Search: “evaluation”

LYRIC: Language-Driven Physics-Based Character Control for Contact-Rich Whole-Body Object Interaction

arXiv:2609.19688v1 Announce Type: new Abstract: We present LYRIC, a generative flow-matching controller for language-driven physics-based contact-rich interaction control, that enables simulated chara

safetyarxiv-cs-ro

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Safety

MAGMA-GEN: Validated Recovery Supervision from Ambiguous Failures via Counterfactual Re-Execution

DGX agent

arXiv:2609.20056v1 Announce Type: new Abstract: Hierarchical robotic systems executing long-horizon manipulation tasks must make high-level semantic decisions that orchestrate stochastic low-level ski

safetyarxiv-cs-ai

18 Sept 2026

MILER: Semantic Mid-Level Representation for Sim-to-Real Reinforcement Learning in Unstructured Autonomous Driving

arXiv:2609.20747v1 Announce Type: cross Abstract: Reinforcement learning constitutes a promising approach owing to its potential for superhuman performance and self-learned policies. However, its appl

MiX: Micro-Inverted-Scaling for End-to-End Low-Bit Vision-Language Model Acceleration

arXiv:2609.19683v1 Announce Type: cross Abstract: The deployment of Vision-Language Models (VLMs) on edge devices is severely bottlenecked by memory bandwidth, necessitating aggressive sub-8-bit quant

Agents

MM-Future: Multi-Mode Joint World-Action Modeling for Autonomous Driving

arXiv:2609.20377v1 Announce Type: new Abstract: Autonomous driving involves coupled decision-making and scene evolution under multi-mode uncertainty. To capture this coupling and uncertainty, we intro

agentsarxiv-cs-cv

Model-Agnostic and Language-Agnostic Voice Pipeline Improvement for the Agriculture Domain

arXiv:2609.20504v1 Announce Type: cross Abstract: FarmerChat is Digital Green's AI-powered agricultural advisory assistant for smallholder farmers, who access it in their own language through text, vo

Model Specific Task Similarity for Vision Language Model Selection via Layer Conductance

arXiv:2602.01346v2 Announce Type: replace Abstract: While open sourced Vision-Language Models (VLMs) have proliferated, selecting the optimal pretrained model for a specific downstream task remains ch

safetyarxiv-cs-ai

MTF-Net: Multi-Modal Temporal Feature Fusion Network for Pedestrian Intention Prediction

arXiv:2609.20178v1 Announce Type: new Abstract: Accurately predicting pedestrian intentions is crucial for ensuring safe and proactive interaction between autonomous vehicles and pedestrians. However,

local-aiarxiv-cs-cv

Multi-Axis Max@K Reinforcement Learning for Representative Diversity in Text-to-Image Generation

arXiv:2607.14962v2 Announce Type: replace-cross Abstract: Text-to-image (T2I) models can synthesize realistic, prompt-aligned images, yet samples generated for the same prompt often cover only a small

safetyarxiv-cs-ai

Multi-center Medical Data Mining with FL-Net - A One-stop Shop for Federated Learning

arXiv:2609.20650v1 Announce Type: new Abstract: Federated learning enables collaborative training without sharing patient-level data, but most studies remain simulations. Based on five requirements de

Near-Optimal Pure Single-Loop Extragradient Method for Strongly Convex--Strongly Concave Minimax Optimization

arXiv:2609.20327v1 Announce Type: cross Abstract: We study smooth strongly convex--strongly concave minimax optimization with general nonlinear coupling in the deterministic unconstrained setting. We

Agents

Neuro-Symbolic Agentic AI for Networked Low-Altitude UAVs

arXiv:2609.19961v1 Announce Type: new Abstract: Networked low-altitude unmanned aerial vehicles (UAVs) need reliable and adaptive decision-making capabilities to operate under uncertain observations,

agentsarxiv-cs-ai

OceanMoE: Structured Conditional Sparse Computation for Long-Horizon Multivariate Ocean Forecasting

arXiv:2609.19768v1 Announce Type: new Abstract: Multivariate ocean forecasting must exploit shared evolution in a coupled ocean system while adapting to the heterogeneous statistical and dynamical cha

Odds-Ratio Thompson Sampling: A Specification and Design Guide for Contrast-Based Multi-Armed Bandits

arXiv:2609.19709v1 Announce Type: new Abstract: Batched multi-armed bandits update on a service's own schedule, and the usual implementation carries each arm's absolute reward rate from one update to

Open ultrasound foundation model for robust segmentation and clinical measurement across heterogeneous settings

arXiv:2609.19230v1 Announce Type: new Abstract: Ultrasound is the most widely deployed imaging modality worldwide, yet clinical AI remains fragmented into narrow single-task models that fail when devi

