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Designing for Interpretation Uncertainty: Architecture and Principles for Topological Learning Analytics Dashboards

arXiv:2610.01749v1 Announce Type: cross Abstract: Topological Data Analysis (TDA) offers novel methods for understanding temporal dynamics in complex systems, yet its application in information system

researcharxiv-cs-lg

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Model Releases

Detect, Explain, Interpret: An End-to-End Benchmark for Time Series Anomaly Detection, Explainability and Interpretability

DGX agent

arXiv:2610.01168v1 Announce Type: cross Abstract: Time Series Anomaly Detection has received increasing attention, driven by the growing availability of complex time series data. This surge has led to

model-releasesarxiv-cs-ai

2 Oct 2026

Detecting Inconsistencies in Model Specifications with LLM-as-Verifier Reasoning

arXiv:2610.01847v1 Announce Type: cross Abstract: Model specifications define how large language models (LLMs) should behave, guiding alignment training, inference-time behavior, and evaluation. Yet t

DexPolicy: Scheduled Exploration for Trajectory-Guided Dexterous Manipulation

arXiv:2610.00360v1 Announce Type: cross Abstract: Reinforcement learning (RL) for dexterous manipulation must discover finger-object contacts and then control the object precisely; the action noise th

DiDE:Direct Injection with Color-Texture DEcoupling for 3D Stylization

arXiv:2610.02044v1 Announce Type: new Abstract: Recent advances in rectified flow-based image-to-3D generative models have enabled high-fidelity 3D asset generation. Building on this, a growing line o

Diffusion Editing with Soft Mask: Pixel Level Redo of Image and Video with Adjustable Strength

arXiv:2610.00359v1 Announce Type: cross Abstract: Diffusion models with prompt and reference image-guided editing have seen rapid progress, yet they remain too coarse for pixel-level control. One prom

Discrete Annotation, Continuous Preference: Rethinking Supervision for Accurate and Generalizable Aesthetic Image Cropping

arXiv:2610.00582v1 Announce Type: new Abstract: Aesthetic image cropping aims to identify the optimal crop of an image in terms of aesthetics and composition. While supervision based on annotated data

Discrete Wasserstein Flows for One-Step Generative Modeling

arXiv:2610.01355v1 Announce Type: cross Abstract: We introduce a new framework for one-step generative modelling on finite state spaces. To extend drifting beyond continuous domains, we use discrete W

Tutorials

Distillation of Tabular Foundation Models into Efficient Predictors

arXiv:2610.01435v1 Announce Type: new Abstract: Tabular foundation models (TFMs) achieve strong predictive performance through in-context learning, yet repeatedly conditioning on labeled data makes in

tutorialsarxiv-cs-lg

Distilling Directional Verification

arXiv:2610.00997v1 Announce Type: cross Abstract: Knowledge distillation aims to transfer the factual knowledge of large language models to smaller models for efficient deployment. Yet a teacher may r

Distilling LLM Reasoning into Graph of Concept Predictors

arXiv:2602.03006v3 Announce Type: replace Abstract: Deploying Large Language Models (LLMs) for discriminative workloads is often limited by inference latency, compute, and API costs at scale. Active d

Distributionally Robust Schrodinger Bridge

arXiv:2610.02043v1 Announce Type: cross Abstract: Schrodinger bridge (SB) learns stochastic transport between prescribed initial and target distributions. When the initial distribution shifts at test

DITTO-X: Forward and Reverse Teleoperation for Dexterous Manipulation and Human Intervention

arXiv:2610.00781v1 Announce Type: new Abstract: Teleoperated demonstrations are a primary source of data for robot manipulation, and teleoperated interventions are a primary mechanism for correcting p

safetyarxiv-cs-ro

DiVid: Diagnosing Dimension-Specific Diversity Collapse in Video Generation Models

arXiv:2610.01661v1 Announce Type: new Abstract: Despite remarkable progress, video generation models often produce highly similar outputs when repeatedly sampled from the same prompt, limiting their u

Divide-and-Remember: Recursive Action-Relevant Memory for Long-Horizon VLA Policies

arXiv:2610.00982v1 Announce Type: cross Abstract: Vision-language-action (VLA) models struggle on history-dependent manipulation tasks, where the current observation alone does not determine the actio

DMAD: Distribution Matching as Adversarial Distillation for Fast Visual Generation

arXiv:2610.02188v1 Announce Type: cross Abstract: Distribution Matching Distillation (DMD) trains a few-step student from the difference between separately estimated target and student scores, so it m

