Search: “models”

Immuno-VLM: Immunizing Large Vision-Language Models via Generative Semantic Antibodies for Open-World Trustworthiness

arXiv:2605.30745v1 Announce Type: new Abstract: Large Vision-Language Models have achieved unprecedented success in zero-shot recognition by aligning visual features with broad semantic concepts. Howe

researcharxiv-cs-cv

Tutorials

Lumos-Nexus: Efficient Frequency Bridging with Homogeneous Latent Space for Video Unified Models

DGX agent

arXiv:2605.31603v1 Announce Type: cross Abstract: Connector-based video unified models have demonstrated strong capability in instruction-grounded video synthesis, but integrating a large high-fidelit

tutorialsarxiv-cs-ai

1 Jun 2026

Protein Language Model Embeddings Improve Generalization of Implicit Transfer Operators

arXiv:2602.11216v2 Announce Type: replace Abstract: Molecular dynamics (MD) is a central computational tool in physics, chemistry, and biology, enabling quantitative prediction of experimental observa

Real-time multilingual ASR using rolling buffers and monolingual models [P]

researchr-machinelearning

This research discusses a technique for enabling real-time multilingual automatic speech recognition (ASR) on edge devices by dynamically switching between compact monolingual models rather than using

Semantic Triplet Restoration: A Novel Protocol for Hierarchical Table Understanding in Large Language Models

arXiv:2605.31550v1 Announce Type: new Abstract: Table question answering requires models to recover semantic relations encoded implicitly by two-dimensional layout, merged cells, and hierarchical head

researcharxiv-cs-cl

SLAP: The Semantic Least Action Principle for Variational Video-Language Modeling

arXiv:2605.30750v1 Announce Type: new Abstract: In the era of Large Video-Language Models (LVLMs), the computational necessity of sparse frame sampling creates a fundamental ``temporal gap'', renderin

researcharxiv-cs-cv

Local Ai

YARD: Y-Architecture Register Decoding for Efficient Hallucination Mitigation in Large Vision-Language Models

arXiv:2605.31429v1 Announce Type: new Abstract: Contrastive decoding (CD) seeks to mitigate hallucinations in Large Vision-Language Models (LVLMs) by contrasting the output distributions of a standard

local-aiarxiv-cs-cv

ActTraitBench: Quantifying the Knowledge-Decision Gap in Large Language Models via Human-Grounded Behavioral Validation

arXiv:2605.29791v1 Announce Type: new Abstract: While Large Language Models (LLMs) can convincingly simulate personas in explicit self-reports, they often deviate in implicit behavioral decisions, rev

safetyarxiv-cs-cl

AutoSizer: Automatic Sizing of Analog and Mixed-Signal Circuits via Large Language Model (LLM) Agents

model-releasesarxiv-cs-ai

arXiv:2602.02849v2 Announce Type: replace Abstract: The design of Analog and Mixed-Signal (AMS) integrated circuits remains heavily reliant on expert knowledge, with transistor sizing a major bottlene

Causal-JEPA: Learning World Models through Object-Level Latent Masking

arXiv:2602.11389v2 Announce Type: replace Abstract: World models require robust relational understanding to support prediction, reasoning, and control. While object-centric representations provide a u

Data filtering methods for training language models

arXiv:2605.29807v1 Announce Type: cross Abstract: Data quality is a critical factor in the effectiveness of machine learning models. Label errors, present even in widely used benchmarks, introduce noi

Dissociative Identity: Language Model Agents Lack Grounding for Reputation Mechanisms

arXiv:2605.30169v1 Announce Type: cross Abstract: As autonomous language model agents proliferate, forming an emerging agentic web with real-world consequences, what credibility signals can you use to

DVSM: Decoder-only View Synthesis Model Done Right

arXiv:2605.29891v1 Announce Type: new Abstract: Recent Large View Synthesis Models (LVSMs) advocate an encoder-decoder architecture that separates reconstruction and rendering into distinct networks.

researcharxiv-cs-cv

Emergent Semantic Representations in World Models through Physical Interaction without Linguistic Supervision

arXiv:2605.28865v1 Announce Type: cross Abstract: What does a world model learn from physical exploration, without any linguistic supervision? We argue the answer is organized by a single principle: t

Estimating the Empowerment of Language Model Agents

arXiv:2509.22504v3 Announce Type: replace Abstract: As language model (LM) agents become increasingly capable and adopted in real-world applications, there is a growing need for scalable evaluation fr

Fingerprinting Inference Systems of Large Language Models

arXiv:2605.29979v1 Announce Type: cross Abstract: The behavior of LLMs does not depend solely on the model itself. Components of the inference system, such as the inference engine, attention backend,

GRPO is Secretly a Process Reward Model

arXiv:2509.21154v4 Announce Type: replace-cross Abstract: Process reward models (PRMs) allow for fine-grained credit assignment in reinforcement learning (RL), and seemingly contrast with outcome rewa

Harnessing non-adversarial robustness in large language models

arXiv:2605.29816v1 Announce Type: new Abstract: The work presents an approach for addressing the challenge of robustness in Large Language Models (LLMs) to alterations and potential errors caused by s

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

Tutorials

Large Depth Completion Model from Sparse Observations

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

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

Tutorials

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

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

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

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

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

Local Ai

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