Search: “models”

NSR-Boost: A Neuro-Symbolic Residual Boosting Framework for Industrial Legacy Models

arXiv:2601.10457v3 Announce Type: replace Abstract: Although the Gradient Boosted Decision Trees (GBDTs) dominate industrial tabular applications, upgrading legacy models in high-concurrency productio

applicationsarxiv-cs-ai

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PALoRA: Projection-Adaptive LoRA for Preserving Reasoning in Large Language Models

DGX agent

arXiv:2605.24549v1 Announce Type: new Abstract: Efficiently updating Large Language Models (LLMs) with new or evolving factual knowledge remains a central challenge, as even parameter-efficient adapta

model-releasesarxiv-cs-ai

26 May 2026

ParkourFormer: Integrating Predictive Supervision and Sequence Modeling into Parkour Locomotion

model-releasesarxiv-cs-ro

arXiv:2605.25782v1 Announce Type: new Abstract: Humanoid parkour requires locomotion policies to coordinate whole-body dynamics across rapidly changing terrains such as stairs, gaps, slopes, and obsta

PolySAE: Modeling Feature Interactions in Sparse Autoencoders via Polynomial Decoding

arXiv:2602.01322v2 Announce Type: replace-cross Abstract: Sparse autoencoders (SAEs) interpret neural network representations by decomposing activations into sparse combinations of dictionary atoms. H

Representation Without Control: Testing the Realization Effect in Language Models

arXiv:2605.25151v1 Announce Type: new Abstract: Large language models are increasingly used as behavioral simulators, but it remains unclear when their outputs reflect human-like cognitive mechanisms

RepSAM: Bridging Foundation Models to Robotic Vision via Representation-Guided Adaptation

model-releasesarxiv-cs-ro

arXiv:2605.25495v1 Announce Type: new Abstract: Robotic perception in unstructured environments remains challenging despite the zero-shot capabilities of foundation models such as SAM. This work attri

Simulating Human Memory with Language Models

arXiv:2605.25680v1 Announce Type: cross Abstract: Language models are increasingly being deployed as user simulators, but their memory is far more reliable than that of real users. To measure this gap

applicationsarxiv-cs-ai

Local Ai

Temporal Score Rescaling for Temperature Sampling in Diffusion and Flow Models

arXiv:2510.01184v2 Announce Type: replace Abstract: We present a mechanism to steer the sampling diversity of denoising diffusion and flow matching models, allowing users to sample from a sharper or b

local-aiarxiv-cs-lg

The meaning of prompts and the prompts of meaning: Semiotic reflections and modelling

arXiv:2509.14250v2 Announce Type: replace Abstract: This paper explores prompts and prompting in large language models (LLMs) as dynamic semiotic phenomena, drawing on Peirce's triadic model of signs,

researcharxiv-cs-cl

UtilityMax Prompting: A Formal Framework for Multi-Objective Large Language Model Tasks

arXiv:2603.11583v4 Announce Type: replace-cross Abstract: The success of a Large Language Model (LLM) task depends heavily on its prompt. Most use-cases specify prompts using natural language, which i

VaaWIT: Visual-Aware Adaptation of Large Language Models for Multilingual Web Image Translation

arXiv:2605.24675v1 Announce Type: cross Abstract: Translating text embedded in Web images is crucial for improving content accessibility and cross-lingual information retrieval, particularly within so

Visual-Redundancy-Controlled Parallel Decoding for Diffusion-Based Multimodal Large Language Models

arXiv:2605.25820v1 Announce Type: new Abstract: Diffusion-based multimodal large language models (dMLLMs) decode by iteratively predicting tokens at multiple masked positions in parallel. This turns e

Agentic-VLA: Efficient Online Adaptation for Vision-Language-Action Models

arXiv:2605.22896v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models have emerged as a promising paradigm for robotic manipulation by leveraging pre-trained vision-language representa

Safety

Convergence Without Understanding: When Language Models Agree on Representations but Disagree on Reasoning

arXiv:2605.23315v1 Announce Type: cross Abstract: Large language models trained under diverse objectives and architectures have been shown to develop increasingly similar internal representations, an

safetyarxiv-cs-ai

Cultural Adaptation in Large Language Models for Political Discourse

arXiv:2605.23332v1 Announce Type: new Abstract: The integration of large language models into political discourse analysis creates new opportunities for comparative research, policy analysis, and civi

