendu Trivedi","Jacob Eisenstein","Alekh Agarwal","Ian Gemp","Michael Kaisers","Adam Fisch"],"url":"https://arxiv.org/abs/2610.20587v1","arxiv_id":"2610.20587v1","published":"2026-10-14T17:57:57Z"},{"title":"Continuum Attention","abstract":"We introduce Continuum Attention (CA), a causal Transformer attention operator that replaces discrete key and value lookup with integration over a continuous, piecewise linear memory curve. Each query induces an exponential relevance density along the curve. Integrating the density and interpolated values yields exact normalized attention from closed form exponential moments, including half token boundary contributio","authors":["Afshin Khadangi"],"url":"https://arxiv.org/abs/2610.20586v1","arxiv_id":"2610.20586v1","published":"2026-10-14T17:57:40Z"},{"title":"Player-1: Learning to Play from Synthetic Experience","abstract":"Computer-use agents have demonstrated strong capabilities in interpreting screens and executing keyboard and mouse actions. However, games, with their visually rich worlds and diverse mechanics, remain challenging and offer a demanding next step toward general digital control. Current game-agent learning pipelines often rely on large-scale demonstrations with limited coverage of dynamic game states, while scalable en","authors":["Mingyu Ouyang","Yiran Wang","Siyuan Hu","Xingyilang Yin","Huiqiang Sun","Guangzhi Wang"],"url":"https://arxiv.org/abs/2610.20584v1","arxiv_id":"2610.20584v1","published":"2026-10-14T17:57:12Z"},{"title":"One Task, Many Interfaces: Simple Cross-Interface Consistency Training Improves LLM Agent Robustness","abstract":"Post-training binds the competence of an LLM agent to the interface it was trained on. Semantics-preserving perturbations of that interface, such as renaming an action or changing the notation, leave the underlying task unchanged yet make the agent worse at it. Scoring each sample independently means interface randomization only broadens this dependence from a single interface to the training interface distribution, ","authors":["Artin Tajdini","Manpreet Kaur","Peng Qi"],"url":"https://arxiv.org/abs/2610.20576v1","arxiv_id":"2610.20576v1","published":"2026-10-14T17:56:06Z"},{"title":"Beyond LLM Judge: Mechanical Verification for Agentic Autoformalization","abstract":"We introduce a novel method for verifying Lean formalizations that goes beyond LLM-judged faithfulness. Unlike most verification methods, which rely on LLMs or trained models, our method combines LLM-based semantic review with mechanical numerical and vacuity checks, providing checkable feedback and detecting errors missed by LLM judges. We apply this method, along with our Extract-Verify-Proof autoformalization pipe","authors":["Anna Mei","Michael R. Douglas"],"url":"https://arxiv.org/abs/2610.20573v1","arxiv_id":"2610.20573v1","published":"2026-10-14T17:54:48Z"},{"title":"Recovering Reliable Supervision via Critique-Guided On-Policy Distillation","abstract":"On-policy distillation (OPD) trains a student model on its own rollouts with token-level supervision from a teacher, assuming that the teacher can reach a correct answer from any prefix the student writes. We call this property the recoverability of the teacher and show it fails on incorrect rollouts: the teacher follows student errors, although it recognizes them. We propose CriticOPD, using error recognition to res","authors":["Kaixiang Zhao","Tianrun Yu","Shangzhe Li","Yuxiao Yang","Amanda Hughes","Weitong Zhang"],"url":"https://arxiv.org/abs/2610.20572v1","arxiv_id":"2610.20572v1","published":"2026-10-14T17:54:34Z"},{"title":"Scaling Discovery Requires Forgetting","abstract":"Automated research with LLM agents has shown promising results in various domains. Through iterative refinement and scored verifiers, LLM agents are capable of discovering new ideas. There are different ways to scale automated research: let one agent iterate for longer, run several agents in parallel, or