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Re-FORC trains a lightweight adapter on reasoning models, demonstrating improved prediction with longer reasoning and larger models. Re-FORC enables: 1) early stopping of unpromising reasoning chains, reducing compute by up to 26\\% compa","authors":["Renos Zabounidis","Aditya Golatkar","Michael Kleinman","Alessandro Achille","Wei Xia","Stefano Soatto"],"url":"http://arxiv.org/abs/2511.02130v2","arxiv_id":"2511.02130v2","published":"2025-11-03T23:47:49Z"},{"title":"Analysis of AdvFusion: Adapter-based Multilingual Learning for Code Large Language Models","abstract":"Programming languages can benefit from one another by utilizing a language model for software engineering tasks. Full fine-tuning and Parameter Efficient Fine-Tuning (PEFT) of Code Language Models (Code-LMs) has been explored for multilingual knowledge transfer. AdapterFusion is a PEFT architecture that aims to enhance task performance by leveraging information from multiple programming languages, but primarily focus","authors":["Amirreza Esmaeili","Fahd Seddik","Yongyi Ji","Fatemeh Fard","Fuxiang Chen"],"url":"http://arxiv.org/abs/2511.02869v1","arxiv_id":"2511.02869v1","published":"2025-11-03T23:45:27Z"},{"title":"Matrix Sensing with Kernel Optimal Loss: Robustness and Optimization Landscape","abstract":"In this paper we study how the choice of loss functions of non-convex optimization problems affects their robustness and optimization landscape, through the study of noisy matrix sensing. In traditional regression tasks, mean squared error (MSE) loss is a common choice, but it can be unreliable for non-Gaussian or heavy-tailed noise. To address this issue, we adopt a robust loss based on nonparametric regression, whi","authors":["Xinyuan Song","Ziye Ma"],"url":"http://arxiv.org/abs/2511.02122v3","arxiv_id":"2511.02122v3","published":"2025-11-03T23:22:37Z"},{"title":"InsurAgent: A Large Language Model-Empowered Agent for Simulating Individual Behavior in Purchasing Flood Insurance","abstract":"Flood insurance is an effective strategy for individuals to mitigate disaster-related losses. However, participation rates among at-risk populations in the United States remain strikingly low. This gap underscores the need to understand and model the behavioral mechanisms underlying insurance decisions. Large language models (LLMs) have recently exhibited human-like intelligence across wide-ranging tasks, offering pr","authors":["Ziheng Geng","Jiachen Liu","Ran Cao","Lu Cheng","Dan M. Frangopol","Minghui Cheng"],"url":"http://arxiv.org/abs/2511.02119v1","arxiv_id":"2511.02119v1","published":"2025-11-03T23:19:27Z"},{"title":"Deep Value Benchmark: Measuring Whether Models Generalize Deep Values or Shallow Preferences","abstract":"We introduce the Deep Value Benchmark (DVB), an evaluation framework that directly tests whether large language models (LLMs) learn fundamental human values or merely surface-level preferences. This distinction is critical for AI alignment: Systems that capture deeper values are likely to generalize human intentions robustly, while those that capture only superficial patterns in preference data risk producing misalig","authors":["Joshua Ashkinaze","Hua Shen","Saipranav Avula","Eric Gilbert","Ceren Budak"],"url":"http://arxiv.org/abs/2511.02109v3","arxiv_id":"2511.02109v3","published":"2025-11-03T22:49:54Z"},{"title":"Metamorphic Testing of Large Language Models for Natural Language Processing","abstract":"Using large language models (LLMs) to perform natural language processing (NLP) tasks has become increasingly pervasive in recent times. The versatile nature of LLMs makes them applicable to a wide range of such tasks. While the performance of recent LLMs is generally outstanding, several studies have shown that they can often produce incorrect results. Automatically identifying these faulty behaviors is extremely us","authors":["Steven Cho","Stefano Ruberto","Valerio