All entries

Dep-LLM: Training-Free Depression Diagnosis via Evidence-Guided Structured Multi-factor with Reliable LLM Reasoning

arXiv:2606.10796v1 Announce Type: cross Abstract: Automatic Depression Detection (ADD) from clinical interviews is a pivotal task in computational mental health, yet it remains challenging due to two

tutorialsarxiv-cs-ai

Model Releases

Deployment-Time Memorization in Foundation-Model Agents

DGX agent

arXiv:2606.10062v1 Announce Type: new Abstract: Foundation-model agents are increasingly long-lived systems that remember users across interactions, making memorization an explicit deployment-time fun

model-releasesarxiv-cs-ai

10 Jun 2026

DeRA-MOS: Optimizing Text-to-Music Evaluation via Decoupled Listwise Ranking and Modality Alignment

arXiv:2606.10010v1 Announce Type: cross Abstract: Evaluating text-to-music (TTM) systems remains expensive because music impression (MI) and text alignment (TA) scores rely on human mean opinion score

safetyarxiv-cs-ai

Designed by Journalists, but Is It for Readers? Rethinking AI Disclosures and Transparency in News

arXiv:2606.11116v1 Announce Type: cross Abstract: As newsrooms integrate generative AI, journalists face a disclosure challenge: how to communicate AI involvement in ways that maintain reader trust. C

tutorialsarxiv-cs-ai

Hardware

Designing Production-Ready Battery Energy Storage Systems for AI Factories

Battery energy storage systems serve as grid-interactive control assets that buffer fast-changing, power-dense AI loads, improve power quality, and enable flexible interconnection with utilities and d

hardwarenvidia-developer

Detecting Knowledge Gaps from Conversational AI Interactions Using Curriculum Prerequisite Graphs

arXiv:2606.10736v1 Announce Type: cross Abstract: Large online courses generate thousands of student questions directed at conversational AI teaching assistants, yet these interaction logs remain larg

Detecting Speculative Language in Biomedical Texts using Recurrent Neural Tensor Networks

arXiv:2606.10471v1 Announce Type: cross Abstract: In this investigation, we delve into the automated detection of speculative language within biomedical articles by utilizing distributed sentence repr

Dexterous Point Policy: Learning Point-based Dexterous Hand Policies from Human Demonstrations

arXiv:2606.10614v1 Announce Type: cross Abstract: Robotic foundation models pre-trained on human demonstration videos have shown promise, but a significant embodiment gap remains when the resulting po

safetyarxiv-cs-cv

Industry

Diabetes org apologizes for ejecting scientists over criticism of Trump

Five prominent obesity scientists, including the editor-in-chief of a leading diabetes journal, were kicked out of the American Diabetes Association's annual meeting while handing out an editorial cri

industryars-technica

Diffusion Forcing Planner: History-Annealed Planning with Time-Dependent Guidance for Autonomous Driving

arXiv:2606.11019v1 Announce Type: cross Abstract: Learning-based motion planners, despite recent progress, often suffer from temporal inconsistency. Small perturbations across frames can accumulate in

safetyarxiv-cs-ai

DiffusionGemma

model-releasessimon-willison

DiffusionGemma Last May Google briefly released an experimental Gemini Diffusion model. I tried the preview at the time and recorded it running at 857 tokens/second. It was an exciting model, but Goog

DiffusionGemma: 4x faster text generation

model-releasesgoogle-deepmind

DiffusionGemma is an experimental open model from Google DeepMind that uses text diffusion for exceptionally fast generation, moving beyond sequential token-by-token processing to generate entire bloc

Applications

Dirichlet-Guided Group Forecasting for Alleviating Over-smoothing in Time Series Forecasting

arXiv:2606.10592v1 Announce Type: new Abstract: Time series forecasting often suffers from over-smoothing, especially when future dynamics are multi-modal. Forecasts may follow the coarse trend of the

applicationsarxiv-cs-lg

Discovering Interpretable Multi-Parameter Control Policies for Evolutionary Algorithms Using Deep Reinforcement Learning

arXiv:2606.10129v1 Announce Type: new Abstract: While deep Reinforcement Learning (deep-RL) has been increasingly applied to parameter control in evolutionary algorithms, rigorous theoretical analysis

discussed more here:

discussed more here: I was first to call the death of tokenmaxxing (ahead of @zerohedge), among the first (in 2023) to warn that the economics of Generative AI was problematic, and first (also 2023) t

safetygary-marcus--x

Disjoint or Overlapping? Inference Windowing for Reconstruction-Based Time Series Anomaly Detection

arXiv:2606.09874v1 Announce Type: new Abstract: Reconstruction-based methods are widely used for time series anomaly detection, where models are trained to reconstruct subsequences, and anomalies are

