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A Dynamic Likelihood Approach to Filtering for Advection-Diffusion Dynamics

arXiv:2406.06837v2 Announce Type: cross Abstract: A Bayesian data assimilation scheme is formulated for advection-dominated advective and diffusive evolutionary problems, based upon the Dynamic Likeli

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A Finite Sample Analysis for Quantile Temporal Difference Learning in Distributional Reinforcement Learning

DGX agent

arXiv:2608.27313v1 Announce Type: cross Abstract: We establish a global finite-sample guarantee for synchronous quantile temporal-difference learning (QTD) in tabular distributional reinforcement lear

researcharxiv-cs-lg

28 Aug 2026

A Flexible Empirical Bayes Approach to Generalized Linear Models, with Applications to Sparse Logistic Regression

arXiv:2601.21217v2 Announce Type: replace-cross Abstract: We introduce a flexible empirical Bayes approach for fitting Bayesian generalized linear models. Specifically, we adopt a novel mean-field var

A Framework for Low-Effort Training Data Generation for Urban Semantic Segmentation

arXiv:2510.11567v2 Announce Type: replace Abstract: Synthetic datasets are widely used for training urban scene recognition models, but even highly realistic renderings show a noticeable gap to real i

researcharxiv-cs-cv

A Geometry-Driven, Framework-Agnostic Optimization for Object Pose Estimation

arXiv:2608.26859v1 Announce Type: new Abstract: Current object pose estimation research remains predominantly model-centric, focusing on architectural innovations and post-processing refinements. This

safetyarxiv-cs-cv

A Layer Importance Metric for Quantization Accounting for the Speed-Quality Trade-off in Autoregressive Models

model-releasesarxiv-cs-lg

arXiv:2608.26926v1 Announce Type: new Abstract: Small language models (sLLMs) are nowadays hosted on devices with limited memory and computational budget. In an autoregressive setup, inference is memo

A Multi-Framework Comparison of Outline Stages in Long-Form Generation with LLMs

arXiv:2608.26177v1 Announce Type: cross Abstract: Long-form generation exposes fundamental limitations of large language models. Even 70B-parameter models exhibit length collapse at 16k-token outputs,

A Multi-Modal AI Framework for Real-Time Queue Prediction, Management and Optimisation in Intelligent Border Control Systems

arXiv:2608.27010v1 Announce Type: new Abstract: In the present work an efficient border control management procedure is proposed. Compared to operational queue management systems, whose operations are

A Point-of-Prescription Safety-Check System for Adverse Drug Reactions in Rural Bangladeshi Hospitals: A Feasibility Study

arXiv:2608.27239v1 Announce Type: cross Abstract: Adverse drug reactions (ADRs) are a major, largely preventable source of patient harm. In high-income settings, electronic health records store a pati

safetyarxiv-cs-lg

A Reranker for Orchestrating Heterogeneous Speech and Text Retrievers

arXiv:2608.26194v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) systems have attracted significant interest for their ability to mitigate hallucinations in Large Language Models

Local Ai

A Safety-Gated Multimodal AI Backend for Mental-Health Support: Hierarchical State Representation, Conservative Risk Fusion, and Controlled Generation in Anian

arXiv:2608.26162v1 Announce Type: new Abstract: Safety-critical mental-health support systems must distinguish when supportive conversation is appropriate from when free-form generation should be bloc

local-aiarxiv-cs-ai

A Single Suffix to Break Them All: Basin-Aware Jailbreaks for Merged Model Families

arXiv:2608.26506v1 Announce Type: cross Abstract: Model merging enables combining multiple fine-tuned models without additional training, but its safety implications remain poorly understood. Prior wo

safetyarxiv-cs-cl

Hardware

A Survey of LLM Prompt Datasets: Taxonomy, Linguistic Patterns, and Practical Uses

arXiv:2510.09316v3 Announce Type: replace-cross Abstract: We compile 129 public LLM prompt datasets with more than 1.22TB and more than 673M instances and organize them into a unified taxonomy. We use

hardwarearxiv-cs-cl

Applications

A Table Is Worth 64 Tokens: Pixel-level Compression for Multi-Table Document Question Answering

arXiv:2608.26949v1 Announce Type: new Abstract: Answering questions over real-world documents requires processing long inputs that interleave text with tables. Optical context compression, which repre

applicationsarxiv-cs-ai

A Task-Centric Ontology and Deterministic Domain Rules as a Verifiable Core for AI-Assisted Chemistry Problem Solving

arXiv:2608.26164v1 Announce Type: new Abstract: Large language models can interpret natural-language chemistry questions, but their internal reasoning is difficult to inspect, constrain, and validate.

