{"date":"2025-11-06","schema_version":1,"captured_at":"2026-08-25T22:55:42.009Z","complete":true,"sources_captured":["attention","frontier","research","history"],"counts":{"attention":30,"frontier":25,"research":25,"history":15},"attention":[{"title":"Zohran Mamdani","views":3842842,"rank":2,"extract":"Zohran Kwame Mamdani is an American politician who has served since 2026 as the 112th mayor of New York City. A member of the Democratic Party and the Democratic Socialists of America, he represented the 36th district in the New York State Assembly from 2021 t","url":"https://en.wikipedia.org/wiki/Zohran_Mamdani","thumb":"https://upload.wikimedia.org/wikipedia/commons/thumb/5/59/Zohran_Mamdani_05.25.25_%28b%29_%28cropped%29.jpg/330px-Zohran_Mamdani_05.25.25_%28b%29_%28cropped%29.jpg?utm_source=en.wikipedia.org&utm_campaign=api&utm_content=thumbnail"},{"title":"Rama Duwaji","views":1281707,"rank":3,"extract":"Rama Sawaf Duwaji is an American animator, illustrator, and ceramist. 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reference summaries and the limitations of existing metrics like ROUGE and BLEU, which depend on surface-level lexical overlap. We introduce REFLEX, a reference-free evaluation metric for log summarization based on large language model (LLM) judgment. REFLEX uses LLMs as zero-shot evaluators to assess summary quality along dimensions ","authors":["Priyanka Mudgal"],"url":"http://arxiv.org/abs/2511.07458v2","arxiv_id":"2511.07458v2","published":"2025-11-06T23:52:36Z"},{"title":"DMA: Online RAG Alignment with Human Feedback","abstract":"Retrieval-augmented generation (RAG) systems often rely on static retrieval, limiting adaptation to evolving intent and content drift. We introduce Dynamic Memory Alignment (DMA), an online learning framework that systematically incorporates multi-granularity human feedback to align ranking in interactive settings. DMA organizes document-, list-, and response-level signals into a coherent learning pipeline: supervise","authors":["Yu Bai","Yukai Miao","Dawei Wang","Li Chen","Fei Long","Rundi Zhai"],"url":"http://arxiv.org/abs/2511.04880v1","arxiv_id":"2511.04880v1","published":"2025-11-06T23:51:08Z"},{"title":"Minimal and Mechanistic Conditions for Behavioral Self-Awareness in LLMs","abstract":"Recent studies have revealed that LLMs can exhibit behavioral self-awareness: the ability to accurately describe or predict their own learned behaviors without explicit supervision. This capability raises safety concerns as it may, for example, allow models to better conceal their true abilities during evaluation. We attempt to characterize the minimal conditions under which such self-awareness emerges, and the mecha","authors":["Matthew Bozoukov","Matthew Nguyen","Shubkarman Singh","Bart Bussmann","Patrick Leask"],"url":"http://arxiv.org/abs/2511.04875v2","arxiv_id":"2511.04875v2","published":"2025-11-06T23:28:16Z"},{"title":"Report from Workshop on Dialogue alongside Artificial Intelligence","abstract":"Educational dialogue -- the collaborative exchange of ideas through talk -- is widely recognized as a catalyst for deeper learning and critical thinking in and across contexts. At the same time, artificial intelligence (AI) has rapidly emerged as a powerful force in education, with the potential to address major challenges, personalize learning, and innovate teaching practices. However, these advances come with signi","authors":["Thomas J McKenna","Ingvill Rasmussen","Sten Ludvigsen","Avivit Arvatz","Christa Asterhan","Gaowei Chen"],"url":"http://arxiv.org/abs/2511.05625v2","arxiv_id":"2511.05625v2","published":"2025-11-06T23:23:05Z"},{"title":"Grounding Foundational Vision Models with 3D Human Poses for Robust Action Recognition","abstract":"For embodied agents to effectively understand and interact within the world around them, they require a nuanced comprehension of human actions grounded in physical space. Current action recognition models, often relying on RGB video, learn superficial