Search: “models”

DreOPD: Degraded-Reference Extrapolative On-Policy Distillation for Flow-matching Models

arXiv:2608.09233v1 Announce Type: cross Abstract: Flow-matching models are now a mainstream method to image generation, but its adaptation to diverse downstream scenarios typically relies on post-trai

safetyarxiv-cs-cv

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Energy-Structured Latent World Models with Neural Time Fields for Physically Constistent Open-World Motion Planning

DGX agent

arXiv:2608.09876v1 Announce Type: cross Abstract: Physically consistent motion planning remains a fundamental challenge in embodied AI, as generated trajectories must strictly conform to real-world ex

safetyarxiv-cs-ai

11 Aug 2026

General OOD Detection via Model-aware and Subspace-aware Variable Priority

arXiv:2512.13003v2 Announce Type: replace-cross Abstract: Out-of-distribution (OOD) detection is essential for determining when a supervised model encounters inputs that differ meaningfully from its t

local-aiarxiv-cs-lg

HarnessWAM: Bridging Prediction and Deliberation in World Action Models

arXiv:2608.09516v1 Announce Type: new Abstract: World Action Models (WAMs) jointly learn environmental dynamics and robot actions, introducing priors over physical evolution into embodied control. How

local-aiarxiv-cs-ro

InfoOps Bench: A live information operations safety benchmark

arXiv:2607.28503v3 Announce Type: replace Abstract: In this paper we present an active, constantly updated AI benchmark which measures the integrity of frontier language models against being co-opted

Applications

Length-MAX Tokenizer for Language Models

arXiv:2511.20849v2 Announce Type: replace-cross Abstract: We introduce a new tokenizer for language models that minimizes the average tokens per character, thereby reducing the number of tokens needed

applicationsarxiv-cs-ai

LLM within MCP Matters: Measuring Inefficient Resource Utilization Driven by LLMs

arXiv:2608.08467v1 Announce Type: new Abstract: The Model Context Protocol (MCP) standardizes how servers expose data and tools to Large Language Models (LLMs). A common server design embeds frequentl

LLMVisor: A Real-Time Latency Attribution Model for Multi-Tenant LLM Serving

arXiv:2608.08382v1 Announce Type: new Abstract: As LLM inference shifts to multi-tenant GPU clusters, co-batching improves throughput but obscures per-tenant usage and limits control. Enabling fractio

Mechanistic Interpretability-Guided Selective Fine-Tuning of Vision-Language Models for Centimeter-Level Flood Depth Estimation

model-releasesarxiv-cs-cv

arXiv:2608.07562v1 Announce Type: new Abstract: Urban flooding poses an escalating threat to transportation infrastructure, yet no operational system provides real-time, street-level flood-depth estim

Persuasive and Compliant Tendencies Predict Group Decision-Making in Humans and Language Models

arXiv:2608.08199v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly involved in group decision-making with other LLMs and humans. Yet it remains unclear whether their influen

Agents

Population-Scalable Multi-Agent World Modeling

arXiv:2608.08600v1 Announce Type: cross Abstract: World models have recently achieved impressive progress in visual prediction and interactive generation, but extending them to multi-agent environment

agentsarxiv-cs-ai

Applications

Reading Cognition as Decisions Unfold in Words: A Factorized Inverse Decision Model

arXiv:2608.09222v1 Announce Type: new Abstract: Inverse decision modeling infers latent properties of decision processes from observed behavior, but existing formulations rely primarily on action traj

applicationsarxiv-cs-cl

SC^{2}-WM: A Self-Correcting World Model with Closed-Loop Feedback for Vision-and-Language Navigation in Continuous Environments

arXiv:2608.07548v1 Announce Type: cross Abstract: Vision-and-Language Navigation in Continuous Environments (VLN-CE) requires agents to make fine-grained navigation decisions under partial observabili

Sign Language Recognition Using Original and Synthetic Depth Image Based Point Cloud Data Models

arXiv:2608.09400v1 Announce Type: cross Abstract: Research regarding the sign language recognition mostly relies on RGB images, whileas sign language datasets that provide depth images are limited. Po

Time Present and Time Past: Benchmarking Large Language Models on Temporally Evolving Document Understanding

arXiv:2608.08512v1 Announce Type: new Abstract: Evolving documents, such as laws, tax codes, and software documentation, are amended, replaced, and sometimes reverted over time, so a question has diff

Topographic Constraints Shape Brain-Like Component Structure in Auditory Models

arXiv:2509.24039v2 Announce Type: replace-cross Abstract: If topography is a fundamental feature of the brain, it should influence both how neurons are arranged in space (i.e. explain brain maps) and

