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🧱 DiffusionBlocks: Training Neural Networks One Block at a Time https://pub.sakana.ai/diffusionblocks

DiffusionBlocks is a novel training method that enables neural networks to be trained sequentially, one architectural block at a time, rather than end-to-end. This approach, developed by Sakana AI, le

researchdavid-ha--x

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Agents

DIG to Heal: Scaling General-purpose Agent Collaboration via Explainable Dynamic Decision Paths

DGX agent

arXiv:2603.00309v2 Announce Type: replace Abstract: The increasingly popular agentic AI paradigm promises to harness the power of multiple, general-purpose large language model (LLM) agents to collabo

agentsarxiv-cs-ai

28 May 2026

Dimensionality Reduction for Robust Federated Learning: A Theoretical Analysis and Convergence Guarantee

model-releasesarxiv-cs-lg

arXiv:2605.28335v1 Announce Type: new Abstract: Federated Learning (FL) enables multiple clients to collaboratively train models without sharing raw data, but it is highly vulnerable to Byzantine atta

DisasterBench: Benchmarking LLM Planning under Typed Tool Interface Constraints

model-releasesarxiv-cs-cl

arXiv:2605.27957v1 Announce Type: new Abstract: Disasters cause severe societal impacts, demanding rapid coordination of heterogeneous AI tools, from satellite analysis to flood prediction and damage

DiscoForcing: A Unified Framework for Real-Time Audio-Driven Character Control with Diffusion Forcing

arXiv:2605.28491v1 Announce Type: new Abstract: We study real-time audio-responsive character control as a deployment-faithful problem: strictly causal, bounded-latency streaming that must generate co

safetyarxiv-cs-cv

Discovery Agents for Real-Time Analytics: Toward Proactive Insight Systems

arXiv:2605.27571v1 Announce Type: new Abstract: Modern analytics systems are fundamentally reactive, requiring users to define queries over increasingly complex and continuously evolving data. In real

Disentangling Adversarial Prompts: A Semantic-Graph Defense for Robust LLM Security

arXiv:2605.27823v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly vulnerable to adversarial prompts that exploit semantic ambiguities to bypass safety mechanisms, resulti

Disentangling Language Roles in Multilingual LLM Task Execution

model-releasesarxiv-cs-cl

arXiv:2605.27649v1 Announce Type: new Abstract: Multilingual LLMs are increasingly used when instruction, source content, and required response languages do not coincide. Existing benchmarks have expa

Do Agents Know What They Can't Do? Evaluating Feasibility Awareness in Tool-Using Agents

arXiv:2605.28532v1 Announce Type: new Abstract: Tool-using agents often incur substantial computational cost due to long reasoning chains and iterative tool usage. In practical scenarios, many tasks b

Do Agents Need Semantic Metadata? A Comparative Study in Agentic Data Retrieval

arXiv:2605.28787v1 Announce Type: cross Abstract: In the era of autonomous agents, machine-actionable data is critical for data-driven workflows. For more than a decade, semantic metadata like schema.

Do Agents Think Deeper? A Mechanistic Investigation of Layer-Wise Dynamics in Sequential Planning

arXiv:2605.27935v1 Announce Type: new Abstract: Recent mechanistic studies suggest that large language models (LLMs) may utilize their depth inefficiently in standard single-turn tasks. Whether this s

Do Audio LLMs Listen or Read? Analyzing and Mitigating Paralinguistic Failures with VoxParadox

model-releasesarxiv-cs-lg

arXiv:2605.27772v1 Announce Type: cross Abstract: Audio large language models (Audio LLMs) demonstrate strong performance on speech understanding tasks, yet their ability to understand paralinguistic

Do Clinical Models Change Treatment Decisions?

arXiv:2605.28129v1 Announce Type: new Abstract: Clinical foundation models are evaluated with factual or exam-style medical QA, but treatment decisions must change when patient context changes. We int

Do LLMs Build World Models From Text? A Multilingual Diagnostic of Spatial Reasoning

arXiv:2605.28277v1 Announce Type: new Abstract: Whether large language models (LLMs) construct internal spatial world models from pure-text descriptions remains contested, and whether such capabilitie

Do LLMs Favor Their Providers? Measuring Vertical Integration Bias in Code Generation

arXiv:2605.28515v1 Announce Type: cross Abstract: Large Language Models (LLMs) have become an integral part of software development, especially with the advent of agentic capabilities. Yet, many front

Do Models Know Why They Changed Their Mind? Interpretability and Faithfulness of Chain-of-Thought Under Knowledge Conflict

arXiv:2605.27773v1 Announce Type: cross Abstract: When a language model sees a document contradicting its training knowledge, it must choose: follow the document or trust itself. Prior work proved thi

Applications

Do readers prefer AI-generated Italian short stories?

