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arXiv:2512.04069v2 Announce Type: replace Abstract: Vision Language Models (VLMs) demonstrate strong qualitative visual understanding, but struggle with metrically precise spatial reasoning required f

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Temporally-Aligned Evaluation for Audio-Driven Talking Head Generation

DGX agent

arXiv:2606.01031v1 Announce Type: cross Abstract: Audio-driven talking-head generation has advanced rapidly, yet existing evaluation protocols mainly rely on frame-wise metrics that assume strict temp

model-releasesarxiv-cs-ai

2 Jun 2026

TempRet: Temporal Enhancement and Two-Stage Reranking for CVPR 2026 EPIC-KITCHENS-100 Multi-Instance Retrieval Challenge

model-releasesarxiv-cs-cv

arXiv:2605.24470v2 Announce Type: replace Abstract: Video-text retrieval has witnessed remarkable progress driven by large-scale vision-language pretraining, yet most existing approaches inherit an im

The Role of Ambiguity in Error Prediction via Uncertainty Quantification

arXiv:2606.02093v1 Announce Type: cross Abstract: The task of Error Prediction, namely predicting whether a model output is correct, is commonly tackled with Uncertainty Quantification (UQ). However,

TimeSage-MT: A Multi-Turn Benchmark for Evaluating Agentic Time Series Reasoning

arXiv:2606.01498v1 Announce Type: cross Abstract: Time series data inform critical decisions across many real-world domains. While large language model (LLM) agents can analyze data through natural la

Towards Simple and Provable Parameter-Free Adaptive Gradient Methods

arXiv:2412.19444v2 Announce Type: replace Abstract: Optimization algorithms such as AdaGrad and Adam have significantly advanced the training of deep models by dynamically adjusting the learning rate

TriLens: Per-Layer Logit-Lens Entropy for White-Box Hallucination Detection

arXiv:2606.01033v1 Announce Type: new Abstract: When a language model hallucinates, the final answer is wrong, but the mistake is not necessarily invisible inside the model. Different internal pathway

URDF-Anything+: End-to-End Generation for Simulation-Ready Articulated Assets

model-releasesarxiv-cs-ro

arXiv:2603.14010v2 Announce Type: replace Abstract: Articulated objects are fundamental for robotics, simulation of physics, and interactive virtual environments. However, recovering them from visual

Value Flows

arXiv:2510.07650v4 Announce Type: replace-cross Abstract: While most reinforcement learning methods today flatten the distribution of future returns to a single scalar value, distributional RL methods

What Do LLMs Know About Alzheimer's Disease? Multi-loss Fine-Tuning and Probing for AD Detection

arXiv:2602.11177v2 Announce Type: replace-cross Abstract: Reliable early detection of Alzheimer's disease (AD) is challenging, particularly due to the limited availability of labeled data. While large

When Parallelism Pays Off: Cohesion-Aware Task Partitioning for Multi-Agent Coding

arXiv:2606.00953v1 Announce Type: new Abstract: Multi-agent Large Language Model (LLM) systems offer a way to decompose complex tasks, such as coding, through parallelization and context isolation. Ho

When Safe Skills Collide: Measuring Compositional Risk in Agent Skill Ecosystems

arXiv:2606.00448v1 Announce Type: cross Abstract: LLM agents increasingly rely on community-contributed skills that expand an agent's operational capability set. We study a core safety problem in agen

You Can Learn Tokenization End-to-End with Reinforcement Learning

arXiv:2602.13940v2 Announce Type: replace-cross Abstract: Tokenization is a hardcoded compression step which remains in the training pipeline of Large Language Models (LLMs), despite a general trend t

A Kinetic Energy Perspective of Flow Matching

arXiv:2602.07928v2 Announce Type: replace-cross Abstract: Flow-based generative models can be viewed through a physics lens: sampling transports a particle from noise to data by integrating a learned

A Padding Method for Enhanced Encoding of Inorganic Structures with Varying Chemical Compositions

arXiv:2605.30743v1 Announce Type: cross Abstract: Designing novel inorganic materials through generative models remains an important challenge for material science, driven by the complexity and divers

researcharxiv-cs-cl

A Visually Impaired Assistance Benchmark for VLM-as-a-Judge Evaluation

model-releasesarxiv-cs-cl

arXiv:2605.31351v1 Announce Type: new Abstract: AI-based Visually Impaired Assistance (VIA) remains challenging, largely due to the high cost of human evaluation. The VLM-as-a-Judge paradigm may offer

