Search: “evaluation”

Personalized Recommendation Tool Learning via Autonomous Language Agents

arXiv:2607.19739v1 Announce Type: cross Abstract: Although large language models (LLMs) have recently gained traction in recommender systems due to their strong reasoning capabilities and extensive wo

agentsarxiv-cs-ai

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PG-KINN: A Physics-Informed Petrov-Galerkin Kolmogorov-Arnold Network for Solving Forward and Inverse PDEs

DGX agent

arXiv:2607.20378v1 Announce Type: new Abstract: Physics-informed learning of partial differential equations (PDEs) has been dominated by multilayer perceptrons (MLPs), whose spectral bias and dense pa

model-releasesarxiv-cs-lg

23 Jul 2026

PGTT: Phase-Guided Terrain Traversal for Perceptive Legged Locomotion

arXiv:2510.18348v2 Announce Type: replace-cross Abstract: State-of-the-art perceptive Reinforcement Learning controllers for legged robots typically either (i) impose oscillator-or IK-based gait prior

PhaseAware: Interpretable Human-in-the-Loop Rehabilitation Scoring with Boundary Monitoring

arXiv:2607.20237v1 Announce Type: new Abstract: Rehabilitation scoring systems are most useful when their outputs can be reviewed and interpreted within clinical workflows. This study presents PhaseAw

agentsarxiv-cs-lg

PhenSPINE: A Standardized Benchmark for Spine Pathology Diagnosis

arXiv:2607.19696v1 Announce Type: cross Abstract: The accurate diagnosis of spinal pathologies depends heavily on radiological interpretation, yet automated systems are hindered by the lack of diverse

Physics-Aware Complex-Valued State Space Model with Scattering-Prior Feature Modulation for PolSAR Image Classification

arXiv:2607.19787v1 Announce Type: cross Abstract: Polarimetric synthetic aperture radar (PolSAR) image classification is a representative task for physics-aware GeoAI, where land-cover semantics are c

Physics Closure Matters for Machine Olfaction: A Maxwell--Stefan Graph Solver for Identifiable Dynamic Gas Unmixing

arXiv:2607.18544v1 Announce Type: new Abstract: Machine olfaction for gas unmixing is an underconstrained inverse problem in which gas compositions must be inferred from low-dimensional, delayed, and

local-aiarxiv-cs-cv

Point-Selection Fine-Tuning Framework for Robust Point Cloud Classification

model-releasesarxiv-cs-cv

arXiv:2607.19711v1 Announce Type: new Abstract: Noisy and corrupted points can substantially degrade point cloud recognition performance, especially under challenging corruption settings. In particula

Pointing-Based Object Recognition

arXiv:2603.15403v2 Announce Type: replace Abstract: This paper presents a comprehensive pipeline for recognizing objects targeted by human pointing gestures using RGB images. As human-robot interactio

PoTRE: Test-Time Reasoning inspired by Cognitive Heterogeneity

arXiv:2607.20268v1 Announce Type: new Abstract: While Large Language Models (LLMs) excel at many tasks, they frequently struggle with complex reasoning that requires long-horizon planning and iterativ

agentsarxiv-cs-ai

Pre-Deployment Complexity Estimation for Federated Perception Systems

arXiv:2603.28282v2 Announce Type: replace-cross Abstract: Edge AI systems increasingly rely on federated learning to train perception models in distributed, privacy-preserving, and resource-constraine

local-aiarxiv-cs-ai

Predicting Groundwater Arsenic Concentrations Using Graph Neural Networks

arXiv:2607.19392v1 Announce Type: new Abstract: Arsenic contamination in groundwater presents a longstanding public health crisis in the United States, especially for households depending on private w

researcharxiv-cs-lg

Predictive Extrema, Unprofitable Policies: An AI-Assisted Audit of Candle-Based Binance Spot Timing Models

arXiv:2607.19453v1 Announce Type: cross Abstract: We audit whether candle-based machine-learning models can turn predictions of cryptocurrency extrema or short-horizon outcomes into positive Binance S

Privileged Lesion-Context Relational Distillation for Mask-Free Skin Lesion Classification

arXiv:2607.18773v1 Announce Type: new Abstract: Accurate skin lesion classification can benefit from lesion segmentation masks, but requiring masks or an auxiliary segmentation model during inference

safetyarxiv-cs-cv

PRO-LONG: Programmatic Memory Enables Long-Horizon Reasoning

arXiv:2607.20064v1 Announce Type: new Abstract: Long-horizon tasks require sustained perception, reasoning, and exploration, and are a persistent challenge for large language model (LLM) agents. This

agentsarxiv-cs-ai

Prompt Programming for Cultural Bias and Alignment of Large Language Models

arXiv:2603.16827v2 Announce Type: replace Abstract: Culture shapes reasoning, values, prioritization, and strategic decision-making, yet large language models (LLMs) often exhibit cultural biases that

