Search: “models”

TEA: Text Encoder Alignment for Robust Concept Erasure in Text-to-Image Models

arXiv:2608.15341v1 Announce Type: new Abstract: Text-to-image diffusion models can be misused to generate harmful content through adversarial or paraphrased prompts that bypass built-in safety mechani

safetyarxiv-cs-cv

18 Aug 2026

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World-in-World: World Models in a Closed-Loop World

DGX agent

arXiv:2510.18135v2 Announce Type: replace Abstract: Generative world models (WMs) can now simulate worlds with striking visual realism, which naturally raises the question of whether they can endow em

agentsarxiv-cs-cv

18 Aug 2026

A Survey of Large Models in Sports

arXiv:2608.14377v1 Announce Type: new Abstract: Sports have witnessed growing global enthusiasm in recent years, serving as a vital force for physical health, cultural exchange, social connection, and

researcharxiv-cs-cl

17 Aug 2026

Classical Limits of Spectral Filtering in Quantum Generative Models

arXiv:2608.14169v1 Announce Type: cross Abstract: Spectral filtering has been proposed as a route to regularization in quantum generative models: the quantum Fourier transform exposes the amplitude sp

17 Aug 2026

Understanding and Mitigating Over-refusal for Large Language Models via Representation Intervention

arXiv:2511.19009v2 Announce Type: replace-cross Abstract: Large language models (LLMs) demonstrate powerful capabilities across various natural language processing tasks,yet their inherent safety vuln

safetyarxiv-cs-cl

17 Aug 2026

From Observation to Intervention: Memory in Brains and Large Language Models

arXiv:2608.12377v1 Announce Type: cross Abstract: Brains and large language models (LLMs) are fundamentally different memory systems, but they can be compared through shared functional questions: wher

14 Aug 2026

JailWAM: Jailbreaking World Action Models in Robot Control

arXiv:2604.05498v2 Announce Type: replace Abstract: World Action Models (WAMs) have emerged as a promising paradigm for robotic manipulation, enabling physical interaction across diverse tasks and env

safetyarxiv-cs-ro

14 Aug 2026

A Reality Check of Language Models as Formalizers on Constraint Satisfaction Problems

arXiv:2505.13252v5 Announce Type: replace Abstract: Recent work shows superior performance when using large language models (LLMs) as formalizers instead of as end-to-end solvers for symbolic reasonin

researcharxiv-cs-cl

Chemically Meaningful Textualization Enables Explainable Validation of Metal-Organic Frameworks by Large Language Models

arXiv:2608.11283v1 Announce Type: cross Abstract: Computation-ready metal-organic framework (MOF) databases are essential for high-throughput screening, yet many reported crystal structures remain che

Tutorials

Fine-Tuning Generative Models for Extreme Events via CVaR-Penalized Wasserstein Gradient Flows

arXiv:2608.11544v1 Announce Type: cross Abstract: We propose CVaR-penalized Generative Particle Algorithm (CVaR-GPA), a robust, tail-agnostic algorithm for fine-tuning generative models to learn heavy

tutorialsarxiv-cs-lg

Grounding Large Language Models as Generalizable Policies in Network Control

arXiv:2512.11839v2 Announce Type: replace Abstract: Designing generalizable control policies that operate reliably under changing conditions is essential for robust network services in modern digital

safetyarxiv-cs-lg

Keep the Future, Drop the Rollout: RIFT for World Action Models

arXiv:2608.11521v1 Announce Type: cross Abstract: World action models (WAMs) condition robot actions on predicted futures, but iterative video rollout increases deployment latency. We ask whether acti

Latent variable models for simultaneous EOV identification and removal in population-based SHM

model-releasesarxiv-cs-lg

arXiv:2608.11995v1 Announce Type: cross Abstract: The robust treatment of environmental and operational variability (EOV) is an open challenge in population-based structural health monitoring (PBSHM).

Chain of Spatial Thoughts: Modality-Agnostic Spatial Grounding for Vision Language Models

arXiv:2608.10278v1 Announce Type: new Abstract: Spatial understanding is fundamental to embodied intelligence, underpinning applications such as robotic manipulation, embodied navigation, and autonomo

agentsarxiv-cs-cv

Covert Visual Prompt Injection against Commercial Multimodal Large Language Models

arXiv:2603.29418v2 Announce Type: replace-cross Abstract: Although multimodal large language models (MLLMs) are increasingly deployed in real-world applications, their instruction-following behavior l

Dynamic Context Adapters: Efficiently Infusing History into Vision-and-Language Models

arXiv:2608.10525v1 Announce Type: cross Abstract: Historical context integration presents a fundamental challenge for Vision-Language Models (VLMs) in sequential decision-making tasks. Current VLMs pr

