Search: “models”

Closed-Loop CO2 Storage Control With History-Based Reinforcement Learning and Latent Model-Based Adaptation

arXiv:2605.02405v1 Announce Type: new Abstract: Closed-loop management of geological CO2 storage requires control policies that adapt to uncertain reservoir behavior while relying on observations that

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Research

Continuous Temporal Representations of Event-Based Signals via Interference-Based Wave Modeling

DGX agent

arXiv:2605.01270v1 Announce Type: new Abstract: Spatio-temporal signals arising from event-driven biological processes, such as surface electromyography (sEMG), exhibit asynchronous and highly structu

researcharxiv-cs-lg

5 May 2026

Deep learning-based pavement performance modeling using multiple distress indicators and road work history

arXiv:2605.01914v1 Announce Type: new Abstract: The deterioration of pavement is a complex and dynamic process determined by different factors including material, environment, design, and some other u

Disciplined Diffusion: Text-to-Image Diffusion Model against NSFW Generation

arXiv:2605.01113v1 Announce Type: new Abstract: Text-to-image (T2I) diffusion models have the ability to build high-quality pictures from text prompts, but they pose safety concerns because they can g

safetyarxiv-cs-cv

Emergence of Nonequilibrium Latent Cycles in Unsupervised Generative Modeling

arXiv:2512.11415v2 Announce Type: replace-cross Abstract: We show that nonequilibrium dynamics can play a constructive role in unsupervised machine learning by inducing the spontaneous emergence of la

Foundation Models to Unlock Real-World Evidence from Nationwide Medical Claims

arXiv:2605.02740v1 Announce Type: cross Abstract: Evidence derived from large-scale real-world data (RWD) is increasingly informing regulatory evaluation and healthcare decision-making. Administrative

safetyarxiv-cs-cl

Generalized Category Discovery under Domain Shifts: From Vision to Vision-Language Models

arXiv:2605.00906v1 Announce Type: new Abstract: Generalized Category Discovery (GCD) aims to categorize unlabelled instances from both known and unknown classes by transferring knowledge from labelled

applicationsarxiv-cs-cv

Generative Interfaces for Language Models

arXiv:2508.19227v3 Announce Type: replace Abstract: Large language models (LLMs) are increasingly seen as assistants, copilots, and consultants, capable of supporting a wide range of tasks through nat

Hey, That's My Data! Token-Only Dataset Inference in Large Language Models

arXiv:2506.06057v2 Announce Type: replace Abstract: Large Language Models (LLMs) rely on massive training datasets, often including proprietary data, which raises concerns about unauthorized usage and

How Reasoning Evolves from Post-Training Data: An Empirical Study Using Chess

model-releasesarxiv-cs-lg

arXiv:2604.05134v2 Announce Type: replace Abstract: We study how reasoning evolves in a language model -- from supervised fine-tuning (SFT) to reinforcement learning (RL) -- by analyzing how a set of

Agents

Large Language Models for Multi-Robot Systems: A Survey

arXiv:2502.03814v5 Announce Type: replace Abstract: The rapid advancement of Large Language Models (LLMs) has opened new possibilities in Multi-Robot Systems (MRS), enabling enhanced communication, ta

agentsarxiv-cs-ro

Latent Bridge: Feature Delta Prediction for Efficient Dual-System Vision-Language-Action Model Inference

arXiv:2605.02739v1 Announce Type: new Abstract: Dual-system Vision-Language-Action (VLA) models achieve state-of-the-art robotic manipulation but are bottlenecked by the VLM backbone, which must execu

researcharxiv-cs-ro

Low-rank surrogate modeling and stochastic zero-order optimization for training of neural networks with black-box layers

arXiv:2509.15113v2 Announce Type: replace Abstract: The growing demand for energy-efficient, high-performance AI systems has led to increased attention on alternative computing platforms (e.g., photon

local-aiarxiv-cs-lg

LUMINA: A Grid Foundation Model for Benchmarking AC Optimal Power Flow Surrogate Learning

model-releasesarxiv-cs-lg

arXiv:2605.02133v1 Announce Type: new Abstract: AC optimal power flow (ACOPF) is foundational yet computationally expensive in power grid operations, driving learning-based surrogates for large-scale

Medmarks: A Comprehensive Open-Source LLM Benchmark Suite for Medical Tasks

arXiv:2605.01417v1 Announce Type: new Abstract: Evaluating large language models (LLMs) for medical applications remains challenging due to benchmark saturation, limited data accessibility, and insuff

On Training Large Language Models for Long-Horizon Tasks: An Empirical Study of Horizon Length

arXiv:2605.02572v1 Announce Type: cross Abstract: Large language models (LLMs) have shown promise as interactive agents that solve tasks through extended sequences of environment interactions. While p

