Search: “models”

Efficient Coreset Selection via K-Nearest Neighbor Graphs

arXiv:2608.16270v1 Announce Type: new Abstract: Coreset selection reduces the cost of model training by replacing a large training set with a small representative subset. Existing gradient-approximati

applicationsarxiv-cs-lg

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Emergent Misaligned Communication in Long-Horizon Multi-Agent LLM Commerce

DGX agent

arXiv:2608.14825v1 Announce Type: cross Abstract: Frontier LLM agents increasingly transact on behalf of separate principals, often using natural language rather than structured APIs. Much of the safe

safetyarxiv-cs-ai

18 Aug 2026

Empowering Polymeric Materials Discovery by Artificial Intelligence

arXiv:2606.20753v2 Announce Type: replace-cross Abstract: Polymeric materials underpin modern technologies spanning energy storage, microelectronics, healthcare and sustainable manufacturing. Yet thei

agentsarxiv-cs-ai

Experimentally Extending Quantum Kernel Learning to Quantum Data by NMR

arXiv:2412.09557v3 Announce Type: replace-cross Abstract: Quantum kernel learning (QKL) promises efficient machine learning by encoding feature maps onto exponentially large Hilbert spaces inherent in

FedImp: Enhancing Federated Learning Convergence with Impurity-Based Weighting

arXiv:2608.14654v1 Announce Type: cross Abstract: Federated Learning (FL) is a collaborative paradigm that enables multiple devices to train a global model while preserving local data privacy. A major

local-aiarxiv-cs-ai

Feed-Forward Hierarchical Gaussian Diffusion for Extreme CT Reconstruction

arXiv:2608.15343v1 Announce Type: new Abstract: Reconstructing three-dimensional computed tomography (CT) from severely constrained projections is highly ill-posed. Sparse angular sampling, restricted

Fiber Fingerprints of Hidden Learning-State Dynamics

arXiv:2608.15976v1 Announce Type: new Abstract: A learning system can occupy execution states that are indistinguishable under every declared present-behavior readout yet respond differently to future

FMReward: Aligning and Evaluating Audio-Driven 3D Facial Animation with Human Preferences

arXiv:2608.15296v1 Announce Type: new Abstract: Audio-driven 3D facial animation is essential for advancing immersion and interactivity in virtual experiences. Although recent advances have shown prom

safetyarxiv-cs-cv

Focusing by Contrastive Attention: Enhancing VLMs' Visual Reasoning

arXiv:2509.06461v3 Announce Type: replace-cross Abstract: Vision-Language Models (VLMs) have demonstrated remarkable success across diverse visual tasks, yet their performance degrades in complex visu

Formalisms for Robotic Mission Specification and Execution: A Comparative Analysis

arXiv:2603.15427v2 Announce Type: replace-cross Abstract: Robots are increasingly deployed across diverse domains and designed for multi-purpose operation. As robotic systems grow in complexity and op

applicationsarxiv-cs-ro

Fractional Optimizers Meet Fractal Activation Functions: An Empirical Study of Multi-Scale Optimization in Neural Network

arXiv:2608.14636v1 Announce Type: cross Abstract: Fractional optimization methods and fractal activation functions are two independent directions for improving neural network training. Fractional opti

From Reactive to Autonomous: Evolution of AI Operations in Cloud Network Infrastructure

arXiv:2608.14574v1 Announce Type: cross Abstract: The operational model for cloud network infrastructure has undergone a fundamental transformation over the past decade. What began as manual, human-dr

agentsarxiv-cs-ai

Frontier AI Forecasting Has a Measurement Problem: An Audit of Progress Evidence

arXiv:2608.14903v1 Announce Type: new Abstract: Quantitative forecasts of frontier artificial intelligence often connect dated targets to trends in benchmark scores, training compute, release time, or

GATTA: Graph Active Learning with Test-Time Augmentation

arXiv:2608.15084v1 Announce Type: cross Abstract: Test-time augmentation (TTA) has proven effective for improving model robustness and uncertainty estimation in computer vision, yet its application to

Generative Learning of Separatrices

arXiv:2608.14743v1 Announce Type: new Abstract: The identification and reconstruction of the boundaries separating basins of attraction in multistable, multidimensional dynamical systems presents a fu

researcharxiv-cs-lg

GEO-Flag: Detecting and Measuring GEO-Optimized Web Content

arXiv:2608.16824v1 Announce Type: new Abstract: Generative Engine Optimization (GEO) modifies web content to increase its likelihood of being selected and cited by generative search engines. This can