Do Better Scores Mean Better Physics? Physics-Grounded Explanations for Sim2Real Neural Operators

model-releasesarxiv-cs-lg

arXiv:2610.00415v1 Announce Type: new Abstract: Machine-learning surrogates accelerate physical simulation, but lower prediction error need not coincide with lower error in physically relevant flow st

Do MLLM Judges Judge the Edit? Auditing Bias in Image Editing Evaluation with Verified Quality Preservation

arXiv:2610.01670v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) are increasingly used as automated judges for instruction-based image editing and as reward signals for model t

Do Vision Language Models Understand Human Engagement in Games?

arXiv:2603.18480v2 Announce Type: replace-cross Abstract: Inferring human engagement from gameplay video is important for game design and player-experience research, yet it remains unclear whether vis

Do Your Own Research: Learning to Forecast by Learning to Search

model-releasesarxiv-cs-lg

arXiv:2610.01955v1 Announce Type: new Abstract: Outcome-based reinforcement learning can train language models to forecast real-world events, but prior forecasting work either freezes research context

Does Continual Imitation Learning Remain Grounded? A Language-Perturbed Benchmark for Robotic Task Retention

model-releasesarxiv-cs-ro

arXiv:2610.00542v1 Announce Type: new Abstract: Continual imitation learning evaluates whether a robot can learn new knowledge without forgetting previously learned skills. However, retaining task per

Does Scaling Reinforcement Learning Really Require More Training?

arXiv:2610.01133v1 Announce Type: cross Abstract: Scaling reasoning typically spends more compute on reinforcement learning (RL) or on inference. We show that a completed RL training history can yield

Domain generalization and synthetic data in object detection: the enabler, the probe, and the gap

arXiv:2610.00030v1 Announce Type: new Abstract: Object detection models often experience performance degradation when deployed under distribution shifts, caused by for example changes in weather type,

Local Ai

Don't Waste the Noise: Importance-Guided Perturbation Allocation under Joint Global and Local Constraints

arXiv:2610.00861v1 Announce Type: cross Abstract: Adversarial optimization under a shared ell_1 budget requires deciding not only how much perturbation to use, but also where that limited budget shoul

local-aiarxiv-cs-ai

Agents

DramaAgent: Agentic Storytelling Video Generation

arXiv:2610.00097v1 Announce Type: new Abstract: Recent diffusion and autoregressive models have substantially improved text-to-video generation, yet producing coherent long-form story videos with cons

agentsarxiv-cs-cv

Hardware

DRelay: Global Draft Context for Prefix-Aware Parallel Speculative Decoding Repair

arXiv:2610.01439v1 Announce Type: new Abstract: Parallel drafting reduces the drafting overhead of speculative decoding for large language models (LLMs), but its gains remain limited by the accepted p

hardwarearxiv-cs-ai

DriftOPD: Sequence-Level Reverse-KL Distillation for One-Step VLA Policies

arXiv:2610.00317v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models increasingly rely on action experts that generate short action chunks under receding-horizon control. While chunk-

DSSR-3D: Decoupled Reasoning for View-Dependent Referring in 3D Gaussians

arXiv:2610.00040v1 Announce Type: new Abstract: Recent advances in 3D Gaussian Splatting have enabled open-vocabulary and referring segmentation by distilling semantic knowledge from 2D foundation mod

DuoMind: Enabling Distributed Multi-Robot Coordination with Semantic Communication

arXiv:2610.02161v1 Announce Type: cross Abstract: Vision-language models (VLMs) and vision-language-action models (VLAs) have recently driven rapid progress in general-purpose robots, yet most progres

DuplexSpeechBench-Document Grounding: Benchmarking Document Grounding and Hallucinations in Voice Agents

arXiv:2610.00316v1 Announce Type: cross Abstract: Voice agents enable low-latency, natural interaction, yet their ability to faithfully ground responses in external documents remains underexplored. We

Dyna-DINO: Efficient ViT Distillation Via Adaptive Representation Anchoring

arXiv:2606.19483v2 Announce Type: replace Abstract: Vision Foundation Models (VFMs) with Vision Transformer (ViT) backbones, such as DINOv2, have become essential for downstream tasks like object reco

researcharxiv-cs-cv

Applications

Dyna3: VLM-Guided Training-Free 4D Reconstruction via Depth Foundation Models

arXiv:2610.01286v1 Announce Type: new Abstract: Recent depth foundation models like Depth Anything 3 (DA3) achieve remarkable multi-view depth estimation but assume static 3D scenes, limiting their ap

applicationsarxiv-cs-cv

DynamicVLA: A Vision-Language-Action Model for Dynamic Object Manipulation

arXiv:2601.22153v2 Announce Type: replace-cross Abstract: Manipulating dynamic objects remains an open challenge for Vision-Language-Action (VLA) models. Although recent VLAs generalize well in static