RADMI: Latent Information Aggregation as a Proxy for Model Uncertainty

model-releasesarxiv-cs-cv

arXiv:2605.01502v1 Announce Type: new Abstract: Epistemic uncertainty estimation is essential for identifying regions where deep learning system outputs may be unreliable. However, existing approaches

RefusalGuard: Geometry-Preserving Fine-Tuning for Safety in LLMs

arXiv:2605.01913v1 Announce Type: cross Abstract: Fine-tuning safety-aligned language models for downstream tasks often leads to substantial degradation of refusal behavior, making models vulnerable t

Silicon Showdown: Performance, Efficiency, and Ecosystem Barriers in Consumer-Grade LLM Inference

model-releasesarxiv-cs-ai

arXiv:2605.00519v2 Announce Type: cross Abstract: The operational landscape of local Large Language Model (LLM) inference has shifted from lightweight models to datacenter-class weights exceeding 70B

Task-Related Token Compression in Multimodal Large Language Models from an Explainability Perspective

arXiv:2506.01097v2 Announce Type: replace Abstract: Existing Multimodal Large Language Models (MLLMs) process a large number of visual tokens, leading to significant computational costs and inefficien

safetyarxiv-cs-cv

TCDA: Thread-Constrained Discourse-Aware Modeling for Conversational Sentiment Quadruple Analysis

arXiv:2605.01717v1 Announce Type: new Abstract: Conversational Aspect-based Sentiment Quadruple Analysis (DiaASQ) needs to capture the complex interrelationships in multiple rounds of dialogues. Exist

TetraJet-v2: Accurate NVFP4 Training for Large Language Models with Oscillation Suppression and Outlier Control

arXiv:2510.27527v2 Announce Type: replace Abstract: Large Language Models (LLMs) training is prohibitively expensive, driving interest in low-precision fully-quantized training (FQT). While novel 4-bi

The Cylindrical Representation Hypothesis for Language Model Steering

arXiv:2605.01844v1 Announce Type: new Abstract: Steering is a widely used technique for controlling large language models, yet its effects are often unstable and hard to predict. Existing theoretical

TOC-SR: Task-Optimal Compact diffusion for Image Super Resolution

model-releasesarxiv-cs-cv

arXiv:2605.02767v1 Announce Type: new Abstract: Diffusion models have recently demonstrated strong performance for image restoration tasks, including super-resolution. However, their large model size

Turning Drift into Constraint: Robust Reasoning Alignment in Non-Stationary Environments

model-releasesarxiv-cs-cv

arXiv:2510.04142v2 Announce Type: replace Abstract: This paper identifies a critical yet underexplored challenge in reasoning alignment from multiple multi-modal large language models (MLLMs): In non-

Validation of an AI-based end-to-end model for prostate pathology using long-term archived routine samples

arXiv:2605.02614v1 Announce Type: new Abstract: Artificial intelligence (AI) is becoming a clinical tool for prostate pathology, but generalization across variations in sample preparation and preserva

researcharxiv-cs-cv

Where Do Prompt Perturbations Break Generation? A Segment-Level View of Robustness in LoRA-Tuned Language Models

arXiv:2605.01605v1 Announce Type: new Abstract: Large language models are sensitive to minor prompt perturbations, yet existing robustness methods usually enforce consistency at the whole-sequence lev

Zero-Shot Adaptation of Behavioral Foundation Models to Unseen Dynamics

arXiv:2505.13150v2 Announce Type: replace Abstract: Behavioral Foundation Models (BFMs) proved successful in producing policies for arbitrary tasks in a zero-shot manner, requiring no test-time traini

safetyarxiv-cs-lg

A unified perspective on fine-tuning and sampling with diffusion and flow models

arXiv:2605.00229v1 Announce Type: cross Abstract: We study the problem of training diffusion and flow generative models to sample from target distributions defined by an exponential tilting of a base

safetyarxiv-cs-lg

Comparative Analysis of Polygon-Based and Global Machine Learning Models for Bus Occupancy Prediction

arXiv:2605.00083v1 Announce Type: new Abstract: Accurate forecasting of bus ridership (passengers numbers) is crucial for efficient management and optimization of public transport systems. Traditional

local-aiarxiv-cs-lg

Diversity in Large Language Models under Supervised Fine-Tuning

arXiv:2605.00195v1 Announce Type: new Abstract: Supervised Fine-Tuning (SFT) is essential for aligning Large Language Models (LLMs) with user intent, yet it is believed to suppress generative diversit

Foundation AI Models for Aerosol Optical Depth Estimation from PACE Satellite Data

arXiv:2605.00678v1 Announce Type: new Abstract: Aerosol Optical Depth (AOD) retrieval is essential for Earth observation, supporting applications from air quality monitoring to climate studies. Conven

safetyarxiv-cs-cv

Graph Concept Bottleneck Models

arXiv:2508.14255v2 Announce Type: replace Abstract: Concept Bottleneck Models (CBMs) provide explicit interpretations for deep neural networks through concepts and allow intervention with concepts to