correlations between patterns and action labels, so they struggle to capture underlying physical interaction dynamics and human poses in complex scenes. We propose a mo","authors":["Nicholas Babey","Tiffany Gu","Yiheng Li","Cristian Meo","Kevin Zhu"],"url":"http://arxiv.org/abs/2511.05622v1","arxiv_id":"2511.05622v1","published":"2025-11-06T23:12:43Z"},{"title":"Epistemic Reject Option Prediction","abstract":"In high-stakes applications, predictive models must not only produce accurate predictions but also quantify and communicate their uncertainty. Reject-option prediction addresses this by allowing the model to abstain when prediction uncertainty is high. Traditional reject-option approaches focus solely on aleatoric uncertainty, an assumption valid only when large training data makes the epistemic uncertainty negligibl","authors":["Vojtech Franc","Jakub Paplham"],"url":"http://arxiv.org/abs/2511.04855v2","arxiv_id":"2511.04855v2","published":"2025-11-06T22:39:21Z"},{"title":"Software Defined Vehicle Code Generation: A Few-Shot Prompting Approach","abstract":"The emergence of Software-Defined Vehicles (SDVs) marks a paradigm shift in the automotive industry, where software now plays a pivotal role in defining vehicle functionality, enabling rapid innovation of modern vehicles. Developing SDV-specific applications demands advanced tools to streamline code generation and improve development efficiency. In recent years, general-purpose large language models (LLMs) have demon","authors":["Quang-Dung Nguyen","Tri-Dung Tran","Thanh-Hieu Chu","Hoang-Loc Tran","Xiangwei Cheng","Dirk Slama"],"url":"http://arxiv.org/abs/2511.04849v1","arxiv_id":"2511.04849v1","published":"2025-11-06T22:27:39Z"},{"title":"GRIP: In-Parameter Graph Reasoning through Fine-Tuning Large Language Models","abstract":"Large Language Models (LLMs) have demonstrated remarkable capabilities in modeling sequential textual data and generalizing across diverse tasks. However, effectively adapting LLMs to structural data, such as knowledge graphs or web graphs, remains a fundamental challenge. Some approaches adopt complex strategies to convert graphs into text sequences, resulting in significant token overhead and rendering them impract","authors":["Jiarui Feng","Donghong Cai","Yixin Chen","Muhan Zhang"],"url":"http://arxiv.org/abs/2511.07457v2","arxiv_id":"2511.07457v2","published":"2025-11-06T21:56:58Z"},{"title":"Prompt-Based Safety Guidance Is Ineffective for Unlearned Text-to-Image Diffusion Models","abstract":"Recent advances in text-to-image generative models have raised concerns about their potential to produce harmful content when provided with malicious input text prompts. To address this issue, two main approaches have emerged: (1) fine-tuning the model to unlearn harmful concepts and (2) training-free guidance methods that leverage negative prompts. However, we observe that combining these two orthogonal approaches o","authors":["Jiwoo Shin","Byeonghu Na","Mina Kang","Wonhyeok Choi","Il-Chul Moon"],"url":"http://arxiv.org/abs/2511.04834v2","arxiv_id":"2511.04834v2","published":"2025-11-06T21:51:03Z"},{"title":"Isaac Lab: A GPU-Accelerated Simulation Framework for Multi-Modal Robot Learning","abstract":"We present Isaac Lab, the natural successor to Isaac Gym, which extends the paradigm of GPU-native robotics simulation into the era of large-scale multi-modal learning. Isaac Lab combines high-fidelity GPU parallel physics, photorealistic rendering, and a modular, composable architecture for designing environments and training robot policies. Beyond physics and rendering, the framework integrates actuator models, mul","authors":["NVIDIA",":","Mayank Mittal","Pascal Roth","James Tigue","Antoine Richard"],"url":"http://arxiv.org/abs/2511.04831v1","arxiv_id":"2511.04831v1","published":"2025-11-06T21:43:02Z"},{"title":"A Standardized Benchmark for Multilabel Antimicrobial Peptide Classification","abstract":"Antimicrobial peptides have emerged as promising molecules to