Self-Geometry: GT-Free and Plug-and-Play Test-Time Adaptation for Geometrically Consistent 3D Vision Foundation Models

arXiv:2608.10708v1 Announce Type: new Abstract: Recent Vision Foundation Models (VFMs) predict depth, camera pose, and pointmap in a single forward pass without per-scene optimization, achieving stron

researcharxiv-cs-cv

Bounding Hallucinations: Merlin-Arthur Protocols for Mutual-Information Bounds in Language Models

arXiv:2512.11614v3 Announce Type: replace-cross Abstract: Retrieval-augmented generation (RAG) relies on retrieved context to guide large language models (LLM), yet treats the retrieval as a heuristic

agentsarxiv-cs-ai

DeepFreqMark: End-To-End Learnable Frequency-Domain Watermarking with Spherical Attack Simulation for Latent Diffusion Models

arXiv:2608.08999v1 Announce Type: cross Abstract: The proliferation of AI-generated images produced by Latent Diffusion Models (LDMs) has raised critical concerns regarding copyright infringement and

Hardware

Depth-adaptive Inference of Looped Language Models via Continuous Depth Batching

arXiv:2608.09444v1 Announce Type: cross Abstract: A main promise of looped language models (LMs) is depth-adaptive inference. By iterating a block of shared layers a variable number of times, the mode

hardwarearxiv-cs-cl

Discovering and Causally Validating Emotion-Sensitive Neurons in Large Audio-Language Models

arXiv:2601.03115v2 Announce Type: replace Abstract: Emotion is a central dimension of spoken communication, yet, we still lack a mechanistic account of how modern large audio-language models (LALMs) e

safetyarxiv-cs-cl

Ground-Truth Neighborhood Regularization for Reinforcement Learning Post-Training of Time Series Foundation Models

arXiv:2608.08010v1 Announce Type: cross Abstract: Time series forecasting (TSF) plays an important role in a wide range of real-world applications. Recently, time series foundation models (TSFMs), pre

How Do Large Language Models Judge Social Attraction? Evidence from Theory-Grounded Persona Ratings Across Multiple LLMs and Humans

arXiv:2608.09717v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used to perform subjective evaluations traditionally made by humans, yet their validity as social judges

Hybrid Neural-Classical Correction for Frozen Time Series Foundation Models: A Comprehensive Ablation Study on High-Frequency Stock Prediction

arXiv:2608.08825v1 Announce Type: cross Abstract: Foundation models for time series forecasting demonstrate impressive zero-shot generalization but often underperform on specialized domains such as hi

KVDiagnosis: A Diagnostic Benchmark for KV-Cache Compression in Long-Context Language Models

model-releasesarxiv-cs-ai

arXiv:2608.09412v1 Announce Type: new Abstract: KV-cache compression reduces long-context memory, but aggregate task scores reveal neither which correct executions fail nor why. We present KVDiagnosis

LF{}^{2}AR: Accounting for Layerwise Dynamics to Improve Multimodal Adaptation of Language Models

arXiv:2503.06211v3 Announce Type: replace-cross Abstract: Text-pretrained language models (LMs) encode rich world knowledge, but adapting them to process and generate perceptual modalities such as aud

Multi-Branch Policy Optimization for Multimodal Large Language Models

arXiv:2608.07581v1 Announce Type: cross Abstract: Group-based reinforcement learning methods for multimodal large language models typically rely on trajectory-level credit assignment that applies a si

NeuroBreak: Unveil Internal Jailbreak Mechanisms in Large Language Models

arXiv:2509.03985v2 Announce Type: replace-cross Abstract: In deployment and application, large language models (LLMs) typically undergo safety alignment to prevent illegal and unethical outputs. Howev

OATS: Online Data Augmentation for Time Series Foundation Models

arXiv:2601.19040v2 Announce Type: replace Abstract: Time Series Foundation Models (TSFMs) are a powerful paradigm for time series analysis and are often enhanced by synthetic data augmentation to impr

Pragmatic Attack Surface: Vulnerabilities of Implicit Context in Large Language Models

arXiv:2608.09551v1 Announce Type: new Abstract: In the era of large language models (LLMs), attackers often manipulate natural language to elicit unsafe or harmful outputs, creating a new natural lang

safetyarxiv-cs-cl

PRISM-Delta: Differential Subspace Steering for Prompt Highlighting in Large Language Models

arXiv:2603.10705v2 Announce Type: replace Abstract: Prompt highlighting steers a large language model to prioritize user-specified text spans during generation. A key challenge of existing Key-editing

researcharxiv-cs-cl

Quality-Diversity Stress Tests for Process Reward Models:What Archive Coverage Can and Cannot Certify

arXiv:2608.08008v1 Announce Type: new Abstract: Process reward models (PRMs) score intermediate reasoning steps and are widely used for search, ranking, and training, but optimization can exploit thes

