Search: “evaluation”

Ranked Right, Sized Wrong: Ensemble Spread Fails Silently at Streamflow Extremes

arXiv:2610.20947v1 Announce Type: cross Abstract: Deep learning models, particularly Long Short-Term Memory networks, are increasingly used for operational streamflow forecasting, with ensemble spread

safetyarxiv-cs-lg

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Model Releases

RDL-Bench: A Benchmark for Task-Aware Question Answering over Relational Databases

DGX agent

arXiv:2610.21382v1 Announce Type: new Abstract: Relational databases (RDBs) remain the primary infrastructure for storing and managing structured data across enterprise and scientific domains. Natural

model-releasesarxiv-cs-lg

16 Oct 2026

Reducing the Cost of Qudit State Preparation by Learning a Sparse Structure for SNAP Gates

arXiv:2610.21557v1 Announce Type: cross Abstract: Preparing quantum states in bosonic qudits with SNAP--Displacement sequences requires both optimizing the gate parameters and deciding which Fock leve

researcharxiv-cs-lg

Reinforcement Learning-driven Diffusion Models: Language, Vision, and Beyond

arXiv:2610.21539v1 Announce Type: new Abstract: Diffusion models (DMs) have steadily demonstrated outstanding performance in high-fidelity, controllable generation across diverse scenarios, following

REKI: Registering Knowledge in VLAs

arXiv:2610.22178v1 Announce Type: cross Abstract: We introduce REKI, a 2.4B vision-language-action model (VLA) pretrained exclusively on public data that sets a new Pareto frontier between performance

safetyarxiv-cs-cv

Resisting Harmful Instructions through Training-Free Weight Editing

arXiv:2610.21189v1 Announce Type: new Abstract: Instruction-following language models can comply with requests that conflict with intended behaviors and safety alignment. We introduce a training-free

safetyarxiv-cs-lg

ReState: Noise-Level-Aware State Correction for Training-Free Object Removal in Few-Step Diffusion Models

arXiv:2610.21287v1 Announce Type: new Abstract: Training-free object removal commonly redirects self-attention away from the target region, but on few-step distilled diffusion models this often leaves

Rethinking the Role of Tool Graph Memory in Multi-Turn Tool Use

arXiv:2610.21571v1 Announce Type: cross Abstract: Large language model (LLM) agents solve complex tasks by calling external tools over multiple turns. Recent work has proposed tool graph memory, which

Retrofitting Recurrent Vision-Language Models from Pretrained Backbones

model-releasesarxiv-cs-cv

arXiv:2610.22075v1 Announce Type: new Abstract: Recent work shows that depth recurrence can be retrofitted into pretrained language models. In this work, we adapt this paradigm for visual understandin

RoboCloth: A Large-Scale Real Cloth Material Dataset for Neural Reflectance Reconstruction

model-releasesarxiv-cs-cv

arXiv:2610.21090v1 Announce Type: new Abstract: We present RoboCloth, a dataset of 500 real cloth materials captured under flexible, calibrated camera and lighting configurations, where both are robot

Robotic-supported Data Loss Detection in Android Applications

arXiv:2610.20807v1 Announce Type: cross Abstract: Smartphones have become integral to modern life due to their diverse applications. However, the development of applications for these devices faces si

researcharxiv-cs-ro

RSIFlow: Adaptive Self-Improvement Workflows with an Evolving Meta-Agent

arXiv:2610.21791v1 Announce Type: new Abstract: Large language model (LLM) agents have demonstrated strong capabilities in complex tasks, including code generation, tool use, and multi-hop question an

Safe and Fuel-Efficient Motion Planning for Lunar Propulsive Drones

arXiv:2610.22225v1 Announce Type: new Abstract: Propulsive drones are a promising new technology for generating high-resolution terrain models of the Moon. In future lunar missions, propulsive drones

agentsarxiv-cs-ro

SATOR: Backpropagation-Free 3D Test-Time Adaptation via Sample-Adaptive Token Routing

arXiv:2610.21367v1 Announce Type: new Abstract: Test-time adaptation (TTA) mitigates the performance degradation of source-pretrained models on unlabeled test data under distribution shifts. Recent ba

SCCL-DT: Semantic-Conflict-Aware Contrastive Learning with Dual-Rule Teacher Distillation for Zero-Shot Chest X-Ray Diagnosis

arXiv:2610.22094v1 Announce Type: new Abstract: Contrastive vision-language pre-training supports zero-shot chest X-ray diagnosis, but standard objectives treat every off-diagonal image-report pair in

Screwtape: Adversarial Transcript Editing for Monitor Red-Teaming

arXiv:2610.20941v1 Announce Type: cross Abstract: We present Screwtape, a pipeline that converts real agent transcripts into attacks that evade action-only monitors. An LLM attacker edits a transcript

