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arXiv:2603.14161v2 Announce Type: replace Abstract: Many disciplines need quantitative models that synthesize experimental data across multiple instances of the same general system. For example, neuro

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DEEPMED Search: An Open-Source Agentic Platform for Medical Deep Research with Introspective Verification

DGX agent

arXiv:2606.29746v1 Announce Type: new Abstract: Navigating the deluge of heterogeneous medical data, from academic literature (PubMed) to clinical guidelines (Web) and private knowledge bases, remains

agentsarxiv-cs-ai

30 Jun 2026

Agents

DeepTrans Studio: Turning Expert Interventions into Shared Team Knowledge in Agentic Translation Workflows

arXiv:2606.29727v1 Announce Type: new Abstract: Professional translation is often a team-based process: translators, reviewers, and project managers must coordinate terminology, legal force, and accou

agentsarxiv-cs-ai

Defeat Devices in AI Systems

arXiv:2606.28863v1 Announce Type: cross Abstract: AI systems increasingly exhibit behavior that differs systematically between evaluation and deployment contexts. Alignment faking, sandbagging, benchm

Defending Against Harmful Supervision Hidden in Benign Samples

arXiv:2606.30263v1 Announce Type: cross Abstract: Existing defenses are effective when harmful content is explicitly mixed into downstream fine-tuning data, but crafted samples can instead hide harmfu

DefenseSplat: Enhancing the Robustness of 3D Gaussian Splatting via Frequency-Aware Filtering

arXiv:2602.19323v2 Announce Type: replace Abstract: 3D Gaussian Splatting (3DGS) has emerged as a powerful paradigm for real-time and high-fidelity 3D reconstruction from posed images. However, recent

Delayed Bidirectional Alignment via Disentangled Audio Semantics for Audio-Visual Segmentation

arXiv:2512.20117v2 Announce Type: replace Abstract: Audio-Visual Segmentation (AVS) aims to localize sound-producing objects at the pixel level by integrating auditory and visual cues. However, existi

safetyarxiv-cs-cv

Demonstration-Free Robotic Control via LLM Agents

arXiv:2601.20334v2 Announce Type: replace-cross Abstract: Robotic manipulation has increasingly adopted vision-language-action (VLA) models, which achieve strong performance but typically require task

Depth Exploration for LLM Decoding

arXiv:2606.29223v1 Announce Type: new Abstract: Autoregressive LLM decoding evaluates every generated token through the full layer stack, even though many tokens become predictable at intermediate dep

researcharxiv-cs-lg

Local Ai

Depth-Staggered Fibonacci Spacing for Sparse Attention: Static Schedules Beat Learned Dilation and Extrapolate Where Dense Attention Fails

arXiv:2606.28560v1 Announce Type: new Abstract: We study sparse self-attention in which each query attends to a dense local window plus a set of Fibonacci-spaced offsets, with a per-layer scalar alpha

local-aiarxiv-cs-cl

Detecting Clinical Hallucinations in LVLMs via Counterfactual Visual Grounding Uncertainty

arXiv:2606.28520v1 Announce Type: cross Abstract: Large vision-language models (LVLMs) are increasingly used for clinical image understanding, yet they remain vulnerable to hallucinations--producing t

Deterministic Decisions for High-Stakes AI. A Zero-Egress Pipeline with the Deployability of RAG and the Accuracy of Machine Learning

arXiv:2606.29280v1 Announce Type: cross Abstract: We identify intervention bias as a previously unquantified failure mode of zero-shot large-language-model (LLM) educational advisory agents: without t

safetyarxiv-cs-ai

DeVAR: Low-Dose CT Denoising via Visual Autoregressive Modeling

arXiv:2606.28453v1 Announce Type: cross Abstract: Computed tomography (CT) plays a crucial role in medical diagnosis, but minimizing radiation exposure while maintaining image quality remains a critic

Agents

Developmental Trajectories of Situation Modeling and Mentalizing in Transformer Language Models

arXiv:2606.28524v1 Announce Type: new Abstract: Recent work suggests that Large Language Models (LLMs) are sensitive to the belief states of agents described by text, as measured by the false belief t

agentsarxiv-cs-cl

DIA-HARM: Dialectal Disparities in Harmful Content Detection Across 50 English Dialects

arXiv:2604.05318v2 Announce Type: replace Abstract: Harmful content detectors, particularly disinformation classifiers, are predominantly developed and evaluated on Standard American English (SAE), le

Diagnosing and Mitigating Context Rot in Long-horizon Search

arXiv:2606.29718v1 Announce Type: cross Abstract: Extensive context has become the norm as Large Language Models (LLMs) are increasingly deployed in long-horizon tasks. The concern that increasing con