Dissect and Prune: Enhancing Robustness in AI-Generated Image Detection

arXiv:2606.10309v1 Announce Type: new Abstract: While existing AI-generated image detectors report high performance, we identify that this is largely driven by a critical prediction asymmetry: a bias

safetyarxiv-cs-cv

Divide-and-Conquer Modeling for the CTF-4-Science Lorenz Benchmark

arXiv:2606.10084v1 Announce Type: cross Abstract: This work presents a divide-and-conquer modeling strategy for the CTF-4-Science Lorenz benchmark, which evaluates chaotic-system prediction across twe

Divide and Cooperate: Role-Decomposed Multi-Agent LLM Training with Cross-Agent Learning Signals

arXiv:2606.10684v1 Announce Type: cross Abstract: Modern language agents which perform multi-step reasoning have shown strong performance in knowledge-intensive question answering. However, existing a

Agents

Dmsh: A Multi-Agent Reinforcement Learning Framework for All-Quad Mesh Generation

arXiv:2606.10601v1 Announce Type: cross Abstract: Generating high-quality meshes for arbitrary geometries remains a fundamental bottleneck in computational engineering, often demanding heuristic tunin

agentsarxiv-cs-ai

DMT: Demographic Conditioning, Morphology-Enhanced Transformer for Cuffless Blood Pressure Estimation from PPG Signals

arXiv:2606.11125v1 Announce Type: cross Abstract: Blood pressure (BP) is a key marker for cardiovascular risk assessment and therapeutic decision-making, and Photoplethysmography (PPG) enables low-cos

tutorialsarxiv-cs-lg

Do LLMsMakeNeural Distinguishers Wise?

arXiv:2606.10692v1 Announce Type: cross Abstract: Neural distinguishers are a cryptanalysis method for symmetric-key cryptography that trains machine learning models on pairs of plaintexts and ciphert

Do Transformers Actually Help Intrusion Detection? A Temporal Sequence Evaluation on CIC-IDS2017

arXiv:2606.11098v1 Announce Type: cross Abstract: Recent deep learning approaches for network intrusion detection increasingly incorporate temporal architectures such as recurrent networks and Transfo

Do Vision-Language Models See or Guess? Measuring and Reducing Textual-Prior Reliance with a Phrasing-Controlled Benchmark

model-releasesarxiv-cs-cl

arXiv:2606.10400v1 Announce Type: new Abstract: Vision-language models (VLMs) are increasingly deployed where answers must follow from what is in the image, yet they often answer from textual priors,

Do VLMs Reason Like Engineers? A Benchmark and a Stage-wise Evaluation

arXiv:2606.10833v1 Announce Type: new Abstract: Vision-Language Models (VLMs) demonstrate strong performance on general multimodal reasoning benchmarks, yet their ability to perform engineering reason

Applications

Does Normalization Choice Matter for Causal Large Time-Series Models?

arXiv:2606.09954v1 Announce Type: cross Abstract: Large models for time-series forecasting have been emerged as a promising paradigm for training models on heterogeneous collections of signals. These

applicationsarxiv-cs-ai

Does Reasoning Preserve Alignment? On the Trustworthiness of Large Reasoning Models

arXiv:2606.11046v1 Announce Type: new Abstract: Instruction-tuned LLMs are increasingly converted into reasoning models through post-training to improve multi-step task performance. This conversion is

safetyarxiv-cs-cl

Domain Adapted Large Language Models for Additive Manufacturing

arXiv:2603.22017v2 Announce Type: replace Abstract: This work presents a collection of multi-modal domain adapted large language models built upon the instruction tuned variants of open weight models

Don't waste SAM

model-releasesarxiv-cs-cv

arXiv:2606.10696v1 Announce Type: new Abstract: Meta AI has recently released the Segment Anything Model (SAM), which demonstrates exceptional zero-shot image segmentation performance across various t

Drawing with Strangers: Population Scaling Drives Zero-Shot Mutual Intelligibility in Emergent Sketching

arXiv:2606.10582v1 Announce Type: cross Abstract: Generalization in emergent communication has largely focused on novel inputs or linguistic structures, yet the capacity for agents to communicate with

Dropout-GRPO: Variational Stochasticity for Continuous Latent Reasoning

arXiv:2606.10184v1 Announce Type: cross Abstract: Group Relative Policy Optimization (GRPO) relies on the diversity of K rollouts within each group; otherwise, the group-mean advantage A^{(k)} = r^{(k

safetyarxiv-cs-ai

Dual-Branch Gated Fusion for Open-Set Audio Deepfake Source Tracing

arXiv:2606.10223v1 Announce Type: cross Abstract: Attributing a synthetic utterance to its originating system remains an open challenge: closed-set models fail to reject unseen synthesizers and produc

