Search: “models”

PiXTime: A Model for Federated Time Series Forecasting with Heterogeneous Data across Nodes

model-releasesarxiv-cs-ai

arXiv:2601.05613v2 Announce Type: replace-cross Abstract: While collaborative forecasting on distributed time series is highly desirable, directly pooling localized datasets is often impractical due t

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Safety

Reason--Imagine--Act: Closed-Loop LLM Decision Making with World Models for Autonomous Driving

DGX agent

arXiv:2605.24004v1 Announce Type: new Abstract: Large language models (LLMs) are promising for autonomous driving, but semantics-only decision policies can yield physically unsafe behavior in dynamic

safetyarxiv-cs-ai

26 May 2026

SAE-FD: Sparse Autoencoder Feature Distillation for Continual Learning of Large Language Models

arXiv:2605.25525v1 Announce Type: new Abstract: Continual learning enables large language models to adapt to evolving tasks without retraining from scratch, yet catastrophic forgetting remains a centr

Structural Abstraction as an Inductive Bias for Non-Stationary Language Model Training

arXiv:2603.17198v2 Announce Type: replace-cross Abstract: A foundational principle in cognitive science holds that intelligent agents do not learn by storing experiences as isolated instances, but by

TimeSpot: Benchmarking Geo-Temporal Understanding in Vision-Language Models in Real-World Settings

arXiv:2603.06687v2 Announce Type: replace-cross Abstract: Geo-temporal understanding, the ability to infer location, time, and contextual properties from visual input alone, underpins applications suc

Universal Boosts, Specific Suppressors: Sparse Autoencoder Steering of Medical Vision-Language Models

arXiv:2605.24977v1 Announce Type: cross Abstract: Medical vision-language models (VLMs) often hallucinate findings when generating chest X-ray reports: they fabricate findings that are not present in

VeriTrace: Evolving Mental Models for Deep Research Agents

model-releasesarxiv-cs-ai

arXiv:2605.26081v1 Announce Type: new Abstract: Deep research agents face vast, interdependent, and pervasively uncertain information. Existing systems explore what evolving intermediate representatio

Beyond VLM-Based Rewards: Diffusion-Native Latent Reward Modeling

arXiv:2602.11146v2 Announce Type: replace-cross Abstract: Preference optimization for diffusion and flow-matching models relies on reward functions that are both discriminatively robust and computatio

safetyarxiv-cs-ai

Differences in Typological Alignment in Language Models' Treatment of Differential Argument Marking

arXiv:2602.17653v2 Announce Type: replace Abstract: Recent work has shown that language models (LMs) trained on synthetic corpora can exhibit typological preferences that resemble cross-linguistic reg

Dithering Defense: Adversarial Robustness of Vision Foundation Models via Multi-Level Floyd-Steinberg Dithering

arXiv:2605.23065v1 Announce Type: cross Abstract: Vision foundation models are widely used as frozen backbones across many downstream tasks, making them a single point of failure under adversarial att

researcharxiv-cs-ai

GEM-4D: Geometry-Enhanced Video World Models for Robot Manipulation

arXiv:2605.22882v1 Announce Type: new Abstract: Video world models can generate realistic futures from a single instruction, but they often fail to preserve consistent point-level motion over time. As

applicationsarxiv-cs-cv

How Human-Like Are Large Language Models? A Register-Aware Linguistic Evaluation Framework

arXiv:2605.23651v1 Announce Type: new Abstract: While factual correctness and task-performance have been in focus of Large Language Model (LLM) research for a long time, the fundamental question of ho

applicationsarxiv-cs-cl

Tutorials

Learnability-Informed Fine-Tuning of Diffusion Language Models

arXiv:2605.22939v1 Announce Type: new Abstract: We aim to improve the reasoning capabilities of diffusion language models (DLMs). While SFT is a popular post-training recipe for autoregressive models,

tutorialsarxiv-cs-cl

Tutorials

VAMP-Diff: VampPrior Latent Diffusion for Photoplethysmography Modeling

arXiv:2605.22851v1 Announce Type: cross Abstract: Photoplethysmography (PPG) has become a ubiquitous physiological signal; however, current generative models still struggle to preserve realistic wavef

tutorialsarxiv-cs-lg

What Does the Server See? Understanding Privacy Leakage from Large Language Models in Split Inference

arXiv:2605.23158v1 Announce Type: cross Abstract: The deployment of large language models (LLMs) on resource-constrained devices remains challenging, spurring interest in split inference, where models

researcharxiv-cs-cl

A Diffusive Classification Loss for Learning Energy-based Generative Models

arXiv:2601.21025v3 Announce Type: replace-cross Abstract: Score-based generative models have recently achieved remarkable success. While they are usually parameterized by the score, an alternative way

