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Twin Worlds: Equivariance-Based Abstention for Evidence-Grounded Reasoning

arXiv:2608.28018v1 Announce Type: new Abstract: Knowledge-intensive reasoning requires Large Language Models (LLMs) to ground answers in provided evidence. When evidence is insufficient, it is desirab

researcharxiv-cs-cl

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UIC-AIHealth4All at ArchEHR-QA 2026: Answer-First Evidence Grounding for Clinical Question Answering

DGX agent

arXiv:2608.27467v1 Announce Type: new Abstract: We describe the UIC-AIHealth4All system for ArchEHR-QA 2026, a shared task on grounded question answering from electronic health records. We participate

safetyarxiv-cs-cl

31 Aug 2026

Under-Mattress Temporal Sensing for Next-Day Agitation Risk Scoring in Dementia Wards

arXiv:2608.28152v1 Announce Type: cross Abstract: Agitation fluctuates over short time horizons in people living with dementia, yet continuous physiological information for anticipating next-day risk

UniLipi: A Unified Multi-Script OCR for Historical Indic Manuscripts

arXiv:2608.28195v1 Announce Type: new Abstract: Optical character recognition (OCR) for handwritten Indic manuscripts is essential for large-scale digitization and computational access to manuscript h

Unsupervised Continual Learning with Growing Self-Organizing Maps and Synthetic Replay

arXiv:2608.27662v1 Announce Type: new Abstract: This work presents a generative continual learning framework based on growing self-organizing maps (GSOMs) that are augmented with learned distributiona

uScenes: A Multimodal RGB and 3D Sonar Dataset for Underwater Robot Perception

arXiv:2608.27795v1 Announce Type: new Abstract: Robust perception is essential for the deployment of autonomous underwater robots. However, optical cameras become unreliable under poor illumination an

agentsarxiv-cs-cv

Applications

Var-JEPA: A Variational Formulation of the Joint-Embedding Predictive Architecture - Bridging Predictive and Generative Self-Supervised Learning

arXiv:2603.20111v2 Announce Type: replace Abstract: The Joint-Embedding Predictive Architecture (JEPA) is often seen as a non-generative alternative to likelihood-based self-supervised learning, empha

applicationsarxiv-cs-lg

VersaGauss: A Versatile Framework for Generating Multiphase Dynamics with 3D Gaussians

arXiv:2608.28069v1 Announce Type: new Abstract: Recent progress has been made in 3D Gaussian representation for reconstruction, generation, and physical simulation. However, current approaches mainly

VICT: Verifier-Instrumented Credit Tracing for Long-Horizon LLM Agent Reinforcement Learning

arXiv:2608.28128v1 Announce Type: new Abstract: Fine-grained credit assignment is a central challenge in reinforcement learning for long horizon LLM agents. Standard objectives often train from progra

agentsarxiv-cs-lg

Video Generative Models as Geometry Learner

arXiv:2608.28549v1 Announce Type: new Abstract: Recent generative approaches to geometry estimation adapt pretrained image diffusion models and treat the task as image-conditioned generation. Leveragi

VidParse: Online Parsing of Egocentric Procedures Like a Pro

arXiv:2608.27562v1 Announce Type: new Abstract: Translating continuous, noisy egocentric video streams into discrete, temporally ordered action steps is fraught with visual challenges. Heavy ego-motio

VISTA: Verifier-Informed Student-to-Teacher Adaptation for On-Policy Self-Distillation

arXiv:2608.28306v1 Announce Type: cross Abstract: On-policy self-distillation (OPSD) improves reasoning by training a problem-only student on its own rollouts using dense token-level supervision from

Visual Token Coding for Video Multimodal Large Language Models

arXiv:2608.28008v1 Announce Type: new Abstract: In this paper, we propose a new token compression paradigm for video Multimodal Large Language Models (MLLMs), termed Visual Token Coding (VTC). Inspire

WALDO: One-Shot Exemplar-Conditioned Object Detection in Cluttered Scenes

arXiv:2608.28216v1 Announce Type: new Abstract: Locating a specific object instance in a cluttered scene using a single reference image and a short description, and reporting when that instance is abs

local-aiarxiv-cs-cv

What Can Low Resource Languages Learn From Each Other?

arXiv:2608.27753v1 Announce Type: new Abstract: Despite the rapid advancement of Vision-Language Models (VLMs), their linguistic reach remains largely confined to high-resource languages, leaving the

What Do Interaction Representations Actually Measure? Pre-Event Separability in Weakly-Supervised Violence Detection

arXiv:2608.27879v1 Announce Type: new Abstract: Articulated human pose provides detailed body-configuration information beyond coarse spatial relationships, but whether this detail yields greater disc

What Makes Agent Memory Useful for Reliable Unanswerable Question Handling?

