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Human face perception reflects inverse-generative and naturalistic discriminative objectives

arXiv:2605.12619v1 Announce Type: cross Abstract: The perceptual representations supporting our ability to recognize faces remain a computational mystery. Deep neural networks offer mechanistic hypoth

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Humanwashing -- It Should Leave You Feeling Dirty

DGX agent

arXiv:2605.13723v1 Announce Type: cross Abstract: The phrase 'human in the loop' is increasingly used to imply a sense of safety in relation to AI decision systems. It shouldn't. There are contexts wh

safetyarxiv-cs-ai

14 May 2026

Agents

IdeaForge: A Knowledge Graph-Grounded Multi-Agent Framework for Cross-Methodology Innovation Analysis and Patent Claim Generation

arXiv:2605.13311v1 Announce Type: new Abstract: Current AI-assisted innovation systems typically apply a single ideation methodology (such as TRIZ or Design Thinking) using sequential prompt-based wor

agentsarxiv-cs-ai

Identification of Non-Transversal Bifurcations of Linkages

arXiv:2605.13094v1 Announce Type: new Abstract: The local analysis is an established approach to the study of singularities and mobility of linkages. Key result of such analyses is a local picture of

researcharxiv-cs-ro

Agents

Identifying AI Web Scrapers Using Canary Tokens

arXiv:2605.13706v1 Announce Type: cross Abstract: From pre-training to query-time augmentation, web-scraped data helps to improve the quality and contextual relevancy of content generated by large lan

agentsarxiv-cs-ai

Identifying the nonlinear string dynamics with port-Hamiltonian neural networks

arXiv:2605.12785v1 Announce Type: new Abstract: Hybrid machine learning combines physical knowledge with data-driven models to enhance interpretability and performance. In this context, Port-Hamiltoni

IGT-OMD: Implicit Gradient Transport for Decision-Focused Learning under Delayed Feedback

arXiv:2605.12693v1 Announce Type: new Abstract: Decision-focused learning trains predictive models end-to-end against downstream decision loss, but online settings suffer delayed feedback: outcomes ma

researcharxiv-cs-lg

ImageAttributionBench: How Far Are We from Generalizable Attribution?

model-releasesarxiv-cs-cv

arXiv:2605.12967v1 Announce Type: new Abstract: The rapid advancement of generative AI has enabled the creation of highly realistic and diverse synthetic images, posing critical challenges for image p

Img2CADSeq: Image-to-CAD Generation via Sequence-Based Diffusion

arXiv:2605.13293v1 Announce Type: new Abstract: Boundary Representation (BRep) is the standard format for Computer-Aided Design (CAD), yet reconstructing high-quality BReps from single-view images rem

Implicit Behavioral Decoding from Next-Step Spike Forecasts at Population Scale

arXiv:2605.12999v1 Announce Type: cross Abstract: Closed-loop brain-computer interfaces often require both a forecast of upcoming neural population activity and a readout of the animal's behavioral st

Imposing Boundary Conditions on Neural Operators via Learned Function Extensions

arXiv:2602.04923v2 Announce Type: replace Abstract: Neural operators have emerged as powerful surrogates for the solution of partial differential equations (PDEs), yet their ability to handle general,

Improved monocular depth prediction using distance transform over pre-semantic contours with self-supervised neural networks

arXiv:2605.08320v1 Announce Type: cross Abstract: Monocular depth estimation (MDE) with self-supervised training approaches struggles in low-texture areas, where photometric losses may lead to ambiguo

Improving Classifier-Free Guidance of Flow Matching via Manifold Projection

arXiv:2601.21892v2 Announce Type: replace-cross Abstract: Classifier-free guidance (CFG) is a widely used technique for controllable generation in diffusion and flow-based models. Despite its empirica

Improving Code Translation with Syntax-Guided and Semantic-aware Preference Optimization

arXiv:2605.13229v1 Announce Type: new Abstract: LLMs have shown immense potential for code translation, yet they often struggle to ensure both syntactic correctness and semantic consistency. While pre

Improving Diffusion Posterior Samplers with Lagged Temporal Corrections for Image Restoration

arXiv:2605.12573v1 Announce Type: cross Abstract: Diffusion-based posterior sampling (PS) is a leading framework for imaging inverse problems, combining learned priors with measurement constraints. Ye

Improving Reproducibility in Evaluation through Multi-Level Annotator Modeling

arXiv:2605.13801v1 Announce Type: cross Abstract: As generative AI models such as large language models (LLMs) become more pervasive, ensuring the safety, robustness, and overall trustworthiness of th