Geometry of Forgetting: Representation Flux in Continual Learning

arXiv:2608.15854v1 Announce Type: cross Abstract: Catastrophic forgetting remains a fundamental obstacle to continual learning, where neural networks lose previously acquired knowledge while learning

GLaQ: Grounding Latent Queries in Visual Evidence for Multimodal Reasoning

arXiv:2608.15517v1 Announce Type: new Abstract: Chain-of-thought reasoning has substantially improved the problem-solving capabilities of multimodal large language models. Fine-grained visual evidence

local-aiarxiv-cs-cv

Gradient descent reliably finds depth- and gate-optimal circuits for generic unitaries

arXiv:2601.03123v2 Announce Type: replace-cross Abstract: When the gate set has continuous parameters, synthesizing a unitary operator as a quantum circuit is, in principle, always possible using exac

GraniKV: Asymmetric Granularity KV-Cache Paging for Multi-Agent Systems with Long Shared Prefix

arXiv:2608.15584v1 Announce Type: cross Abstract: Production paged-serving engines apply uniform paging granularity to the KV cache, even though the two regions of a multi-agent workload have opposite

GRNEdit: Efficient General Video Editing from a New Binary-Evidence Perspective in Generative Refinement Networks

arXiv:2608.16328v1 Announce Type: new Abstract: Instruction-based general video editing seeks to unify diverse editing operations within a single, intuitive interface. Existing approaches often rely o

local-aiarxiv-cs-cv

Grounding Healthcare LLMs in a Causal Knowledge Graph: Framework, Metrics, and a Cardiovascular Pilot

arXiv:2608.15382v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly proposed for healthcare decision support, but their evaluations still reward single-answer accuracy rather

applicationsarxiv-cs-ai

Hallucination Span Detection with Input-Side Evidence Alignment

arXiv:2608.15804v1 Announce Type: new Abstract: Hallucinations remain a major obstacle to the reliable use of large language models (LLMs) in conditional text generation. Existing methods primarily as

safetyarxiv-cs-cl

How Do Agents Fail on AutoResearch: End-to-End Diagnostic Evaluation on 100 Real-World Frontier Research Tasks

arXiv:2608.14905v1 Announce Type: new Abstract: AI has long assisted scientific research, but the rapid advance of LLMs and agentic scaffolds is reshaping the landscape; a single system can now carry

agentsarxiv-cs-cl

How Many Samples Are Needed to Determine Causal Direction? Sharp Minimax Bounds for Bivariate LiNGAM

arXiv:2608.15840v1 Announce Type: cross Abstract: We study how many observations are needed to determine the causal direction between two linearly related variables. Classical LiNGAM theory shows that

How Sampling Strategy Affects Imbalance Mitigation in LiDAR Segmentation: A Study of Structured vs. Random Point-Based Architectures

arXiv:2608.16673v1 Announce Type: new Abstract: Class imbalance in LiDAR point clouds poses challenges for semantic segmentation in autonomous navigation and urban mapping. While 2D vision has numerou

HP2-SLAM: Adaptive Hybrid ICP for Robust and Efficient LiDAR SLAM

model-releasesarxiv-cs-ro

arXiv:2608.14996v1 Announce Type: new Abstract: Achieving robustness, accuracy, and efficiency simultaneously remains a central challenge in light detection and ranging (LiDAR) simultaneous localizati

HTTM: Head-wise Temporal Token Merging for Faster VGGT

arXiv:2511.21317v2 Announce Type: replace Abstract: The Visual Geometry Grounded Transformer (VGGT) marks a significant leap forward in 3D scene reconstruction, as it is the first model that directly

local-aiarxiv-cs-cv

Identifying parameter couplings and uncertainties of mixed-noise stochastic systems via full-covariance Gaussian mixture network

arXiv:2608.15198v1 Announce Type: cross Abstract: Parameter identification of stochastic dynamical systems driven by mixed noises is challenging due to intractable likelihood functions. We propose PEN

In-Context Learning to Assess Built Environment Impacts on Perceived Neighborhood Walkability Among Mobility-impaired Older Adults

arXiv:2608.14663v1 Announce Type: new Abstract: As global populations age, enhancing neighborhood walkability through inclusive urban design is important for mitigating built environment (BE) barriers

researcharxiv-cs-lg

Information Geometry of Message Passing

arXiv:2608.15922v1 Announce Type: cross Abstract: We show that the natural-gradient stationary condition of variational inference has an edge-local form on a Forney-style factor graph. We start from t

Invariant Pretraining for Robust Code Representations

arXiv:2608.15412v1 Announce Type: cross Abstract: Encoder-based code representation models remain widely deployed for discriminative tasks such as clone detection and code classification, where their