Local Ai

Dual-stream attention-guided learning for weakly supervised whole slide image classification

arXiv:2505.23341v3 Announce Type: replace Abstract: Whole slide images (WSIs) play a crucial role in cancer diagnosis due to their ultra-high resolution and rich morphological information, and multipl

local-aiarxiv-cs-cv

Duality for Optimal Multi-Item, Multi-Bidder Auction Design: Revenue Certificates through Deep Learning

arXiv:2606.10112v1 Announce Type: cross Abstract: Characterizing revenue-optimal auctions for multi-item, multi-bidder settings remains a fundamental open problem, with no known closed-form solution e

DUET -- Dual User Embedding Transformers for Offsite Conversion Prediction

arXiv:2606.10243v1 Announce Type: new Abstract: Offsite conversion rate (OCVR) prediction is an important ranking problem in computational recommendation systems. This task presents a modeling challen

Dynamic Linear Attention

arXiv:2606.10650v1 Announce Type: cross Abstract: The scalability of Large Language Models (LLMs) to long contexts is fundamentally constrained by the quadratic complexity of standard attention, motiv

Dynamics of Adversarial Attacks on Large Language Model-Based Search Engines

arXiv:2501.00745v3 Announce Type: replace-cross Abstract: The increasing integration of Large Language Model (LLM) based search engines has transformed the landscape of information retrieval. However,

Early-Token Confidence Predicts Reasoning Quality in Multi-Agent LLM Debate

arXiv:2606.10307v1 Announce Type: new Abstract: Evaluating reasoning quality in multi-agent LLM systems is challenging, especially for open-ended tasks without reference answers. We investigate whethe

safetyarxiv-cs-cl

Earth-OneVision: Extending Remote Sensing Multimodal Large Language Models to More Sensor Modalities and Tasks

arXiv:2606.10819v1 Announce Type: cross Abstract: RS-MLLMs enable natural-language understanding and spatial reasoning over earth observation imagery. However, existing models support only a narrow ra

Easy solution to slow down recursive AI self improvement: - The lab with the top-ranked model must agree THEY must not use it for working on…

tutorialsjeremy-howard--x

Easy solution to slow down recursive AI self improvement: - The lab with the top-ranked model must agree THEY must not use it for working on frontier AI - But everyone else should have access to it. B

EEVEE: Towards Test-time Prompt Learning in the Real World for Self-Improving Agents

arXiv:2606.11182v1 Announce Type: cross Abstract: In this paper, we propose EEVEE, the first multi-dataset test-time prompt learning framework for LLM agents, enabling test-time prompt learning under

Effective Reinforcement Learning for Agentic Search by Recycling Zero-Variance Queries During Training

arXiv:2606.10709v1 Announce Type: cross Abstract: The use of GRPO-style algorithms has become the standard strategy for training LLM search agents under outcome-only rewards. With these algorithms, a

Effective Training Principles of Physical Reservoirs

arXiv:2606.10130v1 Announce Type: cross Abstract: Reservoir computers benefit from the inherent complexity of optical phenomena, which provide rich, often nonlinear dynamics. However, training directl

Efficient AI-Inspired Reduction of Feynman Integrals via Tube Seeding

arXiv:2606.10698v1 Announce Type: cross Abstract: In this paper, we use machine learning to discover a new seeding strategy for integration-by-parts reduction of Feynman integrals, which is a frequent

Efficient RWKV-based Representation Learning for 3D Point Clouds

model-releasesarxiv-cs-cv

arXiv:2606.10395v1 Announce Type: new Abstract: The recent receptance weighted key value (RWKV) model combines RNN-style recurrence, offering a linear-complexity alternative to Transformers' quadratic

Efficient-WAM: A 1B-Parameter World-Action Model with Low-Cost Future Imagination

model-releasesarxiv-cs-ro

arXiv:2606.10040v1 Announce Type: new Abstract: World-Action Models (WAMs) have emerged as a promising paradigm for embodied control by coupling future visual prediction with action generation. Howeve

Efficiently Learning Drifting Halfspaces with Massart Noise

arXiv:2606.11149v1 Announce Type: new Abstract: We study the problem of learning a drifting concept in the presence of Massart noise. In this framework, an online learner has access to a history of in

EM-Fall: Embodied mmWave Sensing for Day-and-Night Fall Detection on Humanoid Robots

arXiv:2606.11109v1 Announce Type: new Abstract: Falls are one of the leading causes of injury and hospitalization among elderly individuals, making reliable fall awareness an essential capability for

safetyarxiv-cs-ro