Decoding the Critique Mechanism in Large Reasoning Models

arXiv:2603.16331v2 Announce Type: replace Abstract: Large Reasoning Models (LRMs) exhibit backtracking and self-verification mechanisms that enable them to revise intermediate steps and reach correct

researcharxiv-cs-lg

Local Ai

How Far Will They Go? Red-Teaming Online Influence with Large Language Models

arXiv:2605.22880v1 Announce Type: cross Abstract: As large language model (LLM)-based agents increasingly participate in online discourse, red-teaming their capacity to support political influence cam

local-aiarxiv-cs-ai

Tutorials

Learning a Particle Dynamics Model with Real-world Videos

arXiv:2605.23845v1 Announce Type: new Abstract: Data-driven learning approaches for physics simulation, sometimes referred to as world models, have emerged as promising alternatives to traditional phy

tutorialsarxiv-cs-cv

LLMs as Noisy Channels: A Shannon Perspective on Model Capacity and Scaling Laws

arXiv:2605.23901v1 Announce Type: cross Abstract: Existing scaling laws for Large Language Models (LLMs), predominantly monotonic power laws, fail to explain emerging non-monotonic phenomena such as c

Model Collapse as Cultural Evolution

arXiv:2605.23054v1 Announce Type: cross Abstract: Model collapse, the progressive degradation of LLMs trained on their own outputs, has been characterized statistically but lacks a linguistic explanat

PROGRESSLM: Towards Progress Reasoning in Vision-Language Models

arXiv:2601.15224v2 Announce Type: replace-cross Abstract: Estimating task progress requires reasoning over long-horizon dynamics rather than recognizing static visual content. While modern Vision-Lang

TEAM: Temporal-Spatial Consistency Guided Expert Activation for MoE Diffusion Language Model Acceleration

arXiv:2602.08404v2 Announce Type: replace Abstract: Diffusion large language models (dLLMs) have recently gained significant attention due to their inherent support for parallel decoding. Building on

Transcoders Trace Visual Grounding and Hallucinations in Vision-Language Models

arXiv:2605.22902v1 Announce Type: cross Abstract: Generative Vision-Language Models (VLMs) perform well on multimodal reasoning, but how visual inputs are transformed to text remains poorly understood

Spectra as Language: Large Language Models for Scalable Stellar Parameter and Abundance Inference

arXiv:2605.22162v1 Announce Type: cross Abstract: Stellar spectra encode key information on the physical properties and chemical compositions of stars. Accurate stellar parameter determination is esse

23 May 2026

VRPRM: Process Reward Modeling via Visual Reasoning

arXiv:2508.03556v3 Announce Type: replace Abstract: Process Reward Model (PRM) is widely used in the post-training of Large Language Model (LLM) because it can perform fine-grained evaluation of the r

researcharxiv-cs-lg

23 May 2026

Beyond Euclidean Proximity: Repairing Latent World Models with Horizon-Matched Trajectory Reachability Metrics

model-releasesarxiv-cs-ro

arXiv:2605.22164v1 Announce Type: cross Abstract: Latent world models can contain the state needed for control, yet their terminal-cost interface can expose the planner to the wrong decision-relevant

CrossVLA: Cross-Paradigm Post-Training and Inference Optimization for Vision-Language-Action Models

arXiv:2605.21854v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models have rapidly converged on a small set of architectural patterns: discrete-token autoregression (e.g. OpenVLA) and co

MAP4TS: A Multi-Aspect Prompting Framework for Time-Series Forecasting with Large Language Models

arXiv:2510.23090v2 Announce Type: replace Abstract: Recent advances have investigated the use of pretrained large language models (LLMs) for time-series forecasting by aligning numerical inputs with L

PIU: Proximity-guided Identity Unlearning in ID-Conditioned Diffusion Models

arXiv:2605.22311v1 Announce Type: new Abstract: Identity-conditioned diffusion models enable high-quality and identity-consistent face generation, but they also raise severe privacy concerns, as model

researcharxiv-cs-cv

Seizure-Semiology-Suite (S3): A Clinically Multimodal Dataset, Benchmark, and Models for Seizure Semiology Understanding

arXiv:2605.21852v1 Announce Type: new Abstract: While Multimodal Large Language Models (MLLMs) have demonstrated remarkable proficiency in general video understanding, their capacity to interpret invo

ArchSIBench: Benchmarking the Architectural Spatial Intelligence of Vision-Language Models

arXiv:2605.20837v1 Announce Type: new Abstract: Architectural spatial intelligence, the ability to recognize and infer architectural space, is fundamental to tasks such as robot navigation, embodied i

