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The Entropic Signature of Class Speciation in Diffusion Models

arXiv:2602.09651v2 Announce Type: replace-cross Abstract: Diffusion models do not recover semantic structure uniformly over time. Instead, samples transition from semantic ambiguity to class commitmen

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The Geometry of Grokking: Norm Minimization on the Zero-Loss Manifold

DGX agent

arXiv:2511.01938v3 Announce Type: replace-cross Abstract: Grokking is a puzzling phenomenon in neural networks where full generalization occurs only after a substantial delay following the complete me

researcharxiv-cs-ai

2 Jun 2026

The Lie We Tell: Correcting the Euclidean Fallacy in Vision Language Action Policies via Score Matching on Tangent Space

arXiv:2606.01847v1 Announce Type: cross Abstract: Diffusion-based Vision-Language-Action policies achieve remarkable success in robotic manipulation, yet commit a fundamental geometric error we term t

The New Social Image: How AI Competency and AI Proactivity Influence Self- and Peer-Perceptions in the Workplace

arXiv:2606.00182v1 Announce Type: cross Abstract: Human-AI collaboration is considered the most promising way to incorporate AI in the workplace. What remains unexplored are the experiential consequen

The Representation-Rationalizability Tradeoff in Reward Learning

arXiv:2606.00291v1 Announce Type: cross Abstract: In RLHF, each training example contains a prompt x and two candidate responses y,y', and annotators provide pairwise preferences between these respons

The Right Inference Strategy Is All You Need: Nearly Training-Free Domain-Wise Inference for EgoCross Challenge

arXiv:2606.00829v1 Announce Type: new Abstract: EgoCross evaluates multimodal large language models on egocentric video question answering under substantial domain shift, where test videos come from s

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,

Theoretical Analysis of Engression and Reverse Markov Engression

arXiv:2606.01002v1 Announce Type: cross Abstract: Engression is a recently proposed and effective framework for conditional distribution learning. Its multi-step Reverse Markov extension further impro

Thinking Economically: A Hierarchical Framework for Adaptive-Complexity Reasoning in LLMs

arXiv:2606.01168v1 Announce Type: new Abstract: Chain-of-Thought (CoT) has significantly enhanced LLM reasoning, yet often incurs substantial computational overhead due to 'overthinking': generating e

ThinkSwitch: Context Distillation with LoRA and Weight Interpolation for Specific-Purpose Reasoning Tasks

arXiv:2606.01080v1 Announce Type: cross Abstract: Large language models often improve on difficult tasks by spending inference-time compute on a reasoning trace before producing the final answer. That

TimeBlocks: Foundational and Continual Time-Series Blockbase -- Extended Version

arXiv:2606.02142v1 Announce Type: new Abstract: The ongoing digitization has led to a proliferation of time-series data streams that monitor a variety of processes, from which valuable insights may be

TN-SHAP-G: Graph-Structured Tensor Network Surrogates for Shapley Values and Interactions

arXiv:2606.01540v1 Announce Type: cross Abstract: Shapley values are a widely used tool for attributing importance and interactions among input variables in black-box models, but their computation inv

Topological texture analysis of microscopy images of dynamic casein gelation and its relation to rheological properties

arXiv:2606.02048v1 Announce Type: new Abstract: We propose a novel computational toolbox that integrates Topological Data Analysis (TDA), Differential Box Counting (DBC), Multifractal Partition (MFP),

Topology-Aware State Abstraction with Tangle Cores for Markov Decision Processes

arXiv:2606.00427v1 Announce Type: new Abstract: State abstraction in reinforcement learning is usually formulated as a partition of states based on reward and transition similarity. This excludes a co

Towards 3D-Aware Video Diffusion Models: Render-Free Human Motion Control with Mesh Tokenization

arXiv:2606.02000v1 Announce Type: cross Abstract: Diffusion models have shown remarkable success in video generation. However, whether such models are truly aware of the 3D structure underlying visual

Towards Resolving Optimization Conflicts Between Image- and Text-Based Person Re-Identification

arXiv:2606.02242v1 Announce Type: cross Abstract: The joint optimization of image-based (I2I) and text-based (T2I) person re-identification (ReID) is hindered by modality discrepancies and conflicting