Opinion Dynamics-based Coalition Formation for Federated Learning in Heterogeneous IoT Systems

arXiv:2609.19695v1 Announce Type: new Abstract: Federated learning (FL) enables privacy-preserving, on-device training across heterogeneous Internet-of-Things (IoT) deployments such as smart-city wate

local-aiarxiv-cs-lg

PaGNet: A Panel-Aware GBDT--Neural Network for Multi-Target Corporate Tax Avoidance Proxy Forecasting

arXiv:2609.20177v1 Announce Type: new Abstract: Forecasting corporate tax avoidance proxies from firm--year panel data is challenging because predictive signals are distributed across short firm histo

PerSeM: Persistent Semantic Memory for Long-Horizon Open-Vocabulary UAV Mapping

arXiv:2609.19542v1 Announce Type: new Abstract: Open-vocabulary segmentation enables rich semantic perception for UAVs, but frame-wise predictions can remain temporally inconsistent across repeated ob

PersonalAI 2.0: Enhancing knowledge graph traversal/retrieval with planning mechanism for Personalized LLM Agents

model-releasesarxiv-cs-cl

arXiv:2605.13481v2 Announce Type: replace Abstract: We introduce PersonalAI 2.0 (PAI-2), a novel framework designed to enhance LLM-based systems through integration of external knowledge graphs (KGs).

PhyRestore: Physics-Structured Latent-Factor Restoration

arXiv:2609.19776v1 Announce Type: new Abstract: Estimating temporal soil-loss change is challenging when physically meaningful input factors are noisy or corrupted, particularly because substantial ch

Physical knowledge on historical data matters more than enforcing physical constraints on the forecast

arXiv:2609.19871v1 Announce Type: new Abstract: Time series forecasting has seen signicant advancements with the emergence of new deep learning models. However, forecasting time series in applications

applicationsarxiv-cs-ai

Physics-Informed Hemodynamic Modeling for Data-Free Prediction and Sparse-Data Assimilation

arXiv:2609.19290v1 Announce Type: cross Abstract: Clinical decision-making for coronary intervention relies mainly on angiography and fractional flow reserve (FFR). However, angiography is two-dimensi

Physics-Informed Support Vector Kernels via Green-Function Analogies and Jackson-Chebyshev Spectral Design

arXiv:2502.11153v4 Announce Type: replace-cross Abstract: Kernel selection for regression of physical observables is often heuristic. We investigate a physics-informed strategy in which functional for

PIVOT: Perception-aware Independent Viewpoint Online Optimization

arXiv:2609.19510v1 Announce Type: new Abstract: A fundamental assumption in robotic perception is that the sensor's field of view (FoV) is fixed relative to the robot body. Motion-decoupled sensors, s

applicationsarxiv-cs-ro

Pose-aware Legged Robot Semantic Exploration with Omnidirectional Perception in Confined Unknown Environments

arXiv:2609.19460v1 Announce Type: new Abstract: Semantic exploration in confined environments requires both environment mapping and detailed observation of target objects. For ground robots, limited s

applicationsarxiv-cs-ro

Pre-train to Gain: Robust Learning Without Clean Labels

arXiv:2511.20844v2 Announce Type: replace-cross Abstract: Training deep networks with noisy labels leads to poor generalization and degraded accuracy due to overfitting to label noise. Existing approa

applicationsarxiv-cs-ai

Precision autotuning for linear solvers via contextual bandit-based RL

arXiv:2601.00728v5 Announce Type: replace Abstract: We propose a reinforcement learning (RL) framework for xy{responsive} precision tuning for linear solvers, which can be extended to general algorith

PrefixBench-H100: Characterizing Prefix Reuse and Time-to-First-Token in H100 LLM Serving

model-releasesarxiv-cs-lg

arXiv:2609.19657v1 Announce Type: cross Abstract: Repeated prompt prefixes are increasingly common in LLM serving workloads, appearing in system prompts, templated retrieval-augmented generation pipel

Agents

QEncodeBench: Can Large Language Models Encode Classical Problems into Verified Quantum Oracles?

arXiv:2609.20319v1 Announce Type: cross Abstract: Grover search, amplitude amplification, and quantum counting all rely on the same reusable subroutine, a phase oracle, whose construction the algorith

agentsarxiv-cs-lg

Quantum Graph Convolutional Networks: Implementation and Trainability Analysis

model-releasesarxiv-cs-lg

arXiv:2609.19983v1 Announce Type: cross Abstract: Graph Neural Networks (GNNs) achieve state-of-the-art performance on graph-structured data, but training and inference on large graphs are often bottl

Queries Knew More Than We Thought: Uncovering Latent Knowledge in Segmentation Models

arXiv:2609.20283v1 Announce Type: new Abstract: Modern segmenters often fail after the expensive computation has already been done: a useful mask is present among the model's query-conditioned candida

QVAC Genesis III: A Large-Scale, High-Quality Open Synthetic STEM Corpus for Efficient Language Model Pre-Training

model-releasesarxiv-cs-ai

arXiv:2609.19513v1 Announce Type: new Abstract: High-quality pre-training data is a critical bottleneck for educational and STEM-specific language models targeting edge AI and on-device deployment whe

