Search: “models”

PQMass: Probabilistic Assessment of the Quality of Generative Models using Probability Mass Estimation

arXiv:2402.04355v4 Announce Type: replace-cross Abstract: We propose a likelihood-free method for comparing two distributions given samples from each, with the goal of assessing the quality of generat

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Local Ai

Small Language Models as Judges for Rubric-Based Reinforcement Learning

DGX agent

arXiv:2608.30005v1 Announce Type: new Abstract: Rubric-based reinforcement learning extends RL beyond tasks with exact answers or rule-based verifiers by scoring responses against instance-specific cr

local-aiarxiv-cs-cl

1 Sept 2026

Statutory AI: Aligning Large Language Models With Legal Norms

arXiv:2608.28593v1 Announce Type: new Abstract: With the increasing development of AI regulatory frameworks, ensuring that artificial intelligence systems, particularly generative models, operate in a

Temporal Forcing: 4D Representation Alignment for Vision-Language-Action Models

arXiv:2608.30643v1 Announce Type: new Abstract: Recent vision-language-action (VLA) methods improve manipulation performance by aligning their representations with 3D scene geometry. However, these me

safetyarxiv-cs-ro

Test-Time Scaling for Video Diffusion Models via Diagnosis-Guided Candidate Recycling

arXiv:2608.29322v1 Announce Type: new Abstract: Recent video diffusion models have achieved remarkable generation quality, but high-fidelity results still largely depend on closed-source systems or co

Triggering Chain-of-Thought via Latent Feature Interventions in Large Language Models

arXiv:2601.08058v2 Announce Type: replace-cross Abstract: Chain-of-Thought (CoT) prompting often improves the reasoning performance of large language models (LLMs), but the internal signal that trigge

Hardware

Beyond Data Scaling: Representation-Centric Continued Pre-training for Vision-Language-Action Models

arXiv:2608.27550v1 Announce Type: cross Abstract: Scaling robot data is crucial for building generalist Vision-Language-Action (VLA) models, yet robot trajectories are harder to scale than web-scale i

hardwarearxiv-cs-cv

31 Aug 2026

Large Reasoning Models Struggle to Transfer Parametric Knowledge Across Scripts

arXiv:2603.17070v2 Announce Type: replace Abstract: In this work, we analyze shortcomings in cross-lingual knowledge transfer in large, modern reasoning LLMs. We demonstrate that the perceived gap in

local-aiarxiv-cs-cl

31 Aug 2026

Cross-Platform Generalisation Failure in Mental Health Natural Language Processing: A Five-Axis Fairness Audit of Transformer Models on Social Media

arXiv:2608.26138v1 Announce Type: new Abstract: We introduce the Cross-Platform Fairness Evaluation (CPFE) framework -- a five-axis audit protocol covering discriminative performance, calibration, sta

safetyarxiv-cs-cl

Agents

Do Language Models Follow Occam's Razor? An Evaluation of Parsimony in Inductive and Abductive Reasoning

arXiv:2509.03345v3 Announce Type: replace Abstract: Non-deductive reasoning, encompassing inductive and abductive reasoning, is essential in addressing complex real-world questions. One key feature of

agentsarxiv-cs-ai

Unsupervised Adaptation of 3D CT Foundation Models for 3D CBCT Segmentation

arXiv:2608.27190v1 Announce Type: new Abstract: Accurate 3D segmentation of cone-beam CT (CBCT) is critical for interventional and radiation therapy applications, yet it remains limited by two compoun

Visual Information-Guided Parallel Decoding for Diffusion Multimodal Large Language Models

arXiv:2608.26580v1 Announce Type: cross Abstract: Diffusion multimodal large language models (dMLLMs) have recently emerged as a new decoding paradigm for multimodal generation. Starting from a fully

Zero-Shot Video Restoration and Enhancement with Text-to-Image Latent Diffusion Models and Multi-Modal References

arXiv:2608.26476v1 Announce Type: new Abstract: Zero-shot image restoration methods with text-to-image latent diffusion models have achieved great success in universal image restoration tasks without

Overview of SHROOM-Visions 2026: A Shared Task on Hallucination Detection in Large Vision-Language Models

arXiv:2608.25662v1 Announce Type: new Abstract: In 2026, we held the fourth iteration of the SHROOM Shared Task series: SHROOM-Visions (extbf{S}hared-task on extbf{H}allucinations and extbf{R}elated e

27 Aug 2026

SAUSS: Stochastic Approximation with Unbiased Simulated Scores for Limited Dependent Variable Models

arXiv:2608.25304v1 Announce Type: cross Abstract: Multinomial choice models allow flexible substitution patterns but become computationally demanding with many alternatives or observations. With a fix

local-aiarxiv-cs-lg

27 Aug 2026

The Blending Ratio Is Not Where the Performance Is: Diagnosing Prototype Blending for Few-Shot Adaptation of Vision-Language Models

arXiv:2608.23634v1 Announce Type: new Abstract: Many few-shot adaptation methods for vision-language models classify with a convex combination of the zero-shot text prototype and the mean of the K lab

