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arXiv:2512.03683v2 Announce Type: replace Abstract: 3D stylization is central to game development, virtual reality, and digital arts, where the demand for diverse assets calls for scalable methods tha

applicationsarxiv-cs-cv

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InterCorrect: Intersection-Aware Correction of Demographic Model Merging for Fair ASR

DGX agent

arXiv:2610.08604v1 Announce Type: new Abstract: Automatic Speech Recognition (ASR) systems often show uneven performance across demographic groups, and errors can be especially difficult to address fo

researcharxiv-cs-cl

7 Oct 2026

Interleaved Projected Gradient Descent for Safe Imitation Learning

arXiv:2610.07167v1 Announce Type: cross Abstract: We propose an imitation-learning design for neural-network control policies under state and input constraints. Training alternates a standard imitatio

safetyarxiv-cs-lg

Internalizing Agent Experience into Diffusion Model Weights via On-Policy Context Distillation

arXiv:2610.07250v1 Announce Type: new Abstract: Wrapping an image generation model in an agentic harness can effectively boost Text-to-Image task performance: the harness can leverage memory, skills,

safetyarxiv-cs-ai

Interpretable Hypergraph Learning via Neural Additive Models

model-releasesarxiv-cs-lg

arXiv:2610.07458v1 Announce Type: new Abstract: Hypergraphs offer a natural framework for modeling networked data, where dependencies among entities are governed by higher-order interactions. While hy

Intersectional Fairness via Mixed-Integer Optimization

arXiv:2601.19595v2 Announce Type: replace-cross Abstract: The deployment of Artificial Intelligence in high-risk domains, such as finance and healthcare, necessitates models that are both fair and tra

safetyarxiv-cs-ai

Investigating Model Compression for Neural Machine Translation in the Biomedical Domain

arXiv:2610.07032v1 Announce Type: cross Abstract: Large-scale pretrained transformer models have achieved state-of-the-art performance across diverse machine translation tasks, including multilingual

IronMan: Information-Constrained Video-Action Learning for Robot Manipulation

arXiv:2610.07961v1 Announce Type: new Abstract: Video Action Models (VAMs) couple visual dynamics modeling with action generation for robot manipulation. However, video representations are not natural

safetyarxiv-cs-ro

Is sqrt{d} Separation Necessary for Gradient EM to Learn Gaussian Mixtures in High Dimensions?

arXiv:2610.07551v1 Announce Type: cross Abstract: Learning Gaussian mixture models (GMMs) using the Expectation-Maximization (EM) algorithm and its gradient-based variants is a fundamental problem in

tutorialsarxiv-cs-lg

Is this machine playing?

arXiv:2610.07130v1 Announce Type: new Abstract: We placed a modern AI coding assistant in an unintended role: as the mind of a body on an unknown digital island. With only a minimal instruction mentio

agentsarxiv-cs-ai

Isotropic Yet Undecodable: The Sequential Content-Sufficiency Gap in Latent-Predictive Text Representations

arXiv:2610.07906v1 Announce Type: new Abstract: We study sequential content sufficiency by investigating whether a representation retains the ordered target information available in its input. An info

Jailbreaking Open-Weight LLMs via Random Embedding Perturbations

arXiv:2610.07125v1 Announce Type: cross Abstract: While open-weight models have enjoyed steady progress in capabilities and wide adoption across multiple domains, their safety remains an important con

Jarvis: A Proactive Speech Agent for Multi-Party Conversations

arXiv:2610.07506v1 Announce Type: cross Abstract: Speech agents are reactive and dyadic: they speak when spoken to, and to one person at a time. We ask what it takes for a speech agent to instead take

agentsarxiv-cs-ai

JEV versus LLMs: Accuracy, Cost and Calibration on Seven Political Science Replications

arXiv:2610.06625v2 Announce Type: replace Abstract: Large language models (LLMs) annotate and scale political text or constructs by generating text tokens. A new class of models, which TypeSafe market

researcharxiv-cs-cl

JIVEAdapter: A Multi-Task Additive Low-Rank Adapter via Joint and Individual Variation Explained (JIVE)

arXiv:2610.07036v1 Announce Type: new Abstract: Parameter-efficient fine-tuning adapts pretrained models at a fraction of the cost of full fine-tuning, yet most low-rank adapters are single-task and r

Joint upper-bound coverage and route-choice utility: an empirical evaluation on two urban proxy tasks

arXiv:2610.06995v1 Announce Type: new Abstract: Whether more accurate traffic forecasts or higher uncertainty coverage improve route decisions is unclear. We evaluate this question with a frozen proto

