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arXiv:2606.29684v1 Announce Type: cross Abstract: This research investigates the optimization of Convolutional and Dense Neural Networks (CNNs and DNNs) for autonomous steering using the (N+M) Evoluti

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Research

FADE: Mitigating Hallucinations by Reducing Language-Prior Dominance in Large Vision-Language Models

DGX agent

arXiv:2606.29431v1 Announce Type: new Abstract: Despite the impressive capabilities of Large Vision-Language Models (LVLMs), they remain susceptible to hallucination, generating content inconsistent w

researcharxiv-cs-ai

30 Jun 2026

Fast Wireless Foundation Models with Early-Exits

arXiv:2606.29640v1 Announce Type: cross Abstract: While wireless foundation models (FMs) are demonstrating strong potential to enable AI-Native 6G networks, their high computational cost remains a cri

Invariant Reasoning Directions in Latent Trajectories of Language Models

arXiv:2606.29164v1 Announce Type: cross Abstract: Latent reasoning models perform multi-step inference directly in hidden-state space, yet the structure of these latent reasoning trajectories remains

Tutorials

LoRA-Tuned Large Language Models for Dementia Detection via Multi-View Speech-Derived Features

arXiv:2606.28445v1 Announce Type: cross Abstract: Early detection of dementia enables timely intervention, and reflecting cognitive impairment, spontaneous speech offers a non-invasive screening modal

tutorialsarxiv-cs-ai

On the Faithfulness of Post-Hoc Concept Bottleneck Models

arXiv:2606.30498v1 Announce Type: cross Abstract: Human decision-making interprets the world through high-level concepts, such as recognizing a bird by its belly color. To bridge the gap between opaqu

applicationsarxiv-cs-ai

Tutorials

Ontology-Guided Reverse Thinking Makes Large Language Models Stronger on Knowledge Graph Question Answering

arXiv:2502.11491v3 Announce Type: replace-cross Abstract: Large language models (LLMs) have shown remarkable capabilities in natural language processing. However, in knowledge graph question answering

tutorialsarxiv-cs-ai

Robustness and Structure Preservation in Flow-Based Generative Models via Wasserstein Path-Space Divergences

arXiv:2410.01244v2 Announce Type: replace-cross Abstract: We introduce a novel Wasserstein-1 (W_1) path-space divergence for stochastic and deterministic dynamics and establish a Wasserstein Uncertain

Understanding Evaluation Illusion in Diffusion Large Language Models

arXiv:2606.29228v1 Announce Type: new Abstract: Despite the capability of parallel decoding, diffusion large language models (dLLMs) require many denoising steps to maintain generation quality, motiva

researcharxiv-cs-cl

Using Large Language Models as Low-Cost Statistical Estimators for Human-Response Data

arXiv:2606.30372v1 Announce Type: new Abstract: Quantitative research across the social and behavioral sciences depends on human subject experiments that are expensive, slow, and subject to sampling b

Bifocal Diffusion Language Models: Asymmetric Bidirectional Context for Parallel Generation

arXiv:2606.27732v1 Announce Type: cross Abstract: Discrete diffusion language models (dLLMs) recover masked tokens in parallel, offering significant speedups over autoregressive (AR) generation. Howev

IWP: Token Pruning as Implicit Weight Pruning in Large Vision Language Models

arXiv:2604.00757v2 Announce Type: replace-cross Abstract: Large Vision Language Models show impressive performance across image and video understanding tasks, yet their computational cost grows rapidl

POTracker: Optimizing Large Language Models for Standard-Compliant Power Outage Report Generation

arXiv:2606.23533v2 Announce Type: replace Abstract: Recent large language models (LLMs) are good at general text generation, but it is still hard to use them for domain-specific data generation becaus

Recall Before Rerank: Benchmarking Deep Learning Models for Large-Scale Code-to-Code Retrieval

arXiv:2606.27401v1 Announce Type: cross Abstract: Semantic code search and clone detection are essential for software development, maintenance, and reuse. This paper evaluates the effectiveness, effic

researcharxiv-cs-cl

Tutorials

Textual Belief States for World Models: Identifiable Representation Learning Under Strict Mediation

arXiv:2606.27681v1 Announce Type: cross Abstract: World models in partially observed environments rely on latent representations that summarize interaction history, but in many modern LLM-based archit

tutorialsarxiv-cs-cl

Einstein World Models

arXiv:2606.26969v1 Announce Type: new Abstract: Does intelligence require the ability to reason about phenomena beyond direct experience? It is natural to suspect that some complex thought cannot be c