Pushing the Frontier of Full-Song Generation: Hierarchical Autoregressive Planning Meets Flow-Matching Rendering

arXiv:2607.20253v1 Announce Type: cross Abstract: In this report, we present a unified song generation framework capable of producing high-quality full-length music from lyrics, text descriptions, and

Recovering Clinical Utility Under Differential Privacy: Empirical Validation of Adaptive Federated Aggregation on Heterogeneous Cardiovascular Datasets

arXiv:2607.19403v1 Announce Type: cross Abstract: Validating federated learning frameworks on real clinical data is an essential step between proof-of-concept demonstrations in controlled synthetic en

ReFace: Reorganizing Facial Spatiotemporal Representations for Improved Pain Assessment

model-releasesarxiv-cs-cv

arXiv:2607.19722v1 Announce Type: new Abstract: Automatic pain assessment from facial video remains challenging due to the spatial heterogeneity of pain-related facial cues. This study proposes ReFace

Reliability-Aware Hard--Soft Physics-Informed Neural Networks for Robust Learning of Challenging Partial Differential Equations

model-releasesarxiv-cs-lg

arXiv:2607.19377v1 Announce Type: cross Abstract: Physics-informed neural networks (PINNs) provide a mesh-free framework for solving partial differential equations, but their training is often affecte

Hardware

ReRAM-aware Model Finetuning addressing I-V Non-linearity and Retention Errors

arXiv:2606.17471v3 Announce Type: replace Abstract: Traditional CPU, GPU, and NPU architectures are increasingly limited by the von Neumann bottleneck. While In-Memory Computing (IMC) using ReRAM cros

hardwarearxiv-cs-lg

RIM: A Retrieval-In-Matching Framework for Cross-Domain Global Visual Localization of UAVs

arXiv:2607.20116v1 Announce Type: new Abstract: Global visual localization of unmanned aerial vehicles (UAVs) using remote-sensing reference maps has attracted increasing attention. However, acquisiti

local-aiarxiv-cs-cv

RoboInter1.5: A Holistic Intermediate Representation Suite for Embodied World Modeling and Robotic Manipulation

model-releasesarxiv-cs-ro

arXiv:2607.18709v2 Announce Type: replace Abstract: Existing robot datasets remain expensive to curate, embodiment-specific, and insufficiently annotated with the fine-grained structure required for g

Robust Multi-View Classification under Noisy Supervision via Global Anchor Consensus

arXiv:2607.18561v1 Announce Type: cross Abstract: In recent years, multi-view learning has attracted increasing attention, as it integrates the complementary information of heterogeneous views. Most e

RPPNet: Perceptually-Grouped Rhythm-Pitch Primitives for Long-Term Structure Melody Generation via Boundary-Aware Modeling

arXiv:2607.19776v1 Announce Type: cross Abstract: Existing symbolic music generation models typically use bars as the basic structural unit. However, human perception of musical phrases often does not

researcharxiv-cs-ai

Tutorials

Scaling Laws for Hypernetwork-Based Knowledge Injection in Large Language Models

arXiv:2607.19604v1 Announce Type: new Abstract: Injecting factual knowledge into large language models (LLMs) reliably and at scale remains an open challenge. Hypernetworks provide a promising solutio

tutorialsarxiv-cs-cl

Hardware

Scaling Time Series Classification via XAI-Driven Data Reduction

arXiv:2607.15774v2 Announce Type: replace-cross Abstract: Explainable AI (XAI) for time series has seen significant algorithmic growth, but its utility in providing measurable performance gains for do

hardwarearxiv-cs-ai

Self-Explaining Reinforcement Learning for Mobile Network Resource Allocation

arXiv:2509.14925v2 Announce Type: replace Abstract: Deep reinforcement learning (DRL) methods, though powerful, often lack transparency, which limits their adoption in critical domains. We apply Self-

safetyarxiv-cs-lg

Self-organizing Architecture of Receptron Units: a Hardware-Aware Framework for Edge Intelligence

arXiv:2607.20162v1 Announce Type: new Abstract: The growing demand for intelligent processing at the edge of IoT networks is constrained by the severe computational and memory limitations of microcont

local-aiarxiv-cs-lg

Semantic Richness or Geometric Reasoning? The Fragility of VLM's Visual Invariance

arXiv:2604.01848v4 Announce Type: replace Abstract: This work investigates the fundamental fragility of state-of-the-art Vision-Language Models (VLMs) under basic geometric transformations. While mode