Terragni"],"url":"http://arxiv.org/abs/2511.02108v1","arxiv_id":"2511.02108v1","published":"2025-11-03T22:48:19Z"},{"title":"Geometric Data Valuation via Leverage Scores","abstract":"Shapley data valuation provides a principled, axiomatic framework for assigning importance to individual datapoints, and has gained traction in dataset curation, pruning, and pricing. However, it is a combinatorial measure that requires evaluating marginal utility across all subsets of the data, making it computationally infeasible at scale. We propose a geometric alternative based on statistical leverage scores, whi","authors":["Rodrigo Mendoza-Smith"],"url":"http://arxiv.org/abs/2511.02100v1","arxiv_id":"2511.02100v1","published":"2025-11-03T22:20:50Z"},{"title":"Automated Reward Design for Gran Turismo","abstract":"When designing reinforcement learning (RL) agents, a designer communicates the desired agent behavior through the definition of reward functions - numerical feedback given to the agent as reward or punishment for its actions. However, mapping desired behaviors to reward functions can be a difficult process, especially in complex environments such as autonomous racing. In this paper, we demonstrate how current foundat","authors":["Michel Ma","Takuma Seno","Kaushik Subramanian","Peter R. Wurman","Peter Stone","Craig Sherstan"],"url":"http://arxiv.org/abs/2511.02094v1","arxiv_id":"2511.02094v1","published":"2025-11-03T22:07:53Z"},{"title":"Uncertainty Guided Online Ensemble for Non-stationary Data Streams in Fusion Science","abstract":"Machine Learning (ML) is poised to play a pivotal role in the development and operation of next-generation fusion devices. Fusion data shows non-stationary behavior with distribution drifts, resulted by both experimental evolution and machine wear-and-tear. ML models assume stationary distribution and fail to maintain performance when encountered with such non-stationary data streams. Online learning techniques have ","authors":["Kishansingh Rajput","Malachi Schram","Brian Sammuli","Sen Lin"],"url":"http://arxiv.org/abs/2511.02092v1","arxiv_id":"2511.02092v1","published":"2025-11-03T22:03:37Z"},{"title":"Natural Building Blocks for Structured World Models: Theory, Evidence, and Scaling","abstract":"The field of world modeling is fragmented, with researchers developing bespoke architectures that rarely build upon each other. We propose a framework that specifies the natural building blocks for structured world models based on the fundamental stochastic processes that any world model must capture: discrete processes (logic, symbols) and continuous processes (physics, dynamics); the world model is then defined by ","authors":["Lancelot Da Costa","Sanjeev Namjoshi","Mohammed Abbas Ansari","Bernhard Schölkopf"],"url":"http://arxiv.org/abs/2511.02091v1","arxiv_id":"2511.02091v1","published":"2025-11-03T22:02:04Z"},{"title":"Energy Loss Functions for Physical Systems","abstract":"Effectively leveraging prior knowledge of a system's physics is crucial for applications of machine learning to scientific domains. Previous approaches mostly focused on incorporating physical insights at the architectural level. In this paper, we propose a framework to leverage physical information directly into the loss function for prediction and generative modeling tasks on systems like molecules and spins. We de","authors":["Sékou-Oumar Kaba","Kusha Sareen","Daniel Levy","Siamak Ravanbakhsh"],"url":"http://arxiv.org/abs/2511.02087v1","arxiv_id":"2511.02087v1","published":"2025-11-03T21:58:36Z"},{"title":"Watermarking Discrete Diffusion Language Models","abstract":"Watermarking has emerged as a promising technique to track AI-generated content and differentiate it from authentic human creations. While prior work extensively studies watermarking for autoregressive large language models (LLMs) and image diffusion models, it remains comparatively underexplored for discrete diffusion language models (DDLMs), which are becoming popular due to their