A MARL Centered Reference Architecture for Large Language Model Augmentation in Smart Manufacturing

arXiv:2608.07148v1 Announce Type: new Abstract: Modern manufacturing imposes six coupled demands on adaptive control: local decisions with global consequences, partial observability, nonstationarity,

local-aiarxiv-cs-ai

Gated-BEPO: Confidence-Gated Bellman Credit Assignment for Large Language Model Agents

arXiv:2608.06861v1 Announce Type: new Abstract: Training large language model agents in long-horizon environments requires assigning credit from sparse terminal outcomes to individual actions. Existin

HRDiT: Training-Free High-Resolution Image Generation with Off-the-Shelf Diffusion Transformer Models

arXiv:2608.07003v1 Announce Type: new Abstract: Training-free text-to-high-resolution image generation has recently attracted growing research attention. However, existing studies on this task primari

Prune Once: Retraining-Free Task-Agnostic Pruning for Vision-Language Models

arXiv:2608.06901v1 Announce Type: new Abstract: Vision-language models (VLMs) have achieved remarkable generalization across diverse multimodal tasks through large-scale pre-training, yet their rapidl

Science Edge Evaluation: SEE the Missing Step Toward Real Scientific Discovery

arXiv:2608.06931v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly involved in scientific discovery, yet it remains unclear whether they can support complex real laboratory

Unsupervised Adaptation of PDE Foundation Models

arXiv:2608.07053v1 Announce Type: new Abstract: Pretrained partial differential equation (PDE) foundation models can generalize across different equations, but adapting them to unseen PDE systems typi

AegisShield: Democratizing Cyber Threat Modeling with Generative AI

arXiv:2509.10482v2 Announce Type: replace-cross Abstract: The increasing sophistication of technology systems makes traditional threat modeling hard to scale, especially for small organizations with l

Beyond Full-Model Rollback: AuroSFT for Adapter-State Multi-Task Fine-Tuning

model-releasesarxiv-cs-lg

arXiv:2608.05250v1 Announce Type: new Abstract: Multi-task supervised fine-tuning (SFT) often casts a heterogeneous data mixture as a single optimization problem, even though different tasks may reach

Can Open-Weight LLMs Produce Kernel-Verified Coq Proofs? A Pilot Study

model-releasesarxiv-cs-lg

arXiv:2608.05420v1 Announce Type: cross Abstract: Large language models (LLMs) can generate text that resembles a mathematical proof, but resemblance does not establish correctness. A formal proof che

Applications

Do Tabular Foundation Models Agree with Themselves?

arXiv:2608.06004v1 Announce Type: new Abstract: Tabular Foundation Models (TFMs) are currently the best approach to tabular prediction problems. They are constructed as transformers that approximate t

applicationsarxiv-cs-lg

How Far Do Simple Transformations Translate Across Text Embedding Models?

arXiv:2608.05980v1 Announce Type: new Abstract: We investigate whether simple transformations can translate representations across heterogeneous text embedding models. Understanding how independently

researcharxiv-cs-lg

In-Context VLA: Endowing Vision-Language-Action Models with Language via In-Context Post-Training and Agentic Tool Use

arXiv:2608.05738v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models have become the dominant recipe for generalist manipulation, yet they are almost universally trained by behavior clo

safetyarxiv-cs-ro

KVAE: Family of Tokenizers for Multimodal Generative Models

arXiv:2608.05798v1 Announce Type: new Abstract: Latent diffusion modeling (LDM), a prominent paradigm, utilizes tokenizers to map input signal to compressed representation. This dependency positions t

LUNAR: Benchmarking Personalized Large Language Models on UNiversal User BehAvioR Logs

arXiv:2608.05246v1 Announce Type: new Abstract: Existing personalized LLM benchmarks primarily rely on textual personas or isolated behavioral signals, providing limited evaluation of cross-domain beh

Tutorials

Mapping Similarity Spaces across Embedding Models with Synthetic Query Probing

arXiv:2608.05857v1 Announce Type: new Abstract: Retrieval-Augmented Generation systems rely on similarity scores to retrieve relevant content, yet scores are not directly comparable across embedding m

tutorialsarxiv-cs-cl

Tutorials

Mean-Field Dynamics of Chain-of-Thought Reasoning in Large Language Models

arXiv:2608.05152v1 Announce Type: cross Abstract: Large language models (LLMs) with chain-of-thought reasoning have been widely applied in recent years, and theoretical explanations of their behavior

tutorialsarxiv-cs-ai

PhyLatent: Learning Dynamics-Relevant Representations for JEPA World Models

arXiv:2608.05720v1 Announce Type: new Abstract: We propose PhyLatent, a dynamics-relevant training objective for JointEmbedding Predictive Architecture (JEPA) world models. Our key observation is that

safetyarxiv-cs-cv

Simulator-Grounded Large Language Models for Industrial Causal Reasoning: Tool-Use, Structured Injection, and Plant-Portable Retrieval for Wastewater Treatment Decision Support

arXiv:2608.05151v1 Announce Type: cross Abstract: Wastewater operators need answers grounded in how their plant's variables interact and how fast effects propagate, not in generic pretraining text, wh