A Unified Descriptive-Complexity Framework for Model Selection under Correlated Designs

model-releasesarxiv-cs-lg

arXiv:2608.26618v1 Announce Type: cross Abstract: Model selection becomes particularly challenging under strong predictor dependence and model-class uncertainty, especially when there are exponentiall

A Unified Framework for Fair and Personalized Decentralized Learning under Communication Constraints

arXiv:2608.26493v1 Announce Type: new Abstract: Decentralized learning systems aim to collaboratively train models across multiple clients without relying on a central coordinator. While decentralizat

safetyarxiv-cs-lg

A Unified Framework for the Mechanics of Information in Convolutional Neural Network Image Space

model-releasesarxiv-cs-cv

arXiv:2608.26363v1 Announce Type: new Abstract: This paper introduces a unified mathematical framework for modeling information propagation through convolutional neural networks (CNNs), with the aim o

A Very Big Video Reasoning Suite

arXiv:2602.20159v3 Announce Type: replace-cross Abstract: Rapid progress in video models has largely focused on visual quality, leaving their reasoning capabilities underexplored. Video reasoning grou

Applications

Absolute indices for determining compactness, separability and number of clusters

arXiv:2510.13065v3 Announce Type: replace Abstract: Finding 'true' clusters in a data set is a challenging problem. Clustering solutions obtained using different models and algorithms do not necessari

applicationsarxiv-cs-lg

Accelerating Scientific Research with Gemini in the Real-World

arXiv:2608.26701v1 Announce Type: new Abstract: We present an extension and comprehensive real-world validation of Co-Scientist, a Gemini-based multi-agent system designed to accelerate end-to-end sci

Accurate Measurement of 3D and 2D Circular Centers With Application to LiDAR-Camera Extrinsic Calibration

arXiv:2511.06611v2 Announce Type: replace Abstract: Accurate measurement of circular centers is a fun-damental geometric sensing problem in instrumentation and measurement tasks involving cameras, LiD

safetyarxiv-cs-cv

Activation Outliers Matter: Robust Recovery for Quantized Multimodal LLMs

arXiv:2608.26581v1 Announce Type: new Abstract: Low-bit quantization offers a promising avenue for reducing the computational and memory demands of Multimodal Large Language Models (MLLMs). Recent har

Active Curriculum Refinement for Reinforcement Learning

model-releasesarxiv-cs-lg

arXiv:2608.26469v1 Announce Type: new Abstract: In many reinforcement learning (RL) domains, environments are connected by prerequisite relations, such as difficulty-increasing edits or parameter incr

Active Diffusion-Based Inference for Ill-Posed Inverse Problems under Incomplete Priors

arXiv:2608.27080v1 Announce Type: cross Abstract: Many scientific and engineering applications require estimating unknown parameters from experimentally observable data -- an inverse problem that is i

Active Preference Learning over Latent Preference Archetypes for Many-Objective Bayesian Optimization

arXiv:2603.28410v2 Announce Type: replace Abstract: Preference-based many-objective Bayesian optimization typically assumes that all pairwise comparisons arise from a single latent utility function, d

safetyarxiv-cs-lg

Active sensing to characterize the heterogeneity of plant stress

arXiv:2608.27088v1 Announce Type: cross Abstract: While most phenotyping platforms rely primarily on image-based measurements, advanced plant characterization requires the integration of active physio

Active Surface-Driven Reconfigurable Gripper: Robust Grasping and Sequential Manipulation of Thin Objects

arXiv:2608.26883v1 Announce Type: new Abstract: Robotic grippers face substantial challenges in grasping and manipulating thin objects. Most existing grippers rely on highly precise approach and grasp

researcharxiv-cs-ro

AdaThinking-E: One-Token Entropy Regulation for Adaptive Thinking

arXiv:2608.26141v1 Announce Type: new Abstract: Multimodal large language models have demonstrated strong document reasoning capabilities by incorporating explicit thinking processes. While this capab

ADeptS-Bench: Measuring the Trustworthiness of Computer Use Agents Across Devices

arXiv:2608.26204v1 Announce Type: cross Abstract: Computer Use Agents (CUAs) are increasingly deployed to navigate mobile and desktop applications on behalf of users, yet no benchmark comprehensively

Adversarial Training Without Input Gradients via Low-Rank Householder Expansions

arXiv:2608.26963v1 Announce Type: new Abstract: This work concerns adversarial training against the small-norm adversarial examples that arise from the inherent input instability of a trained deep neu

