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What Transfers from Text to Vision? Capability Scaling Laws and Transfer Dynamics for VLMs

arXiv:2608.00013v1 Announce Type: new Abstract: Choosing the right large language model (LLM) backbone is the most consequential decision when building a vision-language model (VLM), yet it remains fu

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Agents

When Collaboration Becomes a Trigger: Collective Evidence-Threshold Backdoors in Multi-Agent Systems

DGX agent

arXiv:2608.01085v1 Announce Type: cross Abstract: LLM-based multi-agent systems (MAS) extend LLM capabilities through iterative communication and shared contexts. However, this collaboration introduce

agentsarxiv-cs-lg

4 Aug 2026

Tutorials

When Differential Privacy Meets Wireless Federated Learning: An Improved Analysis for Privacy and Convergence

arXiv:2603.19040v2 Announce Type: replace Abstract: Differentially private wireless federated learning (DPWFL) is a promising framework for protecting sensitive user data. However, foundational questi

tutorialsarxiv-cs-lg

When Do Surrogate Updates Improve Decisions? A Local Theory of Trajectory-Wise Transfer

arXiv:2608.01130v1 Announce Type: new Abstract: A broad range of models face the mismatch where they are updated through trajectory losses but are evaluated by downstream task reward. Here, a trajecto

When Extreme Darkness Meets Motion Blur: MeanFlow for Unified RAW Restoration

arXiv:2608.01720v1 Announce Type: new Abstract: Extremely low-light RAW enhancement aims to recover severely attenuated sensor signals, yet existing methods often focus on illumination and noise while

When LLM Essays Outscore Student Essays: What a Korean Writing Rubric Rewards and Where Readers Disagree

arXiv:2601.19913v4 Announce Type: replace Abstract: LLMs now help students plan, draft, and revise essays. Educational assessment therefore faces a basic question: how should student and LLM writing b

When May a Model Replace the Experiment? Audits, Licenses, and the Price of Trust in Surrogate-Driven Design

arXiv:2608.01378v1 Announce Type: new Abstract: Design campaigns in chemistry, materials science, and machine learning share a bottleneck: determining how good a candidate truly is requires an expensi

safetyarxiv-cs-lg

When Measurement Conventions Masquerade as Calibration Gains in Cardiac Digital Twins

arXiv:2608.01602v1 Announce Type: new Abstract: Cardiac digital twins convert clinical images into physiological measurements through observation operators, yet calibration studies often assume a fixe

Agents

When Only the Final Text Survives: Implicit Execution Tracing for Multi-Agent Auditing

arXiv:2603.17445v5 Announce Type: replace-cross Abstract: When a multi-agent system produces an incorrect or harmful answer, who is accountable if execution logs and agent identifiers are unavailable?

agentsarxiv-cs-cl

Agents

When Prompts Control Robots: Prompt Injection Attacks in Multi-Agent Robotic Systems

arXiv:2608.00747v1 Announce Type: new Abstract: Large language models are increasingly integrated into autonomous robotic systems for task planning and control, but this integration exposes them to pr

agentsarxiv-cs-ro

When Replanning Becomes the Bottleneck: Budgeted Replanning for Embodied Agents

arXiv:2608.01428v1 Announce Type: cross Abstract: Embodied agents replan frequently to recover from execution drift, partial observability, and coordination hazards, but each LLM-based replanning call

When Retrieval Helps and Distracts: Evaluating Evidence-Generating LLMs for Biomedical Claim Verification

arXiv:2608.01409v1 Announce Type: new Abstract: Biomedical fact-checking systems must do more than predict whether a claim is supported, contradicted, or unaddressed: they should also produce evidence

When Words Divide: Diachronic Ideological Polarization in Political Discourse on Social Media

arXiv:2608.01176v1 Announce Type: new Abstract: Political polarization has become a defining feature of online discourse, yet its long-term evolution remains poorly understood. We present a longitudin

Where did the ambiguity go? Examining how multimodal models interpret polysemous words

arXiv:2608.00410v1 Announce Type: cross Abstract: Human language is highly polysemous. Many common words (e.g., 'bank' or 'palm') carry several distinct meanings that shape what humans communicate and

Local Ai

Where Does Generative Difficulty Reside? An Empirical Study of Target Representations

arXiv:2608.00626v1 Announce Type: new Abstract: The target representation defines the distribution an image generator must learn, yet it is often treated as an interchangeable interface. This assumpti

local-aiarxiv-cs-cv

Which Modality Decides? Counterfactual Modality Attribution for Multimodal LLMs

arXiv:2608.00076v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) increasingly support high-stakes decision making by combining complementary information from images and text. W

safetyarxiv-cs-cv

Who Belongs in the Eval Set? A Capability-Taxonomy-Driven Pipeline for Curating Regression Eval Sets in Agent-Extensibility Platforms

arXiv:2608.01004v1 Announce Type: new Abstract: Platform teams hosting agent-extensibility surfaces face a regression-economics paradox: every onboarding customer ships an evaluation set tuned to thei