combat antimicrobial resistance. However, fragmented datasets, inconsistent annotations, and the lack of standardized benchmarks hinder computational approaches and slow down the discovery of new candidates. To address these challenges, we present the Expanded Standardized Collection for Antimicrobial Peptide Evaluation (ESCAPE), an experimental framework ","authors":["Sebastian Ojeda","Rafael Velasquez","Nicolás Aparicio","Juanita Puentes","Paula Cárdenas","Nicolás Andrade"],"url":"http://arxiv.org/abs/2511.04814v1","arxiv_id":"2511.04814v1","published":"2025-11-06T21:10:48Z"},{"title":"Frequency Matters: When Time Series Foundation Models Fail Under Spectral Shift","abstract":"Time series foundation models (TSFMs) have shown strong results on public benchmarks, prompting comparisons to a \"BERT moment\" for time series. Their effectiveness in industrial settings, however, remains uncertain. We examine why TSFMs often struggle to generalize and highlight spectral shift (a mismatch between the dominant frequency components in downstream tasks and those represented during pretraining) as a key ","authors":["Tianze Wang","Sofiane Ennadir","John Pertoft","Gabriela Zarzar Gandler","Lele Cao","Zineb Senane"],"url":"http://arxiv.org/abs/2511.05619v1","arxiv_id":"2511.05619v1","published":"2025-11-06T21:08:51Z"},{"title":"Multimodal Diffusion Forcing for Forceful Manipulation","abstract":"Given a dataset of expert trajectories, standard imitation learning approaches typically learn a direct mapping from observations (e.g., RGB images) to actions. However, such methods often overlook the rich interplay between different modalities, i.e., sensory inputs, actions, and rewards, which is crucial for modeling robot behavior and understanding task outcomes. In this work, we propose Multimodal Diffusion Forci","authors":["Zixuan Huang","Huaidian Hou","Dmitry Berenson"],"url":"http://arxiv.org/abs/2511.04812v2","arxiv_id":"2511.04812v2","published":"2025-11-06T21:08:35Z"},{"title":"An Active Learning Pipeline for Biomedical Image Instance Segmentation with Minimal Human Intervention","abstract":"Biomedical image segmentation is critical for precise structure delineation and downstream analysis. Traditional methods often struggle with noisy data, while deep learning models such as U-Net have set new benchmarks in segmentation performance. nnU-Net further automates model configuration, making it adaptable across datasets without extensive tuning. However, it requires a substantial amount of annotated data for ","authors":["Shuo Zhao","Yu Zhou","Jianxu Chen"],"url":"http://arxiv.org/abs/2511.04811v1","arxiv_id":"2511.04811v1","published":"2025-11-06T21:07:26Z"},{"title":"Early GVHD Prediction in Liver Transplantation via Multi-Modal Deep Learning on Imbalanced EHR Data","abstract":"Graft-versus-host disease (GVHD) is a rare but often fatal complication in liver transplantation, with a very high mortality rate. By harnessing multi-modal deep learning methods to integrate heterogeneous and imbalanced electronic health records (EHR), we aim to advance early prediction of GVHD, paving the way for timely intervention and improved patient outcomes. In this study, we analyzed pre-transplant electronic","authors":["Yushan Jiang","Shuteng Niu","Dongjin Song","Yichen Wang","Jingna Feng","Xinyue Hu"],"url":"http://arxiv.org/abs/2511.11623v1","arxiv_id":"2511.11623v1","published":"2025-11-06T20:56:39Z"},{"title":"PuzzleMoE: Efficient Compression of Large Mixture-of-Experts Models via Sparse Expert Merging and Bit-packed inference","abstract":"Mixture-of-Experts (MoE) models have shown strong potential in scaling language models efficiently by activating only a small subset of experts per input. However, their widespread deployment remains limited due to the high memory overhead associated with storing all expert parameters, particularly as the number of experts increases. To address this challenge, prior works have explored expert dropping and merging str","authors":["Yushu