Search: “models”

Joint Model and Data Sparsification via the Marginal Likelihood

arXiv:2605.29908v1 Announce Type: cross Abstract: Sparse recovery in linear systems underpins applications from signal processing to high-dimensional regression. Sparse Bayesian Learning, grounded in

Large Depth Completion Model from Sparse Observations

DGX agent

arXiv:2605.30115v1 Announce Type: new Abstract: This work presents the Large Depth Completion Model (LDCM), a simple, effective, and robust framework for single-view metric depth estimation with spars

tutorialsarxiv-cs-cv

29 May 2026

Long-Context Modeling with Dynamic Hierarchical Sparse Attention for Memory-Constrained LLM Inference

model-releasesarxiv-cs-cl

arXiv:2510.24606v2 Announce Type: replace Abstract: The quadratic cost of attention limits the scalability of long-context LLMs, especially under limited hardware memory budgets. While attention is of

Micro-Macro Retrieval: Reducing Long-Form Hallucination in Large Language Models

arXiv:2605.28828v1 Announce Type: cross Abstract: Large Language Models (LLMs) achieve impressive performance across many tasks but remain prone to hallucination, especially in long-form generation wh

Position: Stop Chasing the C-index when Evaluating Survival Analysis Models

arXiv:2506.02075v2 Announce Type: replace-cross Abstract: The current state of evaluation in survival analysis is plagued by the persistent use of evaluation metrics in ways that are misaligned with t

safetyarxiv-cs-lg

Reliable Reasoning with Large Language Models via Preference-Based Maximum Satisfiability

arXiv:2605.29687v1 Announce Type: new Abstract: Large Language Models (LLMs) excel at understanding natural language but struggle with optimisation tasks involving multiple constraints and user-define

Riemannian AmbientFlow: Towards Simultaneous Manifold Learning and Generative Modeling from Corrupted Data

arXiv:2601.18728v2 Announce Type: replace Abstract: Modern generative modeling methods have demonstrated strong performance in learning complex data distributions from clean samples. In many scientifi

SM2ITH: Safe Mobile Manipulation with Interactive Human Prediction via Task-Hierarchical Bilevel Model Predictive Control

arXiv:2511.17798v2 Announce Type: replace Abstract: Mobile manipulators are designed to perform complex sequences of navigation and manipulation tasks in human-centered environments. While recent opti

researcharxiv-cs-ro

Applications

Specialty-Specific Medical Language Model for Immune-Mediated Diseases

arXiv:2605.28838v1 Announce Type: cross Abstract: Extracting detailed clinical information from free-text medical narratives remains a practical challenge for researchers and healthcare systems. Termi

applicationsarxiv-cs-ai

SRUG: Shadow-Guided Relightable Urban Scene with Generation Model

arXiv:2605.24700v2 Announce Type: replace Abstract: Creating relightable urban scenes from images or videos is widely useful but highly ill-posed. Urban environments are typically unbounded and extend

tutorialsarxiv-cs-cv

TaxDistill: Improving Metagenomic Taxonomic Annotation via Distilled Genomic Foundation Models

model-releasesarxiv-cs-ai

arXiv:2605.28868v1 Announce Type: cross Abstract: Metagenomic taxonomic annotation aims to identify the microbial origins of DNA fragments in environmental samples. Traditional methods that rely on se

VLA-Trace: Diagnosing Vision-Language-Action Models through Representation and Behavior Tracing

arXiv:2605.30117v1 Announce Type: new Abstract: Understanding how Vision-Language-Action (VLA) models transform multimodal knowledge into embodied control remains an open challenge. We present VLA-Tra

Are Large Pre-trained Vision Language Models Effective Construction Safety Inspectors?

model-releasesarxiv-cs-cv

arXiv:2508.11011v2 Announce Type: replace Abstract: Construction safety inspections typically involve a human inspector identifying safety concerns on-site. With the rise of powerful Vision Language M

Auditable Decision Models with Learned Abstention and Real-Time Steering

arXiv:2605.27768v1 Announce Type: new Abstract: Production AI systems often operate with incomplete, conflicting, or insufficient evidence. Forced classifiers collapse such cases into action labels, w

Can Large Language Models Handle Discourse Particles? A Case Study of Colloquial Malay

model-releasesarxiv-cs-cl

arXiv:2605.28782v1 Announce Type: new Abstract: Discourse particles, such as extit{well} and extit{kind of}, are crucial components that enable LLMs to ``speak'' more like humans. They are used to con

