Search: “models”

arXiv:2607.03551v1 Announce Type: new Abstract: Weight space learning aims to learn representations of neural network (NN) weights, enabling different downstream tasks. Existing approaches show promis

tutorialsarxiv-cs-lg

7 Jul 2026

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Agents

A Practice Auditing Framework for Large Language Model Use: Collective Empiricism, Pseudo-Rational Cognition, and Governance of AI-Generated Content

DGX agent

arXiv:2607.01248v1 Announce Type: cross Abstract: Large language models are increasingly used for knowledge acquisition, code generation, academic writing, and agent-based automation. In these setting

agentsarxiv-cs-ai

3 Jul 2026

BamiBERT: A New BERT-based Language Model for Vietnamese

arXiv:2607.02259v1 Announce Type: new Abstract: In this paper, we introduce BamiBERT, a new BERT-based pre-trained language model for Vietnamese that addresses key limitations of PhoBERT -- the curren

researcharxiv-cs-cl

CreativityNeuro: Steering Language Model Weights to Improve Divergent Thinking and Reduce Mode Collapse

arXiv:2607.01433v1 Announce Type: new Abstract: Divergent thinking is a crucial aspect of creativity, yet large language models (LLMs) tend to consistently generate similar responses to open-ended que

DriveVLM-RL: Neuroscience-Inspired Reinforcement Learning with Vision-Language Models for Safe and Deployable Autonomous Driving

arXiv:2603.18315v2 Announce Type: replace-cross Abstract: Traditional reinforcement learning (RL) methods rely on manually engineered rewards or sparse collision signals, which fail to capture the ric

Estimating Individualized Treatment Effects in Acute Ischemic Stroke with Causal Transformation Models (TRAM-DAG): A Multi-Centre Observational Study with External RCT Validation

arXiv:2606.12623v3 Announce Type: replace-cross Abstract: Personalized medicine in acute ischemic stroke requires moving beyond average treatment effects (ATE) to individualized treatment effect (ITE)

Large language models reshape the language of science

arXiv:2504.12317v2 Announce Type: replace Abstract: Scientific language is a central infrastructure of knowledge production, but it remains unclear whether large language models (LLMs) are altering no

applicationsarxiv-cs-cl

Leveraging Metamemory Agent for Enhanced Data-Free Code Generation in Large Language Models

arXiv:2501.07892v2 Announce Type: replace-cross Abstract: Large language models (LLMs) have shown strong performance in automated code generation, with few-shot prompting widely used for its simplicit

agentsarxiv-cs-ai

Optimizing Visual Generative Models via Distribution-wise Rewards

arXiv:2607.02291v1 Announce Type: new Abstract: Conventional reinforcement learning strategies for visual generation typically employ sample-wise reward functions, yet this practice frequently results

safetyarxiv-cs-lg

Local Ai

Procedural Memory Distillation: Online Reflection for Self-Improving Language Models

arXiv:2607.01480v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards (RLVR), along with recent selfdistillation variants such as SDPO, evaluates each rollout against a verifi

local-aiarxiv-cs-ai

Safe and Adaptive Cloud Healing: Verifying LLM-Generated Recovery Plans with a Neural-Symbolic World Model

arXiv:2607.01595v1 Announce Type: new Abstract: As the scale and complexity of cloud-based AI systems continue to escalate, ensuring service reliability through rapid fault detection and adaptive reco

Scaling Latent Reasoning via Looped Language Models

arXiv:2510.25741v5 Announce Type: replace Abstract: Modern LLMs are trained to 'think' primarily via explicit text generation, such as chain-of-thought (CoT), which defers reasoning to post-training a

researcharxiv-cs-cl

Towards Cellular-Scale Interpretability in Pathology Foundation Models for Biomarker Assessment

arXiv:2511.05150v2 Announce Type: replace-cross Abstract: Molecular biomarker testing in pathology is often costly and tissue-consuming, limiting scalable clinical deployment. Artificial intelligence

Transport Discrepancy as a Reliability Signal for Vision-Language-Action Models

arXiv:2512.01715v2 Announce Type: replace Abstract: Vision-language-action (VLA) models that generate continuous action chunks via flow matching lack an internal signal for judging whether a given pre

safetyarxiv-cs-ro

Local Ai

VT-WAM: Visual-Tactile World Action Model for Contact-Rich Manipulation

arXiv:2607.02503v1 Announce Type: new Abstract: Contact-rich manipulation requires policies to react to local deformation, pressure, slip, and friction, yet these cues are temporally sparse and often

local-aiarxiv-cs-ro

When Sample Selection Bias Precipitates Model Collapse

arXiv:2606.13732v2 Announce Type: replace Abstract: The proliferation of recursive training on synthetic data can alleviate data scarcity but risks model collapse, where repeated training erodes distr