high inference throughput. In this","authors":["Avi Bagchi","Akhil Bhimaraju","Moulik Choraria","Daniel Alabi","Lav R. Varshney"],"url":"http://arxiv.org/abs/2511.02083v2","arxiv_id":"2511.02083v2","published":"2025-11-03T21:43:44Z"},{"title":"Pinching Antennas Meet AI in Next-Generation Wireless Networks","abstract":"Next-generation (NG) wireless networks must embrace innate intelligence in support of demanding emerging applications, such as extended reality and autonomous systems, under ultra-reliable and low-latency requirements. Pinching antennas (PAs), a new flexible low-cost technology, can create line-of-sight links by dynamically activating small dielectric pinches along a waveguide on demand. As a compelling complement, a","authors":["Fang Fang","Zhiguo Ding","Victor C. M. Leung","Lajos Hanzo"],"url":"http://arxiv.org/abs/2511.07442v1","arxiv_id":"2511.07442v1","published":"2025-11-03T21:32:00Z"},{"title":"Human-AI Co-Embodied Intelligence for Scientific Experimentation and Manufacturing","abstract":"Scientific experimentation and manufacturing rely on prolonged protocol development and complex, multi-step implementation, which require continuous human expertise for precise execution and decision-making, limiting interpretability and scalability. Here, we introduce human-artificial intelligence (AI) co-embodied intelligence, a new form of physical AI that unites human researchers, agentic AI, and wearable hardwar","authors":["Xinyi Lin","Yuyang Zhang","Yuanhang Gan","Juntao Chen","Hao Shen","Yichun He"],"url":"http://arxiv.org/abs/2511.02071v2","arxiv_id":"2511.02071v2","published":"2025-11-03T21:12:48Z"},{"title":"Vortex: Hosting ML Inference and Knowledge Retrieval Services With Tight Latency and Throughput Requirements","abstract":"There is growing interest in deploying ML inference and knowledge retrieval as services that could support both interactive queries by end users and more demanding request flows that arise from AIs integrated into a end-user applications and deployed as agents. Our central premise is that these latter cases will bring service level latency objectives (SLOs). Existing ML serving platforms use batching to optimize for ","authors":["Yuting Yang","Tiancheng Yuan","Jamal Hashim","Thiago Garrett","Jeffrey Qian","Ann Zhang"],"url":"http://arxiv.org/abs/2511.02062v1","arxiv_id":"2511.02062v1","published":"2025-11-03T20:59:27Z"},{"title":"Text-VQA Aug: Pipelined Harnessing of Large Multimodal Models for Automated Synthesis","abstract":"Creation of large-scale databases for Visual Question Answering tasks pertaining to the text data in a scene (text-VQA) involves skilful human annotation, which is tedious and challenging. With the advent of foundation models that handle vision and language modalities, and with the maturity of OCR systems, it is the need of the hour to establish an end-to-end pipeline that can synthesize Question-Answer (QA) pairs ba","authors":["Soham Joshi","Shwet Kamal Mishra","Viswanath Gopalakrishnan"],"url":"http://arxiv.org/abs/2511.02046v1","arxiv_id":"2511.02046v1","published":"2025-11-03T20:28:22Z"},{"title":"Regularization Through Reasoning: Systematic Improvements in Language Model Classification via Explanation-Enhanced Fine-Tuning","abstract":"Fine-tuning LLMs for classification typically maps inputs directly to labels. We ask whether attaching brief explanations to each label during fine-tuning yields better models. We evaluate conversational response quality along three axes: naturalness, comprehensiveness, and on-topic adherence, each rated on 5-point scales. Using ensemble-generated data from multiple LLMs, we fine-tune a 7B-parameter model and test ac","authors":["Vivswan Shah","Randy Cogill","Hanwei Yue","Gopinath Chennupati","Rinat Khaziev"],"url":"http://arxiv.org/abs/2511.02044v2","arxiv_id":"2511.02044v2","published":"2025-11-03T20:25:42Z"},{"title":"Quantum-Enhanced Generative Models for Rare Event Prediction","abstract":"Rare events such as financial