AesCanvas: A Large-Scale Dataset and Benchmark for Aesthetic Critique and Contextual Suitability

arXiv:2608.26713v1 Announce Type: cross Abstract: Recent advances in Multimodal Large Language Models (MLLMs) have extended Image Aesthetic Assessment (IAA) beyond scalar scores toward interpretable c

AEScorer: An Agentic Evidence-Grounded Framework for Graded Factuality Verification

arXiv:2601.03605v2 Announce Type: replace Abstract: Despite the significant advancements of Large Language Models (LLMs), their factuality remains a critical challenge, creating a growing need for mor

AffectOmni: RL-Verifiable People-Centric Grounded Affective Reasoning for Social and Art-Related Scenes

arXiv:2608.26193v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) achieve strong performance on VQA and scene understanding, yet affective reasoning remains vulnerable to shortc

Affix Cache for Diffusion Large Language Models

arXiv:2608.26140v1 Announce Type: new Abstract: Diffusion Large Language Models (DLLMs) enable non-autoregressive decoding and bidirectional context modeling, but efficient inference remains challengi

researcharxiv-cs-cl

AfriSwitch: A Benchmark for In-the-Wild African Code-Switched Speech Recognition

arXiv:2608.26434v1 Announce Type: new Abstract: Code-switching is pervasive in bilingual African conversation, yet most ASR systems assume monolingual input and are evaluated on curated monolingual be

Agent Mesh: Reliability Primitives for Non-Idempotent Agent Delegation - Identity Adequacy and Evidence Adequacy

arXiv:2608.26225v1 Announce Type: new Abstract: Autonomous agents increasingly perform bounded software tasks under an orchestrator that retries, resumes, and budgets them. The machinery such orchestr

Agent Seer: Synthesizing Scenarios from Specification Understanding

arXiv:2608.26133v1 Announce Type: new Abstract: Evaluating AI agents that use external tools requires realistic test scenarios that capture how practitioners compose tools and iterate across conversat

AgentFold: Closed-Loop Agentic Search for Protein Folding Model Design

arXiv:2608.26747v1 Announce Type: new Abstract: Scientific LLM agents have shown promise in literature reasoning, tool use, and experiment planning, but it remains unclear whether they can autonomousl

safetyarxiv-cs-ai

Agentic AI for operating scientific instruments for nanoscale characterization

arXiv:2608.26198v1 Announce Type: new Abstract: Operating a scientific instrument such as an atomic force microscope (AFM) requires continuous expert decision-making. A trained user defines the experi

AgentJudgeBench: A Multi-Difficulty Benchmark for Evaluating LLM Judges on Agentic Tool-Calling

arXiv:2608.26623v1 Announce Type: new Abstract: LLM judges are widely used to evaluate agentic tool-calling systems, yet their reliability on structured, dependency-driven workflows remains largely un

Agents Don't Paginate: First-Chunk Selection for LLM Tool Responses

arXiv:2608.26130v1 Announce Type: new Abstract: Coding agents built on large language models (LLMs), such as Claude Code, Cursor, OpenAI Codex, GitHub Copilot, and Aider, receive tool responses that r

AI agents in Algorithmic Electricity Markets: On the Emergence of Tacit Collusion

arXiv:2608.26896v1 Announce Type: new Abstract: As electricity market participants increasingly adopt learning-based agents for their bidding strategies, electricity markets are becoming algorithmic.

AI Control Scientist: LLM-driven Agentic System for Automated Control Design

arXiv:2608.26780v1 Announce Type: new Abstract: Control system design is critical for modern industry, such as chemical process temperature regulation and aero-engine control. However,traditional cont

AI Revealed Preferences

arXiv:2608.26178v1 Announce Type: new Abstract: There is growing interest in whether language models have stable preferences, for technical, safety, and philosophical reasons. We test 20 language mode

AirLLM: Diffusion Policy-based Adaptive LoRA for Remote Fine-Tuning of LLM over the Air

arXiv:2507.11515v2 Announce Type: replace-cross Abstract: Operating Large Language Models (LLMs) on edge devices is increasingly challenged by limited communication bandwidth and strained computationa

safetyarxiv-cs-ai

Algebraic Multigrid Acceleration for Efficient Label Spreading

arXiv:2608.26309v1 Announce Type: new Abstract: Modern machine learning models rely on large amounts of labeled data. However, manual annotation of large-scale datasets is expensive and time-consuming

Algorithmic Principles For Multiclass Learning Are Hard To Come By: Limits of Regularization and Proper Learning

arXiv:2608.26516v1 Announce Type: new Abstract: Two of the most fundamental questions in statistical learning theory are the following: which prediction problems are learnable, and how should they be