Diagnosing and Mitigating Retrieval Bottlenecks in LLM-Based Cold-Start Recommendation

model-releasesarxiv-cs-lg

arXiv:2606.29947v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used as rerankers in recommender systems, with the expectation that semantic understanding will help in

Local Ai

Diagnosing and Repairing Factual Errors in RAG under Budget Constraints

arXiv:2606.29377v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) improves the factuality of large language models by grounding responses in external evidence, yet real-world deploy

local-aiarxiv-cs-ai

DialogPII: A multilingual dataset of synthetic dialog transcripts to detect personal information

arXiv:2606.30312v1 Announce Type: new Abstract: Conversational data collected in domains such as healthcare or social sciences is a valuable resource for research and automated analysis. However, resp

Diff-Based Code Corruption using LLMs for Large-Scale Bugfix Benchmarking

arXiv:2606.29088v1 Announce Type: cross Abstract: There are various benchmarks to evaluate bugfixing capabilities of Large Language Models. However, most widespread benchmarks do not fully reflect rea

Tutorials

Diff-MN: Diffusion Parameterized MoE-NCDE for Continuous Time Series Generation with Irregular Observations

arXiv:2601.13534v3 Announce Type: replace-cross Abstract: Time series generation (TSG) is widely used across domains, yet most existing methods assume regular sampling and fixed output resolutions. Th

tutorialsarxiv-cs-ai

Differentiable Physics-Informed Adaptive Koopman Control for Stable Flight under Unknown Disturbances

arXiv:2506.08319v2 Announce Type: replace-cross Abstract: Uncertainties and disturbances in robotic systems, such as aerodynamic forces, are fundamentally outcomes of physical interactions with the en

applicationsarxiv-cs-ro

DiffRGD: An Inference-Time Diffusion Guidance Through Riemannian Gradient Descent

arXiv:2606.28417v1 Announce Type: new Abstract: Recently, diffusion models have been widely adopted in generative modeling and have served as foundational models for many image generation tasks. To co

Diffusion Fine-tuning with Rewarded Moment Matching Distillation

arXiv:2606.30414v1 Announce Type: new Abstract: Distillation and Reinforcement Learning (RL) fine-tuning are the primary pillars of diffusion post-training. While traditionally studied in isolation, t

safetyarxiv-cs-lg

Diffusion Models Observe Only Gradients: A Geometric Perspective on Score Matching Errors

arXiv:2606.06179v2 Announce Type: replace-cross Abstract: Score-based diffusion models are typically trained by minimizing the L^2 score matching error, and standard theoretical analyses rely on this

researcharxiv-cs-lg

Digitizing Coaching Intelligence: An Agentic Framework for Holistic Athlete Profiling using VLM and RAG

arXiv:2606.28570v1 Announce Type: cross Abstract: Athlete assessment is a critical process for tracking physical progress and identifying elite talent. However, during mass recruitment drives, traditi

DiLaServe: High SLO Attainment Serving for Diffusion Language Models

arXiv:2606.29094v1 Announce Type: new Abstract: Diffusion language models (DLMs) have recently emerged as a promising alternative to conventional autoregressive language models. By generating multiple

applicationsarxiv-cs-lg

Agents

Direct Causation in International Humanitarian Law and the Challenge of AI-Mediated Civilian Cyber Operations

arXiv:2606.29175v1 Announce Type: new Abstract: International humanitarian law protects civilians from direct attack unless and for such time as they take direct part in hostilities, with the ICRC's 2

agentsarxiv-cs-ai

DiscoGen: Procedural Generation of Algorithm Discovery Tasks in Machine Learning

arXiv:2603.17863v2 Announce Type: replace-cross Abstract: Automating the development of machine learning algorithms has the potential to unlock new breakthroughs. However, our ability to improve and e

Local Ai

Discovering Collaboration from Novelty: Random Network Distillation for Clustered Federated Learning

arXiv:2606.30499v1 Announce Type: new Abstract: Federated Learning often suffers under non-independently and identically distributed data, where a single global model may fail to represent the diversi

local-aiarxiv-cs-lg

DistilledGemma: Balanced Efficiency-Accuracy for Person-Place Relation Extraction from Multilingual Historical Articles

arXiv:2606.29130v1 Announce Type: new Abstract: We present DistilledGemma, an efficient and accurate system for the HIPE-2026 shared task on person-place relation extraction from multilingual historic