SemICP: Semantic Non-Rigid Point Cloud Registration with Elastic Energy Regularization

arXiv:2503.00972v4 Announce Type: replace Abstract: Purpose: Accurate point cloud registration is essential in computer-aided interventions (CAI) to align multi-modal medical images for intraoperative

safetyarxiv-cs-cv

SHFormer: Dynamic Spectral Filtering Convolutional Neural Network and High-pass Kernel Generation Transformer for Adaptive MRI Reconstruction

arXiv:2607.20159v1 Announce Type: new Abstract: Attention Mechanism (AM) selectively focuses on essential information for imaging tasks and captures relationships between distant pixel neighborhoods t

local-aiarxiv-cs-cv

Simulating Eutopia: Revisiting Long-term Fairness with Outcomes, Performativity, and Dynamics

arXiv:2607.19389v1 Announce Type: cross Abstract: As AI-driven Decision Makers (ADMs) influence our socioeconomic reality, their roles in both enhancing efficiency and amplifying the social biases hav

Simultaneous Speech-to-Speech Translation Without Aligned Data

model-releasesarxiv-cs-cl

arXiv:2602.11072v2 Announce Type: replace Abstract: Simultaneous speech translation requires translating source speech into a target language in real-time while handling non-monotonic word dependencie

SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD

arXiv:2607.20145v1 Announce Type: cross Abstract: Full-parameter post-training of trillion-parameter-scale MoE models introduces substantial system-level challenges for large-scale distributed trainin

Small, Free, and Effective: Orchestrating Open-Weight Small Language Models to Outperform Single LLM for Malware Analysis

arXiv:2607.20216v1 Announce Type: cross Abstract: Malware analysis demands rapid interpretation of complex detonation reports spanning filesystem, network, and process behaviours. While large language

Sophisticated Policies from Epistemic Priors

arXiv:2607.19518v1 Announce Type: new Abstract: Sophisticated Inference is a variant of active inference often associated with recursive belief modeling and tree search. We argue that its central comp

Sound Probabilistic Safety Bounds for Large Language Models

arXiv:2607.20286v1 Announce Type: cross Abstract: We propose a novel framework for computing rigorous bounds on the probability that a large language model (LLM) generates harmful output to a given pr

Spatiotemporal Facial Action Unit Detection using Twin Cycle Autoencoders for Driver Monitoring

arXiv:2607.16760v2 Announce Type: replace-cross Abstract: Driver monitoring systems (DMS) increasingly rely on facial cues to infer drowsiness, distraction, and cognitive load in real time. Facial Act

Spectral-LSH: Sub-Quadratic Prompt Compression via Krylov-Projected Locality-Sensitive Hashing

arXiv:2607.19368v1 Announce Type: new Abstract: Long-prompt inference remains expensive because prefill attention scales quadratically with sequence length. We propose Spectral-LSH, a training-free pr

Stale but Stable: Staleness-Adaptive Trust Regions for Stabilizing Asynchronous Reinforcement Learning

arXiv:2607.18722v2 Announce Type: replace-cross Abstract: Asynchronous reinforcement learning improves throughput by decoupling rollout generation from optimization, but staleness is an inevitable byp

safetyarxiv-cs-cl

Statistical Early Stopping for Reasoning Models

arXiv:2602.13935v2 Announce Type: replace Abstract: While LLMs have seen substantial improvement in reasoning capabilities, they also sometimes overthink, generating unnecessary reasoning steps, parti

researcharxiv-cs-ai

Statistical Inference for Rank Allocation in Low-Rank Adaptation

model-releasesarxiv-cs-lg

arXiv:2607.20205v1 Announce Type: cross Abstract: Low-rank adaptation (LoRA) has become a widely used parameter-efficient fine-tuning method for large language models. Since different modules and laye

Statistically Grounded Sparse-Feature Interventions for Activation-Space Control in Large Language Models

arXiv:2607.19364v1 Announce Type: new Abstract: Activation steering offers a lightweight alternative to fine-tuning for behavioral control of large language models, but SAE-based steering methods ofte

STN-TGAT: Top-K Portfolio Construction via Prior-Guided Graph Attention with Learnable Soft-Threshold Sparsification

model-releasesarxiv-cs-lg

arXiv:2607.19385v1 Announce Type: new Abstract: This paper tackles the problem of stock ranking and portfolio construction under realistic investment settings by jointly modeling temporal dynamics and

Stochastic Primal-Dual Decoding for Multiobjective Generative Recommender Systems

arXiv:2607.19357v1 Announce Type: new Abstract: Recent advances in recommender systems (RS) have shown substantial performance gains through generative modelling. In practice, recommendation often inv

Applications

STS-NET: Spatio-Temporal Stress Network for Self-Supervised Crop Stress Detection using Satellite Image Time Series

arXiv:2607.18791v1 Announce Type: new Abstract: Early and accurate detection of crop stress is essential to improve agricultural productivity and ensure global food security. However, collecting a lar

applicationsarxiv-cs-cv

SUPER Module for Detail-Sensitive and Cost-Efficient U-Net Variant Decoders

arXiv:2511.11015v2 Announce Type: replace Abstract: Skip-connected U-Net variants are widely used for dense inverse problems, yet their decoders commonly recover resolution through spatial upscaling,