Zeus: Towards Tuning-Free Foundation Model for Time Series Analysis

arXiv:2607.01918v1 Announce Type: new Abstract: We present Zeus, a unified tuning-free Time Series Foundation Model (TSFM) that delivers superior performance across diverse analysis tasks without any

Do Time Series Foundation Model Benchmarks Hide Regime-Dependent Failures? Evidence from Traffic Speed Forecasting

arXiv:2606.18367v2 Announce Type: replace Abstract: Standard benchmarks evaluate time series foundation models (TSFMs) using aggregate metrics, but these can mask severe failures in critical operating

Path Planning in Physically Viable World Models

arXiv:2607.00673v1 Announce Type: new Abstract: Robots deployed in unstructured outdoor environments often plan from scene reconstructions collected before deployment because operators cannot remap la

researcharxiv-cs-ro

Rethinking Robust Adversarial Concept Erasure in Diffusion Models

arXiv:2510.27285v4 Announce Type: replace Abstract: Concept erasure methods aim to remove specific unsafe target concepts in diffusion models while preserving image generation utility. To address the

researcharxiv-cs-cv

Hardware

SNAP-FM: Sparse Nonlinear Accelerated Projection for Physics-Constrained Generative Modeling

arXiv:2607.00095v1 Announce Type: cross Abstract: Generative models have emerged as scalable surrogates for physical simulation, yet they offer no guarantee that their outputs respect the conservation

hardwarearxiv-cs-ai

TRCGL-Net: A Long-Tailed Multi-Label Chest X-Ray Classification Framework with Generative Data Augmentation and Label Co-Occurrence Modeling

arXiv:2607.00975v1 Announce Type: cross Abstract: Chest X-ray multi-label classification is a core task in intelligent medical imaging diagnosis. However, real clinical data often exhibit extreme long

VLM-AR3L: Vision-Language Models for Absolute and Relative Rewards in Reinforcement Learning

arXiv:2607.00483v1 Announce Type: new Abstract: Designing effective reward functions remains a major challenge in reinforcement learning (RL), particularly in open-ended environments where task goals

agentsarxiv-cs-ro

Zero-Shot Distracted Driver Detection via Vision Language Models with Double Decoupling

arXiv:2601.08467v2 Announce Type: replace Abstract: Distracted driving is a major cause of traffic collisions, calling for robust and scalable detection methods. Vision-language models (VLMs) enable s

safetyarxiv-cs-cv

Adapting Foundation ASR Models to Dysarthric Speech: A Case Study

arXiv:2606.31722v1 Announce Type: new Abstract: Automatic speech recognition (ASR) systems often perform poorly in dysarthric speech, limiting their usefulness to affected speakers in everyday communi

applicationsarxiv-cs-cl

Diffusion Crossover: Defining Evolutionary Recombination in Diffusion Models via Noise Sequence Interpolation

arXiv:2604.14790v2 Announce Type: replace Abstract: Interactive Evolutionary Computation (IEC) provides a powerful framework for optimizing subjective criteria such as human preferences and aesthetics

FedXDS: Leveraging Model Attribution Methods to counteract Data Heterogeneity in Federated Learning

arXiv:2606.31742v1 Announce Type: cross Abstract: Explainable AI (XAI) methods have demonstrated significant success in recent years at identifying relevant features in input data that drive deep lear

Interface-Aware Neural Newton Preconditioning for Robust Cohesive Zone Model Simulations

arXiv:2606.31921v1 Announce Type: new Abstract: Cohesive Zone Models (CZMs) are widely used to simulate interface fracture, delamination, adhesive failure, and fiber--matrix debonding in aerospace com

Learning Gaussian Graphical Models from a Glauber Trajectory Without Mixing

arXiv:2606.31230v1 Announce Type: new Abstract: We study the task of learning the structure of a d-sparse Gaussian graphical model on n variables from a single trajectory of Glauber dynamics. Beyond a

Mean-Field Model for Two-Layer Neural Networks Trained with Consensus-Based Optimization

arXiv:2511.21466v3 Announce Type: replace Abstract: We study Consensus-Based Optimization (CBO) for two-layer neural network training. We compare the performance of CBO against Adam on two test cases