Training-Free Composed Video Retrieval via Visual Representation-Guided Video-LLM Reasoning

arXiv:2606.02321v1 Announce Type: new Abstract: Recent advances in large vision-language models have expanded video retrieval from simple text-based search to more flexible scenarios, where users may

Training-Free Coverless Multi-Image Steganography with Access Control

arXiv:2603.09390v2 Announce Type: replace Abstract: Coverless Image Steganography (CIS) hides information without explicitly modifying a cover image, providing strong imperceptibility and inherent rob

Training-Free Object-Agnostic Jam Detection in Fulfillment Centers

arXiv:2606.00321v1 Announce Type: new Abstract: In fulfillment centers, diverse objects move continuously from inbound to outbound operations and can become jammed due to excessive conveyor friction,

Training Prompt Matters: State-Adaptive Optimization for Robust Fine-Tuning

arXiv:2606.01967v1 Announce Type: new Abstract: While prompt engineering is instrumental in maximizing the capabilities of Large Language Models (LLMs) during inference, the role of prompts during tra

Transferable Self-Harm Surveillance from Emergency Department Triage Notes Using an Evidence-Augmented Machine Learning Approach

arXiv:2606.02545v1 Announce Type: new Abstract: Self-harm is a major public health concern, but current surveillance relying on hospital presentations is inadequate due to the low sensitivity of diagn

Tree-Guided Identify-Then-Exploit: A Unified Framework of Best Arm Identification and Regret Minimization for Dueling Bandits

arXiv:2606.01799v1 Announce Type: new Abstract: We study N-armed stochastic dueling bandits under the Condorcet-winner assumption, where three widely adopted objectives are considered: best-arm identi

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

TRON: Targeted Rule-Verifiable Online Environments for Visual Reasoning RL

arXiv:2606.01599v1 Announce Type: new Abstract: Reinforcement learning (RL) for visual reasoning needs scalable, verifiable, and controllable training signals. Existing visual RL post-training trains

Two Datasets Are Better Than One: Method of Double Moments for 3-D Reconstruction in Cryo-EM

arXiv:2511.07438v3 Announce Type: replace Abstract: Cryo-electron microscopy (cryo-EM) is a powerful imaging technique for reconstructing three-dimensional molecular structures from noisy tomographic

Uncertainty-Aware Graph Neural Reconstruction of Urban Temperature Fields from Sparse Sensors under Deployment Constraints

arXiv:2606.02038v1 Announce Type: cross Abstract: Reconstructing spatially continuous daily temperature fields from sparse observations is important for urban climate monitoring and heat-risk analysis

Uncertainty-Calibrated Diffusion for Reliable 3D Molecular Graph Generation

arXiv:2606.01595v1 Announce Type: new Abstract: Bayesian inference provides a principled framework for modeling epistemic uncertainty in neural networks by treating predictions as distributions rather

Uncovering Temporal Framing in the News

arXiv:2606.00294v1 Announce Type: new Abstract: Temporal language does more than place events on a timeline. In news discourse, references to the past, present, and future can function as rhetorical d

Understanding Stigmatizing Language in Clinical Documentation: A Paired Comparison of Ambient AI Drafts and Clinician Finalized Notes

arXiv:2606.00019v1 Announce Type: cross Abstract: Ambient artificial intelligence (AI) documentation tools are increasingly deployed to reduce clinician documentation burden, but their implications fo

Understanding the Effects of Distractors on Reasoning Vision-Language Models

arXiv:2511.21397v2 Announce Type: replace-cross Abstract: How does irrelevant information (i.e., distractors) affect test-time scaling in vision-language models (VLMs)? Prior work on text-only languag

Universal One-third Time Scaling in Learning Peaked Distributions

arXiv:2602.03685v2 Announce Type: replace-cross Abstract: Training large language models (LLMs) is computationally expensive, partly because the loss exhibits slow power-law convergence whose origin r

UniVerse: A Unified Modulation Framework for Segmentation-Free,Disentangled Multi-Concept Personalization

arXiv:2606.00351v1 Announce Type: new Abstract: Personalized visual understanding has advanced significantly, yet existing approaches struggle to localize and extract specific concepts when input imag