crashes, climate extremes, and biological anomalies are notoriously difficult to model due to their scarcity and heavy-tailed distributions. Classical deep generative models often struggle to capture these rare occurrences, either collapsing low-probability modes or producing poorly calibrated uncertainty estimates. In this work, we propose the Quantum-Enhanced Generative Model (QEGM), a","authors":["M. Z. Haider","M. U. Ghouri","Tayyaba Noreen","M. Salman"],"url":"http://arxiv.org/abs/2511.02042v1","arxiv_id":"2511.02042v1","published":"2025-11-03T20:24:55Z"},{"title":"RobustFSM: Submodular Maximization in Federated Setting with Malicious Clients","abstract":"Submodular maximization is an optimization problem benefiting many machine learning applications, where we seek a small subset best representing an extremely large dataset. We focus on the federated setting where the data are locally owned by decentralized clients who have their own definitions for the quality of representability. This setting requires repetitive aggregation of local information computed by the clien","authors":["Duc A. Tran","Dung Truong","Duy Le"],"url":"http://arxiv.org/abs/2511.02029v2","arxiv_id":"2511.02029v2","published":"2025-11-03T20:07:21Z"},{"title":"Path-Coordinated Continual Learning with Neural Tangent Kernel-Justified Plasticity: A Theoretical Framework with Near State-of-the-Art Performance","abstract":"Catastrophic forgetting is one of the fundamental issues of continual learning because neural networks forget the tasks learned previously when trained on new tasks. The proposed framework is a new path-coordinated framework of continual learning that unites the Neural Tangent Kernel (NTK) theory of principled plasticity bounds, statistical validation by Wilson confidence intervals, and evaluation of path quality by ","authors":["Rathin Chandra Shit"],"url":"http://arxiv.org/abs/2511.02025v1","arxiv_id":"2511.02025v1","published":"2025-11-03T19:55:59Z"},{"title":"Shared Parameter Subspaces and Cross-Task Linearity in Emergently Misaligned Behavior","abstract":"Recent work has discovered that large language models can develop broadly misaligned behaviors after being fine-tuned on narrowly harmful datasets, a phenomenon known as emergent misalignment (EM). However, the fundamental mechanisms enabling such harmful generalization across disparate domains remain poorly understood. In this work, we adopt a geometric perspective to study EM and demonstrate that it exhibits a fund","authors":["Daniel Aarao Reis Arturi","Eric Zhang","Andrew Ansah","Kevin Zhu","Ashwinee Panda","Aishwarya Balwani"],"url":"http://arxiv.org/abs/2511.02022v1","arxiv_id":"2511.02022v1","published":"2025-11-03T19:50:24Z"},{"title":"Proof-of-Spiking-Neurons(PoSN): Neuromorphic Consensus for Next-Generation Blockchains","abstract":"Blockchain systems face persistent challenges of scalability, latency, and energy inefficiency. Existing consensus protocols such as Proof-of-Work (PoW) and Proof-of-Stake (PoS) either consume excessive resources or risk centralization. This paper proposes \\textit{Proof-of-Spiking-Neurons (PoSN)}, a neuromorphic consensus protocol inspired by spiking neural networks. PoSN encodes transactions as spike trains, elects ","authors":["M. Z. Haider","M. U Ghouri","Tayyaba Noreen","M. Salman"],"url":"http://arxiv.org/abs/2511.02868v1","arxiv_id":"2511.02868v1","published":"2025-11-03T19:31:47Z"},{"title":"InteracSPARQL: An Interactive System for SPARQL Query Refinement Using Natural Language Explanations","abstract":"In recent years, querying semantic web data using SPARQL has remained challenging, especially for non-expert users, due to the language's complex syntax and the prerequisite of understanding intricate data structures. To address these challenges, we propose InteracSPARQL, an interactive SPARQL query generation and refinement system that leverages natural language explanations (NLEs) to enhance user comprehension and ","authors":["Xiangru