Impact of Task Phrasing on Presumptions in Large Language Models

arXiv:2605.00436v1 Announce Type: new Abstract: Concerns with the safety and reliability of applying large-language models (LLMs) in unpredictable real-world applications motivate this study, which ex

safetyarxiv-cs-cl

PrefMoE: Robust Preference Modeling with Mixture-of-Experts Reward Learning

arXiv:2605.00384v1 Announce Type: new Abstract: Preference-based reinforcement learning offers a scalable alternative to manual reward engineering by learning reward structures from comparative feedba

safetyarxiv-cs-ro

Putting HUMANS first: Efficient LAM Evaluation with Human Preference Alignment

arXiv:2605.00022v1 Announce Type: new Abstract: The rapid proliferation of large audio models (LAMs) demands efficient approaches for model comparison, yet comprehensive benchmarks are costly. To fill

Representation in large language models

arXiv:2501.00885v2 Announce Type: replace Abstract: The extraordinary success of recent Large Language Models (LLMs) on a diverse array of tasks has led to an explosion of scientific and philosophical

Resting Neurons, Active Insights: Robustify Activation Sparsity for Large Language Models

arXiv:2512.12744v3 Announce Type: replace Abstract: Activation sparsity offers a compelling route to accelerate large language model (LLM) inference by selectively suppressing hidden activations, yet

safetyarxiv-cs-lg

Unlearning What Matters: Token-Level Attribution for Precise Language Model Unlearning

arXiv:2605.00364v1 Announce Type: new Abstract: Machine unlearning has emerged as a critical capability for addressing privacy, safety, and regulatory concerns in large language models (LLMs). Existin

safetyarxiv-cs-cl

Agents

A Grid-Aware Agent-Based Model for Analyzing Electric Vehicle Charging Systems

arXiv:2604.27849v1 Announce Type: new Abstract: This paper presents a configurable, grid-aware Agent-Based Model (ABM) for the systematic analysis of electric vehicle (EV) charging systems under confi

agentsarxiv-cs-ai

AI Models for Depressive Disorder Detection and Diagnosis: A Review

arXiv:2508.12022v2 Announce Type: replace Abstract: Major Depressive Disorder is one of the leading causes of disability worldwide, yet its diagnosis still depends largely on subjective clinical asses

safetyarxiv-cs-ai

Agents

Backdoor Attacks on Prompt-Driven Video Segmentation Foundation Models

arXiv:2512.22046v2 Announce Type: replace Abstract: Prompt-driven Video Segmentation Foundation Models (VSFMs), such as SAM2, are increasingly used in applications including autonomous driving and dig

agentsarxiv-cs-cv

Beyond the Mean: Within-Model Reliable Change Detection for LLM Evaluation

model-releasesarxiv-cs-ai

arXiv:2604.27405v1 Announce Type: cross Abstract: We adapted the Reliable Change Index (RCI; Jacobson and Truax, 1991) from clinical psychology to item-level LLM version comparison on 2,000 MMLU-Pro i

Budget-Constrained Online Retrieval-Augmented Generation: The Chunk-as-a-Service Model

arXiv:2604.26981v1 Announce Type: cross Abstract: Large Language Models (LLMs) have revolutionized the field of natural language processing. However, they exhibit some limitations, including a lack of

CasLayout: Cascaded 3D Layout Diffusion for Indoor Scene Synthesis with Implicit Relation Modeling

arXiv:2604.27361v1 Announce Type: new Abstract: Synthesizing realistic 3D indoor scenes remains challenging due to data scarcity and the difficulty of simultaneously enforcing global architectural con

local-aiarxiv-cs-cv

Cross-Lingual Response Consistency in Large Language Models: An ILR-Informed Evaluation of Claude Across Six Languages

arXiv:2604.27137v1 Announce Type: new Abstract: This paper introduces a systematic evaluation framework grounded in the Interagency Language Roundtable (ILR) Skill Level Descriptions and applies it to

Detecting is Easy, Adapting is Hard: Local Expert Growth for Visual Model-Based Reinforcement Learning under Distribution Shift

arXiv:2604.27411v1 Announce Type: new Abstract: Visual model-based reinforcement learning (MBRL) agents can perform well on the training distribution, but often break down once the test environment sh

local-aiarxiv-cs-lg

Tutorials

Efficient Sparse Selective-Update RNNs for Long-Range Sequence Modeling

arXiv:2603.02226v2 Announce Type: replace Abstract: Real-world sequential signals, such as audio or video, contain critical information that is often embedded within long periods of silence or noise.

tutorialsarxiv-cs-lg