Explainable Ensemble-Based Machine Learning Models for Detecting the Presence of Cirrhosis in Hepatitis C Patients

arXiv:2606.26561v1 Announce Type: new Abstract: Hepatitis C is a liver infection caused by a virus, which results in mild to severe inflammation of the liver. Over many years, hepatitis C gradually da

Metaphors are a Source of Cross-Domain Misalignment of Large Reasoning Models

arXiv:2601.03388v3 Announce Type: replace-cross Abstract: Earlier research has shown that metaphors influence human decision-making, raising the question of whether metaphors also influence large lang

Mitigating Hallucinations via Inter-Layer Consistency Aggregation in Large Vision-Language Models

arXiv:2505.12343v2 Announce Type: replace-cross Abstract: Despite the impressive capabilities of Large Vision-Language Models (LVLMs), they remain susceptible to hallucinations, where generated conten

Prompt Injection in Automated Resume Screening with Large Language Models: Single and Multi-Injection Settings

arXiv:2606.27287v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used to screen and rank job applicants, creating incentives for candidates to strategically manipulate alg

World Action Models Enable Continual Imitation Learning with Recurrent Generative Replays

arXiv:2606.27374v1 Announce Type: cross Abstract: Going beyond predicting robot actions, World Action Models (WAMs) can also generate future visual observations. We build on this generative capability

safetyarxiv-cs-cv

Action ControlNet: A Lightweight Delay-Aware Adapter for Smooth Asynchronous Control in Vision-Language-Action Models

arXiv:2606.25985v1 Announce Type: new Abstract: Vision-language-action (VLA) models have shown strong potential for general-purpose robot manipulation, but their inference latency remains a major obst

safetyarxiv-cs-ro

BCoughBench: Benchmarking Respiratory Acoustic Foundation Models Under Body-Coupled Wearable Sensor Conditions

arXiv:2606.25116v1 Announce Type: cross Abstract: Respiratory acoustic foundation models (FMs) are benchmarked exclusively on smartphone recordings, yet clinical deployment increasingly targets body-c

Dense Supervision Is Not Enough: The Readout Blind Spot in Looped Language Models

arXiv:2606.24898v1 Announce Type: new Abstract: Looped language models turn hidden states into runtime state: each state is decoded for prediction and fed back into future computation. This creates a

Agents

MANGO: Automated Multi-Agent Test Oracle Generation for Vision-Language-Action Models

arXiv:2606.24815v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models are emerging robotic control systems that integrate perception, language understanding, and action generation in a

agentsarxiv-cs-ro

ROAD-VLA: Robust Online Adaptation via Self-Distillation for Vision-Language-Action Models

arXiv:2606.25800v1 Announce Type: new Abstract: Effective online adaptation of vision-language-action (VLA) models remains challenging, as sparse rewards provide weak supervision for high-dimensional

AI Tokenomics: The Economics of Tokens, Computation, and Pricing in Foundation Models

arXiv:2606.24616v1 Announce Type: new Abstract: Tokens have become the practical accounting unit for modern foundation model services, linking information processing, computation, memory use, energy e

applicationsarxiv-cs-ai

Cyclic Denoising Reveals Ultrastable Memories in Diffusion Models

arXiv:2606.24000v1 Announce Type: cross Abstract: We introduce cyclic denoising -- repeated forward and reverse diffusion at controlled noise amplitudes -- as an extraction attack for image diffusion

G^3VLA: Geometric inductive bias for Vision-Language-Action Models

arXiv:2606.24472v1 Announce Type: cross Abstract: Vision-language-action (VLA) models have made rapid progress in generalist robot manipulation by harnessing semantic knowledge from pretrained vision-

Quantum Cinema: An Interactive Cinematic Exploration of Quantum Computing Hardware via Generative World Models

arXiv:2606.17102v2 Announce Type: replace-cross Abstract: Quantum computing promises transformative advances across science and industry, yet the physical hardware that enables these computations rema