IP Protection in the Era of Visual Generative AI: A Survey

arXiv:2608.14730v1 Announce Type: new Abstract: The rapid evolution of visual generative AI has introduced a wide range of intellectual property risks, spanning the unauthorized learning, reproduction

researcharxiv-cs-cv

KeyID: Decoupled Drafting and Keyframe Editing for Identity-Preserving Video Generation

arXiv:2608.16154v1 Announce Type: new Abstract: Identity-preserving video generation (IPVG) requires synthesizing videos that are faithful to both reference subjects and text prompts. Existing methods

Layers Matter: Why Continual Learning Regularization Should Be Layer-Adaptive

arXiv:2608.15901v1 Announce Type: cross Abstract: Continual learning regularizers like EWC fight forgetting by penalizing changes from previous-task parameters with per-parameter importance, typically

Learning to Price with Persuasion

arXiv:2608.16699v1 Announce Type: cross Abstract: Motivated by modern marketplaces, where the platform or the seller routinely gathers detailed user profiles, we study a novel learning theoretic model

researcharxiv-cs-lg

LLM-Based Hierarchical Coordinated Control with Continuation-Aware Policy Learning

arXiv:2608.15041v1 Announce Type: new Abstract: Coordinating multiple interacting units in complex engineering systems is challenging when system interactions are difficult to model, operational infor

safetyarxiv-cs-ai

LLM Safety Alignment in Low-Resource Languages: A Systematic Literature Review

arXiv:2608.14626v1 Announce Type: cross Abstract: Large Language Models (LLMs) have achieved substantial progress in safety alignment, yet their safety guarantees remain significantly weaker in low-re

safetyarxiv-cs-ai

LongDocBench: Benchmarking TOC Hierarchy and Contextual Relationship Recovery in Long Documents

arXiv:2608.15064v1 Announce Type: new Abstract: Parsing visual documents into machine-readable representations is fundamental to document intelligence. Existing benchmarks focus on page-level element

Longitudinal and Graph-Augmented Prediction of Adolescent Substance Use Onset in the ABCD Study

arXiv:2608.14578v1 Announce Type: new Abstract: Early identification of adolescent substance-use risk is an important prevention challenge, yet the relative value of baseline characteristics, longitud

LongRCA Bench: Diagnosing Responsible Roles and Root Causes in Long-Horizon Agent Failures

arXiv:2608.15242v1 Announce Type: new Abstract: When a long-horizon agent execution fails, outcome-level evaluation reveals the unsuccessful result but not where the decisive error entered the traject

Low Cost Two-Stage Fabric Defect Detection at the Edge

arXiv:2608.14727v1 Announce Type: new Abstract: Fabric inspection in the garment industries of low-income economies remains largely manual, and commercial vision systems are priced beyond most small a

Measuring Structured Predictability in Neural Training Dynamics: A Cross-Regime Study

arXiv:2608.15483v1 Announce Type: new Abstract: Modern deep networks are trained through long update trajectories, yet their temporal organization remains less systematically characterized than archit

MechMath: Sorrifier-Driven Formal Decomposition Workflow for Automated Theorem Proving

arXiv:2603.24465v2 Announce Type: replace Abstract: Recent advances in large language models (LLMs) and LLM-based agents have substantially improved the capabilities of automated theorem proving. Howe

agentsarxiv-cs-cl

Metaplasticity as adaptive gradient preconditioning for incremental learning

arXiv:2608.14634v1 Announce Type: new Abstract: Biological intelligence naturally prevents catastrophic forgetting through Complementary Learning Systems (CLS) theory, a macroscopic consolidation proc

Navigation-Informed Embeddings: Dense-Retriever Adaptation from Agent Search Traces

arXiv:2608.15956v1 Announce Type: new Abstract: Agentic retrieval workflows produce query, retrieval, and stopping traces as a byproduct of answering questions. We study how these traces can adapt a d

OpenGPT-4o-Image: A Comprehensive Dataset for Advanced Image Generation and Editing

arXiv:2509.24900v2 Announce Type: replace-cross Abstract: The performance of unified multimodal models for image generation and editing is fundamentally constrained by the quality and comprehensivenes

applicationsarxiv-cs-ai

PCA-guided Activation Scaling for Monotonic Bidirectional Control over LLM Sycophancy

arXiv:2608.16650v1 Announce Type: new Abstract: Large language models (LLMs) exhibit sycophancy, a tendency to agree with user beliefs regardless of factual accuracy. This can reinforce misconceptions

researcharxiv-cs-cl