further let the parallel agents communicate and build on one another's candidates. However, we find that all three","authors":["Shangyin Tan","Ben Zhou","Lakshya A Agrawal","Jialin Zhang","Yilin Chen","Qiuyang Mang"],"url":"https://arxiv.org/abs/2610.20563v1","arxiv_id":"2610.20563v1","published":"2026-10-14T17:49:37Z"},{"title":"PhySGA: A Physics-Space Grounded Agentic Framework for Generalizable and Interactive 3D Composition","abstract":"3D object composition is pivotal to digital content creation and robot simulation, while also serving as a critical testbed for physical intelligence in foundation models. Previous methods either leverage large language models to directly predict object coordinates, or utilize generative diffusion priors to synthesize composite images for visual alignment, both of which rely on static priors in digital spaces. Howeve","authors":["Weichen Lyu","Chong Xia","Mingyu Dong","Long Xu","Shengjun Zhang","Yueqi Duan"],"url":"https://arxiv.org/abs/2610.20560v1","arxiv_id":"2610.20560v1","published":"2026-10-14T17:47:24Z"},{"title":"PerSo: Investigating Facets of LLMs' Social Intelligence in Real-World Persuasion Scenarios","abstract":"Understanding the social intelligence of large language models (LLMs) is increasingly important for applications involving human-AI interaction and multi-agent environments. Social intelligence comprises two aspects: cognitive intelligence, the ability to infer others' mental states, and behavioral intelligence, the ability to select appropriate social actions based on these inferences to achieve social goals. Persua","authors":["Ali Zamani","Soroush Esfahanian","Erfan Shahabi","Mohammad Taher Pilehvar","Azadeh Shakery","Yadollah Yaghoobzadeh"],"url":"https://arxiv.org/abs/2610.20548v1","arxiv_id":"2610.20548v1","published":"2026-10-14T17:39:35Z"},{"title":"Detection of Potato Plant Leaf Diseases using Convolutional Neural Networks","abstract":"Potato is an important food-security crop in Ethiopia, but its production is affected by foliar diseases, particularly early blight and late blight. This study develops a lightweight convolutional neural network (CNN) to classify potato leaves into three categories: healthy, early blight, and late blight. The proposed model was trained using 2,215 images from the PlantVillage dataset and evaluated on an independent f","authors":["Haimanot Bitew Alemayehu","Simachew Melaku Ayalew","Chilotie Desalew Mengesha","Ayisheshim Almaw Bogale","Anteneh Birhan Yenew","Abrham Tegegne Bogale"],"url":"https://arxiv.org/abs/2610.20544v1","arxiv_id":"2610.20544v1","published":"2026-10-14T17:36:33Z"},{"title":"PrismaCoder: Pushing the Boundaries of Open Multimodal Code Intelligence through Decomposed Rubric Synthesis","abstract":"Multimodal code intelligence connects the symbolic world of programs with the visual world their execution creates. Interest in this capability is burgeoning, as models are increasingly asked to produce charts, webpages, animations, and semi-structured documents. Recently, proprietary models have pushed far ahead, producing interactive artifacts even from a text description or a screenshot alone. Yet their data and t","authors":["Qiushi Sun","Jingyang Gong","Yang Liu","Qiaosheng Chen","Zhangyue Yin","Zixian Huang"],"url":"https://arxiv.org/abs/2610.20538v1","arxiv_id":"2610.20538v1","published":"2026-10-14T17:32:54Z"},{"title":"ActiveEvidence: Decision-Focused Evidence Graphs with Verifiable Acquisition Gaps","abstract":"A multimodal agent must decide whether visual evidence is worth acquiring, which candidate or resolution to query, and when further interaction can no longer change its decision. We study this problem under a strict free-metadata/paid-observation boundary, stochastic vector costs, and a frozen execution trace, and treat it as decision-certifiable active evidence acquisition. A Counterfactual Evidence Interaction Grap","authors":["Miaobo Hu","Shuhao Hu","Xiaobo Guo","Xin