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DPsurv: Dual-Prototype Evidential Fusion for Uncertainty-Aware and Interpretable Whole-Slide Image Survival Prediction

arXiv:2510.00053v2 Announce Type: replace-cross Abstract: Pathology whole-slide images (WSIs) are widely used for cancer survival analysis because of their comprehensive histopathological information

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Human DGX agent

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Dr. DocBench: A Comprehensive Benchmark for Expert-Level and Difficult Document Parsing

DGX agent

arXiv:2606.01393v1 Announce Type: cross Abstract: Document parsing and recognition are fundamental capabilities for vision-language models (VLMs) and document processing systems. However, existing Opt

model-releasesarxiv-cs-ai

2 Jun 2026

DraDDP: A Multimodal Multi-Party Dialogue Discourse Parsing Dataset

arXiv:2606.00012v1 Announce Type: cross Abstract: Multi-party dialogue discourse parsing aims to identify dependency structures and relation types between utterances in conversations. Previous studies

DREAM-S: Speculative Decoding with Searchable Drafting and Target-Aware Refinement for Multimodal Generation

arXiv:2606.00535v1 Announce Type: new Abstract: Speculative decoding (SD) has proven to be an effective technique for accelerating autoregressive generation in large language models (LLMs) however, it

Drift Q-Learning

arXiv:2606.00350v1 Announce Type: cross Abstract: Offline reinforcement learning requires improving a policy from fixed data while avoiding out-of-distribution actions with unreliable value estimates.

Drifting Preference Optimization for One-Step Generative Models

arXiv:2606.02521v1 Announce Type: cross Abstract: One-step text-to-image generators are attractive for deployment because they generate an image with a single forward pass, but preference finetuning t

safetyarxiv-cs-cv

DriveAnchor: Progressive Anchor-based Flow Learning for Autonomous Driving Planning

arXiv:2606.00519v1 Announce Type: new Abstract: We present DriveAnchor, a three-stage framework for autonomous driving planning that achieves behavioral diversity, controllability, and safety in a com

safetyarxiv-cs-ro

DRL-Based Pose Control for Double-Ackermann Robots Under Actuation Uncertainties

arXiv:2606.00313v1 Announce Type: cross Abstract: Robust deployment of deep reinforcement learning (DRL) policies on real robots remains challenging due to discrepancies between simulation and real-wo

DrugClaw and DrugAudit: A Primary-Source-Grounded Agent and Authority-Aware Benchmark for Drug-Information Question Answering

model-releasesarxiv-cs-cl

arXiv:2606.01434v1 Announce Type: new Abstract: Drug-information question answering is a high-stakes setting where hallucinated facts can mislead clinical decision-making and the provenance of each ci

Local Ai

DSL-LLaDA: Scaling Continuous Denoising to 8B Masked Diffusion LMs

arXiv:2606.01024v1 Announce Type: cross Abstract: Discrete Masked diffusion language models generate text by iterative parallel decoding, but few-step decoding suffers from a tradeoff between length a

local-aiarxiv-cs-ai

Dual-Route Top-K Retrieval with 1v1 VLM Reranking for the CoVR-R

arXiv:2606.01097v1 Announce Type: new Abstract: We describe Dual-Route Top-K Retrieval with 1v1 VLM Reranking for the CoVR-R challenge. The method treats composed video retrieval as two coupled proble

Hardware

DuetServe: Harmonizing Prefill and Decode for LLM Serving via Adaptive GPU Multiplexing

arXiv:2511.04791v2 Announce Type: replace Abstract: Modern LLM serving systems must sustain high throughput while meeting strict latency SLOs across two distinct inference phases: compute-intensive pr

hardwarearxiv-cs-lg

DyLLM: Efficient Diffusion LLM Inference via Saliency-based Token Selection and Partial Attention

arXiv:2603.08026v2 Announce Type: replace-cross Abstract: Masked diffusion language models enable parallel token decoding, providing a promising alternative to the sequential nature of autoregressive

Dynamic Coordination Strategy Selection for Enterprise Multi-Agent Systems

arXiv:2606.00804v1 Announce Type: cross Abstract: Enterprise multi-agent systems increasingly expose multiple coordination patterns, but deployments often lack evidence for when to use consensus, deba

Dynamic Entropy Tuning in Reinforcement Learning Low-Level Quadcopter Control: Stochasticity vs Determinism

arXiv:2512.18336v2 Announce Type: replace-cross Abstract: This paper explores the impact of dynamic entropy tuning in Reinforcement Learning (RL) algorithms that train a stochastic policy. Its perform

Dynamic Proxy-Mixing: Transferring Replay Controllers from Small to Large Models for Continual Instruction Tuning

arXiv:2606.00400v1 Announce Type: new Abstract: Continual instruction tuning updates a language model through a sequence of new domains, yet each update can progressively erode previously learned capa