Jian","Zhengyuan Dong","M. Tamer Özsu"],"url":"http://arxiv.org/abs/2511.02002v1","arxiv_id":"2511.02002v1","published":"2025-11-03T19:15:51Z"},{"title":"TRACE: Textual Reasoning for Affordance Coordinate Extraction","abstract":"Vision-Language Models (VLMs) struggle to translate high-level instructions into the precise spatial affordances required for robotic manipulation. While visual Chain-of-Thought (CoT) methods exist, they are often computationally intensive. In this work, we introduce TRACE (Textual Reasoning for Affordance Coordinate Extraction), a novel methodology that integrates a textual Chain of Reasoning (CoR) into the affordan","authors":["Sangyun Park","Jin Kim","Yuchen Cui","Matthew S. Brown"],"url":"http://arxiv.org/abs/2511.01999v1","arxiv_id":"2511.01999v1","published":"2025-11-03T19:13:26Z"},{"title":"Trove: A Flexible Toolkit for Dense Retrieval","abstract":"We introduce Trove, an easy-to-use open-source retrieval toolkit that simplifies research experiments without sacrificing flexibility or speed. For the first time, we introduce efficient data management features that load and process (filter, select, transform, and combine) retrieval datasets on the fly, with just a few lines of code. This gives users the flexibility to easily experiment with different dataset config","authors":["Reza Esfandiarpoor","Max Zuo","Stephen H. Bach"],"url":"http://arxiv.org/abs/2511.01857v1","arxiv_id":"2511.01857v1","published":"2025-11-03T18:59:57Z"}],"history":{"events":[{"year":1996,"text":"Abdullah Çatlı, a leader of the ultra-nationalist Grey Wolves, was killed in a car crash near Susurluk, Turkey, sparking a scandal that exposed the depth of the state's complicity in organized crime.","url":"https://en.wikipedia.org/wiki/Abdullah_%C3%87atl%C4%B1"},{"year":1969,"text":"U.S. president Richard Nixon made a plea for support from the \"silent majority\", referring to those Americans who did not join in the large demonstrations against the Vietnam War at the time.","url":"https://en.wikipedia.org/wiki/Richard_Nixon"},{"year":1957,"text":"The Soviet Union launched Sputnik 2, carrying the space dog Laika as the first living creature to enter orbit around Earth.","url":"https://en.wikipedia.org/wiki/Sputnik_2"},{"year":1956,"text":"Suez Crisis: During an invasion of the Gaza Strip, Israeli soldiers shot and killed hundreds of Palestinian refugees and local inhabitants in Khan Yunis.","url":"https://en.wikipedia.org/wiki/Suez_Crisis"},{"year":1954,"text":"The first film featuring the giant monster known as Godzilla was released (poster pictured) nationwide in Japan.","url":"https://en.wikipedia.org/wiki/Godzilla_(1954_film)"},{"year":1948,"text":"The Chicago Daily Tribune published the erroneous headline \"Dewey Defeats Truman\" (pictured) in its early morning edition shortly after incumbent U.S. president Harry S. Truman officially upset the heavily favored governor of New York Thomas Dewey in the presidential election.","url":"https://en.wikipedia.org/wiki/Chicago_Tribune"},{"year":1943,"text":"The Holocaust: The largest massacre of Jews by German forces began at Majdanek concentration camp.","url":"https://en.wikipedia.org/wiki/The_Holocaust"},{"year":1942,"text":"World War II: U.S. Marines and U.S. Army forces began an attempt to encircle and destroy a regiment of Imperial Japanese Army troops on Guadalcanal.","url":"https://en.wikipedia.org/wiki/World_War_II"},{"year":1935,"text":"In a widely criticised referendum, almost 98 percent of Greek voters supported the restoration of George II (pictured) as King of the Hellenes.","url":"https://en.wikipedia.org/wiki/1935_Greek_monarchy_referendum"},{"year":1898,"text":"The Fashoda Incident ended with French forces withdrawing after several months of military stalemate with the British in Fashoda (now in South Sudan).","url":"https://en.wikipedia.org/wiki/Fashoda_Incident"},{"year":1881,"text":"Indigenous Mapuche began an uprising against the occupation of Araucanía