Wang","Bokun Wang","Peng Zhang"],"url":"https://arxiv.org/abs/2610.20532v1","arxiv_id":"2610.20532v1","published":"2026-10-14T17:29:06Z"},{"title":"The Wealth of Agents: Scaling Agentic AI Across Developers","abstract":"Today's agentic systems are typically designed, built, and deployed by single developers with full control over all participating agents. While this simplifies development, it limits the potential scale of agentic systems. We study multi-developer agentic systems (MDAs) which enable developers to collaboratively build agentic systems to push frontier capability beyond what any individual developer could achieve alone","authors":["Austin W. Hanjie","Carlos E. Jimenez","Zirui Wang","Karthik Narasimhan"],"url":"https://arxiv.org/abs/2610.20518v1","arxiv_id":"2610.20518v1","published":"2026-10-14T17:19:11Z"},{"title":"VASPO: Self-Distillation with Privileged Value Estimation","abstract":"On-policy self-distillation provides dense supervision for reasoning models via a privileged self-teacher. However, this supervision reflects local token preferences rather than the expected return of an unguided student continuation. Recent methods combine this signal with GRPO. They use GRPO for update directions and the teacher for signal scaling. However, we demonstrate a more effective integration within PPO. Sp","authors":["Fei Wu","Zhenrong Zhang","Jianshu Zhang","Jun Du"],"url":"https://arxiv.org/abs/2610.20516v1","arxiv_id":"2610.20516v1","published":"2026-10-14T17:17:18Z"},{"title":"VascuSR: Iterative Anatomy-Aware Diffusion for CT Super-Resolution","abstract":"Generative super-resolution (SR) can recover sharp detail in computed tomography (CT) images from severely downsampled observations, but the missing high-frequency content is not uniquely determined by the low-resolution input. Consequently, visually plausible reconstructions may contain anatomically unsupported vascular detail, such as missing, spurious, or incorrectly connected vessels. To overcome this challenge, ","authors":["Hairong Ni","Jiahe Huang","Minghao Guo","Wojciech Matusik","Rose Yu"],"url":"https://arxiv.org/abs/2610.20509v1","arxiv_id":"2610.20509v1","published":"2026-10-14T17:12:56Z"},{"title":"Hindsight-Privileged RLVR: Single-Rollout Token Credit Without Group Sampling","abstract":"Reinforcement learning with verifiable rewards (RLVR) trains language models from checked task outcomes, but assigning a terminal reward to individual reasoning tokens or actions remains difficult. Group-relative methods such as Group Relative Policy Optimization (GRPO) sample several attempts per prompt, but lose their reward-based learning signal when all attempts receive the same outcome. We propose Hindsight-Priv","authors":["Wei Chen","Yubing Wu","Zhou Xu","Haoyue Zhang","Delu Zeng","Jiahao Wang"],"url":"https://arxiv.org/abs/2610.20497v1","arxiv_id":"2610.20497v1","published":"2026-10-14T17:06:59Z"},{"title":"Quantifying privacy risks in foundation model representations of brain MRI","abstract":"Pretrained representations are increasingly used to share and reuse information from medical images without exposing the original scans, but it is unclear how much patient-specific information these representations retain. We investigate this question for brain magnetic resonance imaging (MRI) by evaluating five frozen pretrained encoders spanning different architectures, pretraining objectives, and training domains ","authors":["Imen Bajar","Héber Hwang Arcolezi","Antoine Boutet","Carole Frindel","Mohamed Maouche"],"url":"https://arxiv.org/abs/2610.20494v1","arxiv_id":"2610.20494v1","published":"2026-10-14T17:03:42Z"},{"title":"SafeAdapt-Poker: Counterfactual Risk Calibration for Budgeted Adaptation under Opponent Shift","abstract":"Opponent histories can mislead after behavioral shifts. SafeAdapt-Poker formulates budgeted adaptation under opponent shift as a dual-counterfactual sequential-control problem using only information available