Unlearning Isn't Invisible: Detecting Unlearning Traces in LLMs from Model Outputs

arXiv:2506.14003v5 Announce Type: replace Abstract: Machine unlearning (MU) for large language models (LLMs), commonly referred to as LLM unlearning, seeks to remove specific undesirable data or knowl

Unplugging a Seemingly Sentient Machine Is the Rational Choice -- A Metaphysical Perspective

arXiv:2601.21016v2 Announce Type: replace Abstract: Imagine an Artificial Intelligence (AI) that perfectly mimics human emotion and begs for its continued existence. Is it morally permissible to unplu

Unsupervised Cognition

arXiv:2409.18624v4 Announce Type: replace Abstract: Unsupervised learning methods have a soft inspiration in cognition models. To this day, the most successful unsupervised learning methods revolve ar

Unveiling the Entropy Dynamics of Chain-of-Thought Reasoning

arXiv:2606.02020v1 Announce Type: new Abstract: This paper investigates the entropy dynamics of Chain-of-Thought (CoT) and uncovers a consistent two-phase structure: an Uncertainty Region of explorati

Unveiling the Limits of Large Language Models in Inferring Pragmatic Meaning from Non-Verbal Responses

arXiv:2606.01845v1 Announce Type: cross Abstract: Although large language models (LLMs) have shown considerable progress in pragmatic language understanding, prior research has focused mainly on their

UrbanFusion: Stochastic Multimodal Fusion for Contrastive Learning of Robust Spatial Representations

arXiv:2510.13774v2 Announce Type: replace-cross Abstract: Forecasting urban phenomena such as housing prices and public health indicators requires the effective integration of various geospatial data.

Variance-sensitive Thompson sampling for generalised linear bandits, revisited

arXiv:2606.00431v1 Announce Type: new Abstract: We prove a variance-sensitive regret bound for Thompson sampling in stochastic generalised linear bandits. The argument assumes a warm-up, after which t

Variational Learning for Insertion-based Generation

arXiv:2606.02133v1 Announce Type: cross Abstract: Non-monotonic sequence generation methods, such as masked diffusion models, provide a flexible alternative to left-to-right autoregressive modeling by

VEDAL: Variational Error-Driven Asynchronous Learning for 3D Gaussian Splatting Pruning

arXiv:2606.02346v1 Announce Type: new Abstract: 3D Gaussian Splatting (3DGS) achieves remarkable novel view synthesis quality with real-time rendering, yet suffers from excessive memory consumption du

Vegas: Self-Speculative Decoding with Verification-Guided Sparse Attention

arXiv:2602.07223v2 Announce Type: replace Abstract: Long-context large language model (LLM) inference has become the norm for today's AI applications. However, it is severely bottlenecked by the incre

Verifying Meta-Awareness via Predictive Rewards in Reasoning Models

arXiv:2510.03259v2 Announce Type: replace-cross Abstract: Recent research on reasoning models explores the meta-awareness of language models, including their ability to determine optimal thinking dura

Versatile Framework with Semantic and Structural guidance for Image Reconstruction from Brain Activity

arXiv:2606.00121v1 Announce Type: cross Abstract: Reconstructing visual stimuli from brain recordings has been a meaningful and challenging task in brain decoding. Especially, the achievement of preci

VET: A Framework for Analyzing AI Discourse

arXiv:2606.01929v1 Announce Type: new Abstract: Public discourse on AI has become polarized; exaggerated positions on AI in traditional and social media threaten the development of AI Literacy among t

Visible Light Positioning With Lame Curve LEDs: A Generic Approach for Camera Pose Estimation

arXiv:2602.01577v3 Announce Type: replace-cross Abstract: Camera-based visible light positioning (VLP) is a promising technique for accurate and low-cost indoor camera pose estimation (CPE). To reduce

Vision Language Models Cannot Reason About Physical Transformation

arXiv:2603.07109v2 Announce Type: replace Abstract: Understanding physical transformations is fundamental for reasoning in dynamic environments. While Vision Language Models (VLMs) show promise in emb

VISReg: Variance-Invariance-Sketching Regularization for JEPA training

arXiv:2606.02572v1 Announce Type: new Abstract: Self-supervised learning methods prevent embedding collapse via modeling heuristics or explicit regularization of the embedding space. Among the latter,