Who Is Responsible? Self-Adaptation Under Multiple Concurrent Uncertainties With Unknown Sources in Complex ROS-Based Systems

arXiv:2504.20477v4 Announce Type: replace Abstract: Robotic systems increasingly operate in dynamic, unpredictable environments, where tightly coupled sensors and software modules increase the probabi

researcharxiv-cs-ro

Who Should Be Generated? Justifying Demographic Targets in Open-Ended Generation

arXiv:2608.02551v1 Announce Type: cross Abstract: Fairness evaluation concerns not only what a model produces, but also what its outputs ought to be compared against. When a model generates 'a CEO in

safetyarxiv-cs-cl

Why Does Action Chunking Improve Behavioral Cloning Performance in Robotic Control?

arXiv:2608.02547v1 Announce Type: new Abstract: Action chunking---predicting and executing multiple actions instead of a single action---has proven to be a critical component for learning effective ro

safetyarxiv-cs-ro

Why Formal Monitors Fail: Attack Distribution Entropy as a Coverage Bound for LTL-Based LLM Agent Safety

arXiv:2608.01388v1 Announce Type: cross Abstract: Runtime safety monitors based on Linear Temporal Logic (LTL) and finite automata (FSA) are increasingly deployed to intercept unsafe tool-call sequenc

Why Large Language Models Fail at Tabular Prediction

arXiv:2608.02412v1 Announce Type: new Abstract: Large language models (LLMs) have become the default tool for a remarkable range of tasks, yet they have had conspicuously little success at one of the

Why LLMs Give In: Conversational Factors and Reasoning Behind Medical Sycophancy

arXiv:2608.01017v1 Announce Type: new Abstract: A language model that abandons a correct medical answer under user pushback is more dangerous than one that was simply wrong, because it lends the credi

WiFuse: An Attention Mechanism for Human Activity Recognition using Fused CSI Amplitude and Delay-Doppler Channel Features

arXiv:2608.00642v1 Announce Type: new Abstract: Recently, Wi-Fi sensing has played a significant role in Human Activity Recognition (HAR), as it enables the detection of various activities using only

World Action Models in Real Time: An Empirical Study of Smooth Execution via Asynchronous Deployment

arXiv:2608.01880v1 Announce Type: new Abstract: World Action Models generate fixed-horizon action chunks through iterative denoising, creating substantial inference latency that can cause pauses, stal

safetyarxiv-cs-ro

WorldDynCache: Risk-Controlled Latent Dynamics Approximation for Diffusion World Model

arXiv:2608.01845v1 Announce Type: cross Abstract: Diffusion world models generate high-quality futures, but re- peated transformer evaluations make inference prohibitively slow. Existing caches reuse

WorldExam: Benchmarking World Models from Apparent Appearance to Inherent Reactivity

arXiv:2608.02603v1 Announce Type: new Abstract: Controllable video generation models are increasingly being developed as world models. Accordingly, evaluating them in this role extends beyond the appa

WorldMirror: Universal 3D World Reconstruction with Any-Prior Prompting

arXiv:2510.10726v2 Announce Type: replace Abstract: We present WorldMirror, a unified feed-forward model for comprehensive 3D geometric prediction tasks. Unlike existing methods constrained to image-o

Writing-System-Level Tokenizer Adaptation for Byte-Level BPE

arXiv:2608.00582v1 Announce Type: new Abstract: Pretrained byte-level BPE tokenizers can segment underrepresented languages inefficiently. Replacing a tokenizer changes the meaning of nearly every tok

XL-DocBench: Benchmarking Evidence-Grounded Extra-Long Document Understanding

arXiv:2608.00036v1 Announce Type: new Abstract: Real-world document tasks often ask professionals to answer questions from annual reports, regulations, clinical guidelines, and technical manuals that

xMICD: Explainable Representation of Multiple ICD Codes

arXiv:2608.00935v1 Announce Type: new Abstract: Electronic Health Records (EHRs) are widely used for clinical risk prediction using machine learning. International Classification of Diseases (ICD) cod

XSPA: Crafting Imperceptible X-Shaped Sparse Adversarial Perturbations for Transferable Attacks on VLMs

arXiv:2603.28568v2 Announce Type: replace Abstract: Vision-language models (VLMs) share visual-textual representations across zero-shot classification, image captioning, and visual question answering

Z-PEFT: Zero-shot Backdoor Detection in Parameter-Efficient Fine-Tuning via Canonical Spectral Signatures

arXiv:2608.02271v1 Announce Type: new Abstract: Parameter-Efficient Fine-tuned (PEFT) models are frequently downloaded from open repositories by practitioners. This widespread practice creates a signi

Zero-Cost Virtual RNA: Approximating Immunotherapy Signatures via Cross-Modal WSI Retrieval

arXiv:2608.00544v1 Announce Type: new Abstract: Identifying the ``Inflamed'' immunophenotype in Gastric Adenocarcinoma predicts immunotherapy response but requires an expensive 10-gene RNA signature.