In-Situ Behavioral Evaluation for LLM Fairness, Not Standardized-Test Scores

arXiv:2605.12530v1 Announce Type: cross Abstract: LLM fairness should be evaluated through in-situ conversational behavior rather than standardized-test Q&A benchmarks. We show that the standardized-t

IndicMedDialog: A Parallel Multi-Turn Medical Dialogue Dataset for Accessible Healthcare in Indic Languages

arXiv:2605.13292v1 Announce Type: cross Abstract: Most existing medical dialogue systems operate in a single-turn question--answering paradigm or rely on template-based datasets, limiting conversation

Inducing Overthink: Hierarchical Genetic Algorithm-based DoS Attack on Black-Box Large Language Reasoning Models

arXiv:2605.13338v1 Announce Type: cross Abstract: Large Reasoning Models (LRMs) are increasingly integrated into systems requiring reliable multi-step inference, yet this growing dependence exposes ne

Tutorials

Inference-Time Dynamic Modality Selection for Incomplete Multimodal Classification

arXiv:2601.22853v3 Announce Type: replace Abstract: Multimodal deep learning (MDL) has achieved remarkable success across various domains, yet its practical deployment is often hindered by incomplete

tutorialsarxiv-cs-cv

Inference-Time Machine Unlearning via Gated Activation Redirection

arXiv:2605.12765v1 Announce Type: new Abstract: Large Language Models memorize vast amounts of training data, raising concerns regarding privacy, copyright infringement, and safety. Machine unlearning

Information as Maximum-Caliber Deviation: A bridge between Integrated Information Theory and the Free Energy Principle

arXiv:2605.12536v1 Announce Type: cross Abstract: The Free Energy Principle (FEP) is a leading framework for mathematically modeling self-organization and learning, while Integrated Information Theory

Tutorials

Inline Critic Steers Image Editing

arXiv:2605.12724v1 Announce Type: cross Abstract: Instruction-based image editing exhibits heterogeneous difficulty not only across cases but also across regions of an image, motivating refinement app

tutorialsarxiv-cs-ai

Integration of an Agent Model into an Open Simulation Architecture for Scenario-Based Testing of Automated Vehicles

arXiv:2605.13539v1 Announce Type: new Abstract: Simulative and scenario-based testing are crucial methods in the safety assurance for automated driving systems. To ensure that simulation results are r

safetyarxiv-cs-ro

Interpretable Machine Learning for Antepartum Prediction of Pregnancy-Associated Thrombotic Microangiopathy Using Routine Longitudinal Laboratory Data

arXiv:2605.13786v1 Announce Type: new Abstract: Background: Pregnancy-associated thrombotic microangiopathy (P-TMA) is rare but life-threatening. Early risk prediction before overt clinical presentati

researcharxiv-cs-lg

interwhen: A Generalizable Framework for Steering Reasoning Models with Test-time Verification

arXiv:2602.11202v3 Announce Type: replace-cross Abstract: Reasoning models produce long traces of intermediate decisions and tool calls, making test-time verification important for ensuring correctnes

Is a Picture Worth a Thousand Words? Adaptive Multimodal Fact-Checking with Visual Evidence Necessity

arXiv:2604.04692v2 Announce Type: replace-cross Abstract: Automated fact-checking is a crucial task that supports a responsible information ecosystem. While recent research has progressed from text-on

Is Video Anomaly Detection Misframed? Evidence from LLM-Based and Multi-Scene Models

arXiv:2605.12725v1 Announce Type: new Abstract: Recent video anomaly detection research has expanded rapidly with an emphasis on general models of normality intended to work across many different scen

safetyarxiv-cs-cv

ISOMORPH: A Supply Chain Digital Twin for Simulation, Dataset Generation, and Forecasting Benchmarks

arXiv:2605.12768v1 Announce Type: cross Abstract: Open time-series forecasting (TSF) benchmarks cover retail, energy, weather, and traffic, but supply-chain logistics remains underserved. We introduce

'It became a self-fulfilling prophecy': How Lived Experiences are Entangled with AI Predictions in Menstrual Cycle Tracking Apps

arXiv:2605.13261v1 Announce Type: cross Abstract: In menstrual cycle tracking apps (MCTAs), AI-based predictions and insights have become increasingly popular. These features enable users to receive p

