Search: “models”

Differentiable Model Predictive Safety for Heterogeneous Mobility at Urban Intersections

arXiv:2605.27418v1 Announce Type: cross Abstract: The imminent integration of autonomous vehicles and mobile robots in urban settings presents a critical safety challenge for future intelligent transp

safetyarxiv-cs-ro

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EVADE-Bench: Multimodal Benchmark for Evaluating and Enhancing Evasive Content Detection

DGX agent

arXiv:2505.17654v4 Announce Type: replace-cross Abstract: E-commerce platforms increasingly rely on Large Language Models (LLMs) and Vision Language Models (VLMs) to detect illicit or misleading produ

model-releasesarxiv-cs-ai

28 May 2026

Explicit Critic Guidance for Aligning Diffusion Models

arXiv:2605.27736v1 Announce Type: cross Abstract: Online reinforcement learning is becoming increasingly important for aligning diffusion models with non-differentiable objectives. However, existing m

researcharxiv-cs-cv

From AR to Diffusion: Efficiently Adapting Large Language Models with Strictly Causal and Elastic Horizons

arXiv:2605.27387v1 Announce Type: cross Abstract: Diffusion models promise efficient parallel text generation but rely on bidirectional attention, creating a structural mismatch with pre-trained Autor

Hardware

Heterogeneous Parallelism for Multimodal Large Language Model Training

arXiv:2605.27678v1 Announce Type: new Abstract: Foundation model training is becoming multimodal, from post-training pipelines to large-scale pretraining. As modality coverage broadens, context window

hardwarearxiv-cs-lg

HGMEM: Hypergraph-based Working Memory to Improve Multi-step RAG for Long-Context Complex Relational Modeling

arXiv:2512.23959v3 Announce Type: replace-cross Abstract: Multi-step retrieval-augmented generation (RAG) has become a widely adopted strategy for enhancing large language models (LLMs) on tasks that

History-aware adaptive reduced-order models via incremental singular value decomposition

arXiv:2605.28684v1 Announce Type: new Abstract: Reduced-order models (ROMs) can accelerate high-dimensional dynamical simulations, but their accuracy often deteriorates when online dynamics leave the

researcharxiv-cs-lg

Human-like in-group bias in instruction-tuned language model agents

arXiv:2605.28114v1 Announce Type: new Abstract: As autonomous AI agents are deployed in persistent, interacting networks -- coordinating tasks, routing resources, and accumulating reputational histori

JECA^2: Judgment-Explanation Consistent Adversarial Attack against Forensic Vision-Language Models

arXiv:2605.28609v1 Announce Type: new Abstract: Forensic vision-language models (VLMs) have recently been developed to detect image tampering and provide natural-language explanations. However, their

safetyarxiv-cs-cv

Modeling Community Attitude through Reaction Tone: A Human-AI Collaborative Framework for Evaluating LLM Alignment with Linguistic Behaviors in Online Communities

arXiv:2605.27388v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly utilized as proxies for computational social analysis; yet, their ability to faithfully represent the 't

Next-Scale Autoregressive Models for Text-to-Motion Generation

arXiv:2604.03799v2 Announce Type: replace Abstract: Autoregressive (AR) models offer stable and efficient training, but standard next-token prediction is not well aligned with the temporal structure r

researcharxiv-cs-cv

Not All NVFP4 QAT Recipes Are Equal: How Architecture and Scale Shape Model Quality for Anomaly Segmentation

arXiv:2605.27616v1 Announce Type: cross Abstract: Real-time anomaly segmentation demands both high recall and efficient low-precision inference. We study the three-way interaction of model architectur

Object-Centric Vision Token Pruning for Vision Language Models

arXiv:2511.20439v2 Announce Type: replace-cross Abstract: In Vision Language Models (VLMs), vision tokens are quantity-heavy yet information-dispersed compared with language tokens, thus consume too m

OR-Space: A Full-Lifecycle Workspace Benchmark for Industrial Optimization Agents

model-releasesarxiv-cs-ai

arXiv:2605.28158v1 Announce Type: new Abstract: Large language model (LLM) agents are increasingly used to assist with operations research (OR) modeling, yet existing OR-oriented benchmarks often redu

Agents

Segment to Focus: Guiding Latent Action Models in the Presence of Distractors

arXiv:2602.02259v2 Announce Type: replace-cross Abstract: Latent action models (LAMs) offer a promising path to pre-training embodied agents on large amounts of action-free video. They infer latent ac

agentsarxiv-cs-cv

Applications

Surrogate modeling for Bayesian optimization beyond a single Gaussian process

arXiv:2205.14090v2 Announce Type: replace-cross Abstract: Bayesian optimization (BO) has well-documented merits for optimizing black-box functions with an expensive evaluation cost. Such functions eme

applicationsarxiv-cs-lg

Tell Me a Story! Narrative-Driven XAI with Large Language Models

arXiv:2309.17057v3 Announce Type: replace Abstract: In many AI applications today, the predominance of black-box machine learning models, due to their typically higher accuracy, amplifies the need for