Applications

Algorithmic Recourse of In-Context Learning for Tabular Data

arXiv:2605.31272v1 Announce Type: new Abstract: As predictive models are increasingly deployed in high-stakes settings such as credit approval, there is a growing need for post-hoc methods that provid

applicationsarxiv-cs-lg

Balanced LoRA: Removing Parameter Invariance to Accelerate Convergence

arXiv:2605.31484v1 Announce Type: new Abstract: Low-Rank Adaptation (LoRA) is the most widely adopted method for fine-tuning large language models. Notably, LoRA is inherently overparameterized: multi

Benchmarking Uncertainty and its Disentanglement in multi-label Chest X-Ray Classification

arXiv:2508.04457v2 Announce Type: replace-cross Abstract: Reliable uncertainty quantification is crucial for trustworthy decision-making and the deployment of AI models in medical imaging. While prior

Beyond Additive Decompositions: Interpretability Through Separability

arXiv:2605.31200v1 Announce Type: new Abstract: Interpretable machine learning requires models that are accurate and structurally faithful to the data.Existing explainability methods rely heavily on a

researcharxiv-cs-lg

Beyond Classification: Dynamic Adapter Routing for Continual Multimodal Retrieval

arXiv:2605.31229v1 Announce Type: cross Abstract: While retrieval is a core function of vision-language models, continually updating these models for retrieval tasks remains critically underexplored.

Can Subgraph Explanations Be Weaponized to Steal Graph Neural Networks?

arXiv:2605.30470v1 Announce Type: new Abstract: Graph Machine Learning as a Service (GMLaaS) platforms increasingly implement explainability interfaces to meet regulatory transparency requirements. Ho

Applications

Cognitive Fatigue in Autoregressive Transformers: Formalization and Measurement

arXiv:2605.30981v1 Announce Type: new Abstract: Autoregressive language models frequently degrade during long-horizon generation, producing repetitive text, losing instruction adherence, and exhibitin

applicationsarxiv-cs-cl

Tutorials

Constrained Flow Optimization via Sequential Fine Tuning for Molecular Design

arXiv:2605.30610v1 Announce Type: new Abstract: Adapting generative foundation models, in particular diffusion and flow models, to optimize given reward functions (e.g., binding affinity) while satisf

tutorialsarxiv-cs-lg

Counterfactual Trace Auditing of LLM Agent Skills

arXiv:2605.11946v2 Announce Type: replace Abstract: Large Language Model agents are increasingly augmented with agent skills. Current evaluation methods for skills remain limited. Most deployed benchm

DecMem: Towards Minute-Long Consistent World Generation with Decoupled Memory

arXiv:2605.31336v1 Announce Type: new Abstract: Recent advances in video generative models have promoted rapid progress in controllable world models. However, maintaining fine-grained spatio-temporal

researcharxiv-cs-cv

Tutorials

Destruction is a General Strategy to Learn Generation; Diffusion's Strength is to Take it Seriously; Exploration is the Future

arXiv:2605.30553v1 Announce Type: new Abstract: I present diffusion models as part of a family of machine learning techniques that withhold information from a model's input and train it to guess the w

tutorialsarxiv-cs-lg

Diving into Kronecker Adapters: Component Design Matters

arXiv:2602.01267v2 Announce Type: replace Abstract: Kronecker adapters have emerged as a promising approach for fine-tuning large-scale models, enabling high-rank updates through tunable component str

Tutorials

dMoE: dLLMs with Learnable Block Experts

arXiv:2605.30876v1 Announce Type: new Abstract: Diffusion Large Language Models (dLLMs) have recently emerged as a promising alternative to autoregressive models, offering competitive performance whil

tutorialsarxiv-cs-cl

Eywa: Provenance-Grounded Long-Term Memory for AI Agents

model-releasesarxiv-cs-cl

arXiv:2605.30771v1 Announce Type: new Abstract: AI agents that persist across sessions need memory they can retrieve, audit, update, and erase. Existing memory systems often collapse source evidence,

Safety

Feat2Go: Visual Feature-Grounded Value Estimation for Embodied Reinforcement Learning

arXiv:2605.30795v1 Announce Type: new Abstract: Reinforcement learning is a promising approach for improving the capabilities of vision-language-action (VLA) models while avoiding the heavy data requi

safetyarxiv-cs-ro

FOCUS: Forcing In-Context Object Localization through Visual Support Constraints and Policy Optimization

arXiv:2605.31145v1 Announce Type: cross Abstract: In-context localization (ICL) seeks to localize a target object specified by a small set of support examples in a query image, operating on the fly wi