Agents

HEART: Achieving Timely Multi-Model Training for Vehicle-Edge-Cloud-Integrated Hierarchical Federated Learning

arXiv:2501.09934v3 Announce Type: replace-cross Abstract: The rapid growth of AI-enabled Internet of Vehicles (IoV) calls for efficient Machine Learning (ML) solutions that can handle high vehicular m

agentsarxiv-cs-ai

Agents

Hierarchical Prompt-Domain Control and Learning for Resource-Constrained Agentic Language Models

arXiv:2605.27703v1 Announce Type: new Abstract: Large Language Models are increasingly deployed inside agentic systems, where they must follow structured protocols, adapt to evolving states, and opera

agentsarxiv-cs-ai

Imitating and Finetuning Model Predictive Control for Robust and Symmetric Quadrupedal Locomotion

arXiv:2311.02304v3 Announce Type: replace Abstract: Control of legged robots is a challenging problem that has been investigated by different approaches, such as model-based control and learning algor

safetyarxiv-cs-ro

Agents

Large Language Models Approach Expert Pedagogical Quality in Math Tutoring but Differ in Instructional and Linguistic Profiles

arXiv:2512.20780v3 Announce Type: replace Abstract: Recent work has explored the use of large language models (LLMs) to generate tutoring responses in mathematics, yet it remains unclear how closely t

agentsarxiv-cs-cl

Law of Neural Interaction: Depth-Width Shape, Interaction Efficiency, and Generalization

model-releasesarxiv-cs-lg

arXiv:2605.27989v1 Announce Type: new Abstract: The guidance of scaling laws has increased the resource demands of modern large language models (LLMs), yet it remains questionable whether these models

Localizing Input Uncertainty Quantification for Large Language Models via Shapley Values

arXiv:2605.28170v1 Announce Type: new Abstract: As large language models (LLMs) are increasingly integrated into high-stakes decision-making, the ability to reliably quantify uncertainty has become a

local-aiarxiv-cs-ai

Mitigating Adaptive Attacks against Reasoning Models with Activation Consistency Training

arXiv:2605.28467v1 Announce Type: new Abstract: As LLMs gain stronger reasoning capabilities, their extended chain-of-thought introduces new degrees of complexity for defending against adversarial jai

safetyarxiv-cs-lg

Modeling Vehicle-Type-Specific Pedestrian Crash Avoidance Behavior in Safety-Critical Interactions Using Smooth-Mamba Deep Reinforcement Learning

arXiv:2605.28552v1 Announce Type: new Abstract: As automated vehicles (AVs) increasingly share roadways with human-driven vehicles (HDVs), understanding how pedestrians respond to different vehicle ty

Neural Implicit Action Fields: From Discrete Waypoints to Continuous Functions for Vision-Language-Action Models

arXiv:2603.01766v2 Announce Type: replace Abstract: Despite the rapid progress of vision-language-action (VLA) models, the prevailing practice of predicting action chunks as discrete waypoints remains

safetyarxiv-cs-ro

Poison with Style: A Practical Poisoning Attack on Code Large Language Models

arXiv:2605.27631v1 Announce Type: cross Abstract: Code Large Language Models (CLLMs) serve as the core of modern code agents, enabling developers to automate complex software development tasks. In thi

Prompt Codebooks: Discrete Compositional Optimization for Language Model Instruction Refinement

model-releasesarxiv-cs-ai

arXiv:2605.28360v1 Announce Type: new Abstract: Automatic prompt optimization (APO) has driven significant gains in LLM-based agentic workflows. However, existing methods treat each task's prompt as a

Reasoning Matters: Mitigate Hallucination in Multimodal Large Reasoning Models via Reasoning-Conditioned Preference Optimization

arXiv:2605.27906v1 Announce Type: new Abstract: Multimodal Large Reasoning Models introduce the reasoning paradigm, demonstrating strong capabilities on complex vision-language tasks. However, they st

Applications

REC-CBM: Rubric-Aware Error-Correction Concept Bottleneck Models for Trustworthy Open-Ended Grading

arXiv:2605.27402v1 Announce Type: cross Abstract: Open-ended grading is central to equitable and personalized education, yet manual grading remains time-consuming and costly, underscoring the need for

applicationsarxiv-cs-ai

Refining Multidimensional Video Reward Models via Disentangled Influence Functions

arXiv:2605.28203v1 Announce Type: new Abstract: As Text-to-Video (T2V) generation models continue to evolve, the complexity of video evaluation necessitates a fine-grained assessment across various ax

safetyarxiv-cs-lg

Revisiting Anthropomorphic Reflection Markers in Large Language Model Reasoning

arXiv:2605.28305v1 Announce Type: cross Abstract: Large Language Models (LLMs) often produce explicit reflective traces during complex reasoning, accompanied by anthropomorphic markers such as wait, h

Structured Agent Distillation for Large Language Model

arXiv:2505.13820v5 Announce Type: replace-cross Abstract: Large language models (LLMs) exhibit strong capabilities as decision-making agents by interleaving reasoning and actions, as seen in ReAct-sty

The Attentional White Bear Effect in Transformer Language Models

arXiv:2605.28639v1 Announce Type: cross Abstract: Instruction-based suppression is widely used to prevent language models from generating prohibited content, yet it remains unclear whether suppression