Joint Workflow and Prompt Optimization for User Behavior Simulation

arXiv:2610.07663v1 Announce Type: cross Abstract: User behavior simulation is the computational modeling of user interactions within information systems through the use of simulated agents in place of

agentsarxiv-cs-ai

JudgeMoE: Distributional Aggregation for LLM-as-a-Judge

model-releasesarxiv-cs-cl

arXiv:2610.07109v1 Announce Type: new Abstract: When an LLM judge scores an output, its score distribution retains uncertainty and disagreement information that is lost after scalar compression. We in

Knee3DVLM: Dual-Sequence Full-Volume Vision-Language Modeling for Comprehensive Knee MRI Assessment

arXiv:2610.08482v1 Announce Type: cross Abstract: Vision-language models (VLMs) are increasingly being applied to three-dimensional medical imaging, but their application to knee MRI remains limited,

Knowing When Not to Answer: Cross-Domain and Multi-Turn Generalization of Latent Underspecification Signals

arXiv:2610.08413v1 Announce Type: cross Abstract: Large language models routinely answer questions that cannot be answered from the information given, and in dialogue they answer before enough has bee

Knowing When to Trust a Prior: Reliability-Gated Cue Fusion for Video Gaze Prediction

arXiv:2610.08663v1 Announce Type: new Abstract: Video gaze prediction is led by gaze-trained models, yet gaze-free priors carry signal those models have not absorbed, if one knows when to trust them.

researcharxiv-cs-cv

KO: Kinetics-inspired Neural Optimizer with PDE Simulation Approaches

arXiv:2505.14777v2 Announce Type: replace-cross Abstract: The design of effective optimization algorithms for neural networks remains a fundamental challenge, and most existing methods rely on heurist

Koopman Model Predictive Control of An Origami-Inspired Soft Exoskeleton for Knee Rehabilitation

arXiv:2510.11094v2 Announce Type: replace Abstract: Knee rehabilitation plays a critical role in restoring patients' mobility and functional independence. Traditional rigid rehabilitation exoskeletons

researcharxiv-cs-ro

Kurate: Scalable Scientific Quality Analysis

arXiv:2610.07306v1 Announce Type: new Abstract: Scientific search systems can find papers that are relevant to a question, but they generally do not assess the quality of the evidence that those paper

researcharxiv-cs-cl

Label-Efficient Deep Learning for ECG Delineation: A Multi-Dataset Benchmark against Widely Used Delineation Tools

arXiv:2610.07885v1 Announce Type: cross Abstract: Electrocardiogram (ECG) delineation, the identification of waveform boundaries, is a foundational step that translates raw ECG signals into clinically

Language Carries the Expert's Impression: Instrument-Anchored LLM Judges Transfer Counseling-Quality Assessment and Beat In-Domain Training

arXiv:2610.08055v1 Announce Type: new Abstract: Automatic assessment of communication quality in dyadic counseling conversations is bottlenecked by data: expert-rated corpora are small and expensive t

researcharxiv-cs-cl

Language-model ratings of depression reflect the rater more than the patient

arXiv:2610.08501v1 Announce Type: cross Abstract: Depression has no diagnostic blood test. Language models promise tireless, consistent assessment, but can accurate raters disagree about individuals?

Language Unalignability: Why Some Concepts Resist Cross-Cultural Benchmark Evaluation

model-releasesarxiv-cs-cl

arXiv:2610.08303v1 Announce Type: new Abstract: Current evaluation of multilingual Large Language Models (LLMs) rests on an implicit Translation-Isomorphism Assumption (TIA): that semantic structures

Large Language Model Orchestration under Heterogeneous Preferences via Explicit Persona Inference

arXiv:2610.07587v1 Announce Type: new Abstract: LLM orchestration investigates how an orchestrator coordinates a group of autonomous agents to achieve common goals or maximize collective welfare. The

agentsarxiv-cs-cl

Applications

Large Language Models for Cryptocurrency Transaction Analysis: A Bitcoin Case Study

arXiv:2501.18158v4 Announce Type: replace-cross Abstract: Cryptocurrencies are widely used, yet current methods for analyzing transactions often rely on opaque, black-box models. While these models ma

applicationsarxiv-cs-lg

Large Pretraining Datasets Don't Guarantee Robustness after Fine-Tuning in Image Classification

arXiv:2410.21582v4 Announce Type: replace-cross Abstract: Large-scale pretrained models are widely leveraged as foundations for learning new specialized tasks via fine-tuning, with the goal of maintai

