Search: “models”

Learning Latent Energy-Based Models via Interacting Particle Langevin Dynamics

arXiv:2510.12311v2 Announce Type: replace-cross Abstract: We develop interacting particle algorithms for learning latent variable models with energy-based priors. To do so, we leverage recent developm

researcharxiv-cs-lg

15 Jul 2026

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Learning Mechanistic Reasoning for Chemical Reactions with Large Language Models

DGX agent

arXiv:2607.12771v1 Announce Type: cross Abstract: Reaction mechanisms consist of the step-by-step sequences of elementary reactions that explain chemical transformations. Learning the mechanism logic

model-releasesarxiv-cs-cl

15 Jul 2026

Applications

TraceSynth: Generating Production-Quality Kernel Traces with Constraint-Guided Diffusion Models

arXiv:2607.12104v1 Announce Type: cross Abstract: Machine learning models for system diagnostics rely on kernel execution traces to capture fine-grained system behavior, but collecting production trac

applicationsarxiv-cs-lg

15 Jul 2026

Evaluating the Generalizability of Foundation Models for Extreme Environmental Events: Case Study of California Wildfire PM2.5

model-releasesarxiv-cs-lg

arXiv:2607.07951v1 Announce Type: new Abstract: Wildfire smoke events produce extreme PM_{2.5} concentrations that pose severe public health risks, yet forecasting rare, hazardous-level spikes remains

10 Jul 2026

Reinforcing the Generation Order of Multimodal Masked Diffusion Models

arXiv:2607.08056v1 Announce Type: cross Abstract: Diffusion Language Models (DLMs) have recently achieved substantial progress in natural language generation tasks. Recent research demonstrates that a

10 Jul 2026

Local Ai

Distributed Sparse Interventions in Language Models

arXiv:2607.07128v1 Announce Type: new Abstract: Language models perform a wide range of tasks at varying levels of abstraction with the capacity to flexibly infer tasks from context, execute multiple

local-aiarxiv-cs-lg

Enhancing deep learning models for time series classification via knowledge distillation

arXiv:2607.06796v1 Announce Type: cross Abstract: Deep learning has achieved remarkable success in various domains including time series analysis, computer vision and natural language processing. Howe

LiveOIBench: Can Large Language Models Outperform Human Contestants in Informatics Olympiads?

arXiv:2510.09595v3 Announce Type: replace Abstract: Competitive programming problems are increasingly used to evaluate the coding capabilities of large language models (LLMs) due to their complexity a

What's on My Network? Using Large Language Models to Identify Real-World IoT Devices at Scale

model-releasesarxiv-cs-lg

arXiv:2510.13817v2 Announce Type: replace Abstract: The growth of IoT devices in shared environments has outpaced our ability to identify them, posing urgent risks to privacy, safety, and accountabili

Agents

Driving the Wrong Way: Leveraging Interpretability in End2End Autonomous Driving Models

arXiv:2607.06328v1 Announce Type: new Abstract: The increasing adoption of end-to-end learning for autonomous driving introduces increased model complexity and opacity, raising the risk of learning un

agentsarxiv-cs-ai

8 Jul 2026

Mitigating Factual Hallucination in Large Reasoning Models via Mixed-Mode Advantage Regularization

arXiv:2607.05861v1 Announce Type: new Abstract: Large reasoning models (LRMs) improve language model capabilities by generating explicit thinking traces before final answers. In factuality-oriented qu

researcharxiv-cs-cl

8 Jul 2026

Fair-GPTQ: Bias-Aware Quantization for Large Language Models

arXiv:2509.15206v3 Announce Type: replace Abstract: The high memory demands of generative language models have drawn attention to quantization, which reduces memory usage by mapping model weights to l

safetyarxiv-cs-cl

Hardware

OctoPipe: Reducing Pipeline Bubbles for Heterogeneous Models via Co-Optimizing Partitioning, Placement, and Scheduling

arXiv:2509.23722v2 Announce Type: replace-cross Abstract: Pipeline parallelism is widely used to train large language models (LLMs). However, increasing heterogeneity in model architectures exacerbate

hardwarearxiv-cs-ai

Overloading Large Vision-Language Models for Jailbreaking

arXiv:2607.02961v1 Announce Type: new Abstract: Large Vision-Language Models (LVLMs) exhibit remarkable vision-language capabilities and are increasingly deployed in real-world applications such as pe

safetyarxiv-cs-cv

RayTun3R: Online Camera Adaptation in 3D Foundation Models

arXiv:2607.02711v1 Announce Type: new Abstract: Recent 3D foundation models, such as DUSt3R, MASt3R, VGGT, pi^3, and Depth Anything 3, provide strong feed-forward depth and pose estimates on pinhole i

