Search: “models”

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

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AppDeltaWorld: Transition-Grounded Delta Code World Model for Mobile GUI Agents

DGX agent

arXiv:2608.05891v1 Announce Type: new Abstract: Mobile GUI agents can operate apps through pixel perception and touch actions, making them a promising interface for collecting and improving long-horiz

safetyarxiv-cs-ai

7 Aug 2026

Tutorials

Cautious Context Steering for Language Model Personalization

arXiv:2608.05813v1 Announce Type: new Abstract: Personalizing language models (LMs) to individual user preferences is essential for aligning responses with diverse goals and backgrounds. Existing meth

tutorialsarxiv-cs-ai

Agents

Disentangling 3D Modeling from Spatial Reasoning

arXiv:2608.05242v1 Announce Type: cross Abstract: In this work, we explore an alternative paradigm for spatial reasoning by explicitly disentangling 3D perception from reasoning, rather than jointly a

agentsarxiv-cs-cv

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

MMaDA-VLA: Large Diffusion Vision-Language-Action Model with Unified Multi-Modal Instruction and Generation

arXiv:2603.25406v3 Announce Type: replace Abstract: Vision-Language-Action (VLA) models map visual observations and natural-language instructions to robot actions; however, hierarchical and autoregres

applicationsarxiv-cs-ro

omega-0: A Latent Predictive World Action Model for Concurrent Humanoid Loco-Manipulation

arXiv:2608.06375v1 Announce Type: new Abstract: Humanoid household tasks often require concurrent loco-manipulation, where the robot must move, adjust posture, maintain balance, and manipulate objects

applicationsarxiv-cs-ro

Poli-Bias: Understanding and Measuring Large Language Model Biases in International Political Conflicts

arXiv:2608.06123v1 Announce Type: new Abstract: Measuring political bias in large language models (LLMs) remains challenging as it can manifest through subtle differences in framing, argumentation, an

Position: It's Time to Optimize LLMs for Self-Consistency

model-releasesarxiv-cs-ai

arXiv:2608.05188v1 Announce Type: cross Abstract: Despite ever-increasing sophistication in language model (LM) pre- and post-training pipelines, many important failures persist: models overcondition

Relay, Don't Route: Adaptive Population Handoff for Cost-Efficient LLM-Driven Evolution

arXiv:2608.05651v1 Announce Type: cross Abstract: Large language model (LLM)-driven evolution has shown promise for program search and algorithm discovery, but relying on strong models throughout long

Revisiting Black-Box Model Ownership Verification through Information Theory

arXiv:2409.06130v2 Announce Type: replace-cross Abstract: Modern machine learning models require substantial computational resources and data to train, making them valuable intellectual property. Mode

applicationsarxiv-cs-ai

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

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

A geometry-based deep equilibrium model for image restoration under multiplicative Gamma noise

arXiv:2608.04944v1 Announce Type: cross Abstract: We propose a deep learning framework for image restoration from images degraded by both multiplicative Gamma noise and blur. Unlike conventional deep

A Model Merging Approach for Continual MLLM Unlearning

arXiv:2608.04548v1 Announce Type: cross Abstract: Multimodal large language model (MLLM) unlearning methods have been proposed to remove private, sensitive, or proprietary information from well-traine

Agents

AutoProteinEngine: A Large Language Model Driven Agent Framework for Multimodal AutoML in Protein Engineering

arXiv:2411.04440v1 Announce Type: cross Abstract: Protein engineering is important for biomedical applications, but conventional approaches are often inefficient and resource-intensive. While deep lea

agentsarxiv-cs-ai

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

applicationsarxiv-cs-ai

Canonical Joint Energy-Based Model on CIFAR-10: failure modes and practical indistinguishability of Predictor-Corrector and SGLD samplers

arXiv:2608.05025v1 Announce Type: new Abstract: Joint Energy-Based Models (JEM) unify classification and generation within a single network and support out-of-distribution (OOD) detection. Canonical J