26 Aug 2026

When Does Self-Supervised Pretraining Help Tabular Models? A Study of Label Scarcity and Missing Data

arXiv:2608.24381v1 Announce Type: new Abstract: Self-supervised learning (SSL) has emerged as a promising approach for tabular data, yet its efficacy under extreme label scarcity and test-time missing

researcharxiv-cs-lg

26 Aug 2026

A Cross-Architecture Audit of Direction-Based Inference-Time Defences in Vision-Language Models

arXiv:2607.27910v2 Announce Type: replace Abstract: Inference time defences against vision language model jailbreaks often subtract a calibrated direction from the residual stream at a chosen decoder

Applications

Act with Intent: Distilling Behavior Intent for Vision-Language-Action Models

arXiv:2608.23478v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models can turn multimodal context into robot actions, but their action decoders are still trained largely by behavior cl

applicationsarxiv-cs-ai

Tutorials

CAI-DLLM: Convergence Aware Inference for Diffusion Language Models

arXiv:2608.22646v1 Announce Type: new Abstract: Diffusion language models can generate many tokens in parallel, but they still require repeated denoising steps during inference. This makes generation

tutorialsarxiv-cs-ai

Can Large Language Models 'Hyper-Thread'?

arXiv:2608.22376v1 Announce Type: new Abstract: Large language models generate tokens sequentially, but can they execute multiple tasks concurrently while forming each token? Broader attention allocat

DefaultShift: Auditing Semantic Default Shift in Accelerated Text-to-Image Models

arXiv:2608.21784v1 Announce Type: new Abstract: Few-step text-to-image models increasingly replace slower generators, yet acceleration can silently change distributions over unspecified attributes eve

Do Spoken Language Models Hear Speech as They Read Text? Bridging Structural Gaps Between Speech and Text

arXiv:2608.22908v1 Announce Type: cross Abstract: Spoken Language Models (SLMs) generate textual responses directly from speech, offering an alternative to cascaded systems. Despite recent advances, e

Efficient Regression Models for Scan Statistics

arXiv:2608.22201v1 Announce Type: cross Abstract: We introduce a new class of regression models for scan statistics on real-valued signals. These allow for improved fitting of non-stationary signals t

researcharxiv-cs-lg

Guardian: Detecting Robotic Planning and Execution Errors with Vision-Language Models

arXiv:2512.01946v4 Announce Type: replace-cross Abstract: Robust robotic manipulation requires reliable failure detection and recovery. Although recent Vision-Language Models (VLMs) show promise in ro

Knowing Isn't Always Saying: When Do Spatial Encodings Reach Answers in Vision-Language Models?

arXiv:2608.22916v1 Announce Type: new Abstract: Vision-language models are known to encode spatial information in their hidden states, yet often fail to use it when answering. However, it remains uncl

Perturb the Thought, Not the Pixels: Latent-Space Rollout Diversification for Reinforcement Learning of Vision-Language Models

arXiv:2608.21595v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards (RLVR) improves the reasoning ability of vision-language models (VLMs), and diversifying the rollouts wit

Selective Cross-View Consistency for World Action Models: Held-Out Viewpoint Robustness Without Test-Time Camera Information

arXiv:2608.21402v1 Announce Type: cross Abstract: World action models (WAMs) jointly denoise future video frames and robot actions, and the video prior is expected to generalize their control. Camera

Small Reasoning Models are Instruction Followers in Function Calling

arXiv:2608.22472v1 Announce Type: new Abstract: Function calling represents the core capability of agentic large language models (LLMs). Existing research has focused on enhancing LLMs function-callin

local-aiarxiv-cs-ai

Efficient Inference for Inverse Reinforcement Learning and Dynamic Discrete Choice Models

arXiv:2512.24407v2 Announce Type: replace Abstract: In many sequential decision-making problems, researchers observe actions but not the rewards that drive behavior, yet still wish to evaluate and com

safetyarxiv-cs-lg

Jacobian-guided Noise Injection for Quantization Robustness in Large Language Models

arXiv:2608.20988v1 Announce Type: cross Abstract: Quantization of Large Language Models (LLMs) is often hindered by the sensitivity of the self-attention mechanism to discretization errors. We identif

local-aiarxiv-cs-ai

Learning to Orchestrate Vision Foundation Models for Multi-Task Dense Prediction

arXiv:2606.15765v2 Announce Type: replace Abstract: Vision foundation models (VFMs) exhibit complementary strengths shaped by their pretraining objectives. Yet prevailing methods for multi-task dense

Semantically Compatible Knowledge Distillation for Cross-Domain Object Detection with Vision Foundation Models

arXiv:2608.20916v1 Announce Type: new Abstract: Vision foundation models (VFMs) offer strong generalization capabilities for domain-adaptive object detection (DAOD). However, existing VFM-based method

