Search: “evaluation”

Uncertainty Is Not Enough: Value-of-Information Routing for Mixtures of LoRA Experts

arXiv:2608.02528v1 Announce Type: new Abstract: Mixtures of low-rank adaptation experts increase parameter-efficient capacity by routing each input through a subset of adapters. Recent dynamic routers

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Uncertainty Quantification for Visual Object Pose Estimation: S-Lemma Ellipsoidal Bounds

DGX agent

arXiv:2511.21666v2 Announce Type: replace-cross Abstract: Quantifying the uncertainty of an object's pose estimate is essential for robust control and planning. Although pose estimation is a well-stud

applicationsarxiv-cs-cv

4 Aug 2026

Understanding and Correcting Low-Frequency Bias in EEG Foundation Model

arXiv:2608.01898v1 Announce Type: new Abstract: Increasing EEG pretraining data scale or model capacity does not consistently improve downstream performance. We identify a persistent low-frequency bia

safetyarxiv-cs-lg

Understanding Synergistic Interactions among Pathology Foundation Models via Adaptive Fusion

arXiv:2608.01370v1 Announce Type: new Abstract: Pathology foundation models (PFMs) provide strong tile-level representations via self-supervised pre-training on large-scale pathology images. Yet, PFMs

Tutorials

UniqueSplat: View-conditioned 3D Gaussian Splatting for Generalizable 3D Reconstruction

arXiv:2608.02145v1 Announce Type: new Abstract: In this paper, we propose UniqueSplat, a view-conditioned feed-forward 3D Gaussian Splatting model to reconstruct customized 3D radiance fields for each

tutorialsarxiv-cs-cv

Unleashing the Potential of Large Language Models: A Blueprint for Real-Time, Enterprise-Ready Deployments

arXiv:2608.00419v1 Announce Type: cross Abstract: Large language models deployed in real-time, regulated settings face knowledge staleness, catastrophic forgetting, hallucination, and weak feedback lo

safetyarxiv-cs-cl

VertiAKD: Adaptive Off-Road Kinodynamics on Vertically Challenging Terrain

arXiv:2608.00945v1 Announce Type: new Abstract: Off-road mobility requires autonomous mobile robots to generalize across heterogeneous vehicle fleets and continuously changing terrain conditions. Exis

local-aiarxiv-cs-ro

VespaSeg: A Resource-Aware Ground-then-Segment Pipeline for Referring Expression Segmentation

arXiv:2608.01077v1 Announce Type: new Abstract: Referring expression segmentation requires language conditioned localization and pixel-accurate masks, but monolithic models can be costly to deploy. We

local-aiarxiv-cs-ro

VGER: Voxel-Guided Global Event Ranking for Event Cloud Attribution

arXiv:2608.01470v1 Announce Type: new Abstract: Event cameras produce sparse and asynchronous event streams that provide rich spatio-temporal information for efficient perception. Recent advances in e

WAM-Diff2: Hierarchical AR-to-Diffusion Distillation for Highly Efficient Autonomous Driving VLA

arXiv:2608.01035v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models have emerged as a prominent paradigm for end-to-end autonomous driving; however, their efficient deployment is sev

safetyarxiv-cs-cv

When Collaboration Becomes a Trigger: Collective Evidence-Threshold Backdoors in Multi-Agent Systems

arXiv:2608.01085v1 Announce Type: cross Abstract: LLM-based multi-agent systems (MAS) extend LLM capabilities through iterative communication and shared contexts. However, this collaboration introduce

agentsarxiv-cs-lg

When Extreme Darkness Meets Motion Blur: MeanFlow for Unified RAW Restoration

arXiv:2608.01720v1 Announce Type: new Abstract: Extremely low-light RAW enhancement aims to recover severely attenuated sensor signals, yet existing methods often focus on illumination and noise while

When Prompts Control Robots: Prompt Injection Attacks in Multi-Agent Robotic Systems

arXiv:2608.00747v1 Announce Type: new Abstract: Large language models are increasingly integrated into autonomous robotic systems for task planning and control, but this integration exposes them to pr

agentsarxiv-cs-ro

Which Modality Decides? Counterfactual Modality Attribution for Multimodal LLMs

arXiv:2608.00076v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) increasingly support high-stakes decision making by combining complementary information from images and text. W

safetyarxiv-cs-cv

Why Does Action Chunking Improve Behavioral Cloning Performance in Robotic Control?

arXiv:2608.02547v1 Announce Type: new Abstract: Action chunking---predicting and executing multiple actions instead of a single action---has proven to be a critical component for learning effective ro

safetyarxiv-cs-ro

Why Large Language Models Fail at Tabular Prediction

arXiv:2608.02412v1 Announce Type: new Abstract: Large language models (LLMs) have become the default tool for a remarkable range of tasks, yet they have had conspicuously little success at one of the