arXiv:2608.27924v1 Announce Type: new Abstract: Reliable handling of unanswerable questions (UAQs) is critical for trustworthy LLM-based agents. Although memory is widely used in agent systems, its ro

agentsarxiv-cs-cl

What Will This Copper Look Like Later? Forecasting Surface Appearance and Rendering It as a PBR Material

arXiv:2608.28102v1 Announce Type: cross Abstract: Digital design requires predicting how a metal surface will look later in its oxidation; this paper presents such a pipeline for copper. Given a fixed

When Can Conditional Flow Matching Replace Pointwise Negative Log-Likelihood?

arXiv:2608.28010v1 Announce Type: new Abstract: Flow matching enables likelihood-free training, yet alignment methods increasingly reuse conditional flow matching (CFM) losses as endpoint negative log

safetyarxiv-cs-lg

When Linguistic and Internal Confidence Diverge in Large Language Models

arXiv:2608.28382v1 Announce Type: new Abstract: Users often ask large language models (LLMs) to report how confident they are, but it is unclear whether such linguistic confidence tracks the model's i

When Muon Meets Task Interference: A Spectral Perspective on Continual Learning and Model Merging

arXiv:2608.27518v1 Announce Type: new Abstract: Continual learning (CL) and model merging (MM) both aim to obtain a single model that performs well across multiple tasks, challenged respectively by ca

When Robots Mishear Us: Mapping the Safety Risks of Voice-Controlled Embodied AI

arXiv:2608.28518v1 Announce Type: cross Abstract: We investigate whether automatic speech recognition (ASR) errors in user input can lead to unsafe outputs from Embodied AI (EAI) models. We find that

When the City Teaches the Car: Label-Free 3D Perception from Infrastructure

arXiv:2603.16742v2 Announce Type: replace Abstract: Building robust 3D perception for self-driving still relies heavily on large-scale data collection and manual annotation, yet this paradigm becomes

local-aiarxiv-cs-cv

When Tokenizers Fail: Byte-Level Chunking for Zero-Shot Transfer to Low-Resource Languages

arXiv:2608.27658v1 Announce Type: new Abstract: Subword tokenization hinders low-resource language processing by imposing frequency patterns from dominant languages onto script-sharing variants. Byte-

Why Didn't It Check? Unsupported Final Claims and Their Repair in Two Tool-Equipped Language Models

model-releasesarxiv-cs-cl

arXiv:2608.27768v1 Announce Type: cross Abstract: A language model with access to tools can commit to a final claim unsupported by the evidence it has seen, even when a single available tool call woul

WilLaGS: Latent-Conditional 3D Appearance Fields for Robust Gaussian Splatting In-the-Wild

arXiv:2608.28240v1 Announce Type: new Abstract: 3D Gaussian Splatting (3DGS) delivers real-time and high-fidelity rendering but remains challenged by unconstrained in-the-wild scenes, where drastic ap

local-aiarxiv-cs-cv

XHotpotQA: A Benchmark for Cross-Lingual Knowledge Composition in Multi-Hop Question Answering

model-releasesarxiv-cs-cl

arXiv:2608.27481v1 Announce Type: new Abstract: Knowledge-intensive multi-hop question answering requires systems to select evidence and compose dependent facts, yet multilingual benchmarks usually tr

Hardware

ZipMVS: Multi-View Stereo with Compressed Cost Volumes

arXiv:2608.28033v1 Announce Type: new Abstract: Multi-view stereo (MVS) methods typically deliver highly accurate 3D reconstructions from multiple registered RGB images, thanks to the highly informati

hardwarearxiv-cs-cv

4DSynth: Controllable Procedural World Synthesis for Dynamic Embodied Simulation

arXiv:2608.26947v1 Announce Type: cross Abstract: Embodied agents need environments that are visually diverse, physically interactive, and changing over time. Procedural simulators can generate large

6.5% of the Neuro-Symbolic Literature Can Be Reproduced from Its Published Artifacts, a Six-Stage Audit Framework and First Instantiation

arXiv:2608.26236v1 Announce Type: new Abstract: We present a six-stage framework for auditing the reproducibility of scientific claims across a research literature within the computer science domain,

A causal graph-informed temporal convolution architecture for interpretable retail electricity price forecasting

arXiv:2608.26234v1 Announce Type: cross Abstract: Retail electricity markets in deregulated systems face significant price volatility and complex interactions with forward and futures products, posing

A Contract-Centered Architecture for Scalable and Manageable Agentic Runtimes

arXiv:2608.27086v1 Announce Type: new Abstract: Enterprise AI deployment is a coordination problem across business units, application and AI teams, testing, platform engineering, infrastructure, secur

agentsarxiv-cs-ai

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

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

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

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

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

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

model-releasesarxiv-cs-ai

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

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

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

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