Zhao","Zheng Wang","Minjia Zhang"],"url":"http://arxiv.org/abs/2511.04805v2","arxiv_id":"2511.04805v2","published":"2025-11-06T20:53:02Z"},{"title":"Data Efficiency and Transfer Robustness in Biomedical Image Segmentation: A Study of Redundancy and Forgetting with Cellpose","abstract":"Generalist biomedical image segmentation models such as Cellpose are increasingly applied across diverse imaging modalities and cell types. However, two critical challenges remain underexplored: (1) the extent of training data redundancy and (2) the impact of cross domain transfer on model retention. In this study, we conduct a systematic empirical analysis of these challenges using Cellpose as a case study. First, t","authors":["Shuo Zhao","Jianxu Chen"],"url":"http://arxiv.org/abs/2511.04803v1","arxiv_id":"2511.04803v1","published":"2025-11-06T20:45:56Z"},{"title":"Quantifying the Role of OpenFold Components in Protein Structure Prediction","abstract":"Models such as AlphaFold2 and OpenFold have transformed protein structure prediction, yet their inner workings remain poorly understood. We present a methodology to systematically evaluate the contribution of individual OpenFold components to structure prediction accuracy. We identify several components that are critical for most proteins, while others vary in importance across proteins. We further show that the cont","authors":["Tyler L. Hayes","Giri P. Krishnan"],"url":"http://arxiv.org/abs/2511.14781v1","arxiv_id":"2511.14781v1","published":"2025-11-06T20:41:34Z"},{"title":"MDM: Manhattan Distance Mapping of DNN Weights for Parasitic-Resistance-Resilient Memristive Crossbars","abstract":"Manhattan Distance Mapping (MDM) is a post-training deep neural network (DNN) weight mapping technique for memristive bit-sliced compute-in-memory (CIM) crossbars that reduces parasitic resistance (PR) nonidealities. PR limits crossbar efficiency by mapping DNN matrices into small crossbar tiles, reducing CIM-based speedup. Each crossbar executes one tile, requiring digital synchronization before the next layer. At t","authors":["Matheus Farias","Wanghley Martins","H. T. Kung"],"url":"http://arxiv.org/abs/2511.04798v1","arxiv_id":"2511.04798v1","published":"2025-11-06T20:34:10Z"},{"title":"Ask WhAI:Probing Belief Formation in Role-Primed LLM Agents","abstract":"We present Ask WhAI, a systems-level framework for inspecting and perturbing belief states in multi-agent interactions. The framework records and replays agent interactions, supports out-of-band queries into each agent's beliefs and rationale, and enables counterfactual evidence injection to test how belief structures respond to new information. We apply the framework to a medical case simulator notable for its multi","authors":["Keith Moore","Jun W. Kim","David Lyu","Jeffrey Heo","Ehsan Adeli"],"url":"http://arxiv.org/abs/2511.14780v1","arxiv_id":"2511.14780v1","published":"2025-11-06T20:31:19Z"},{"title":"Causal Structure and Representation Learning with Biomedical Applications","abstract":"Massive data collection holds the promise of a better understanding of complex phenomena and, ultimately, better decisions. Representation learning has become a key driver of deep learning applications, as it allows learning latent spaces that capture important properties of the data without requiring any supervised annotations. Although representation learning has been hugely successful in predictive tasks, it can f","authors":["Caroline Uhler","Jiaqi Zhang"],"url":"http://arxiv.org/abs/2511.04790v1","arxiv_id":"2511.04790v1","published":"2025-11-06T20:17:58Z"},{"title":"Small Vocabularies, Big Gains: Pretraining and Tokenization in Time Series Models","abstract":"Tokenization and transfer learning are two critical components in building state of the art time series foundation models for forecasting. In this work, we systematically study the effect of tokenizer design, specifically scaling and quantization strategies, on model performance, alongside the impact of