Gait2Hip-60: A Unified Deep Learning Benchmark for Predicting Hip Muscle Forces and Joint Moments from Multi-Cadence Gait Kinematics

arXiv:2605.30374v1 Announce Type: new Abstract: Estimating hip muscle forces and joint moments during gait typically relies on musculoskeletal simulation, which is informative but time-consuming and d

GradMem: Learning to Write Context into Memory with Test-Time Gradient Descent

arXiv:2603.13875v2 Announce Type: replace Abstract: Many large language model applications require conditioning on long contexts. Transformers typically support this by storing a large per-layer KV-ca

researcharxiv-cs-cl

GraphARC: A Comprehensive Benchmark for Graph-Based Abstract Reasoning

arXiv:2605.31031v1 Announce Type: new Abstract: Relational reasoning lies at the heart of intelligence, but existing benchmarks are typically confined to formats such as grids or text. We introduce Gr

GUI-C^2: Coarse-to-Fine GUI Grounding via Difficulty-Aware Reinforcement Learning

model-releasesarxiv-cs-cv

arXiv:2605.30884v1 Announce Type: new Abstract: Existing agentic reinforcement learning methods for GUI grounding have limitations at two levels. At the data level, current approaches typically treat

HypoSpace: A Diagnostic Benchmark for Set-Valued Hypothesis Generation under Underdetermination and Sublinear Coverage Bounds

model-releasesarxiv-cs-cl

arXiv:2510.15614v3 Announce Type: replace Abstract: Many scientific problems are underdetermined: multiple distinct hypotheses are equally consistent with the same observations. In such settings, effe

Agents

IDOL: Inverse-Dynamics-Guided Future Prediction for End-to-End Autonomous Driving

arXiv:2605.31476v1 Announce Type: new Abstract: End-to-end autonomous driving has emerged as a compelling paradigm for learning planning directly from sensor observations, while recent world-model-bas

agentsarxiv-cs-ro

idSCD: Identifying Training Datasets through Semantic Correlation Descriptors

arXiv:2605.30462v1 Announce Type: cross Abstract: Can a dataset be recognized from the spurious correlations it induces during training? We argue that datasets leave dataset-specific traces in a model

Improving Selective Classification with Pairwise Queries for Binary Classification

arXiv:2605.30615v1 Announce Type: new Abstract: In selective classification, a model predicts the labels of data samples where it is confident, and abstains from predicting labels for samples on which

researcharxiv-cs-lg

Inconsistency-Aware Minimization: Improving Generalization with Unlabeled Data

arXiv:2605.31324v1 Announce Type: cross Abstract: Estimating the generalization gap and developing optimization methods that improve generalization are crucial for deep learning models, for both theor

LLM Bias Evaluation: Gender, Racial, and Age Disparities in Occupational and Crime Scenarios

arXiv:2409.14583v4 Announce Type: replace Abstract: LLM bias evaluation is critical as large language models (LLMs) increasingly influence high-stakes decisions. This paper provides a comprehensive as

Local Ai

Measuring, Localizing, and Ablating Alignment Signatures in LLMs

arXiv:2605.30526v1 Announce Type: cross Abstract: Aligned language models often exhibit a recognizable AI-like style, yet its connection to post-training and internal representations remains poorly un

local-aiarxiv-cs-cl

Minibatch Optimal Transport and Perplexity Bound Estimation in Discrete Flow Matching

arXiv:2411.00759v5 Announce Type: replace Abstract: Discrete flow matching, a recent framework for modeling categorical data, has shown competitive performance with autoregressive models. However, unl

researcharxiv-cs-lg

MLIPilot: LLM-Driven Auto-Research for Machine-Learned Interatomic Potentials

arXiv:2605.30889v1 Announce Type: cross Abstract: Constructing production-quality machine-learned interatomic potentials (MLIPs) requires balancing accuracy, dynamical stability, and computational thr

PEEK: Picking Essential frames via Efficient Knowledge distillation

arXiv:2605.31029v1 Announce Type: new Abstract: Video-language models can process only a limited number of frames, making frame selection a key bottleneck for efficient video captioning. Most captioni

researcharxiv-cs-cv

Plain Transformers are Surprisingly Powerful Link Predictors

arXiv:2602.01553v2 Announce Type: replace-cross Abstract: Link prediction is a core challenge in graph machine learning, demanding models that capture rich and complex topological dependencies. While

Safety

Preference-Aware Rubric Learning for Personalized Evaluation

arXiv:2605.31545v1 Announce Type: new Abstract: As Large Language Models (LLMs) evolve from general-purpose assistants to user-centric agents, personalization has become central to aligning model beha

safetyarxiv-cs-cl