Tutorials

FlowLM: Few-Step Language Modeling via Diffusion-to-Flow Adaptation

arXiv:2605.20199v1 Announce Type: new Abstract: We present FlowLM, a flow matching language model transformed from pre-trained diffusion language models via efficient fine-tuning. By re-aligning the c

tutorialsarxiv-cs-cl

GradPower: Powering Gradients for Faster Language Model Pre-Training

arXiv:2505.24275v3 Announce Type: replace Abstract: We propose GradPower, a lightweight gradient-transformation technique for accelerating language model pre-training. Given a gradient vector g=(g_i)_

Matryoshka Concept Bottleneck Models

arXiv:2605.20612v1 Announce Type: new Abstract: Concept Bottleneck Models (CBMs) have emerged as a prominent paradigm for interpretable deep learning, learning by grounding predictions in human-unders

researcharxiv-cs-lg

Parameters as Experts: Adapting Vision Models with Dynamic Parameter Routing

arXiv:2602.06862v2 Announce Type: replace Abstract: Adapting pre-trained vision models using parameter-efficient fine-tuning (PEFT) remains challenging, as it aims to achieve performance comparable to

The Economics of Model Collapse: Equilibrium, Welfare, and Optimal Provenance Subsidies in Synthetic Data Markets

arXiv:2605.20279v1 Announce Type: cross Abstract: Generative artificial intelligence is rapidly transforming the supply side of training data: an increasing share of new tokens, images, and structured

Adaptive Residual-Update Steering for Low-Overhead Hallucination Mitigation in Large Vision Language Models

arXiv:2511.10292v2 Announce Type: replace-cross Abstract: Large Vision-Language Models (LVLMs) typically process visual inputs as a prefix to the language decoder. As the model autoregressively genera

Base Models Look Human To AI Detectors

arXiv:2605.19516v1 Announce Type: cross Abstract: As AI-generated text enters the real-world at scale, institutions increasingly use commercial AI-text detectors, especially in education and academic-

CLIF: Concept-Level Influence Functions for Transparent Bottleneck Models

arXiv:2605.19848v1 Announce Type: new Abstract: In recent years, the black-box nature of deep learning models has limited their application in high-stakes domains such as medical diagnosis and finance

applicationsarxiv-cs-cl

Local Ai

Code-Guided Reasoning for Small Language Models: Evaluating Executable MCQA Scaffolds

arXiv:2605.18827v1 Announce Type: cross Abstract: Multiple-choice QA benchmarks usually evaluate small language models (SLMs) as direct answerers, but deployed language-model systems increasingly rely

local-aiarxiv-cs-lg

DarkLLM: Learning Language-Driven Adversarial Attacks with Large Language Models

arXiv:2605.18868v1 Announce Type: cross Abstract: While vision and multimodal foundation models underpin critical tasks from perception to complex reasoning, they remain highly vulnerable to adversari

applicationsarxiv-cs-ai

Eyes on VLM: Benchmarking Gaze Following and Social Gaze Prediction in Vision Language Models

arXiv:2605.19859v1 Announce Type: new Abstract: Vision-language models (VLMs) have rapidly evolved into general-purpose multimodal reasoners with strong zero-shot generalization. In this context, VLMs

researcharxiv-cs-cv

Fine-tuning Large Language Model for Automated Algorithm Design

arXiv:2507.10614v2 Announce Type: replace-cross Abstract: The integration of large language models (LLMs) into automated algorithm design has shown promising potential. A prevalent approach embeds LLM

Safety

Improved visual-information-driven model for crowd simulation and its modular application

arXiv:2504.03758v4 Announce Type: replace-cross Abstract: Crowd movement simulation is crucial for pedestrian safety management and facility design. Data-driven models offer the potential to improve r

safetyarxiv-cs-cv

Inference-Time Scaling in Diffusion Models through Iterative Partial Refinement

arXiv:2605.19317v1 Announce Type: cross Abstract: Inference-time scaling has emerged as a major approach for improving reasoning capabilities, and has been increasingly applied to diffusion models. Ho

Safety

Jailbreaking on Text-to-Video Models via Scene Splitting Strategy

arXiv:2509.22292v2 Announce Type: replace-cross Abstract: Along with the rapid advancement of numerous Text-to-Video (T2V) models, growing concerns have emerged regarding their safety risks. While rec

safetyarxiv-cs-ai

MetaRA: Metamorphic Robustness Assessment for Multimodal Large Language Model-based Visual Question Answering Systems

arXiv:2605.19307v1 Announce Type: new Abstract: Visual Question Answering (VQA), as the representative multimodal task, serves as a key benchmark for evaluating the reasoning capabilities of Multimoda

MiMuon: Mixed Muon Optimizer with Improved Generalization for Large Models

arXiv:2605.19619v1 Announce Type: cross Abstract: Matrix-structured parameters frequently appear in many artificial intelligence models such as large language models. More recently, an efficient Muon