REACT: A Fully Spiking State-Space Model for Real-Time Event-Driven Temporal Perception

arXiv:2609.19204v1 Announce Type: cross Abstract: Robotic systems operating in dynamic environments require visual perception that evolves continuously with the incoming sensory stream. Event cameras

Recursive Quantum Long Short-Term Memory for Stable Short-Horizon Temperature Forecasting

arXiv:2609.20594v1 Announce Type: new Abstract: Quantum long short-term memory (QLSTM) models extend recurrent sequence learning with variational quantum circuits, but their optimization behavior can

Redact or Keep? A Fully Local AI Cascade for Educational Dialogue De-Identification

arXiv:2606.18372v2 Announce Type: replace-cross Abstract: Educational dialogue is a valuable but sensitive resource for research: the same transcripts that capture authentic learning often capture per

Refinement Is Inherently Editable: Training-Free Prompt-to-Prompt Image Editing with Generative Refinement Network

arXiv:2609.20633v1 Announce Type: new Abstract: Text-guided image editing must introduce the requested changes while preserving unrelated source content. Diffusion-based editors rely on spatial contro

Reflective Recovery: A Self-Supervised Method for Reasoning by Learning from Mistakes

model-releasesarxiv-cs-cl

arXiv:2609.19156v1 Announce Type: new Abstract: Data-driven fine-tuning is widely adopted to enhance reasoning in Large Language Models (LLMs) due to its simplicity and efficiency. However, mainstream

Relational Attention for Data-Efficient Language Modeling

arXiv:2609.20530v1 Announce Type: new Abstract: We present Relational BabyLM, a system submission to the BabyLM 2026 challenge that combines two cognitively motivated inductive biases in a single deco

researcharxiv-cs-cl

Reproducing Transparent and Scrutable Recommendations: Exploring Open-Weight Models via Natural-Language User Profiles

arXiv:2609.19831v1 Announce Type: cross Abstract: In this reproducibility study, we investigate the transparency and scrutability of recommender systems enhanced by incorporating generated natural-lan

Resilient Motion Planning for Free-Flying Space Robots under Actuator Failures

arXiv:2609.20407v1 Announce Type: new Abstract: Free-flying robots rely on multiple thrusters to maneuver in space. If one or more of these thrusters fail, the robot may lose control authority and ris

local-aiarxiv-cs-ro

Hardware

RoboGPU: Accelerating GPU Collision Detection for Robotics

arXiv:2603.01517v2 Announce Type: replace-cross Abstract: Autonomous robots are anticipated to be deployed soon in domains ranging from transportation to healthcare and home assistance. Enabling auton

hardwarearxiv-cs-ro

Robotic Video World Models: A Survey of Applications, Research Challenges, Future Directions

arXiv:2601.07823v2 Announce Type: replace-cross Abstract: Video world models have emerged as promising candidates for high-fidelity world models, offering the potential to synthesize high-quality vide

safetyarxiv-cs-ro

Robust Conformal Intrusion Detection via Traffic-Aware Calibration and Attack-Orbit Invariance

arXiv:2609.19241v1 Announce Type: cross Abstract: Large language models fine-tuned for network intrusion detection emit single-point predictions without statistical validity guarantees. Conformal pred

Runtime Safety Filtering for Two-Terminal Hazards in Robotic Battery Recycling

arXiv:2609.19665v1 Announce Type: new Abstract: Runtime safety filters for learned manipulation policies typically define unsafe states as unions of object-wise keep-out regions. This representation c

safetyarxiv-cs-ro

Safety Beyond the Interface: Detecting Harm via Latent States in Large Language Models

model-releasesarxiv-cs-ai

arXiv:2609.19472v1 Announce Type: new Abstract: Autonomous systems increasingly rely on Large Language Models (LLMs) yet the safety infrastructure surrounding these models introduces latency and compu

Sample Count Is Not Enough: Candidate-Generation Strategy Shapes the Energy and Performance of LLM Test-Time Scaling

model-releasesarxiv-cs-lg

arXiv:2609.19499v1 Announce Type: new Abstract: Test-time scaling can improve large language model reasoning by generating and combining multiple candidate responses. In sampling-based methods, the in

SceneTeract: Probing and Improving Agent-Aware Activity Reasoning in 3D Indoor Scenes

model-releasesarxiv-cs-cv

arXiv:2603.29798v2 Announce Type: replace Abstract: Indoor 3D scenes are ultimately meant to be used: an embodied agent should be able to navigate, reach objects, and complete diverse activities. Yet

Scientific Image Quality Assessment via Multi-modal Retrieval-Augmented Generation

arXiv:2609.19634v1 Announce Type: cross Abstract: This paper proposes a Retrieval-Augmented Generation (RAG) framework for scientific image quality assessment, designed to simultaneously address both

researcharxiv-cs-cl