Bringing Stability to Diffusion: Decomposing and Reducing Variance of Training Masked Diffusion Models

arXiv:2511.18159v2 Announce Type: replace Abstract: Masked diffusion models (MDMs) are a promising alternative to autoregressive models (ARMs), but they suffer from inherently much higher training var

CellFluxRL: Biologically-Constrained Virtual Cell Modeling via Reinforcement Learning

arXiv:2603.21743v4 Announce Type: replace Abstract: Building virtual cells with generative models to simulate cellular behavior in silico is emerging as a promising paradigm for accelerating drug disc

DecepChain: Inducing Deceptive Reasoning in Large Language Models

arXiv:2510.00319v2 Announce Type: replace Abstract: Large Language Models (LLMs) have been demonstrating strong reasoning capability with their chain-of-thoughts (CoT), which are routinely used by hum

safetyarxiv-cs-lg

Tutorials

MDM-Prime-v2: Binary Encoding and Index Shuffling Enable Scaling of Diffusion Language Models

arXiv:2603.16077v3 Announce Type: replace Abstract: Masked diffusion models (MDM) exhibit superior generalization when learned using a Partial masking scheme (Prime). This approach converts tokens int

tutorialsarxiv-cs-lg

Soft Bayesian Context Tree Models for Real-Valued Time Series

arXiv:2601.11079v2 Announce Type: replace Abstract: This paper proposes the soft Bayesian context tree model (Soft-BCT), which is a novel BCT model for real-valued time series. The Soft-BCT considers

Boiling the Frog: A Multi-Turn Benchmark for Agentic Safety

arXiv:2605.22643v1 Announce Type: new Abstract: Background. Traditional safety benchmarks for language models evaluate generated text: whether a model outputs toxic language, reproduces bias, or follo

Circle-RoPE: Cone-like Decoupled Rotary Positional Embedding for Large Vision-Language Models

arXiv:2505.16416v3 Announce Type: replace Abstract: Rotary Position Embedding (RoPE) is widely adopted in large language models, but when applied to vision-language models (VLMs) it couples text and i

safetyarxiv-cs-cv

Comparing LLM and Fine-Tuned Model Performance on NVDRS Circumstance Extraction with Varying Prompt Complexity

arXiv:2605.21845v1 Announce Type: new Abstract: Suicide is a leading cause of death in the United States, and understanding the circumstances that precede it requires extracting structured information

GesVLA: Gesture-Aware Vision-Language-Action Model Embedded Representations

arXiv:2605.22812v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models have shown strong potential for general-purpose robot manipulation by unifying perception and action. However, exi

applicationsarxiv-cs-cv

Local Ai

I built a free demo for Pixal3D (Tencent new image-to-3D model)

local-air-stablediffusion

Pixal3D is a Tencent image-to-3D model that generates high-fidelity 3D assets from a single image by explicitly lifting pixel features into 3D through back-projection to establish direct pixel-to-3D c

Open Materials 2024 (OMat24) Inorganic Materials Dataset and Models

arXiv:2410.12771v2 Announce Type: replace-cross Abstract: The ability to discover new materials with desirable properties is critical for numerous applications from helping mitigate climate change to

researcharxiv-cs-ai

Hardware

PALS: Power-Aware LLM Serving for Mixture-of-Experts Models

arXiv:2605.21427v1 Announce Type: new Abstract: Large language model (LLM) inference has become a dominant workload in modern data centers, driving significant GPU utilization and energy consumption.

hardwarearxiv-cs-ai

SDGBiasBench: Benchmarking and Mitigating Vision--Language Models' Biases in Sustainable Development Goals

model-releasesarxiv-cs-cv

arXiv:2605.21919v1 Announce Type: new Abstract: Assessing progress toward the Sustainable Development Goals (SDGs) requires multi-step reasoning over visual cues, contextual knowledge, and development

Industry

Today, Zyphra Research is sharing fundamental work extending Equilibrium Propagation beyond Energy-Based Models to biologically realistic ne…

Today, Zyphra Research is sharing fundamental work extending Equilibrium Propagation beyond Energy-Based Models to biologically realistic neuron models. A step toward more efficient AI, local learning

industryemad-mostaque--x

UniSD: Towards a Unified Self-Distillation Framework for Large Language Models

arXiv:2605.06597v2 Announce Type: replace Abstract: Self-distillation (SD) offers a promising path for adapting large language models (LLMs) without relying on stronger external teachers. However, SD

Deeper Thought, Weaker Aim: Understanding and Mitigating Perceptual Impairment during Reasoning in Multimodal Large Language Models

arXiv:2603.14184v2 Announce Type: replace Abstract: Multimodal large language models (MLLMs) often suffer from perceptual impairments under extended reasoning modes, particularly in visual question an