arXiv:2601.17363v2 Announce Type: replace-cross Abstract: This study investigates whether readers prefer AI-generated short stories in Italian over one written by a renowned Italian author. In a blind

applicationsarxiv-cs-ai

Do We Really Need Quantum Machine Learning?: A Multidimensional Empirical Study

arXiv:2605.27923v1 Announce Type: cross Abstract: The rapid growth of computer vision and increasingly complex image recognition tasks has exposed fundamental computational limitations of classical ma

Industry

Doc: the EU is preparing emergency powers to intervene in Europe's chip supply chains during shortages, including by forcing chipmakers to override contracts (Barbara Moens/Financial Times)

Barbara Moens / Financial Times: Doc: the EU is preparing emergency powers to intervene in Europe's chip supply chains during shortages, including by forcing chipmakers to override contracts — Chipmak

industrytechmeme

DODO: Discrete OCR Diffusion Models

arXiv:2602.16872v2 Announce Type: replace Abstract: Optical Character Recognition (OCR) is a fundamental task for digitizing information, serving as a critical bridge between visual data and textual u

researcharxiv-cs-cv

Domain size asymptotics for Markov logic networks

arXiv:2509.04192v2 Announce Type: replace Abstract: A Markov logic network (MLN) M determines a probability distribution P_n^M on the set mathbf{W}_n of structures, or ``possible worlds'', with domain

Tools

Download Windsurf to try it out: http://windsurf.com/download

Windsurf is a software application available for download from windsurf.com, allowing users to try the program. This appears to be a promotional post from the official Windsurf social media account en

toolswindsurf--x

Dr-CiK: A Testbed for Foresight-Driven Agents

arXiv:2605.27904v1 Announce Type: new Abstract: Time series forecasting in real-world settings often depends not only on historical observations, but also on external context that must be actively dis

DREAM-R: Multimodal Speculative Reasoning with RL-Based Refined Drafting, Precise Verification, and Fully Parallel Execution

arXiv:2605.28678v1 Announce Type: new Abstract: Speculative reasoning has recently been proposed as a means to accelerate reasoning-intensive generation in large multimodal models, but its effectivene

DriveWAM: Video Generative Priors Enable Scalable World-Action Modeling for Autonomous Driving

model-releasesarxiv-cs-cv

arXiv:2605.28544v1 Announce Type: new Abstract: Pretrained foundation models have become an important basis for end-to-end autonomous driving. In contrast to vision-language models pretrained primaril

DRTriton: Large-Scale Synthetic Data Driven Reinforcement Learning for Triton Kernel Generation

model-releasesarxiv-cs-cl

arXiv:2603.21465v2 Announce Type: replace Abstract: Developing efficient CUDA kernels is a fundamental yet challenging task in the generative AI industry. Recent research leverages Large Language Mode

DSSE: a drone swarm search environment

arXiv:2307.06240v2 Announce Type: replace-cross Abstract: The Drone Swarm Search project is an environment, based on extsc{PettingZoo}, that is to be used in conjunction with multi-agent (or single-ag

Local Ai

Dual-branch Distilled Transformer for Efficient Asymmetric UAV Tracking

arXiv:2605.28018v1 Announce Type: new Abstract: Given the real-time demands of UAV tracking, many methods simplify the backbone to reduce computation, but this often weakens feature representation and

local-aiarxiv-cs-cv

Dynamic Topic Modeling with a Higher-Order Hypergraphical Representation

arXiv:2605.28269v1 Announce Type: new Abstract: Dynamic topic modeling is widely used to analyze evolving trends in scientific literature, medical records, and social media. Traditional topic models r

researcharxiv-cs-lg

DynaSchedBench: Calibrated Dynamic Scheduling Benchmarks and Observability Paradox in LLM-based Scheduling Agents

arXiv:2605.27566v1 Announce Type: new Abstract: Progress in neural combinatorial optimization for Dynamic Flexible Job Shop Scheduling Problem (DFJSP) is currently hindered by a methodological tension

E^3-Agent: An Executable and Evolving Agent for Resource Management of Edge Generative Inference

arXiv:2605.27428v1 Announce Type: new Abstract: Edge deployments of generative inference increasingly face two practical realities: per-device per-model performance is often unknown at deployment time

agentsarxiv-cs-lg

EAGer: Entropy-Aware GEneRation for Adaptive Inference-Time Scaling

arXiv:2510.11170v2 Announce Type: replace-cross Abstract: With the rise of reasoning language models and test-time scaling methods as a paradigm for improving model performance, substantial computatio

EAPO: Entropy-Driven Adaptive Positive-Negative Sample Weighting for Policy Optimization in Open-Ended QA

arXiv:2605.27846v1 Announce Type: new Abstract: Large Reasoning Models are typically trained via reinforcement learning from verifiable rewards (RLVR). However, existing approaches adopt fixed weights