ECHO: A Participatory Framework for Bias-Anchored AI Harm Anticipation

arXiv:2512.03068v2 Announce Type: replace-cross Abstract: Artificial Intelligence (AI) systems increasingly shape consequential decisions, creating value but also potential harms for individuals, soci

EchoPress: Query-Agnostic KV Cache Pruning via Virtual Context Reconstruction

model-releasesarxiv-cs-lg

arXiv:2610.00412v1 Announce Type: new Abstract: KV cache pruning reduces long-context inference memory usage by evicting less important key-value pairs. KVzip estimates importance through context reco

ECoMEM: Explicit Concept Memory for Memory-Dependent Robot Control

arXiv:2610.00801v1 Announce Type: new Abstract: A robot may lose sight of an object it must later retrieve, need to recall what a person demonstrated earlier, or track which steps of a task it has alr

safetyarxiv-cs-ro

Local Ai

Effective Resistance and Graph Neural Network Reliability in Tissue-Specific Interactomes

arXiv:2610.02175v1 Announce Type: new Abstract: Protein function annotation needs to know which predictions to distrust, not only what a model predicts. We ask whether tissue-specific interaction stru

local-aiarxiv-cs-lg

Tutorials

Efficient Generative Modeling beyond Memoryless Diffusion via Adjoint Schrodinger Bridge Matching

arXiv:2602.15396v2 Announce Type: replace Abstract: Diffusion models often yield highly curved trajectories and noisy score targets due to an uninformative, memoryless forward process that induces ind

tutorialsarxiv-cs-cv

EfficientRollout: System-Aware Self-Speculative Decoding for RL Rollouts

arXiv:2606.18967v2 Announce Type: replace Abstract: Reinforcement learning (RL) has become a representative post-training paradigm for large language models (LLMs), enabling strong reasoning and agent

safetyarxiv-cs-lg

Ego2Act: Evaluating Goal-Directed Manipulation in Egocentric Video Generation

arXiv:2610.01092v1 Announce Type: new Abstract: Video generation models are increasingly being explored as world simulators for embodied planning and learning. To do so effectively, these models must

EgoForge: Goal-Directed Egocentric World Simulator

arXiv:2603.20169v2 Announce Type: replace Abstract: Generative world models have shown promise for simulating dynamic environments, yet egocentric video remains challenging due to rapid viewpoint chan

EgoFound3R: End-to-End Egocentric Hand Reconstruction in World Space with Point-Wise Interaction Attributes

arXiv:2610.01210v1 Announce Type: new Abstract: Egocentric video has become a primary source of supervision for embodied models, and its value rests on recovering hand motion in world coordinates, whi

EgoRefine: Ego-Referenced Predictive Alignment and Trajectory-Conditioned Reliability-Aware Fusion for Asynchronous Collaborative Perception

arXiv:2610.00319v1 Announce Type: new Abstract: Collaborative perception enables connected agents to share complementary observations for 3D object detection, extending sensing range and mitigating oc

EIDA: Execution-Interface Dynamics Adaptation for Real-to-Sim-to-Real Robot Navigation

model-releasesarxiv-cs-ro

arXiv:2610.01219v1 Announce Type: new Abstract: Simulation-to-robot transfer can fail when velocity commands produce motion and feedback that differ from those modeled during policy training. We prese

Embedding Prediction Helps Image Generation

arXiv:2610.02203v1 Announce Type: new Abstract: In diffusion transformers, a class label or a text prompt is embedded once, and the same condition is reused at every denoising step. We ask whether pre

researcharxiv-cs-cv

Emergent Object Binding Has a Finite Spatial Horizon

arXiv:2610.00006v1 Announce Type: new Abstract: Pretrained Vision Transformers encode whether two image patches belong to the same object. This IsSameObject signal is decodable from frozen patch embed

researcharxiv-cs-cv

Emergent Unfaithfulness: How Alignment Training Causes Language Models to Silently Override Task Faithfulness

arXiv:2610.00568v1 Announce Type: cross Abstract: Large language models are characterized by three key properties: capability, alignment, and faithfulness. Prior work studies the tradeoffs between cap

Agents

Empty Commitments: When Agents Promise What Their Runtime Cannot Deliver

arXiv:2610.01045v1 Announce Type: new Abstract: A chatbot that says 'I will remind you tomorrow' will not run again until the user writes. We call such a promise an empty commitment: a promise of an a

agentsarxiv-cs-ai