pretraining versus random initialization. We show that tokenizer configuration primarily governs the representation","authors":["Alexis Roger","Gwen Legate","Kashif Rasul","Yuriy Nevmyvaka","Irina Rish"],"url":"http://arxiv.org/abs/2511.11622v1","arxiv_id":"2511.11622v1","published":"2025-11-06T20:16:21Z"},{"title":"ScheduleStream: Temporal Planning with Samplers for GPU-Accelerated Multi-Arm Task and Motion Planning & Scheduling","abstract":"Bimanual and humanoid robots are appealing because of their human-like ability to leverage multiple arms to efficiently complete tasks. However, controlling multiple arms at once is computationally challenging due to the growth in the hybrid discrete-continuous action space. Task and Motion Planning (TAMP) algorithms can efficiently plan in hybrid spaces but generally produce plans, where only one arm is moving at a ","authors":["Caelan Garrett","Fabio Ramos"],"url":"http://arxiv.org/abs/2511.04758v2","arxiv_id":"2511.04758v2","published":"2025-11-06T19:17:42Z"},{"title":"CPO: Condition Preference Optimization for Controllable Image Generation","abstract":"To enhance controllability in text-to-image generation, ControlNet introduces image-based control signals, while ControlNet++ improves pixel-level cycle consistency between generated images and the input control signal. To avoid the prohibitive cost of back-propagating through the sampling process, ControlNet++ optimizes only low-noise timesteps (e.g., $t < 200$) using a single-step approximation, which not only i","authors":["Zonglin Lyu","Ming Li","Xinxin Liu","Chen Chen"],"url":"http://arxiv.org/abs/2511.04753v1","arxiv_id":"2511.04753v1","published":"2025-11-06T19:02:06Z"},{"title":"Personalized Image Editing in Text-to-Image Diffusion Models via Collaborative Direct Preference Optimization","abstract":"Text-to-image (T2I) diffusion models have made remarkable strides in generating and editing high-fidelity images from text. Yet, these models remain fundamentally generic, failing to adapt to the nuanced aesthetic preferences of individual users. In this work, we present the first framework for personalized image editing in diffusion models, introducing Collaborative Direct Preference Optimization (C-DPO), a novel me","authors":["Connor Dunlop","Matthew Zheng","Kavana Venkatesh","Pinar Yanardag"],"url":"http://arxiv.org/abs/2511.05616v1","arxiv_id":"2511.05616v1","published":"2025-11-06T18:59:54Z"}],"history":{"events":[{"year":2016,"text":"Syrian civil war: The Syrian Democratic Forces launched a successful military campaign to isolate and eventually capture Raqqa, the Islamic State's capital.","url":"https://en.wikipedia.org/wiki/Syrian_civil_war"},{"year":2012,"text":"Tammy Baldwin became the first openly gay politician to be elected to the United States Senate.","url":"https://en.wikipedia.org/wiki/Tammy_Baldwin"},{"year":2004,"text":"A man committing suicide parked his car on the railway tracks in Ufton Nervet, Berkshire, England, causing a derailment that also killed six people on the train.","url":"https://en.wikipedia.org/wiki/Ufton_Nervet"},{"year":1988,"text":"Two earthquakes occurring 12 minutes apart struck Yunnan near the China–Myanmar border, killing more than 730 people.","url":"https://en.wikipedia.org/wiki/1988_Lancang%E2%80%93Gengma_earthquakes"},{"year":1977,"text":"The Kelly Barnes Dam in Stephens County, Georgia, collapsed; the resulting flood killed 39 people and caused US$2.8 million in damages.","url":"https://en.wikipedia.org/wiki/Kelly_Barnes_Dam"},{"year":1963,"text":"Nguyễn Ngọc Thơ was appointed by the junta of General Dương Văn Minh to head the South Vietnamese government, five days after Minh deposed and assassinated President Ngô Đình Diệm.","url":"https://en.wikipedia.org/wiki/Nguy%E1%BB%85n_Ng%E1%BB%8Dc_Th%C6%A1"},{"year":1944,"text":"The B Reactor at the Hanford Site in the U.S. state of Washington began producing plutonium, with the facility later going on to create more for nearly the entire American nuclear