WiFuse: An Attention Mechanism for Human Activity Recognition using Fused CSI Amplitude and Delay-Doppler Channel Features

arXiv:2608.00642v1 Announce Type: new Abstract: Recently, Wi-Fi sensing has played a significant role in Human Activity Recognition (HAR), as it enables the detection of various activities using only

World Action Models in Real Time: An Empirical Study of Smooth Execution via Asynchronous Deployment

arXiv:2608.01880v1 Announce Type: new Abstract: World Action Models generate fixed-horizon action chunks through iterative denoising, creating substantial inference latency that can cause pauses, stal

safetyarxiv-cs-ro

A Frozen Pixel-Space Diffusion Model Can Guide Itself with Its Own Samples

model-releasesarxiv-cs-cv

arXiv:2607.29122v1 Announce Type: new Abstract: Pixel-space diffusion models aim to learn an end-to-end generator directly over raw pixels. This is challenging because a single model must capture both

A Human-Centered Validation of the Explainability-Performance Coefficient

arXiv:2607.29614v1 Announce Type: cross Abstract: The rapid adoption of deep learning models in high-risk domains has intensified the need for trustworthy Explainable Artificial Intelligence (XAI). Ho

Tutorials

A user's guide to PINNs in geometric analysis: lessons from the asymptotic Plateau problem

arXiv:2607.28733v1 Announce Type: cross Abstract: This proceedings contribution elaborates on the findings of arXiv:2605.26234v2: a joint work with Marco Usula, where we introduced a machine learning

tutorialsarxiv-cs-ai

Adaptivity via a Parallel Architecture for Stochastic Gradient Methods Adaptivity via a Parallel Architecture for Stochastic Gradient Methods Adaptivity via a Parallel Architecture for Stochastic Gradient Methods

arXiv:2607.28902v1 Announce Type: new Abstract: We develop a parallel framework that assembles static gradient methods to achieve better adaptivity. A static gradient method, denoted by GD(x_0,T), tak

researcharxiv-cs-lg

Agentic Harness for Real-World Compilers

model-releasesarxiv-cs-ai

arXiv:2603.20075v2 Announce Type: replace-cross Abstract: Compilers are critical to modern computing, yet fixing compiler bugs is difficult. While recent large language model (LLM) advancements enable

AgenticRepair: Multi-Faceted Program Context Engineering for Agentic Vulnerability Repair

arXiv:2607.29422v1 Announce Type: cross Abstract: Automated vulnerability repair aims to reduce the time and effort required to patch security flaws from a vulnerability triage report. Recent agentic

agentsarxiv-cs-ai

An analysis of machine learning approaches for enhancing decision-making in complex discrete choice tasks

arXiv:2607.28854v1 Announce Type: new Abstract: Discrete choice modeling is a common tool used for preference elicitation during policy-making, but this is typically done through parametric models. Ma

safetyarxiv-cs-lg

An Ontology-Guided, Deduplication-Aware Extraction Layer for Knowledge Graph Construction from Heterogeneous Documents

arXiv:2607.28662v1 Announce Type: new Abstract: Large language models extract entities and relationships from unstructured documents fluently but inconsistently: type vocabularies fracture across docu

local-aiarxiv-cs-ai

Analytical and Bootstrap Confidence Intervals of Double Machine Learning: Simulation studies and an application to rural-urban difference in obesity prevalence

arXiv:2607.29456v1 Announce Type: cross Abstract: Double Machine Learning (DML) is a popular approach for treatment effect estimation in various settings, which allows a wide range of flexible machine

AniCrafter: Customizing Realistic Human-Centric Animation via Avatar-Background Conditioning in Video Diffusion Models

arXiv:2505.20255v3 Announce Type: replace Abstract: Recent advances in video diffusion models have substantially enhanced character animation techniques. However, existing methods primarily depend on

Application of machine learning to monster level prediction in tabletop RPG game design

arXiv:2607.09196v2 Announce Type: replace Abstract: Designing balanced adversaries is a central but labor-intensive task in tabletop role-playing game (TTRPG) development. In systems such as Pathfinde

researcharxiv-cs-lg

Assessing the Generalization of Graph Neural Networks for Fault Location Across Increasing Distributed Energy Resource Penetration Levels

arXiv:2607.29293v1 Announce Type: new Abstract: Accurate fault location is critical for distribution network reliability. However, increasing distributed energy resource (DER) penetration complicates

Automated classification method of COVID-19 cases from chest CT volumes using 2D and 3D hybrid CNN for anisotropic volumes

arXiv:2607.28950v1 Announce Type: new Abstract: This paper proposes an automated classification method of chest CT volumes based on likelihood of COVID-19 cases. Novel coronavirus disease 2019 (COVID-

BeatEdit: Symbolic Music Generation as Explicit Editing

arXiv:2607.11124v2 Announce Type: replace-cross Abstract: Music creation is fundamentally a process of revision. Yet symbolic music generation remains dominated by paradigms that produce complete sequ