The Grammar of Transformers: A Systematic Review of Interpretability Research on Syntactic Knowledge in Language Models

arXiv:2601.19926v2 Announce Type: replace-cross Abstract: We present a systematic review of 337 articles evaluating the syntactic abilities of Transformer-based language models (TLMs), reporting on ov

Transfer learning RGB models to hyperspectral images with trainable tensor decompositions

arXiv:2605.28331v1 Announce Type: new Abstract: Transfer learning makes it possible to use large vision networks on a variety of domains, by specializing their models' general filters to new tasks. Ho

researcharxiv-cs-cv

VLA-Hijack: A Transferable Patch Attack against Vision-Language-Action Models via Visual Proprioception Hijacking

arXiv:2605.28083v1 Announce Type: new Abstract: While Vision-Language-Action (VLA) models have emerged as powerful generalist policies, their severe vulnerability to adversarial patches significantly

safetyarxiv-cs-cv

Where Does Toxicity Live? Mechanistic Localization and Targeted Suppression in Language Models

arXiv:2605.27997v1 Announce Type: cross Abstract: Large language models frequently generate toxic, hateful, or harmful content, yet existing mitigation methods rely on costly retraining or output-leve

local-aiarxiv-cs-ai

Anchored Decoding: Provably Reducing Copyright Risk for Any Language Model

arXiv:2602.07120v2 Announce Type: replace Abstract: Language models (LMs) tend to memorize portions of their training data and emit verbatim spans. When the underlying sources are sensitive or copyrig

researcharxiv-cs-cl

Athena: Enhancing Multimodal Reasoning with Data-efficient Process Reward Models

arXiv:2506.09532v5 Announce Type: replace-cross Abstract: We present Athena-PRM, a multimodal process reward model (PRM) designed to evaluate the reward score for each step in solving complex reasonin

Can VLA Models Learn from Real-World Data Continually without Forgetting?

arXiv:2605.26820v1 Announce Type: new Abstract: Vision-language-action (VLA) models provide a promising foundation for general-purpose robotics. However, their successful deployment in real-world scen

tutorialsarxiv-cs-ro

Industry

Cisco report finds no closed frontier AI model is safe from multi-turn attacks

A new report out today from Cisco Systems Inc. argues that none of the closed flagship large language models it tested can be considered safe once an attacker is allowed to push past a single prompt,

industrysiliconangle

EEG-FM-Audit: A Systematic Evaluation and Analysis Pipeline for EEG Foundation Models

arXiv:2605.26910v1 Announce Type: cross Abstract: Large EEG Foundation Models (FMs) have shown great potential for decoding EEG signals across diverse cognitive tasks. However, existing EEG-FM studies

HydraPrompt: An Adaptive and Asymmetric Framework of Vision-Language Models for Synthetic Image Detection

arXiv:2605.26421v1 Announce Type: new Abstract: The rapid evolution of generative models has precipitated a proliferation of fabricated content, posing significant challenges to existing Synthetic Ima

researcharxiv-cs-cv

Innovative Silicosis and Pneumonia Classification: Leveraging Graph Transformer Post-hoc Modeling and Ensemble Techniques

arXiv:2501.00520v2 Announce Type: replace Abstract: This paper presents a comprehensive study on the classification and detection of Silicosis-related lung inflammation. Our main contributions include

researcharxiv-cs-cv

Learning Nonlinear Factor Models with Unknown Monotone Links from Incomplete and Noisy Data

arXiv:2605.26271v1 Announce Type: cross Abstract: We study a nonlinear factor model in which observed responses depend on low-rank latent factors through an unknown monotone link function. This settin

Lifting Data-Tracing Machine Unlearning to Knowledge-Tracing for Foundation Models

arXiv:2506.11253v2 Announce Type: replace Abstract: Machine unlearning removes certain training data points and their influence from AI models (e.g., when a data owner revokes their consent to allow m

tutorialsarxiv-cs-cv

Localizing Memorized Regions in Diffusion Models via Coordinate-Wise Curvature Differences

arXiv:2605.26756v1 Announce Type: new Abstract: Diffusion models can unintentionally memorize training samples, raising concerns about privacy and copyright. While recent methods can detect memorizati

local-aiarxiv-cs-lg

Model Merging on Loss Landscape: A Geometry Perspective

arXiv:2605.26693v1 Announce Type: cross Abstract: Model merging offers a promising avenue for knowledge integration and parallel development without retraining. Yet, existing methods either ignore the

MONA: Muon Optimizer with Nesterov Acceleration for Scalable Language Model Training

arXiv:2605.26842v1 Announce Type: cross Abstract: The Muon optimizer has recently offered a promising alternative to AdamW for large language model training, leveraging matrix orthogonalization to pro

local-aiarxiv-cs-cl