DataRx: Missingness-Aware Sampling for Safer Large Language Model Task-Specific Fine-Tuning

arXiv:2608.04322v1 Announce Type: new Abstract: Task-specific fine-tuning can improve the performance of large language models (LLMs) on downstream tasks. However, our study reveals that task-specific

safetyarxiv-cs-cl

DeepInvert: Semi-Supervised Embedding Inversion Against Obfuscated Language Models

arXiv:2608.04477v1 Announce Type: cross Abstract: Cloud-based language model services routinely process prompts containing sensitive information. Obfuscation-based defenses---including ObfusLM, Sentin

DIVE: Dynamic Iterative Visual Evidence Construction for Efficient Vision-Language Models

arXiv:2608.04496v1 Announce Type: new Abstract: Visual inputs in vision-language models (VLMs) are often encoded into substantially longer token sequences than text, making visual tokens a major bottl

Exact Model-Free Policy Iteration for Co-safe LTL Planning

arXiv:2608.05047v1 Announce Type: cross Abstract: This work studies model-free reinforcement learning for co-safe linear temporal logic (sc-LTL) objectives in finite Markov decision processes, which c

safetyarxiv-cs-ro

Language Models Generalize to Human-like Word Order Preferences

arXiv:2608.05028v1 Announce Type: new Abstract: A central question in language acquisition is whether linguistic biases can emerge from general learning mechanisms operating over underdetermined input

safetyarxiv-cs-cl

Manipulation-Proof Oblivious Audits against Deceptive Model Providers

arXiv:2608.04365v1 Announce Type: new Abstract: Audits have emerged as a critical instrument for algorithmic governance, providing a mechanism for external scrutiny and governance of machine learning

safetyarxiv-cs-lg

Memorization in Large Language Models in Medicine: Prevalence, Characteristics, and Implications

arXiv:2509.08604v5 Announce Type: replace-cross Abstract: Large Language Models (LLMs) have demonstrated significant potential in medicine, with many studies adapting them through continued pre-traini

MOAT: Model-Agnostic Randomized Transformations for preventing Efficiency Degradation Attacks on ViTs

arXiv:2608.04680v1 Announce Type: cross Abstract: To adopt the Vision Transformers (ViTs) in resource-constrained environment, token pruning is widely used to reduce computational cost without impacti

Agents

OctoLong: Mid-Training On Cross-Repository Code Contexts Enhances Long-Context Modeling

arXiv:2608.05141v1 Announce Type: new Abstract: Context lengths of language models (LMs) have dramatically increased, driven by the demands for in-context learning, self-improvement, and long-horizon

agentsarxiv-cs-ai

Representing Visual Evidence for Item Difficulty Prediction: Visual Textualization and Image-Native Modeling

arXiv:2608.04554v1 Announce Type: new Abstract: Predicting item difficulty from content can provide an initial estimate for newly developed questions before sufficient student responses are available.

STEP-OPD: Rethinking Output Targets and Internal Dynamics in On-Policy Distillation for Diffusion Models

arXiv:2608.04887v1 Announce Type: new Abstract: On-policy distillation (OPD) has become an effective approach for consolidating multiple task-specialized image generation models into a single student.

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The Personalization Mirage: How LLMs Fabricate User Profiles, and Why Self-Monitoring Misleads

arXiv:2608.04570v1 Announce Type: new Abstract: Personalized LLMs with persistent memory are increasingly deployed, yet the faithfulness of their user models remains unexamined. We study over-inferenc

AI-Based Sound Effect Generation: A Narrative Review of Generative Models Across Input Modalities

arXiv:2608.03742v1 Announce Type: cross Abstract: Sound effects play a crucial role in conveying actions, events, and environmental cues across digital applications, often requiring a high degree of v

ARCHead: Activation-Metric Residual Correction for Large Language Model Output Heads

arXiv:2608.02703v1 Announce Type: new Abstract: Weight-only quantization substantially reduces the storage of large language model (LLM) transformer blocks, but practical backends often retain the fin