Short-Horizon Position Accuracy of Single-Track Models: Implications for Motion Planning of Autonomous Vehicles

arXiv:2606.14216v2 Announce Type: replace Abstract: Accurate and computationally efficient vehicle models are essential for motion planning of autonomous vehicles, where positional accuracy directly a

safetyarxiv-cs-ro

Unbiased Alignment for Large Language Models with Noisy Preferences

arXiv:2607.03248v1 Announce Type: cross Abstract: The alignment of large language models with human preferences is commonly achieved through Reinforcement Learning from Human Feedback or Direct Prefer

When Does Small Data Work? Accuracy and Efficiency Trade-offs Between Tabular Foundation Models and Conventional Methods for Crowd-State Classification at Hajj and Umrah

arXiv:2607.04013v1 Announce Type: cross Abstract: Learning from few labeled examples is a central challenge in tabular machine learning, and it becomes the binding constraint in domains where labeling

researcharxiv-cs-ai

AnyGroundBench: A Specialized-Domain Benchmark for Video Grounding in Vision-Language Models

arXiv:2607.02269v1 Announce Type: cross Abstract: Vision-Language Models (VLMs) have demonstrated immense promise in Spatio-Temporal Video Grounding (STVG). However, current evaluation protocols are l

Mixture-of-Parallelisms: Towards Memory-Efficient Training Stack for Mixture-of-Experts Models

arXiv:2607.01844v1 Announce Type: cross Abstract: This paper showcases a memory-efficient training stack for Mixture-of-Experts (MoE) models. It is a training paradigm that combines and specializes va

On the Utility and Factual Reliability of Pruned Mixture-of-Experts Models in the Biomedical Domain

arXiv:2607.01444v1 Announce Type: cross Abstract: Mixture-of-Experts (MoE) models offer inference speedups via selective activation but impose substantial memory requirements because the whole network

Agents

Recursive Models for Long-Horizon Reasoning

arXiv:2603.02112v2 Announce Type: replace-cross Abstract: Modern language models reason within bounded context, an inherent constraint that poses a fundamental barrier to long-horizon reasoning. We id

agentsarxiv-cs-cl

Restoring Linguistic Grounding in VLA Models via Train-Free Attention Recalibration

arXiv:2603.06001v2 Announce Type: replace-cross Abstract: Vision-Language-Action (VLA) models enable robots to perform manipulation tasks directly from natural language instructions and are increasing

Unlocking Speech-Text Compositional Powers: Instruction-Following Speech Language Models without Instruction Tuning

arXiv:2607.02214v1 Announce Type: new Abstract: Instruction tuning for speech language models (SLMs) is substantially more challenging than for text-based large language models (LLMs), as it requires

researcharxiv-cs-cl

VLSA: Vision-Language-Action Models with Plug-and-Play Safety Constraint Layer

model-releasesarxiv-cs-ro

arXiv:2512.11891v2 Announce Type: replace Abstract: Vision-Language-Action (VLA) models have demonstrated remarkable capabilities in generalizing across diverse robotic manipulation tasks. However, de

Large language models replicate and predict human cooperation across experiments in game theory

arXiv:2511.04500v3 Announce Type: replace Abstract: Large language models (LLMs) are increasingly deployed as decision-making agents in high-stakes domains and as imitators of human behavior in the so

2 Jul 2026

RetailSMV: Exocentric vs. Egocentric Adaptation of Foundation Video World Models in Retail

arXiv:2607.00310v1 Announce Type: cross Abstract: Foundation video diffusion models are increasingly viewed as world simulators for embodied agents, yet their pretraining on internet-scale generic vid

2 Jul 2026

Curvature-Guided Module Localization for Low-Rank Detoxification of Backdoored Large Language Models

arXiv:2606.30899v1 Announce Type: cross Abstract: Backdoor attacks pose a serious threat to large language models (LLMs) by causing otherwise benign systems to produce attacker-specified malicious beh

Local Ai

Large Databases Need Small, Open-Weight Language Models

arXiv:2606.31808v1 Announce Type: new Abstract: Language model systems built around proprietary APIs often operate on a token-based cost model. This becomes prohibitively expensive in the context of l

local-aiarxiv-cs-ai

Learning to Deny: Action Denial in Multimodal Large Language Models

arXiv:2606.31187v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) have rapidly advanced video understanding, achieving strong zero-shot and few-shot recognition across standard

Applications

MemLearner: Learning to Query Context memory for Video World Models

arXiv:2606.31734v1 Announce Type: new Abstract: Video World Models are interactive video generation models that predict future world states based on user actions and history video frames. A critical c

applicationsarxiv-cs-cv

Probing Stylistic Appropriation using Large Language Models: An Evaluation Framework for Copyright Infringement under EU Law

arXiv:2606.31250v1 Announce Type: cross Abstract: Large language models (LLM) trained on web-scale corpora generate output that may infringe copyright, yet existing technical safeguards focus narrowly