Local Ai

Dynamic Resilient Spatio-Semantic Memory with Hybrid Localization for Mobile Manipulation

arXiv:2606.00576v1 Announce Type: new Abstract: Reliable mobile manipulation in dynamic indoor environments requires a scene representation that remains geometrically consistent, semantically queryabl

local-aiarxiv-cs-ro

Agents

Dynamic Trust-Aware Sparse Communication Topology for LLM-Based Multi-Agent Consensus

arXiv:2606.01828v1 Announce Type: cross Abstract: Large language model-driven multi-agent systems enhance the reliability of complex reasoning tasks through multi-round deliberation, role specializati

agentsarxiv-cs-ai

Dynamics Are Learned, Not Told: Semi-Supervised Discovery of Latent Dynamics Geometries For Zero-Shot Policy Adaptation

model-releasesarxiv-cs-ro

arXiv:2606.02280v1 Announce Type: new Abstract: Real-world dynamics shifts pose a critical challenge for reinforcement learning in robotics, as policies tightly coupled to nominal environments often f

Tutorials

E4GEN: Event-level Explainable Extreme-Enhanced Time-series Generation

arXiv:2606.01634v1 Announce Type: cross Abstract: Generating realistic time series is essential for scientific research and real-world applications. However, existing methods often emphasize overall d

tutorialsarxiv-cs-ai

Agents

Early Diagnosis of Wasted Computation in Multi-Agent LLM Systems via Failure-Aware Observability

arXiv:2606.01365v1 Announce Type: new Abstract: Tool-using multi-agent large language model (LLM) systems spend computation through model tokens, tool calls, retries, and code execution before produci

agentsarxiv-cs-ai

Early Prediction of Liver Cirrhosis Up to Two Years in Advance: A Machine Learning Study Benchmarking Against the FIB-4 and APRI Scores

arXiv:2601.00175v2 Announce Type: replace Abstract: Objective: Develop and evaluate machine learning (ML) models for predicting incident liver cirrhosis (LC) one and two years prior to diagnosis using

Agents

Easier to Mislead Than to Correct: Harmful and Beneficial Revision in LLM Conformity

arXiv:2606.01637v1 Announce Type: cross Abstract: Large language models are increasingly used in multi-agent systems, where they see and respond to other agents' answers. A key risk is conformity: a m

agentsarxiv-cs-ai

Echo: A Joint-Embedding Predictive Architecture for Speaker Diarization and Speech Recognition in a Shared Latent Space

model-releasesarxiv-cs-ai

arXiv:2606.01909v1 Announce Type: cross Abstract: We present Echo, a proof-of-concept audio system built around a single 25 M-parameter ViT encoder. The encoder is pretrained with a JEPA objective and

Echo State Networks for Time Series Forecasting: Hyperparameter Sweep and Benchmarking

arXiv:2602.03912v4 Announce Type: replace Abstract: This paper investigates the performance of Echo State Networks (ESNs) for univariate forecasting of monthly and quarterly time series from the M4 Fo

Edge-aware Decoding for Neural Asymmetric Routing

arXiv:2606.02136v1 Announce Type: new Abstract: Neural asymmetric routing models increasingly encode directionality through matrix representations and asymmetry-aware attention. The final routing acti

Edge-Based QoS-Aware Adaptive Task Placement: A Closed-Loop Control in Multi-Robot Systems

arXiv:2606.00552v1 Announce Type: cross Abstract: Multi-robot systems (MRS) increasingly offload compute-intensive perception tasks to edge nodes to meet strict time-sensitive Quality-of-Service (QoS)

researcharxiv-cs-ro

Edge-directed geometric partitioning for versatile video coding

arXiv:2606.01641v1 Announce Type: new Abstract: To improve the coding performance, geometric partition (GEO) was proposed for the upcoming VVC standard. GEO provides 140 partition candidates. The inde

Edge Prediction for Roof Wireframe Reconstruction with Transformers

arXiv:2606.02406v1 Announce Type: new Abstract: This paper presents a competitive solution to the S23DR Challenge 2026, which aims to reconstruct 3D house roof wireframe models from sparse SfM point c

EEG-FuseFormer: A Transformer-Driven Feature Fusion Framework for Seizure Onset Prediction

arXiv:2606.02166v1 Announce Type: new Abstract: Epilepsy is one of the most common neurological disorders globally, characterized by recurring seizures and significantly impacting the quality of life.