The em-dash em-beds in Congress: A population-level rise in em-dash frequency in U.S. congressional press releases at the dawn of the large-language-model era, 2021-2025

arXiv:2608.05889v1 Announce Type: cross Abstract: Large language models (LLMs) can leave small stylistic traces in text written with their help. The most discussed is the em-dash (U+2014), especially

TRW: TRACE-RealWorld---An Auditable Consistency Contract for World Models as Materialized Views

arXiv:2607.21910v2 Announce Type: replace Abstract: World models let agents plan against predicted physical state, but that state drifts; re-observation is costly and delayed, and repair can fail. We

local-aiarxiv-cs-ai

Vorch-Director: Interactive World Story Model via Noise-Aware Error Rectification

model-releasesarxiv-cs-cv

arXiv:2608.05776v1 Announce Type: new Abstract: Autoregressive continuation provides a natural path toward minute-scale audio-visual generation by repeatedly extending a short-window generator conditi

What Current AI Benchmarks Leave Unmeasured: Modality, Search, Citations, and Implications (for Safety Evaluations)

arXiv:2608.06202v1 Announce Type: cross Abstract: Large language model (LLM) benchmark evaluations are routinely used to support claims about model safety, reliability, and deployment readiness. Yet m

AudioDER: A Deduplication-Enhanced Reasoning Dataset for Post-Training Large Audio-Language Models

arXiv:2606.14591v2 Announce Type: replace-cross Abstract: Recent advances in pretrained large audio-language models (LALMs) have demonstrated strong capabilities across speech, sound, and music. To ad

Causal Evidence Extraction and Triangulation in Crisis Reports using Large Language Models: A ReliefWeb-based Study

model-releasesarxiv-cs-cl

arXiv:2608.04576v1 Announce Type: new Abstract: Humanitarian reports are long, noisy, and multi-topic, making it difficult to consolidate decision-relevant causal evidence. We present a ReliefWeb stud

Curiosity-Diffuser: Curiosity Guide Diffusion Models for Reliability

arXiv:2503.14833v2 Announce Type: replace-cross Abstract: One of the bottlenecks in robotic intelligence is the instability of neural network models. This leads to risks when applying intelligence in

EndoVLM: An Endoscopy Vision-Language Pre-training Model via Anatomy-Guided Sparsity and Progressive Alignment

arXiv:2608.04472v1 Announce Type: cross Abstract: The development of foundation models (FMs) is crucial for advancing endoscopic image analysis. However, existing endoscopy FMs mainly rely on self-sup

GeoReward: Mitigating Contextual Variable Overestimation in Vision-Language Models for Cross-Market Preference Prediction

arXiv:2608.04504v1 Announce Type: cross Abstract: Vision-language models excel in many multimodal tasks but remain prone to a subtle yet impactful failure mode: they tend to overestimate dominant visu

Kitchen Robotic Manipulation utilizing Foundation Models

model-releasesarxiv-cs-ro

arXiv:2608.04042v1 Announce Type: new Abstract: Deploying robots in everyday human environments requires perception systems that are both robust and adaptable to diverse, dynamic conditions. In this w

LoRetta: A Foundation Model and Extensive Dataset for Global-Scale Remote Sensing Dense Image Matching

model-releasesarxiv-cs-cv

arXiv:2608.04106v1 Announce Type: new Abstract: Dense image matching establishes pixel-wise correspondences and underpins broad applications in computer vision and photogrammetry. However, extending d

Poly-OPD: Heterogeneous Multi-Teacher On-Policy Distillation for Capability-Selectable Flow Models

arXiv:2608.04349v1 Announce Type: new Abstract: Leading open text-to-image models often carry complementary strengths: one may lead on preference-aligned aesthetics while another follows compositional

safetyarxiv-cs-cv

TS2TabPFN: Time Series Classification and Extrinsic Regression through Feature Extraction and a Tabular Foundation Model

arXiv:2608.04174v1 Announce Type: new Abstract: Time series data are ubiquitous in practical applications, where classification (TSC) and extrinsic regression (TSER) have emerged as essential tasks fo

researcharxiv-cs-lg

Understanding Fault Tolerance of Adversarially Robust Pruned Models

arXiv:2608.04173v1 Announce Type: new Abstract: Deep neural networks (DNNs) deployed on resource-constrained neuromorphic hardware face three concurrent challenges: the need for model compression thro

researcharxiv-cs-lg