arsenal.","url":"https://en.wikipedia.org/wiki/B_Reactor"},{"year":1939,"text":"As part of their plan to eradicate the Polish intellectual elite, the Gestapo arrested 184 professors, students and employees of the Jagiellonian University (location pictured) in Kraków.","url":"https://en.wikipedia.org/wiki/Intelligenzaktion"},{"year":1924,"text":"The Spanish anarchist group Los Solidarios unsuccessfully attempted to launch an insurrection against the dictatorship of Miguel Primo de Rivera.","url":"https://en.wikipedia.org/wiki/Los_Solidarios"},{"year":1917,"text":"First World War: Canadian forces captured Passendale, Belgium, after three months of fighting against the Germans at the Battle of Passchendaele.","url":"https://en.wikipedia.org/wiki/World_War_I"},{"year":1868,"text":"Red Cloud (pictured), a Native American leader of the Oglala Lakota tribe, signed the Treaty of Fort Laramie, ending Red Cloud's War and establishing the Great Sioux Reservation.","url":"https://en.wikipedia.org/wiki/Red_Cloud"},{"year":1863,"text":"American Civil War: A Union brigade defeated a Confederate force at the Battle of Droop Mountain in Pocahontas County, West Virginia.","url":"https://en.wikipedia.org/wiki/American_Civil_War"},{"year":1856,"text":"The first story from the collection Scenes of Clerical Life by the English author George Eliot was submitted for publication.","url":"https://en.wikipedia.org/wiki/Scenes_of_Clerical_Life"},{"year":1794,"text":"French Revolutionary Wars: Two British ships were intercepted by a French squadron, leading to the French seizure of HMS Alexander.","url":"https://en.wikipedia.org/wiki/French_Revolutionary_Wars"},{"year":1217,"text":"King Henry III of England issued the Charter of the Forest, re-establishing the rights of access of free men to royal forests.","url":"https://en.wikipedia.org/wiki/Henry_III_of_England"}],"births":[{"year":2001,"text":"Day'Ron Sharpe, American basketball player","url":"https://en.wikipedia.org/wiki/Day'Ron_Sharpe"},{"year":1997,"text":"Aliona Bolsova, Spanish-Moldovan tennis player","url":"https://en.wikipedia.org/wiki/Aliona_Bolsova"},{"year":1997,"text":"Hero Fiennes-Tiffin, English actor and model","url":"https://en.wikipedia.org/wiki/Hero_Fiennes_Tiffin"},{"year":1997,"text":"Elena-Gabriela Ruse, Romanian tennis player","url":"https://en.wikipedia.org/wiki/Elena-Gabriela_Ruse"},{"year":1995,"text":"Addin Fonua-Blake, Australian-Tongan rugby league player","url":"https://en.wikipedia.org/wiki/Addin_Fonua-Blake"},{"year":1995,"text":"Sam Reinhart, Canadian ice hockey player","url":"https://en.wikipedia.org/wiki/Sam_Reinhart"},{"year":1994,"text":"Isaah Yeo, Australian rugby league player","url":"https://en.wikipedia.org/wiki/Isaah_Yeo"},{"year":1993,"text":"Josh Wakefield, English footballer","url":"https://en.wikipedia.org/wiki/Josh_Wakefield"}],"deaths":[{"year":2025,"text":"Rick Hauck, American naval officer, fighter pilot, and NASA astronaut (born 1941)","url":"https://en.wikipedia.org/wiki/Frederick_Hauck"},{"year":2025,"text":"James Watson, American molecular biologist, geneticist, and zoologist (born 1928)","url":"https://en.wikipedia.org/wiki/James_Watson"},{"year":2024,"text":"Dorothy Allison, American writer (born 1949)","url":"https://en.wikipedia.org/wiki/Dorothy_Allison"},{"year":2024,"text":"John Nott, British politician (born 1932)","url":"https://en.wikipedia.org/wiki/John_Nott"},{"year":2024,"text":"Madeleine Riffaud, French poet, journalist and Resistance member (born 1924)","url":"https://en.wikipedia.org/wiki/Madeleine_Riffaud"},{"year":2024,"text":"Tony Todd, American actor (born 1954)","url":"https://en.wikipedia.org/wiki/Tony_Todd"},{"year":2023,"text":"Antoni Martí, Andorran politician, former Prime Minister of Andorra (born 1963)","url":"https://en.wikipedia.org/wiki/Antoni_Mart%C3%AD"},{"year":2020,"text":"Ken Spears, American writer (born 1938)","url":"https://en.wikipedia.org/wiki/Ken_Spears"}]},"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."}