Effective Multi-sensor Conditioning for Street-view Novel-view Synthesis

arXiv:2606.01590v1 Announce Type: new Abstract: Modern vehicle platforms are equipped with a rich sensor suite, including LiDAR, calibrated multi-camera rigs, and accurate ego-motion, that in principl

Effects of Varying LLM Access on Essay Writing Behavior

arXiv:2606.00250v1 Announce Type: cross Abstract: Investigating the degree to which large language models (LLMs) affect teaching and learning in universities can help identify strategies for integrati

Efficient Approximation for Encoder--Decoder Neural Operators via Variation Spaces

arXiv:2606.01244v1 Announce Type: cross Abstract: We study operator learning using encoder--decoder neural networks. Inspired by the function-space theory of neural networks, we introduce a variation

Efficient Exploration for Iterative Nash Preference Optimization

model-releasesarxiv-cs-ai

arXiv:2606.01382v1 Announce Type: cross Abstract: Preference alignment is central to improving large language models, but standard reward-based formulations can be restrictive when human preferences a

Tutorials

Efficient Hamiltonian, structure and trace distance learning of Gaussian states

arXiv:2411.03163v4 Announce Type: replace-cross Abstract: In this work, we initiate the study of Hamiltonian learning for positive temperature bosonic Gaussian states, the quantum generalization of th

tutorialsarxiv-cs-lg

Efficient LLM Moderation with Multi-Layer Latent Prototypes

arXiv:2502.16174v4 Announce Type: replace-cross Abstract: Although modern LLMs are aligned with human values during post-training, robust moderation remains essential to prevent harmful outputs at dep

Efficient RAG with Intent-Aware Retrieval and Semantics-Preserving Chunking

model-releasesarxiv-cs-cl

arXiv:2606.01240v1 Announce Type: new Abstract: The demand for powerful instruction following and reasoning capability of large language models (LLMs) has promoted rapid development of retrieval-augme

Efficient Synthetic Network Generation via Latent Embedding Reconstruction

arXiv:2606.00934v1 Announce Type: cross Abstract: Network data are ubiquitous across the social sciences, biology, and information systems. Generating realistic synthetic network data has broad applic

Efficient Test-time Inference for Generative Planning Models

arXiv:2606.00618v1 Announce Type: new Abstract: Generative models have emerged as a powerful paradigm for AI planning, yet their performance remains constrained by the training data distribution. One

Efficient Weighted Sampling via Score-based Generative Models

arXiv:2502.04646v2 Announce Type: replace-cross Abstract: Weighted sampling -- sampling from a probability density function (PDF) proportional to the product of a base PDF and a weight function -- is

Ego-METAS: Egocentric online Multimodal Energy-efficient Temporal Action Segmentation benchmark

model-releasesarxiv-cs-cv

arXiv:2606.02246v1 Announce Type: new Abstract: To operate in the physical world, embodied agents must perceive their environment in an 'always-on' fashion, selectively accessing the most informative

EIVE: End-to-End Instance-Specific Visual Explanations for Detection Transformers

arXiv:2606.01601v1 Announce Type: new Abstract: Visual explainability for object detection remains challenging due to the multi-instance nature of detection. Existing approaches predominantly adopt po

ELF: A Family of Encoder-Free ECG-Language Models

arXiv:2601.18798v2 Announce Type: replace-cross Abstract: ECG-Language Models (ELMs) extend recent advances in Multimodal Large Language Models (MLLMs) to automated ECG interpretation. However, most e

Embedding Semantic Risk into Distance Fields and CBFs for Online Monocular Safe Control

arXiv:2606.01605v1 Announce Type: new Abstract: We propose an online monocular perception-to-control framework that embeds semantic risk into the distance field used by Control Barrier Function (CBF)-

safetyarxiv-cs-ro

Embedding-Space Diffusion for Zero-Shot Environmental Sound Classification

arXiv:2412.03771v3 Announce Type: replace-cross Abstract: Zero-shot learning enables models to generalise to unseen classes by leveraging semantic information, bridging the gap between training and te

Emergence of Exploration in Policy Gradient Reinforcement Learning via Retrying

model-releasesarxiv-cs-ai

arXiv:2606.00151v1 Announce Type: cross Abstract: In reinforcement learning (RL), agents benefit from exploration only because they repeatedly encounter similar states: trying different actions can im

Emergent Collaborative Deliberation in Multi-Model AI Systems: A BFT-Derived Protocol for Epistemic Synthesis

arXiv:2606.00005v1 Announce Type: new Abstract: We present the Consilium Protocol, a Byzantine Fault Tolerance-derived architecture for structured multi-model AI deliberation that treats inter-model d

Emergent Ordinal Geometry in Transformers Trained on Local Comparisons

arXiv:2606.01269v1 Announce Type: new Abstract: Transitive inference is the challenge of inferring that A < C from knowing only adjacent relations (A < B, B < C). It is solved by humans and animals no