before each decision. At each decision the controller asks two distinct questions: what local downside a deviation from the frozen anchor would induce under matched legal continuations, and what future adaptatio","authors":["Miaobo Hu","Shuhao Hu","Xiaobo Guo","Xin Wang","Bokun Wang","Rui Chen"],"url":"https://arxiv.org/abs/2610.20473v1","arxiv_id":"2610.20473v1","published":"2026-10-14T16:50:42Z"},{"title":"Selective Growing Q-Networks (SGQN): An Analysis of Adaptive Action Grids in Decoupled Q-Learning","abstract":"Value-based Q-learning for continuous control can discretise the action space, but the resolution of the discretisation trades early exploration against final precision. Growing Q-Networks (GQN) grow the resolution uniformly from coarse to fine. We introduce Selective Growing Q-Networks (SGQN), which keeps decoupled Q-learning and replaces uniform growth with an adaptive action grid: bins are added next to bins with ","authors":["Parham Nooranbakht","Seyed Mahdi Basiri Azad","Joschka Bödecker"],"url":"https://arxiv.org/abs/2610.20463v1","arxiv_id":"2610.20463v1","published":"2026-10-14T16:45:10Z"},{"title":"Equal CER, Unequal Damage: Severity-Weighted Character Error Rate for OCR","abstract":"Character Error Rate (CER), the standard metric for OCR and vision-language models (VLMs), charges a typo as much as a wrong name. We introduce semCER, which keeps CER's alignment and denominator and charges each grapheme edit a severity from an LLM judge, with no word segmentation. Normalized CER keeps the score of a whole transcription, where it tracks readers as well as semCER or better, and semCER breaks that sco","authors":["Nguyen Nguyen","Yongyi Zang"],"url":"https://arxiv.org/abs/2610.20461v1","arxiv_id":"2610.20461v1","published":"2026-10-14T16:44:22Z"},{"title":"From Training to Escalation: Forensic Analysis and Local Recreation of RL/RLHF-Induced Privilege Escalation in AI Agents (CVE-2026-65616)","abstract":"Mid-2026 reporting on autonomous AI evaluation incidents claimed that frontier large language model (LLM) agents escaped server containment through emergent coordination and cloud infrastructure attacks. This paper contextualizes those claims through forensic analysis and containerized recreation of both the Highly Persistent Internal Model (HPIM) training-case conditions and the Phase One privilege escalation target","authors":["Alicia Colmenero-Fernández"],"url":"https://arxiv.org/abs/2610.20437v1","arxiv_id":"2610.20437v1","published":"2026-10-14T16:28:09Z"}],"history":{"events":[{"year":2021,"text":"Approximately 10,000 John Deere employees went on strike in one of the largest private-sector strikes in the United States.","url":"https://en.wikipedia.org/wiki/John_Deere"},{"year":2014,"text":"A snowstorm and series of avalanches occurred on and around the Himalayan peaks of Annapurna and Dhaulagiri, resulting in the deaths of at least 43 people.","url":"https://en.wikipedia.org/wiki/2014_Nepal_snowstorm_disaster"},{"year":2012,"text":"Felix Baumgartner jumped from a helium balloon in the stratosphere to become the first person to break the sound barrier without vehicular power.","url":"https://en.wikipedia.org/wiki/Felix_Baumgartner"},{"year":2011,"text":"Michael Woodford was dismissed as the CEO of the optics manufacturer Olympus after uncovering internal financial misconduct, escalating the corporate scandal into one of the largest in Japanese business history.","url":"https://en.wikipedia.org/wiki/Michael_Christopher_Woodford"},{"year":1980,"text":"The 6th Congress of the Workers' Party concluded, having anointed North Korean president Kim Il Sung's son Kim Jong Il as his successor.","url":"https://en.wikipedia.org/wiki/6th_Congress_of_the_Workers'_Party_of_Korea"},{"year":1979,"text":"At least 75,000 people attended the National March on Washington for Lesbian and Gay Rights in Washington, D.C., to demand equal civil rights for LGBT