Ensemble RL through Classifier Models: Enhancing Risk-Return Trade-offs in Trading Strategies

arXiv:2502.17518v2 Announce Type: replace Abstract: This paper presents a comprehensive study on the use of ensemble Reinforcement Learning (RL) models in financial trading strategies, leveraging clas

EvalMORAAL: Interpretable Chain-of-Thought and LLM-as-Judge Evaluation for Moral Alignment in Large Language Models

arXiv:2510.05942v3 Announce Type: replace Abstract: We present EvalMORAAL, a transparent chain-of-thought (CoT) framework that uses two scoring methods (log-probabilities and direct ratings) plus a mo

Local Ai

Explainable AI: Context-Aware Layer-Wise Integrated Gradients for Explaining Transformer Models

arXiv:2602.16608v2 Announce Type: replace Abstract: Transformer models achieve state-of-the-art performance across domains and tasks, yet their deeply layered representations make their predictions di

local-aiarxiv-cs-cl

Industry

Grok 4.3 stands out for being the most intelligent model in its price range

Grok 4.3 stands out for being the most intelligent model in its price range We built a live job board of all the frontier labs hiring right now. Plus a way to see news, funding rounds, compare model r

industryelon-musk--x

Hybrid Machine Learning Model for Forest Height Estimation from TanDEM-X and Landsat Data

arXiv:2605.20997v1 Announce Type: new Abstract: Integrating machine learning (ML) with physical models (PM) has emerged as a promising way of retrieving geophysical parameters from remote sensing data

Lighting-aware Unified Model for Instance Segmentation

arXiv:2605.20436v1 Announce Type: new Abstract: Foundation models like the Segment Anything Model (SAM) demonstrate impressive zero-shot generalization but frequently degrade under diverse real-world

applicationsarxiv-cs-cv

MedicalBench: Evaluating Large Language Models Toward Improved Medical Concept Extraction

arXiv:2605.20197v1 Announce Type: new Abstract: Medical concept extraction from electronic health records underpins many downstream applications, yet remains challenging because medically meaningful c

Miller-Index-Based Latent Crystallographic Fracture Plane Reasoning with Vision-Language Models

arXiv:2605.20416v1 Announce Type: new Abstract: We study whether multimodal large language models (MLLMs) can leverage crystallographic plane indices (Miller indices) as a structured latent representa

applicationsarxiv-cs-lg

Neural Estimation of Pairwise Mutual Information in Masked Discrete Sequence Models

arXiv:2605.20187v1 Announce Type: new Abstract: Understanding dependencies between variables is critical for interpretability and efficient generation in masked diffusion models (MDMs), yet these mode

Hardware

PulseCol: Periodically Refreshed Column-Sparse Attention for Accelerating Diffusion Language Models

arXiv:2605.20813v1 Announce Type: new Abstract: Inference in diffusion large language models (dLLMs) is computationally expensive, as full self-attention must be repeatedly executed at each step of th

hardwarearxiv-cs-cl

Q-ARVD: Quantizing Autoregressive Video Diffusion Models

arXiv:2605.21072v1 Announce Type: new Abstract: Autoregressive video diffusion models (ARVDs) have emerged as a promising architecture for streaming video generation, paving the way for real-time inte

RISE: Reliable Improvement in Self-Evolving Vision-Language Models

arXiv:2605.20914v1 Announce Type: new Abstract: Vision-language models (VLMs) have achieved strong multimodal reasoning capabilities, but further improving them still relies heavily on large-scale hum

Synchronization and Turn-Taking in Full-Duplex Speech Dialogue Models

arXiv:2605.20356v1 Announce Type: new Abstract: Full-duplex spoken dialogue models (SDMs) can listen and speak simultaneously, enabling interaction dynamics closer to human conversation than turn-base

TabPFN Extensions for Interpretable Geotechnical Modelling

model-releasesarxiv-cs-lg

arXiv:2603.21033v2 Announce Type: replace-cross Abstract: Geotechnical site characterisation relies on sparse, heterogeneous borehole data, where uncertainty quantification and interpretability matter

Under Pressure: Emotional Framing Induces Measurable Behavioral Shifts and Structured Internal Geometry in Small Language Models

arXiv:2605.20202v1 Announce Type: new Abstract: I study whether emotionally framed evaluation follow-ups change both the behavior and the calm-relative internal representations of small, locally deplo

Can Large Language Models Reliably Correct Errors in Low-Resource ASR? A Contamination-Aware Case Study on West Frisian

arXiv:2605.19711v1 Announce Type: new Abstract: Automatic speech recognition (ASR) has improved substantially in recent years, yet performance remains limited for low-resource languages. Large languag