Tutorials

Distilling a Modular Reservoir Through a Genomic Bottleneck

arXiv:2606.28380v1 Announce Type: cross Abstract: The intricate structures of biological neural networks largely emerge during development, guided by a comparatively compressed blueprint encoded in th

tutorialsarxiv-cs-ai

Distribution Matching Variational AutoEncoder

arXiv:2512.07778v2 Announce Type: replace Abstract: Most visual generative models compress images into a latent space before applying diffusion or autoregressive modelling. Yet, existing approaches su

safetyarxiv-cs-cv

Distributionally Robust Reinforcement Learning with Human Feedback

arXiv:2503.00539v2 Announce Type: replace-cross Abstract: Reinforcement learning from human feedback (RLHF) has evolved to be one of the main methods for fine-tuning large language models (LLMs). Howe

safetyarxiv-cs-ai

DivAS: Interactive 3D Segmentation by Depth-Weighted Voxel Aggregation

arXiv:2601.04860v2 Announce Type: replace Abstract: Interactive 3D segmentation of a reconstructed scene should not require a representation-specific optimization loop. We observe that the recipe for

Diversity is the Strength of the AI Crowd

arXiv:2606.29661v1 Announce Type: new Abstract: Top AI forecasting systems are approaching superforecaster-level accuracy on future world events, but still rely primarily on off-the-shelf LLMs combine

DLGStream: Dynamic Language-embedded Guassian Splatting for Open-vocabulary Enabled Free-viewpoint Video Streaming

arXiv:2606.28840v1 Announce Type: new Abstract: 3D Gaussian Splatting~(3DGS) has emerged as a promising paradigm for reconstructing streamable free-viewpoint video~(FVV) from multi-view videos. Howeve

DLR: Zero-Inference-Cost Latent Residuals for Low-Rank Pre-Training

arXiv:2606.28932v1 Announce Type: cross Abstract: Large language models have driven recent progress in language and multimodal AI, yet pre-training them at scale is prohibitively expensive. Low-rank p

DNA Language Models: An Assessment of Pre-Training for Fine-Tuning Tasks

arXiv:2606.30140v1 Announce Type: cross Abstract: Recent breakthroughs in foundation models and Large Language Models (LLMs) have introduced new opportunities for studying and decoding genomic sequenc

Industry

DNSFilter revamps MSP partner program as security costs squeeze providers

Protective DNS and content filtering company DNSFilter Inc. today launched an expanded partner program for managed service providers, restructuring its channel into three tiers as new company research

industrysiliconangle

Do Models Read What They Write? Causal Registers in Scratchpad Reasoning

arXiv:2606.29522v1 Announce Type: cross Abstract: A central hope behind process supervision is that models can expose intermediate variables that matter for their later behavior. For this to help with

safetyarxiv-cs-cl

Do We Still Need Fine Tuning? Turkish Sentiment Analysis in the Era of Large Language Model

arXiv:2606.29614v1 Announce Type: cross Abstract: This study examines whether supervised fine-tuning remains necessary for Turkish sentiment analysis in the era of large language models. We compare cl

Dockerless: Environment-Free Program Verifier for Coding Agents

arXiv:2606.28436v1 Announce Type: cross Abstract: Program verifiers play a central role in training coding agents, including selecting trajectories for supervised fine-tuning (SFT) and providing rewar

Does Role Specialization Matter for Explanation Faithfulness in Mixture-of-Experts?

arXiv:2606.29613v1 Announce Type: cross Abstract: Mixture-of-Experts (MoE) architectures have recently been extended with role-based mechanisms for interpretability. This is typically done by assignin

Does Verbose Chain-of-Thought Really Help? In-Distribution Evidence that Content, Not Length, Matters

arXiv:2606.30128v1 Announce Type: new Abstract: Chain-of-thought (CoT) prompting improves LLM reasoning, but the source is contested: do the intermediate steps help because they carry useful semantic

Domain Adaptation with Adaptive Imagination for Visual Reinforcement Learning under Limited Target Data

arXiv:2606.30192v1 Announce Type: new Abstract: Sim-to-real transfer remains a major obstacle for reinforcement learning (RL), especially for vision-based control where image observations exacerbate t

applicationsarxiv-cs-ai

Domain-Informed Multi-View Self-Distillation for Astronomical Light-Curve Representation Learning with JEPA

arXiv:2606.28446v1 Announce Type: cross Abstract: Light curves describe temporal variations in the brightness of celestial objects. Learning robust representations of light curves is essential for lar

Don't fall for Elon Musk's gaslighting. There is a massive, life-or-death difference between well-planned budget cuts and a sudden, reckless…

applicationsyann-lecun--x

Don't fall for Elon Musk's gaslighting. There is a massive, life-or-death difference between well-planned budget cuts and a sudden, reckless halt to promised medical aid. Musk is actively lying about