Seeing is Believing: Benchmarking and Mitigating the Interference of Language Priors on Visual Perception in MLLMs

model-releasesarxiv-cs-cv

arXiv:2610.21902v1 Announce Type: new Abstract: Language priors in MLLMs can override visual evidence, impairing visual perception. Existing benchmarks assume shared priors across models and rely sole

Selection Is Not Learning: Choosing Model Futures

arXiv:2610.22029v1 Announce Type: cross Abstract: Self-evolving language models train on tasks they generate, so every round must decide which updated model the next round inherits. Current loops such

SHIP: Structured and Hierarchical Insight with Privacy

arXiv:2610.21048v1 Announce Type: new Abstract: Extracting insights from textual data while protecting user privacy is increasingly important. Existing methods produce private insights in diverse form

researcharxiv-cs-cl

SHRED: Self-Play for Heterogeneous Road-Users without Expert Demonstrations

arXiv:2610.20866v1 Announce Type: cross Abstract: Realistic traffic simulation and autonomous planning both depend on modeling interactions among heterogeneous road users whose dynamics, constraints,

SkillNEO: Non-Elitist Evolutionary Optimization of Agent Skills

arXiv:2610.20926v1 Announce Type: new Abstract: Agent skills turn procedural knowledge into reusable artifacts, allowing LLM agents to accumulate and refine expertise through experience. Building on t

Slip-Induced Fall Suppression During Cornering for a Remotely Operated Motorcycle with a Rear-Wheel-Swing Mechanism

arXiv:2610.21089v1 Announce Type: new Abstract: Slip-induced falls when riding motorcycles are difficult to avoid even for expert riders and represent a major cause of single-vehicle accidents. Despit

researcharxiv-cs-ro

Sparse Attention for Video Generation Acceleration via Growing Sparsity and Reduced Search

arXiv:2610.21129v1 Announce Type: new Abstract: Diffusion Transformers (DiTs) dominate video generation but suffer from high inference cost due to the quadratic complexity of self-attention. Sparse at

Spectral-Spatial Interpretation

arXiv:2610.21114v1 Announce Type: cross Abstract: Post-hoc image explanations usually show where a model looks using spatial heatmaps or indicate which channels matter using global channel scores. The

safetyarxiv-cs-cv

Spurious Attention Heads in Vision Transformers: Mechanistic Identification of Annotation-Driven Bias in Medical Chest X-Ray Analysis

arXiv:2610.21638v1 Announce Type: cross Abstract: Vision Transformers deployed in medical imaging exploit spurious correlations, yet the internal mechanisms remain unidentified and impossible to fix.

safetyarxiv-cs-cl

STAMP: Symmetric Temporal Averaging Message Passing for Domain Adaptation in Chronologically Split Graphs

arXiv:2411.10957v2 Announce Type: replace-cross Abstract: This paper addresses domain adaptation in graph-structured data under chronological splits. We consider transductive semi-supervised node clas

Stateful Testing for Deep Learning Compilers

arXiv:2610.22003v1 Announce Type: cross Abstract: Modern deep learning compilers compile models lazily and cache artifacts across invocations. Retained compiler state can make a later invocation incor

researcharxiv-cs-lg

SUPN: Shallow Universal Polynomial Networks for Efficient Function Approximation

model-releasesarxiv-cs-lg

arXiv:2511.21414v2 Announce Type: replace Abstract: Deep neural networks (DNNs) and Kolmogorov-Arnold networks (KANs) are flexible function approximators, but their accuracy can require substantial ne

T-GADE: Thermodynamical Generative-AI-Driven Evolution of LLM Artifacts

arXiv:2609.12286v2 Announce Type: replace Abstract: Introducing large language models (LLMs) into evolutionary computation enables semantic generation of candidates, but the classical problem of maint

researcharxiv-cs-ai

Applications

Task-Centric Multimodal Learning for Cognitive Impairment Detection

arXiv:2610.20906v1 Announce Type: cross Abstract: Dementia affects millions of older adults worldwide, yet early detection remains difficult outside clinical settings. Traditional models primarily rel

applicationsarxiv-cs-cl

Temporal Periodic Image Registration with Implicit Neural Representations

arXiv:2610.21845v1 Announce Type: new Abstract: Implicit Neural Representations (INRs) have recently gained popularity for their ability to model functions using simple and lightweight networks. These