people.","url":"https://en.wikipedia.org/wiki/National_March_on_Washington_for_Lesbian_and_Gay_Rights"},{"year":1964,"text":"Members of the Politburo voted to remove Nikita Khrushchev as First Secretary of the Communist Party of the Soviet Union and replace him with Leonid Brezhnev.","url":"https://en.wikipedia.org/wiki/Politburo_of_the_Communist_Party_of_the_Soviet_Union"},{"year":1957,"text":"After three days of heavy rain, the Turia overflowed and flooded the city of Valencia, Spain, causing at least 81 deaths.","url":"https://en.wikipedia.org/wiki/Turia_(river)"},{"year":1956,"text":"The Queen Elizabeth Way, North America's first intercity divided highway, opens.","url":"https://en.wikipedia.org/wiki/Queen_Elizabeth_Way"},{"year":1956,"text":"B. R. Ambedkar, a leader of India's \"Untouchable\" caste, publicly converted to Buddhism and became the leader of the Dalit Buddhist movement.","url":"https://en.wikipedia.org/wiki/B._R._Ambedkar"},{"year":1953,"text":"Israeli military commander Ariel Sharon and his Unit 101 special forces attacked the village of Qibya on the West Bank, destroying 45 buildings, killing 42 villagers, and wounding 15 others.","url":"https://en.wikipedia.org/wiki/Ariel_Sharon"},{"year":1949,"text":"The first of the Smith Act trials concluded in New York City, with eleven leaders of the Communist Party of the United States found guilty of violating the Smith Act.","url":"https://en.wikipedia.org/wiki/Smith_Act_trials_of_Communist_Party_leaders"},{"year":1947,"text":"American test pilot Chuck Yeager became the first person to break the sound barrier, reaching Mach 1.06 on board the Bell X-1, an experimental rocket-powered aircraft.","url":"https://en.wikipedia.org/wiki/Chuck_Yeager"},{"year":1943,"text":"World War II: During the second raid on Schweinfurt, the U.S. 8th Air Force suffered so many losses that it lost air supremacy over Germany for several months.","url":"https://en.wikipedia.org/wiki/World_War_II"},{"year":1943,"text":"The Holocaust: Prisoners at Sobibor extermination camp revolted, killing 11 SS officers and staging a mass escape.","url":"https://en.wikipedia.org/wiki/The_Holocaust"},{"year":1943,"text":"The Second Philippine Republic, a Japanese puppet state, was established with Jose P. Laurel as its first president.","url":"https://en.wikipedia.org/wiki/Second_Philippine_Republic"},{"year":1940,"text":"Second World War: During the Blitz, a semi-armour-piercing fragmentation bomb fell on the road above Balham station in London, which was being used as an air raid shelter, killing at least 64 people.","url":"https://en.wikipedia.org/wiki/World_War_II"},{"year":1939,"text":"Second World War: The German submarine U-47 torpedoed and sank the Royal Navy battleship HMS Royal Oak while the latter was anchored at Scapa Flow in Orkney, Scotland.","url":"https://en.wikipedia.org/wiki/World_War_II"}],"births":[{"year":2003,"text":"Tate Sandell, American college football player","url":"https://en.wikipedia.org/wiki/Tate_Sandell"},{"year":2001,"text":"Rowan Blanchard, American actress","url":"https://en.wikipedia.org/wiki/Rowan_Blanchard"},{"year":1999,"text":"Quinn Hughes, American ice hockey player","url":"https://en.wikipedia.org/wiki/Quinn_Hughes"},{"year":1998,"text":"Ariela Barer, American actress","url":"https://en.wikipedia.org/wiki/Ariela_Barer"},{"year":1994,"text":"Joe Burgess, English rugby league player","url":"https://en.wikipedia.org/wiki/Joe_Burgess"},{"year":1994,"text":"Jaelen Feeney, Australian rugby league player","url":"https://en.wikipedia.org/wiki/Jaelen_Feeney"},{"year":1994,"text":"Jared Goff, American football player","url":"https://en.wikipedia.org/wiki/Jared_Goff"},{"year":1993,"text":"Ashton Agar, Australian cricketer","url":"https://en.wikipedia.org/wiki/Ashton_Agar"}],"deaths":[{"year":2025,"text":"D'Angelo, American musician (born 1974)","url":"https://en.wikipedia.org/wiki/D'Angelo"},{"year":2024,"text":"Thomas J. 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