Classification of COVID-19 cases from chest CT volumes using hybrid model of 3D CNN and 3D MLP-Mixer

arXiv:2607.28978v1 Announce Type: new Abstract: This paper proposes an automated classification method of COVID-19 chest CT volumes using improved 3D MLP-Mixer. Novel coronavirus disease 2019 (COVID-1

local-aiarxiv-cs-cv

CodeShrink: Adaptive Visual Compression for Efficient Multimodal Code Understanding

arXiv:2607.29637v1 Announce Type: new Abstract: Rendering source code as images offers a promising way to reduce the input costs of Multimodal Large Language Models (MLLMs). Adjusting image resolution

agentsarxiv-cs-cv

Combining Large Language Models and Symbolic Reasoning for Multi-Robot Temporal Planning through Explainable Knowledge Bases

model-releasesarxiv-cs-ai

arXiv:2502.19135v2 Announce Type: replace Abstract: We present PLANTOR, a framework for generating and executing multi-robot task plans from natural-language task descriptions through LLM-assisted kno

Communication-Efficient Secure Aggregation in Decentralized Learning

arXiv:2405.07708v3 Announce Type: replace Abstract: Decentralized learning (DL) enables participants to collaboratively train models without a central server, yet it faces significant scalability chal

Conditioning Tree-Based Diffusions and Flows for Probabilistic Tabular Regression

arXiv:2607.28864v1 Announce Type: cross Abstract: Tree-based diffusion models fit flexible conditional predictive distributions for tabular regression without a neural density estimator, but they inhe

researcharxiv-cs-lg

COntExt: Towards Context-Aware Ontology Extension from Operational Metrics

arXiv:2607.29553v1 Announce Type: new Abstract: Organizations increasingly define operational metrics in structured, machine-readable formats to monitor systems, processes, and compliance. These metri

Tutorials

Copy Less, Ground More: Overcoming Repetitive Copying in Long-Context Reasoning via Evidence-Aware Reinforcement Learning

arXiv:2607.19345v2 Announce Type: replace-cross Abstract: Large language models that generate step-by-step reasoning traces have achieved strong performance on complex tasks, and extending them to lon

tutorialsarxiv-cs-ai

CorrelationFlow: A Training-Free Geometric Approach for LiDAR Scene Flow Estimation

arXiv:2607.29237v1 Announce Type: new Abstract: LiDAR scene flow estimation has settled into a monoculture: nearly all recent methods share the same feed-forward architecture and the same family of se

Cross-Lingual Transfer for Machine Translation in Turkic Languages

arXiv:2607.29355v1 Announce Type: cross Abstract: Cross-lingual transfer is central to low-resource machine translation, but its behavior within closely related language families remains insufficientl

Curriculum Matters: Data-Efficient Relational PFN Pretraining with Synthetic Data

arXiv:2607.29120v1 Announce Type: new Abstract: Relational Prior-Data Fitted Networks (PFNs) such as RDB-PFN approximate Bayesian inference over multi-table relational databases by pretraining on mill

Deformable Medical Image Registration with KAN-based Implicit Neural Representations

arXiv:2509.22874v2 Announce Type: replace Abstract: Deformable image registration (DIR) is central to medical image analysis, supporting spatial alignment for longitudinal studies and multi-modal fusi

safetyarxiv-cs-cv

Demystifying Entropy-based Selection for Chain-of-Thought Compression in Large Reasoning Models

arXiv:2607.28707v1 Announce Type: new Abstract: Entropy-based pruning has been proposed as an effective method for compressing Chain-of-Thought (CoT) reasoning with negligible accuracy loss. We test t

researcharxiv-cs-cl

Detecting Experiential Intertextuality Across Migration Routes: Beyond Surface Similarity in French Narratives

model-releasesarxiv-cs-cl

arXiv:2607.29188v1 Announce Type: new Abstract: Migrants traversing geographically distinct routes such as the Trans-Saharan and Balkan corridors often recount strikingly parallel lived experiences: p

Development of FDD-ON: an Ontology for VAV HVAC System Fault Detection and Diagnostics

arXiv:2607.29657v1 Announce Type: new Abstract: Fault detection and diagnosis (FDD) technology is essential for improving HVAC system reliability, energy efficiency, and maintenance effectiveness. How

DFSC: Error-Controlled Differentiable Mittag-Leffler Propagation for Fractional Scientific Machine Learning

arXiv:2607.29038v1 Announce Type: new Abstract: Fractional scientific machine learning requires numerical operators that can be differentiated, batched, accelerated, and composed with neural networks.

local-aiarxiv-cs-lg

DiffAttack: Evasion Attacks Against Face Recognition via Latent Diffusion Models

model-releasesarxiv-cs-ai

arXiv:2607.28936v1 Announce Type: cross Abstract: Facial biometric identification relies on the distinctiveness of user attributes within a high-dimensional embedding space. However, the decision boun