On Optimizing Multimodal Jailbreaks for Spoken Language Models

arXiv:2603.19127v2 Announce Type: replace Abstract: As Spoken Language Models (SLMs) integrate speech and text modalities, they inherit the safety vulnerabilities of their LLM backbone while introduci

safetyarxiv-cs-lg

Tutorials

Region-Aware Multimodal Large Language Model via SlowFast Tokenization and Pseudo-Mask Guidance for 3D CT Report Generation

arXiv:2506.23102v2 Announce Type: replace-cross Abstract: Current CT report generation frameworks predominantly rely on global feature representations, often failing to capture region-specific details

tutorialsarxiv-cs-cv

Robust Text Watermarking for Large Language Models via Dual Semantic Embeddings

arXiv:2606.31602v1 Announce Type: new Abstract: This work presents Dual-Embedding Watermarking (DEW), a semantic watermarking scheme for large language models (LLMs) that leverages contextual and toke

safetyarxiv-cs-cl

SAGE: A Search-AuGmented Evaluation of Large Language Models on Free-Form QA

arXiv:2504.07385v3 Announce Type: replace-cross Abstract: As Large Language Models (LLMs) become increasingly used for question-answering (QA), relying on static, pre-annotated references for evaluati

agentsarxiv-cs-ai

Wavelet-Optimized Pseudo-3D Accelerated Diffusion Model for Truncated Computed Laminography

arXiv:2606.31318v1 Announce Type: new Abstract: Computed Laminography (CL) is a key technology for the nondestructive testing of large plate-shaped objects. However, field-of-view (FOV) limitations in

applicationsarxiv-cs-cv

Wind and State Estimation on SE(3): Comparative Evaluation of EKF and UKF with Continuous and Discrete Quadrotor Models

arXiv:2606.30804v1 Announce Type: new Abstract: Use of quadrotor UAVs for wind velocity estimation is gaining popularity in recent studies, leveraging their maneuverability, compact size and low cost.

researcharxiv-cs-ro

ZEBRA: Zero-Shot Entropy-Regularized Prompt Learning for Base-to-Novel Generalization in Audio-Language Models

arXiv:2606.31587v1 Announce Type: cross Abstract: Audio-Language Models (ALMs) achieve strong zero-shot performance by aligning audio with textual class descriptions. Although prompt learning improves

Adam's Law: Textual Frequency Law on Large Language Models

arXiv:2604.02176v3 Announce Type: replace Abstract: While textual frequency has been validated as relevant to human cognition in reading speed, its relatedness to Large Language Models (LLMs) is seldo

agentsarxiv-cs-cl

Agentic Tool Use in Large Language Models

arXiv:2604.00835v2 Announce Type: replace Abstract: Large language models are increasingly being deployed as autonomous agents yet their real world effectiveness depends on reliable tools for informat

safetyarxiv-cs-cl

Analyzing Uncertainty in the Spatial Representation of the Kinematic Bicycle Model

arXiv:2606.29566v1 Announce Type: new Abstract: Locating a vehicle and determining its orientation in an uncertain environment is a critical challenge in autonomous vehicle navigation and path plannin

agentsarxiv-cs-ro

Bandwidth Selection in Kernel Density Estimation for Model Calibration

arXiv:2606.29925v1 Announce Type: new Abstract: As deep learning models are increasingly deployed in high-stakes applications, providing well-calibrated uncertainty estimates has become as critical as

safetyarxiv-cs-lg

Characterizing Large Language Model Agentic Workflows: A Study on N8n Ecosystem

arXiv:2606.29116v1 Announce Type: new Abstract: Large Language Models (LLMs) are rapidly being adopted in low-code and no-code automation platforms, where non-expert users design workflows that combin

CLMASP: Coupling Large Language Models with Answer Set Programming for Robotic Task Planning

arXiv:2406.03367v2 Announce Type: replace Abstract: Large Language Models (LLMs) possess extensive foundational knowledge and moderate reasoning abilities, making them suitable for general task planni

COMPASS: Grounding Composition-Intent Guidance in Unified Multimodal Models

arXiv:2606.28696v1 Announce Type: new Abstract: Composition is a high-level visual intent that governs where subjects are placed and how a scene is organized, yet current unified multimodal models rem

Connectivity Estimation using Stochastic Graph Heat Modelling

arXiv:2606.29098v1 Announce Type: cross Abstract: A growing number of techniques leverage the spatial structures that underlie many real-world datasets. Despite these advances, the complementary task

applicationsarxiv-cs-lg

Conversational Domain Adaptation of IndicTrans2 across 21 Indic Languages via Experience Replay and Model Soups

arXiv:2606.29024v1 Announce Type: new Abstract: IndicTrans2 is the strongest open English to Indic translation system, but like most systems it is trained on general text and tends to sound stiff on c

researcharxiv-cs-cl

ENC-ODE: Event-level Neurodegenerative Modeling in Continuous Time with Neural ODEs

arXiv:2606.30398v1 Announce Type: new Abstract: Accurately predicting the temporal evolution of clinical biomarkers is crucial for the early diagnosis and management of neurodegenerative diseases such

Hardware

Enhanced Diffusion Sampling: Efficient Rare Event Sampling and Free Energy Calculation with Diffusion Models

arXiv:2602.16634v2 Announce Type: replace-cross Abstract: The rare-event sampling problem has long been the central limiting factor in molecular dynamics (MD), especially in biomolecular simulation. R

hardwarearxiv-cs-ai