1-Lipschitz Neural Networks on Hadamard Manifolds

arXiv:2607.19335v2 Announce Type: replace-cross Abstract: Controlling the Lipschitz constant of a neural network is a standard way to promote robustness and stability. Most existing constraining strat

A Benchmark for Strategic Auditee Gaming Under Continuous Compliance Monitoring

arXiv:2605.06340v2 Announce Type: replace-cross Abstract: Continuous post-deployment compliance audits, mandated by emerging regulations such as the EU AI Act and Digital Services Act, create a class

Local Ai

A Biometric Sensor Network to Enable Real-Time Measurement of Individual Student Engagement in STEM Lecture Environments

arXiv:2607.28944v1 Announce Type: cross Abstract: Student engagement (SE) is a critical predictor of academic performance and retention in STEM education, yet existing measurement approaches are often

local-aiarxiv-cs-cv

A Frozen Pixel-Space Diffusion Model Can Guide Itself with Its Own Samples

arXiv:2607.29122v1 Announce Type: new Abstract: Pixel-space diffusion models aim to learn an end-to-end generator directly over raw pixels. This is challenging because a single model must capture both

A Generalized-Bayes Perspective on Counterfactual Explanations: Posterior-Based Decision-Making and Evaluation

arXiv:2607.29077v1 Announce Type: new Abstract: Counterfactual explanations (CEs) enhance the interpretability of machine learning models by identifying the smallest change to an input required to obt

researcharxiv-cs-ai

A Hamiltonian driven Geometric Construction of Neural Networks via the Lognormal family, Application to Financial Fraud Detection and to Network Security

arXiv:2509.25778v3 Announce Type: replace Abstract: We presents a method for constructing neural networks intrinsically on statistical manifolds via the lognormal distribution. We demonstrate this app

A Human-Centered Validation of the Explainability-Performance Coefficient

arXiv:2607.29614v1 Announce Type: cross Abstract: The rapid adoption of deep learning models in high-risk domains has intensified the need for trustworthy Explainable Artificial Intelligence (XAI). Ho

researcharxiv-cs-ai

A Machine Learning Surrogate for Component Criticality Ranking in Interdependent Power-Communication Networks

arXiv:2607.08918v2 Announce Type: replace Abstract: Cyber-physical power systems are vulnerable to cascading failures caused by interdependencies between power and communication infrastructures. Becau

Local Ai

A Model-Driven Approach for Developing Families of Reinforcement Learning Environments

arXiv:2606.20324v2 Announce Type: replace-cross Abstract: Virtual training environments are software-intensive systems in which reinforcement learning (RL) agents learn, adapt, and demonstrate meaning

local-aiarxiv-cs-lg

A Neurosymbolic Approach for Explainable Early Diagnosis of Alzheimer's Disease

arXiv:2607.29530v1 Announce Type: new Abstract: Identifying reliable Alzheimer's disease (AD) markers typically requires manual, labor-intensive transcription and expert analysis, limiting its scale.

A Novel XAI-Enhanced Quantum Adversarial Networks for Velocity Dispersion Modeling in MaNGA Galaxies

arXiv:2510.24598v2 Announce Type: replace Abstract: Current quantum machine learning approaches often face challenges balancing predictive accuracy, robustness, and interpretability. To address this,

A robust association between LLM use and scientific productivity: Assessing stopping-time selection

model-releasesarxiv-cs-ai

arXiv:2607.28968v1 Announce Type: cross Abstract: Renault, Bergeaud, and Bosquet (hereafter RBB) argue that dating LLM adoption as the first month in which an author's abstract is flagged induces a st

A Unified Benchmark of Deep Learning Models for Multi-task 3D Brain Tumor Segmentation from Magnetic Resonance Imaging

model-releasesarxiv-cs-ai

arXiv:2607.28858v1 Announce Type: cross Abstract: Automatic brain tumor segmentation from magnetic resonance imaging (MRI) has become a fundamental task in computer-assisted diagnosis, treatment plann

Tutorials

A user's guide to PINNs in geometric analysis: lessons from the asymptotic Plateau problem

arXiv:2607.28733v1 Announce Type: cross Abstract: This proceedings contribution elaborates on the findings of arXiv:2605.26234v2: a joint work with Marco Usula, where we introduced a machine learning

tutorialsarxiv-cs-ai