ECHO: Entropy-Confidence Hybrid Optimization for Test-Time Reinforcement Learning

arXiv:2602.02150v2 Announce Type: replace-cross Abstract: Test-time reinforcement learning generates multiple candidate answers via repeated rollouts and performs online updates using pseudo-labels co

EchoAvatar: Real-time Generative Avatar Animation from Audio Streams

arXiv:2605.28272v1 Announce Type: new Abstract: Real-time synthesis of high-fidelity 3D character motion from audio is a pivotal component for next-generation interactive avatars and virtual assistant

researcharxiv-cs-cv

Economy then vs now: GDP growth: 2024 2.8% 2026 1.6% Inflation: 2024 2.9% 2026 3.8% Wages: 2024 rising faster than inflation 2026 rising slo…

Economy then vs now: GDP growth: 2024 2.8% 2026 1.6% Inflation: 2024 2.9% 2026 3.8% Wages: 2024 rising faster than inflation 2026 rising slower than inflation Unemployment rate: 2024 4% 2026 4.3% Jobs

researchyann-lecun--x

Efficient and Scalable Provenance Tracking for LLM-Generated Code Snippets

arXiv:2605.28510v1 Announce Type: cross Abstract: Large language models (LLMs) for code completion and generation are increasingly used in software development, yet they may reproduce training example

Efficient Post-training of LLMs for Code Generation With Offline Reinforcement Learning

arXiv:2605.28409v1 Announce Type: new Abstract: Post-training using online reinforcement learning (RL) is an important training step for LLMs, including code-generating models. However, online RL for

Efficient Pre-Training of LLMs through Truncated SVD Layers

arXiv:2605.28573v1 Announce Type: cross Abstract: The massive scaling of Large Language Models (LLMs) has made pretraining increasingly cost-prohibitive. While low-rank representation and orthonormal

EgoBench: An Interactive Egocentric Multimodal Benchmark for Tool-Using Agents

arXiv:2605.27820v1 Announce Type: new Abstract: As AI agents increasingly operate in open, real-world environments, they require a deep synergy of multimodal perception, tool invocation with multi-hop

EgoRelight: Egocentric Human Capture and Illumination Recovery for Relightable and Photoreal Avatar Rendering

arXiv:2605.28401v1 Announce Type: new Abstract: Mixed Reality (MR) headsets promise a future of immersive telepresence where virtual humans blend indistinguishably into real or virtual surroundings. A

researcharxiv-cs-cv

Applications

EigeNet: Geometry-Informed Multi-Modal Learning for Few-shot Novel View RIR Prediction

arXiv:2605.28101v1 Announce Type: cross Abstract: Predicting spatially varying Room Impulse Response (RIR) from sparse observations is a critical but highly challenging inverse problem for immersive s

applicationsarxiv-cs-ai

EIT-Pneumatic Hybrid Robotic Skin for Practical and Accurate Force Map Reconstruction

arXiv:2605.28468v1 Announce Type: new Abstract: We present a hybrid robotic skin that combines electrical impedance tomography (EIT) with pneumatic tactile sensing to improve force reconstruction capa

researcharxiv-cs-ro

Eliot: Interactively nderline{E}xploring Fast-Changing Scientific nderline{Li}terature Trends with nderline{O}nline Danderline{t}a and Learning

arXiv:2605.27610v1 Announce Type: cross Abstract: The rapid growth of scientific publishing has made it increasingly difficult to track how fast-moving areas evolve. Search engines and LLM-based assis

Industry

Elon Musk explains the importance of Starlink and how it will increase GDPs of the countries “If you don’t have access to the internet, or i…

Elon Musk explains the importance of Starlink and how it will increase GDPs of the countries “If you don’t have access to the internet, or it’s too expensive or low bandwidth, you cannot access MIT le

industryelon-musk--x

Elon Musk: If Kamala wins she'll hide the Epstein files, & be a threat to democracy. If Trump wins he'll release the Epstein files, & focus …

Elon Musk: If Kamala wins she'll hide the Epstein files, & be a threat to democracy. If Trump wins he'll release the Epstein files, & focus on the people. This idiot has NEVER been right about anythin

researchyann-lecun--x

Emerging Extrinsic Dexterity in Cluttered Scenes via Dynamics-aware Policy Learning

arXiv:2603.09882v2 Announce Type: replace-cross Abstract: Extrinsic dexterity leverages environmental contact to overcome the limitations of prehensile manipulation. However, achieving such dexterity

Energy-Structured Low-Rank Adaptation for Continual Learning

arXiv:2605.27482v1 Announce Type: cross Abstract: While orthogonal subspace methods try to mitigate task interference in Continual Learning (CL), they often suffer from energy diffusion across the bas