Text-to-Image Synthesis: A Decade Survey

arXiv:2411.16164v2 Announce Type: replace Abstract: Text-to-image synthesis (T2I) focuses on generating high-quality images from textual descriptions In this survey, we review 490 papers on T2I spanni

safetyarxiv-cs-cv

The Right Byte First: How UTF-16 Helps Multilingual Boundary Prediction

arXiv:2610.22071v1 Announce Type: new Abstract: Many hierarchical byte-level language models group bytes into patches using a learned boundary predictor whose view of the next codepoint depends on enc

researcharxiv-cs-cl

TimeCap: A Scalable Open-Source Pipeline for Captioning Wearable Health Time-Series Data

model-releasesarxiv-cs-cl

arXiv:2610.21274v1 Announce Type: cross Abstract: Time-series data generated continuously across industrial, environmental, and especially health domains produce multimodal signals whose scale and het

Topology-Inspired Fine-Tuning for Wind-Field Super-Resolution

model-releasesarxiv-cs-lg

arXiv:2610.22262v1 Announce Type: new Abstract: Scientific super-resolution (SR) models are typically optimized with pointwise reconstruction losses, yet scientific interpretation often hinges on extr

Towards automatic digital garment assembly

arXiv:2610.20651v1 Announce Type: new Abstract: Creating digital garments from existing two-dimensional patterns remains a time-consuming process. Although patterns are widely available in digital for

Towards Emotion Consistency Analysis of Large Language Models in Emotional Conversational Contexts

model-releasesarxiv-cs-cl

arXiv:2605.06476v2 Announce Type: replace Abstract: In this work, we conduct an analysis to examine the consistency of Large Language Models (LLMs) with respect to their own generated responses in an

Towards Reliable Long-Context Reasoning: Detecting Unanswerable Questions via FactGuard

arXiv:2504.05607v2 Announce Type: replace-cross Abstract: Despite the success of Large Language Models (LLMs) in long-context tasks, their reliability in recognizing unanswerable questions remains a c

TRACER: A Multimodal Harness for Patient Care in Hospitals

arXiv:2610.21266v1 Announce Type: cross Abstract: Continuous video and clinical records provide complementary information for understanding how patient behavior evolves over a hospital stay. A central

Translation Circuit Discovery and Targeted Optimization for Low-Resource Multilingual Translation

model-releasesarxiv-cs-cl

arXiv:2610.21456v1 Announce Type: new Abstract: Recent multilingual large language models achieve strong translation performance, yet the internal mechanisms responsible for low-resource multilingual

Hardware

UltraDiff: Transferring High-Fidelity Priors to Compressed Latent Spaces for High-Resolution Image Generation

arXiv:2610.21202v1 Announce Type: new Abstract: High-resolution synthesis with Latent Diffusion Models (LDMs) faces a fundamental trade-off between computational cost and generative fidelity. This tra

hardwarearxiv-cs-cv

Unfragmenting Nawatl NMT: Single-Variant Specialization for Masewalkopa

arXiv:2610.22110v1 Announce Type: new Abstract: In minoritized (low-resource) language technologies, a common response to data sparsity is to aggregate text across multiple varieties and treat the com

researcharxiv-cs-cl

Unsupervised Clustering of Public Transport Vehicles Using Deep Morphological Features and Color Histograms

arXiv:2610.22270v1 Announce Type: new Abstract: Surveillance cameras along urban roads produce large volumes of unlabeled video that could support the monitoring of public transport, but grouping the

Value as Memory: Online Value-Guided Post-training for General Robot Policies

arXiv:2610.22368v1 Announce Type: new Abstract: Real-world post-training of generalist robot policies faces two challenges: partial observability makes action selection ambiguous, while offline post-t

safetyarxiv-cs-ro

Value-Informed Schedule Trimming for Accelerated Sequential Monte Carlo Guidance in Discrete Diffusion

arXiv:2610.20669v1 Announce Type: new Abstract: Reward-guided discrete diffusion enables test-time alignment with downstream objectives, but applying Sequential Monte Carlo (SMC) guidance at every den

safetyarxiv-cs-ai

Vertical Sliced Wasserstein Distances for Meta-Measures

arXiv:2610.20743v1 Announce Type: new Abstract: Many modern learning problems require comparing probability distributions whose individual observations are themselves probability measures, naturally g

researcharxiv-cs-lg

VesperMUSE: Multi-Modal Fusion Vision Transformer for Image Semantic Segmentation Under Diverse Light Conditions

arXiv:2610.21990v1 Announce Type: new Abstract: Semantic segmentation is critical for the reliable navigation and scene understanding of autonomous systems, yet its performance degrades severely in lo

agentsarxiv-cs-cv

VOiLA: Vectorized Online Planning with Learned Diffusion Models for POMDP Agents

arXiv:2606.19729v3 Announce Type: replace-cross Abstract: Planning under uncertainty is an essential capability for autonomous robots. The Partially Observable Markov Decision Process (POMDP) provides