by Chile.","url":"https://en.wikipedia.org/wiki/Mapuche"},{"year":1880,"text":"The current melody of Kimigayo, the national anthem of Japan, was adopted.","url":"https://en.wikipedia.org/wiki/Kimigayo"},{"year":1848,"text":"A new constitution drafted by Johan Rudolph Thorbecke was proclaimed, limiting the powers of the Dutch monarchy.","url":"https://en.wikipedia.org/wiki/Constitution_of_the_Netherlands"},{"year":1812,"text":"French invasion of Russia: As Napoleon's Grande Armée began its retreat, its rear guard was defeated at the Battle of Vyazma.","url":"https://en.wikipedia.org/wiki/French_invasion_of_Russia"},{"year":1805,"text":"War of the Third Coalition: The Treaty of Potsdam was signed compelling Prussia to join the coalition if it failed to negotiate peace between Russia and France.","url":"https://en.wikipedia.org/wiki/War_of_the_Third_Coalition"},{"year":1793,"text":"French Revolution: Playwright, journalist and outspoken feminist Olympe de Gouges was guillotined.","url":"https://en.wikipedia.org/wiki/French_Revolution"},{"year":1534,"text":"The Parliament of England passed the first Act of Supremacy, making King Henry VIII head of the Church of England, supplanting the pope and the Catholic Church.","url":"https://en.wikipedia.org/wiki/Parliament_of_England"}],"births":[{"year":2007,"text":"Ever Anderson, American actress","url":"https://en.wikipedia.org/wiki/Ever_Anderson"},{"year":2005,"text":"Fina Strazza, American actress","url":"https://en.wikipedia.org/wiki/Fina_Strazza"},{"year":2005,"text":"Lara Raj, American singer","url":"https://en.wikipedia.org/wiki/Lara_Raj"},{"year":2001,"text":"Hailey Baptiste, American tennis player","url":"https://en.wikipedia.org/wiki/Hailey_Baptiste"},{"year":2001,"text":"Jake LaRavia, American basketball player","url":"https://en.wikipedia.org/wiki/Jake_LaRavia"},{"year":1998,"text":"Maddison Elliott, Australian paralympic swimmer","url":"https://en.wikipedia.org/wiki/Maddison_Elliott"},{"year":1997,"text":"Izuchuckwu Anthony, Nigerian footballer","url":"https://en.wikipedia.org/wiki/Izuchukwu_Anthony"},{"year":1997,"text":"Sarthak Golui, Indian footballer","url":"https://en.wikipedia.org/wiki/Sarthak_Golui"}],"deaths":[{"year":2025,"text":"Dick Cheney, American businessman and politician, 46th Vice President of the United States (born 1941)","url":"https://en.wikipedia.org/wiki/Dick_Cheney"},{"year":2025,"text":"Kim Yong-nam, North Korean politician (born 1928)","url":"https://en.wikipedia.org/wiki/Kim_Yong-nam"},{"year":2024,"text":"Quincy Jones, American producer (born 1933)","url":"https://en.wikipedia.org/wiki/Quincy_Jones"},{"year":2018,"text":"Sondra Locke, American actress and director (born 1944)","url":"https://en.wikipedia.org/wiki/Sondra_Locke"},{"year":2016,"text":"Kay Starr, American singer (born 1922)","url":"https://en.wikipedia.org/wiki/Kay_Starr"},{"year":2015,"text":"Ahmed Chalabi, Iraqi businessman and politician (born 1944)","url":"https://en.wikipedia.org/wiki/Ahmed_Chalabi"},{"year":2015,"text":"Howard Coble, American captain, lawyer, and politician (born 1931)","url":"https://en.wikipedia.org/wiki/Howard_Coble"},{"year":2015,"text":"Tom Graveney, English cricketer and sportscaster (born 1927)","url":"https://en.wikipedia.org/wiki/Tom_Graveney"}]},"sources":[{"id":"attention","name":"Wikipedia most-read","url":"https://api.wikimedia.org","licence":"CC BY-SA 4.0","attribution":"Wikipedia contributors"},{"id":"frontier","name":"Hacker News","url":"https://news.ycombinator.com","licence":"Public API — titles and links, credited","attribution":"Hacker News / Y Combinator"},{"id":"research","name":"arXiv cs.AI","url":"https://arxiv.org","licence":"Open metadata, per arXiv API terms","attribution":"arXiv.org"},{"id":"history","name":"Wikipedia On this day","url":"https://api.wikimedia.org","licence":"CC BY-SA 4.0","attribution":"Wikipedia contributors"}],"licence":"Records are CC BY-SA 4.0, inheriting from Wikipedia. Every item links to its source."}