LARK: A Low-Cost, Accurate, Occlusion-Resilient, Kalman Filter-Assisted Tracking System for Image-Guided Surgery

arXiv:2610.07561v1 Announce Type: new Abstract: Image-guided surgery (IGS) depends on accurate tracking of surgical instruments to provide real-time navigation relative to anatomical structures. Comme

researcharxiv-cs-cv

Latent space bias directions in LLMs capture confidence, not fairness

arXiv:2610.08559v1 Announce Type: cross Abstract: Activation steering has gained popularity as a lightweight inference-time debiasing technique for large language models. However, prior work reports t

safetyarxiv-cs-ai

Later Is Better: Token Reduction for ViTs Under Distribution Shift

arXiv:2610.07758v1 Announce Type: cross Abstract: Training-free token reduction accelerates vision transformers by removing redundant tokens across layers, recovering most of the original accuracy at

LBA-CBF: Rapidly Adaptive Safety Filters via Parallel Dynamics Inference

arXiv:2610.08765v1 Announce Type: new Abstract: Control barrier functions (CBFs) certify commands through an assumed dynamics model, so an abrupt, unmeasured regime change can undermine the certificat

safetyarxiv-cs-ro

LeanPlan: Optimal Planning with LLM-Generated Heuristics and Admissibility Proofs

arXiv:2610.08246v1 Announce Type: new Abstract: Frontier large language models (LLMs) can generate heuristic functions that guide search to achieve state-of-the-art performance in satisficing planning

LEAP: Making Privileged Geometry Supervision Effective for Visuomotor Learning

model-releasesarxiv-cs-ro

arXiv:2610.07015v1 Announce Type: new Abstract: Privileged 3D supervision uses additional geometric information during training to guide RGB-based visuomotor policy learning, without requiring geometr

Learn2Play Bench: How Well Do LLM Agents Learn from Experience in Unfamiliar Environments?

arXiv:2610.08215v1 Announce Type: new Abstract: Learning from experience is essential for LLM agents to adapt to unfamiliar and dynmaic environments. Evaluating this ability is therefore important for

Learnable Spectral Activations

arXiv:2610.07419v1 Announce Type: cross Abstract: Implicit neural representations (INRs) are shaped by the spectral structure induced by their input encodings and activation functions. Existing method

researcharxiv-cs-cv

Learned Adaptive Multiresolution Diffusion Imaging

model-releasesarxiv-cs-lg

arXiv:2610.07884v1 Announce Type: cross Abstract: Adaptive multiresolution methods reduce representation cost by concentrating fine-scale degrees of freedom where needed, but their tree updates are us

Learning a Mixture of GFlowNets

arXiv:2610.07562v1 Announce Type: cross Abstract: Learning an ensemble of GFlowNets to sample from a discrete target distribution has become a common approach for achieving better state space explorat

Learning a Ranking from Human Feedback in Log-Concave Random Utility Models

arXiv:2610.07973v1 Announce Type: new Abstract: We study the problem of recovering the ranking of a fixed set of items according to their unknown numerical utilities. At each interaction with the envi

researcharxiv-cs-lg

Local Ai

Learning consistent molecular mechanics force fields from first principles

arXiv:2610.08020v1 Announce Type: cross Abstract: Classical force fields (FFs) remain the workhorse for large-scale simulations even as machine-learned interatomic potentials (MLIPs) approach ab initi

local-aiarxiv-cs-lg

Learning Decision-Stump Thresholds in Context: Dynamics of Softmax Attention

model-releasesarxiv-cs-lg

arXiv:2610.07074v1 Announce Type: cross Abstract: Estimating a decision threshold requires locating observations near an unknown boundary. We study how gradient-based pretraining learns this statistic

Learning Disentangled Representations with Quantum Variational Autoencoders

arXiv:2610.07196v1 Announce Type: cross Abstract: Variational autoencoders are powerful representation learning models that map complex data into low-dimensional latent spaces, enabling the discovery

tutorialsarxiv-cs-lg

Learning Explainable Representations of Complex Game-playing Strategies

arXiv:2610.07638v1 Announce Type: new Abstract: As part of learning to play complex games, human players develop develop abstractions for concepts and strategies of gameplay consistent with game rules

tutorialsarxiv-cs-ai

Learning Force-Regulated Robotic Manipulation with a Low-Cost Tactile-Force-Controlled Gripper

arXiv:2602.10013v2 Announce Type: replace Abstract: Successfully manipulating many everyday objects, such as potato chips, requires precise force regulation. Failure to modulate force can lead to task

safetyarxiv-cs-ro

Learning from Failures: A Failure-Driven Prompt Refinement for LLM-Based Vulnerability Analysis

arXiv:2610.08405v1 Announce Type: cross Abstract: Large Language Models have emerged as promising tools for software vulnerability analysis, but their effectiveness depends heavily on prompt design. E

tutorialsarxiv-cs-ai