Thermo-FL: Thermal-Aware Robust Federated Fine-Tuning of Large Language Models for Edge AI

arXiv:2608.21172v1 Announce Type: new Abstract: Federated fine-tuning enables large language models to adapt on edge devices without centralizing private data, but practical deployments must address h

local-aiarxiv-cs-lg

TriPLU: Bypassing the Gate with Direct Trilinear Product FFNs in Tiny Language Models

arXiv:2608.20360v1 Announce Type: new Abstract: We study whether tiny decoder-only language models benefit from feed-forward layers that directly multiply learned feature projections. TriPLU, a Trilin

HBVLA: Pushing 1-Bit Post-Training Quantization for Vision-Language-Action Models

arXiv:2602.13710v2 Announce Type: replace Abstract: Vision-Language-Action (VLA) models enable instruction-following embodied control, but their large compute and memory footprints hinder deployment o

safetyarxiv-cs-lg

21 Aug 2026

Agents

The Third Restructuring of Software Form: From the Three-Tier Architecture to Storage, Models, and Agents

arXiv:2608.20201v1 Announce Type: new Abstract: Software form has undergone two paradigm shifts since its inception: Software 1.0, in which instructions determine behavior, and Software 2.0, in which

agentsarxiv-cs-ai

21 Aug 2026

Dream2Reward: Transition-Alignment Reward Models from Positive Demonstrations for Robotic Manipulation

arXiv:2608.18787v1 Announce Type: new Abstract: Learning robotic policies requires dense rewards that remain informative when behavior departs from successful demonstrations. Progress-based rewards es

safetyarxiv-cs-ro

Institutional Prestige as Geographic Bias in Large Language Models: Evidence from Three Factorial Experiments with Bootstrap Confidence Intervals

arXiv:2608.18107v1 Announce Type: cross Abstract: We investigate whether large language models (LLMs) systematically discriminate in candidate evaluations based on applicant name ethnicity and/or inst

Intercepting the Kangaroo: Experimental Astrolinguistics with Constructed Lexicons, Active Probing, and Large Language Models as Informants and Hypothesis Proposers

arXiv:2608.19124v1 Announce Type: cross Abstract: Astrolinguistics -- communication with minds that categorize reality differently from ours -- has been purely speculative since Freudenthal's Lincos (

Applications

Large Language Models in Mental Health: A Systematic Review of Applications, Innovations, and Ethical Challenges

arXiv:2608.18080v1 Announce Type: new Abstract: We present a review on the applications of large language models (LLMs) in health, e.g., social media analysis, clinical conversational agents, therapy

applicationsarxiv-cs-ai

Model Releases

Auditing Exposure to Harmful Content on TikTok using Multimodal Language Models: A Cross-National, Age-Stratified Study

model-releasesarxiv-cs-cl

arXiv:2608.17583v1 Announce Type: new Abstract: Online video platforms can expose young users to harmful content, but independent audits remain difficult because video annotation is costly and moderat

19 Aug 2026

Graph Surgery and the Do-Operator: A Precise Correspondence for Acyclic Structural Causal Models

arXiv:2608.17634v1 Announce Type: new Abstract: The operatorname{do}-operator is described graphically by deleting arrows into its targets and functionally by replacing their mechanisms with constants

19 Aug 2026

GUPO: Gradient Uncertainty-aware Policy Optimization for Post-Training Large Language Models

arXiv:2608.17411v1 Announce Type: new Abstract: Group Relative Policy Optimization (GRPO) has become a widely used approach for post-training Large Language Models (LLMs) for reasoning. In GRPO, the g

safetyarxiv-cs-lg

19 Aug 2026

A Declarative-Procedural Perspective on Expert Routing in Bilingual Mixture-of-Experts Language Models

arXiv:2608.15102v1 Announce Type: new Abstract: We investigate whether Mixture-of-Experts (MoE) language models develop linguistically structured expert routing during bilingual language acquisition.

Beyond Natural-Image Foundation Models: Benchmarking Satellite Pretraining for Ophthalmic Image Analysis

arXiv:2608.15195v1 Announce Type: new Abstract: Vision Foundation Models (VFMs) have emerged as a promising approach in medical imaging, producing broadly applicable systems that can be efficiently ad

Beyond Single Object: Learning 3D Relations with Large Language Models

arXiv:2608.15710v1 Announce Type: cross Abstract: We address a fundamental gap in 3D-LLMs: existing models focus on single-object/scene description, struggling with detailed, inter-object comparison.

local-aiarxiv-cs-ai

Discrete Diffusion Language Models Are Training-Free Multi-Label Classifiers

arXiv:2608.14649v1 Announce Type: new Abstract: We present dLLM-SetScore, a training-free method that uses discrete masked-diffusion language models for multi-label text classification. For each candi

researcharxiv-cs-lg