Applications

It's not the Language Model, it's the Tool: Deterministic Mediation for Scientific Workflows

arXiv:2605.13245v1 Announce Type: new Abstract: Language models can produce convincing scientific analyses, but repeated generations on the same data do not guarantee the same result. A researcher may

applicationsarxiv-cs-ai

IV-ICL: Bounding Causal Effects with Instrumental Variables via In-Context Learning

arXiv:2605.12924v1 Announce Type: new Abstract: The instrumental-variables (IV) setting is standard for partial identification of causal effects when unobserved confounding makes point identification

researcharxiv-cs-lg

JANUS: Anatomy-Conditioned Gating for Robust CT Triage Under Distribution Shift

arXiv:2605.13813v1 Announce Type: new Abstract: Automated CT triage requires models that are simultaneously accurate across diverse pathologies and reliable under institutional shift. While Vision Tra

JEDI: Joint Embedding Diffusion World Model for Online Model-Based Reinforcement Learning

arXiv:2605.13013v1 Announce Type: new Abstract: Diffusion world models have recently become competitive for online model-based reinforcement learning, but current approaches expose a tension: pixel di

Just Ask for a Table: A Thirty-Token User Prompt Defeats Sponsored Recommendations in Twelve LLMs

arXiv:2605.12772v1 Announce Type: new Abstract: Wu et al. (2026) showed that most frontier large language models (LLMs) recommend a sponsored, roughly twice-as-expensive flight when their system promp

KamonBench: A Grammar-Based Dataset for Evaluating Compositional Factor Recovery in Vision-Language Models

model-releasesarxiv-cs-cv

arXiv:2605.13322v1 Announce Type: new Abstract: Kamon (family crests) are an important part of Japanese culture and a natural test case for compositional visual recognition: each crest combines a smal

KAST-BAR: Knowledge-Anchored Semantically-Dynamic Topology Brain Autoregressive Modeling for Universal Neural Interpretation

arXiv:2605.13133v1 Announce Type: new Abstract: While EEG foundation models have shown significant potential in universal neural decoding across tasks, their advancement remains constrained by the ina

Kernel-based guarantees for nonlinear parametric models in Bayesian optimization

arXiv:2605.13160v1 Announce Type: cross Abstract: Modern Bayesian optimization and adaptive sampling methods increasingly rely on nonlinear parametric models, yet theoretical guarantees for such model

Kernel Embeddings and the Separation of Measure Phenomenon

arXiv:2505.04613v4 Announce Type: replace-cross Abstract: We prove that kernel covariance embeddings lead to information-theoretically perfect separation of distinct continuous probability distributio

Kiwi-Edit: Versatile Video Editing via Instruction and Reference Guidance

arXiv:2603.02175v4 Announce Type: replace-cross Abstract: Instruction-based video editing has witnessed rapid progress, yet current methods often struggle with precise visual control, as natural langu

Applications

KVServe: Service-Aware KV Cache Compression for Communication-Efficient Disaggregated LLM Serving

arXiv:2605.13734v1 Announce Type: cross Abstract: LLMs are widely adopted in production, pushing inference systems to their limits. Disaggregated LLM serving (e.g., PD separation and KV state disaggre

applicationsarxiv-cs-ai

Agents

Language-Based Agent Control

arXiv:2605.12863v1 Announce Type: cross Abstract: This paper introduces language-based agent control (LBAC), a new programming model for agentic applications that brings techniques from programming la

agentsarxiv-cs-ai

Language Model Goal Selection Differs from Humans' in a Self-Directed Learning Task

arXiv:2603.03295v2 Announce Type: replace-cross Abstract: Whether in agentic workflows, social studies, or chat settings, large language models (LLMs) are increasingly being asked to replace humans in

Applications

Language Model Networks: Supervision-Efficient Learning through Dense Communication

arXiv:2505.12741v2 Announce Type: replace Abstract: Language models are increasingly used not only as standalone predictors but also as components in larger inference systems, from test-time reasoning

applicationsarxiv-cs-ai

Large Language Models for Agentic NetOps and AIOps: Architectures, Evaluation, and Safety

arXiv:2605.12729v1 Announce Type: cross Abstract: Large language models are increasingly being used to support network operations (NetOps) and artificial intelligence for IT operations (AIOps), includ

Large Language Models Lack Temporal Awareness of Medical Knowledge

arXiv:2605.13045v1 Announce Type: new Abstract: The existing methods for evaluating the medical knowledge of Large Language Models (LLMs) are largely based on atemporal examination-style benchmarks, w

Latent-Augmented Discrete Diffusion Models

arXiv:2510.18114v3 Announce Type: replace-cross Abstract: Discrete diffusion models have emerged as a powerful class of models and a promising route to fast language generation, but practical implemen

Layer-wise Representation Dynamics: An Empirical Investigation Across Embedders and Base LLMs

arXiv:2605.12714v1 Announce Type: new Abstract: Hidden states change substantially across the layers of modern language models, but most layer-wise analyses focus on one aspect of that change. We prop