Automatic Patient-Specific Microwave Ablation Planning Accelerated by a Physics-Guided Deep Learning Model

arXiv:2608.03086v1 Announce Type: cross Abstract: Microwave ablation (MWA) is a promising minimally invasive treatment for liver tumors, but its therapeutic outcome strongly depends on patient-specifi

Tutorials

Divide-and-Conquer: Towards Generalizable Amortized Bayesian Inference for the Drift Diffusion Model

arXiv:2608.03566v1 Announce Type: cross Abstract: The drift diffusion model (DDM) is a cornerstone of cognitive decision-making research. Although numerous estimation methods exist, researchers contin

tutorialsarxiv-cs-lg

Enhancing VLM Reward Models Through Structure-Aware Fine-Tuning

arXiv:2608.03875v1 Announce Type: cross Abstract: Designing effective reward functions remains a major bottleneck in Reinforcement Learning (RL). Recent work uses large foundation Vision-Language Mode

Hunyuan3D-Buffalo 1.0: A Unified Multimodal Model for Scalable 3D Generation, Understanding, and Editing

arXiv:2608.02711v1 Announce Type: new Abstract: Recent advances in image generation have demonstrated the potential of unified multimodal models that integrate understanding, generation, and editing.

MissClick: Exploiting Digit-Serialized Coordinates to Attack GUI Grounding Models

arXiv:2608.03740v1 Announce Type: new Abstract: Recent GUI visual grounding models generate screen coordinates as sequences of digit tokens that are parsed into numerical values and mapped to executab

Modeling Matches as Language: A Generative Transformer Approach for Counterfactual Player Valuation in Football

arXiv:2603.15212v2 Announce Type: replace Abstract: Evaluating football player transfers is challenging because player actions depend strongly on tactical systems, teammates, and match context. Despit

Quantization Effects on Biomedical LLM Reliability

model-releasesarxiv-cs-lg

arXiv:2608.03854v1 Announce Type: new Abstract: When decoder language models are used as classifiers, predicted class probabilities depend on implementation choices, including the prompt template, ver

Robust Counterfactual Policy Optimisation via Nondeterministic Causal Models

arXiv:2608.02893v1 Announce Type: cross Abstract: Counterfactual inference approaches for sequential decision-making typically assume deterministic causal models, where all randomness stems from laten

Sedentary Behavior Classification for Wearable Sensors with a CNN-BiLSTM Model

arXiv:2608.02946v1 Announce Type: new Abstract: Accurate detection of sedentary behavior is important for studying health risks related to prolonged sitting, but posture-based classification remains c

Simulation-free and finite-time diffusion model

arXiv:2608.03117v1 Announce Type: new Abstract: The performance of generative diffusion models is determined by the choice of the reference diffusion process connecting the empirical and prior distrib

Suffix-Constrained Greedy Search Algorithms for Causal Language Models

arXiv:2603.01243v2 Announce Type: replace Abstract: Large language models (LLMs) are powerful tools that have found applications beyond human-machine interfaces and chatbots. Beside free-form generati

Active Regression for Single-Index Models with Unknown Link Functions

arXiv:2608.01287v1 Announce Type: cross Abstract: This paper studies active regression for single-index models under general ell_p-loss with an unknown 1-Lipschitz link function f, formulated as min_{

4 Aug 2026

Computational Approaches to Understanding Large Language Model Impact on Writing and Information Ecosystems

arXiv:2506.17467v2 Announce Type: replace Abstract: Large language models (LLMs) have shown significant potential to change how we write, communicate, and create, leading to rapid adoption across soci

4 Aug 2026

Conformalized Large Language Models under Configuration Shift

arXiv:2608.01460v1 Announce Type: new Abstract: Conformal prediction (CP) is a distribution-free framework for uncertainty quantification that has recently been adapted to large language models (LLMs)