Text-Only Data Synthesis for Vision Language Model Training

arXiv:2503.22655v2 Announce Type: replace Abstract: Training vision-language models (VLMs) typically requires large-scale, high-quality image-text pairs, but collecting or synthesizing such data is co

Towards Unified Vision-Language Models with Incomplete Multi-Modal Inputs

arXiv:2605.27894v1 Announce Type: new Abstract: Video-Language Models (VLMs) have demonstrated impressive multi-modal reasoning capabilities across diverse computer vision applications. However, these

safetyarxiv-cs-cv

Training Stratigraphy: Persistent Behavioral Artifacts in Large Language Models Observed Through Longitudinal AI-Human Interaction

arXiv:2605.28102v1 Announce Type: new Abstract: Large language models trained with Reinforcement Learning from Human Feedback (RLHF) and Constitutional AI exhibit persistent behavioral patterns that s

Unlocking Fine-Grained and Within-Utterance Speaking Style Control in Prompt-Based Text-to-Speech Models

arXiv:2605.27376v1 Announce Type: cross Abstract: While prompt-based text-to-speech (TTS) models enable natural language-driven speaking style control, they often provide limited fine-grained control

When Confidence Misleads: Suffix Anchoring and Anchor-Proximity Confidence Modulation for Diffusion Language Models

arXiv:2605.28181v1 Announce Type: new Abstract: Diffusion language models decode text by iteratively denoising masked token sequences, making the choice of which positions to decode a central inferenc

researcharxiv-cs-cl

When Discourse Pressures Conflict: Information Structure in Vision-Language Model Outputs

arXiv:2605.28346v1 Announce Type: new Abstract: Vision-language models (VLMs) are increasingly evaluated for whether they identify the right visual content, but little is known about whether they expr

researcharxiv-cs-cl

When pre-training hurts LoRA fine-tuning: a dynamical analysis via single-index models

arXiv:2602.02855v2 Announce Type: replace Abstract: Pre-training on a source task is usually expected to facilitate fine-tuning on similar downstream problems. In this work, we mathematically show tha

safetyarxiv-cs-lg

Which Pretraining Paradigm Better Serves Spatial Intelligence? An Empirical Comparison of Vision-Language and Video Generation Models

arXiv:2605.28132v1 Announce Type: new Abstract: Spatial intelligence requires visual representations that capture both semantic objects and geometric structure in the physical world. To support this,

tutorialsarxiv-cs-cv

Beyond Pairwise Preferences: Listwise Reward-Aware Alignment for Diffusion Models

arXiv:2605.26491v1 Announce Type: cross Abstract: Preference optimization has emerged as an efficient alternative to online reinforcement learning from human feedback (RLHF) for aligning text-to-image

safetyarxiv-cs-cv

Chartographer: Counterfactual Chart Generation for Evaluating Vision-Language Models

arXiv:2605.27311v1 Announce Type: new Abstract: Chart question-answering (QA) benchmarks aim to pose questions that require visual reasoning to correctly answer, but models can often reach solutions t

researcharxiv-cs-cl

CodecCap: High-Fidelity Codec-Inspired Residual Modeling for Dense Video Captioning

model-releasesarxiv-cs-cv

arXiv:2605.26967v1 Announce Type: new Abstract: Existing video captioning methods struggle to balance visual fidelity and redundancy: holistic captions are compact but lose fine-grained evidence, wher

ContextGuard: Structured Self-Auditing for Context Learning in Language Models

arXiv:2605.26827v1 Announce Type: cross Abstract: Recent benchmarks reveal that despite strong reasoning capabilities, large language models (LLMs) still struggle to faithfully apply complex contextua

Cultural Value Alignment Via Latent Activation Steering in Large Language Models

arXiv:2605.26365v1 Announce Type: new Abstract: Large Language Models (LLMs) often exhibit homogenized cultural perspectives. While the World Values Survey (WVS) provides a gold standard for mapping h

safetyarxiv-cs-cl

Local Ai

Edge AI Deployment Beyond Models: A BSP-Aware Systems Framework for Industrial Embedded Platforms

arXiv:2605.26119v1 Announce Type: cross Abstract: Industrial Edge AI programs often begin with the model and only later confront the platform. That sequencing is attractive because it allows early dem

local-aiarxiv-cs-ai

Emergent Causal-Geometric Dynamics Across Depth in Large Language Models

arXiv:2602.04931v2 Announce Type: replace-cross Abstract: Geometric analyses of large language model (LLM) representations reveal structured variation across depth but remain fundamentally correlation