Revisiting Parameter Redundancy in Vision-Language-Action Models: Insights from VLM-to-VLA Adaptation

model-releasesarxiv-cs-ro

arXiv:2606.31382v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models have made significant strides in embodied intelligence by integrating the powerful representations of pre-trained Vi

Unsupervised Thermodynamics of Molecular Diffusion Models: Action-Operator Semantics and Auditable Free-Energy Readout

arXiv:2606.30687v1 Announce Type: cross Abstract: Diffusion models are increasingly utilized for modeling molecular structures and conformational ensembles, yet the thermodynamic meaning of their lear

researcharxiv-cs-ai

When Does Learning to Stop Help? A Cost-Aware Study of Early Exits in Reasoning Models

arXiv:2606.30852v1 Announce Type: new Abstract: Reasoning models spend different amounts of useful computation across instances, but it remains unclear when a learned stopping rule improves over simpl

Wisdom Of The (AI) Crowd: Investigating Artificial Swarm Intelligence In Large Language Models

arXiv:2606.31404v1 Announce Type: new Abstract: Human swarm intelligence demonstrates remarkable collective accuracy but faces scalability constraints in cost, coordination, and time. We investigate w

WorldRoamBench: An Open-World Benchmark for Long-Horizon Stability of Interactive World Models

arXiv:2606.31672v1 Announce Type: cross Abstract: Despite rapid progress in interactive world models (IWMs), existing benchmarks evaluate action following only at trajectory level and ignore memory an

Z-1: Efficient Reinforcement Learning for Vision-Language-Action Models

arXiv:2606.31846v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models offer a promising framework for robotic manipulation by connecting language instructions, visual observations, and

Can AI Draw Science? A Benchmark for Evaluating Scientific Figure Generation by Text-to-Image and Multimodal Models

arXiv:2606.28406v1 Announce Type: cross Abstract: Text-to-image and multimodal generative models are increasingly used to produce scientific figures such as mechanism diagrams, experimental-design sch

CAREBench: A Child-Safety Risk Benchmark for Language Models

model-releasesarxiv-cs-lg

arXiv:2606.29685v1 Announce Type: new Abstract: How can we evaluate whether frontier AI systems recognize child-safety risks before they escalate into explicit harm? Existing child safety evaluations

DataComp-VLM: Improved Open Datasets for Vision-Language Models

model-releasesarxiv-cs-cl

arXiv:2606.28551v1 Announce Type: cross Abstract: Building performant Vision-Language Models (VLMs) requires carefully curating large-scale training datasets, yet the community lacks systematic benchm

Do Models Read What They Write? Causal Registers in Scratchpad Reasoning

arXiv:2606.29522v1 Announce Type: cross Abstract: A central hope behind process supervision is that models can expose intermediate variables that matter for their later behavior. For this to help with

safetyarxiv-cs-cl

Early Estimation of Language to Latent Alignment in Diffusion Models

arXiv:2512.08505v2 Announce Type: replace Abstract: Conditional diffusion models frequently suffer from language-image misalignments. Due to the ambiguity of intermediate noise corrupted latents, asse

Applications

Gradient Boosted Mixed Models: Flexible Estimation of Mean and Variance Components for Clustered Data

arXiv:2511.00217v2 Announce Type: replace-cross Abstract: We introduce Gradient Boosted Mixed Models (GBMixed), a framework which extends boosting to clustered data by jointly modeling the mean and va

applicationsarxiv-cs-lg

KnowsTFM: Knowledge-Informed Fine-Tuning of Small Tabular Foundation Models

arXiv:2606.30258v1 Announce Type: cross Abstract: Tabular foundation models have advanced deep learning for tabular data by delivering strong default performance across many small and medium tasks. Ye

Physics-Informed Distillation of Diffusion Models for PDE-Constrained Generation

arXiv:2505.22391v2 Announce Type: replace-cross Abstract: Modeling physical systems in a generative manner offers several advantages, including the ability to handle partial observations, generate div

researcharxiv-cs-ai

RoboGaze: Evaluating Robot World Models via Structured Vision-Language Analysis

arXiv:2606.28385v1 Announce Type: cross Abstract: Recent advances in robot world models enable synthetic video generation for embodied prediction and planning. However, evaluating these videos is chal

Statistically Indistinguishable, Operationally Distinct: A Formal Barrier for Tabular Foundation Models

arXiv:2606.29091v1 Announce Type: cross Abstract: Tabular foundation models cannot reason about data produced by running systems without access to the rules that govern them. We make this statement fa