RynnValue: Scaling Robotic Value Foundation Models with Temporal Distance

arXiv:2608.09853v1 Announce Type: cross Abstract: General-purpose reward models are increasingly the bottleneck for scaling robot learning, yet the recipe for learning value-related capabilities from

safetyarxiv-cs-cv

Subjective Multi-Bias Detection with Large Language Models

model-releasesarxiv-cs-cl

arXiv:2608.09126v1 Announce Type: new Abstract: In this project, we delved into the pervasive challenge of bias detection within the text content. More specifically, our focus lies on the identificati

Twin Rollouts: Noise-Coupled Counterfactual Branching in Interactive Video World Models

arXiv:2608.08982v1 Announce Type: new Abstract: Interactive video world models generate rollouts autoregressively under an action stream, yet they are trained and evaluated almost exclusively on factu

UniMoMo: Expert Merging-Based MoE Acceleration for Large Recommendation Models

model-releasesarxiv-cs-ai

arXiv:2608.08627v1 Announce Type: new Abstract: Sparse mixture-of-experts (MoE) layers expand recommendation capacity through conditional computation, yet a trained checkpoint still stores and routes

Visual Distortion Detection in UGC Images Using Large Multimodal Models

arXiv:2608.09122v1 Announce Type: cross Abstract: The localized depiction of perceptual quality has long been a crucial, yet underexplored, challenge in image quality assessment (IQA). Existing approa

WA-SpecDec: World-Aware Speculative Decoding for Vision-Language-Action Models

arXiv:2608.08725v1 Announce Type: new Abstract: Vision-language-action (VLA) policies generate robot controls autoregressively, making closed-loop latency dominated by repeated target-model forward pa

researcharxiv-cs-ro

AgentPatch: Coarse-to-Fine Weak-Task Repair for Merging Agentic Multimodal Large Language Models

arXiv:2608.06699v1 Announce Type: new Abstract: Agentic multimodal large language models (MLLMs) extend multimodal perception and reasoning with planning, tool use, and interaction in dynamic environm

agentsarxiv-cs-ai

10 Aug 2026

Provable Training Data Identification for Large Language Models

arXiv:2510.09717v3 Announce Type: replace-cross Abstract: Identifying training data of large-scale models is critical for copyright litigation, privacy auditing, and ensuring fair evaluation. However,

10 Aug 2026

Recipes for Creativity: Iterative Generation and Evaluation in Large Language Models

arXiv:2608.07243v1 Announce Type: new Abstract: Generative models are often evaluated through singular artifacts, whereas human creativity typically emerges through iterative generation, appraisal, an

10 Aug 2026

Adapting Vision Foundation Models with Cascaded Semantics

model-releasesarxiv-cs-cv

arXiv:2608.05393v1 Announce Type: new Abstract: Prompt tuning, a leading parameter-efficient adaptation paradigm in NLP, has recently been extended to computer vision. Visual prompt tuning (VPT) adapt

Evaluating Investment Logic in Large Language Models: A Real-World Benchmark Towards Personalzied Financial Agents

model-releasesarxiv-cs-ai

arXiv:2608.06108v1 Announce Type: new Abstract: Investment competence is inherently personalized: the same market evidence can justify different actions for investors with different goals, horizons, p

Explanations of Large Language Models Explain Language Representations in the Brain

arXiv:2502.14671v4 Announce Type: replace-cross Abstract: Large Language Model (LLM) representations are known to align with brain activity during language processing, but it remains unclear what driv

Scalable estimation of VARMA models

arXiv:2608.06340v1 Announce Type: cross Abstract: Vector autoregressive moving-average (VARMA) models have long been considered impractical beyond moderate dimensions: the likelihood is non-convex, th

safetyarxiv-cs-lg

STAIL: Semantic Text-Anchored Incremental Learning for Medical Imaging via Large Language Models

arXiv:2608.05808v1 Announce Type: new Abstract: Deep learning models applied to medical image analysis suffer from severe catastrophic forgetting when continually adapting to new clinical tasks in dyn

researcharxiv-cs-cv

Test-Time Scaling in Reasoning Models Is Not Effective for Knowledge-Intensive Tasks Yet

arXiv:2509.06861v3 Announce Type: replace Abstract: Test-time scaling increases inference-time computation through longer reasoning chains and has shown strong performance gains across many domains. H

Beyond Linear Dynamics: Neural Bilinear Dynamical Models for Time Series Forecasting

arXiv:2608.04471v1 Announce Type: cross Abstract: Time series in real-world applications are often generated by nonlinear dynamical systems, making accurate forecasting challenging. Existing approache