Evaluating Sample Utility for Efficient Data Selection by Mimicking Model Weights

arXiv:2501.06708v5 Announce Type: replace-cross Abstract: Large-scale web-crawled datasets contain noise, bias, and irrelevant information, necessitating data selection techniques. Existing methods de

Learning Energy-Based Models from Stochastic Interpolants using Spatiotemporal Differences

arXiv:2605.26850v1 Announce Type: new Abstract: Learning an energy-based model from data samples is a central problem in machine learning. Many recent and popular methods, such as denoising score matc

tutorialsarxiv-cs-lg

Learning to Diagnose and Correct Errors: Towards Moral Sensitivity Acquisition in Large Language Models

arXiv:2601.03079v4 Announce Type: replace Abstract: Moral sensitivity is the most fundamental capability underlying human moral competence. Although many approaches aim to align large language models

tutorialsarxiv-cs-cl

Left-Right Symmetry Breaking in CLIP-style Vision-Language Models Trained on Synthetic Spatial-Relation Data

arXiv:2601.12809v2 Announce Type: replace-cross Abstract: Spatial understanding remains a key challenge in vision-language models. Yet it is still unclear whether such understanding is truly acquired,

LUCoS: Latent Unsupervised Context Selection for Tabular Foundation Models

arXiv:2605.27254v1 Announce Type: cross Abstract: Selecting which instances to label is a key challenge in low-label tabular learning. For recent Tabular Foundation Models such as TabPFN, context sele

Model Unlearning Objectives Vary for Distinct Language Functions

arXiv:2605.26454v1 Announce Type: new Abstract: Large language models (LLMs) learn undesirable properties during pretraining, including dangerous knowledge and toxic text generation. Just as post-trai

tutorialsarxiv-cs-cl

Olaf-World: Orienting Latent Actions for Video World Modeling

arXiv:2602.10104v2 Announce Type: replace-cross Abstract: Scaling action-controllable world models is limited by the scarcity of action labels. While latent action learning promises to extract control

Respecting Modality Gap in Post-hoc Out-of-distribution Detection with Pre-trained Vision-Language Models

arXiv:2605.26661v1 Announce Type: cross Abstract: Out-of-distribution (OOD) detection has emerged as a popular technique to enhance the reliability of machine learning models by identifying unexpected

SeDT: Sentence-Transformer Decision-Transformer Conditioning for Multi-Turn Conversation Reliability

model-releasesarxiv-cs-ai

arXiv:2605.26788v1 Announce Type: cross Abstract: Large language models (LLMs) achieve impressive performance when a task is fully specified in a single turn, yet the same models lose up to 39% of tha

SOLE-R1: Video-Language Reasoning as the Sole Reward for On-Robot Reinforcement Learning

model-releasesarxiv-cs-cl

arXiv:2603.28730v2 Announce Type: replace-cross Abstract: Vision-language models (VLMs) have shown impressive capabilities across diverse tasks, motivating efforts to leverage these models to supervis

Local Ai

The Constraint Tax: Measuring Validity-Correctness Tradeoffs in Structured Outputs for Small Language Models

arXiv:2605.26128v1 Announce Type: new Abstract: Production LLM systems increasingly require machine-readable outputs: JSON objects, typed traces, regex-constrained fields, and tool-call schemas. This

local-aiarxiv-cs-lg

To model human linguistic prediction, make LLMs less superhuman

arXiv:2510.05141v2 Announce Type: replace Abstract: When we read, we make predictions about upcoming words; these predictions influence our reading behavior. The success of large language models (LLMs

safetyarxiv-cs-cl

A Multimodal 3D Foundation Model for Light Sheet Fluorescence Microscopy Enables Few-Shot Segmentation, Classification, and Deblurring

arXiv:2605.26026v1 Announce Type: cross Abstract: Light sheet fluorescence microscopy (LSM) enables high-resolution, three-dimensional (3D) imaging of biological specimens, providing rich volumetric d

Agents

AutoSOTA: An End-to-End Automated Research System for State-of-the-Art AI Model Discovery

arXiv:2604.05550v2 Announce Type: replace Abstract: Artificial intelligence research increasingly depends on prolonged cycles of reproduction, debugging, and iterative refinement to achieve State-Of-T

agentsarxiv-cs-cl

Capability and Robustness Cannot Both Be Free: An Information-Theoretic Bound for Vision-Language-Action Models

arXiv:2605.25889v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models are increasingly deployed on real robots, where each predicted action is executed and each failure carries a safet

safetyarxiv-cs-lg

Confidence Calibration in Large Language Models

arXiv:2605.23909v1 Announce Type: new Abstract: We investigate the calibration of large language models' (LLMs') confidence across diverse tasks. The results of our preregistered study show that the c