Causal Reward World Models: Zero-shot Reward Design for Automated Skill Generation

arXiv:2606.23280v1 Announce Type: new Abstract: Automated Reward Design (ARD) aims to replace manual reward engineering in reinforcement learning with language-driven reward function synthesis. Howeve

researcharxiv-cs-ro

Conceptual Design of an Ecosystem for Real Farm Data Collection toward Agricultural AI Foundation Models

arXiv:2606.23258v1 Announce Type: new Abstract: Data scarcity is a fundamental challenge in developing AI and foundation models for agricultural robots. Existing open-source data platforms do not prov

researcharxiv-cs-ro

FlowTrain: Flow-Based Decoupled Training for Industrial-Grade Vision-Language Models

arXiv:2606.23087v1 Announce Type: new Abstract: Industrial-grade distributed training of vision-language models (VLMs) remains far less efficient than that of unimodal LLMs. Existing solutions either

applicationsarxiv-cs-lg

Foundation Models for Epileptogenic Zone Identification in Drug-Resistant Epilepsy

arXiv:2606.22657v1 Announce Type: new Abstract: Accurate identification of the epileptogenic zone (EZ) is essential for seizure freedom after resective surgery in drug-resistant epilepsy, yet seizure

From Pixels to Concepts: Growing Rich 3D Semantic Scene Graph Forests utilizing Foundation Models

arXiv:2606.23312v1 Announce Type: new Abstract: Operating in complex real-world environments requires robots to understand their surroundings on a functional semantic level. This demands a detailed mu

applicationsarxiv-cs-ro

Learning with Multiple Correct Answers -- Regret Bounds under Different Feedback Models

arXiv:2602.09402v2 Announce Type: replace Abstract: We study the problem of learning with multiple correct answers, where each instance admits a set of valid labels. We primarily focus on the online s

Local Ai

Mind the Noise: Sensitivity of Transformer-based Interaction-Aware Trajectory Prediction Models to Noisy Data

arXiv:2606.21344v1 Announce Type: cross Abstract: Trajectory prediction allows autonomous vehicles to anticipate the future behavior of surrounding objects (or agents) and, accordingly, maximize the s

local-aiarxiv-cs-lg

Local Ai

Numerical stability analysis of large language models

arXiv:2503.10251v2 Announce Type: replace-cross Abstract: Transformers are the state-of-the-art architecture for large language models, and a key to their scalability is the strategic usage of low-pre

local-aiarxiv-cs-lg

Pre-Generation Hallucination Detection in Large Language Models via Soft-Target Attention Probing

arXiv:2606.21917v1 Announce Type: cross Abstract: Detecting hallucination risk before generation enables abstention, retrieval augmentation, and routing decisions without incurring the cost of decodin

RECALL: Recovery Experience Collection for Active Lifelong Learning in Vision-Language-Action Models

arXiv:2606.23617v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models are commonly fine-tuned through passive imitation learning, where additional demonstrations are collected for task

Rethinking the Adaptation of Vision Foundation Models for Efficient Cell Segmentation

arXiv:2606.21913v1 Announce Type: new Abstract: Cell segmentation is critical for computational pathology and biomedical discovery. While recent Vision Foundation Models (VFMs) have demonstrated remar

Robust Diffusion Models via Divergence-Induced Weighted Denoising

arXiv:2606.22521v1 Announce Type: cross Abstract: We show that replacing the standard MSE denoising loss in diffusion models with a nonlinear transformation induced by an f-divergence yields a simple

SignVLA: Real-Time Sign Language-Guided Robotic Manipulation via Attention LSTM and Vision-Language-Action Models

arXiv:2606.20857v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models enable robots to execute manipulation tasks from natural-language instructions grounded in visual observations. Ho

T-VSS: Test-Time Visual Subspace Steering for Adversarial Robustness of Vision-Language Models

arXiv:2606.23132v1 Announce Type: new Abstract: Vision-language models (VLMs) achieve strong zero-shot recognition, but they remain highly vulnerable to adversarial perturbations. Recent test-time ada

The Trilemma of Truth in Large Language Models

arXiv:2506.23921v5 Announce Type: replace-cross Abstract: The public often attributes human-like qualities to large language models (LLMs), assuming that they 'know' certain things. In reality, LLMs e

TraceMark-LDM: Authenticatable Watermarking for Latent Diffusion Models via Binary-Guided Rearrangement

arXiv:2503.23332v2 Announce Type: replace Abstract: Image generation algorithms are increasingly integral to diverse aspects of human society, driven by their practical applications. However, insuffic

Variance-Tilted Diffusion Models for Diverse Sampling

arXiv:2606.22239v1 Announce Type: cross Abstract: Diffusion models are typically sampled independently, even when the downstream objective is to obtain a diverse set of candidates. We introduce a vari

VLA-FAIL: Efficient Task Failure Detection for Finetuned Vision-Language-Action Models

arXiv:2606.21386v1 Announce Type: cross Abstract: Vision-language-action models (VLAs) achieve state-of-the-art performance on many robotic manipulation tasks, yet they can still behave unpredictably

applicationsarxiv-cs-cv