Search: “benchmark”

MeshGraphNet-Transformer: Scalable Mesh-based Learned Simulation for Solid Mechanics

arXiv:2601.23177v4 Announce Type: replace Abstract: We present MeshGraphNet-Transformer (MGN-T), a novel architecture that combines the global modeling capabilities of Transformers with the geometric

safetyarxiv-cs-lg

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Safety

MM-VeriAgent: Learning to Use Extensive Tools to Verify Multimodal Misinformation with Reinforcement Learning

DGX agent

arXiv:2609.30698v1 Announce Type: new Abstract: Real-world multimodal misinformation often involves mixed forgery sources, requiring sample-specific detection strategies. Existing tool-augmented metho

safetyarxiv-cs-cv

28 Sept 2026

Models Got Talent: Identifying High Performing Wearable Human Activity Recognition Models Without Training

arXiv:2511.06157v3 Announce Type: replace-cross Abstract: Discovering high performing model architectures for wearables-based Human Activity Recognition (HAR) applications is challenging. The astonish

Multi-agent Scaling Across Disjunctive and Compensatory Tasks

arXiv:2609.31563v1 Announce Type: new Abstract: Multi-agent LLM systems are often expected to improve as team size increases, yet the scaling behavior may depend on task structure. Our central contrib

NEMSim: Learning Control-Conditioned Multi-Event Physical Dynamics via Executable Event-Mechanism Priors

arXiv:2609.30718v1 Announce Type: new Abstract: High-fidelity simulation of control-conditioned multi-event physical systems is computationally expensive, especially across broad control spaces and lo

New LoRA Skills Should Read but Never Write

arXiv:2609.31600v1 Announce Type: new Abstract: Low-rank adapters (LoRA) make it cheap to fine-tune a large language model once per task, but combining several independently trained adapters into one

NEXT: Physics-Informed Neuro-Spectral Exponential Time Differencing Architectures

arXiv:2609.31539v1 Announce Type: new Abstract: Physics-Informed Neural Networks (PINNs) build neural representations of time-dependent PDE solutions, naturally incorporating physics knowledge and obs

Nice Fold or Hero Call: Learning Budget-Efficient Thinking under Policy-Dependent Solvability

arXiv:2605.11625v2 Announce Type: replace Abstract: Large reasoning models (LRMs) improve problem solving through extended reasoning, but often misallocate test-time compute. Existing efficiency metho

Offline Policy Evaluation as a decision support tool for designing Adaptive Experiments

arXiv:2609.30273v1 Announce Type: new Abstract: We investigate how historical data from fixed randomized experiments (A/B tests) can be used to inform the deployment of adaptive experiments based on c

safetyarxiv-cs-lg

ORCA: Evaluating LLMs on Data Science Code Translation

arXiv:2609.30749v1 Announce Type: new Abstract: Data Science Code Translation (DSCT) is the process of converting code between data science libraries while preserving functional equivalence and enabli

OSPO: Object-Centric Self-Improving Preference Optimization for Text-to-Image Generation

arXiv:2506.02015v4 Announce Type: replace Abstract: Recent advances in Multimodal Large Language Models (MLLMs) have enabled unified multimodal understanding and generation. However, they still strugg

safetyarxiv-cs-cv

Persistent Homology of Time Series through Complex Networks

arXiv:2605.01624v1 Announce Type: cross Abstract: We present a unified pipeline for univariate time series classification via complex networks and persistent homology. A time series is mapped to a gra

researcharxiv-cs-lg

Persistent Negatives for Adversarial Black-Box On-Policy Distillation

arXiv:2609.30864v1 Announce Type: cross Abstract: Black-box On-Policy Distillation (OPD) seeks to improve a student from its own generations when the teacher provides sampled responses but not token p

Applications

PhoenixSR: Generative Heterogeneous Distillation Unleashes Efficient Models for Real-World Super-Resolution

arXiv:2609.30988v1 Announce Type: new Abstract: Real-world image super-resolution (SR) requires recovering perceptually realistic high-resolution images from complex low-resolution observations while

applicationsarxiv-cs-cv

PICO: Projection-Informed Consistency Optimisation for 6DoF Surgical Tool Pose Estimation

arXiv:2609.30989v1 Announce Type: new Abstract: Purpose: Accurate 6 DoF pose estimation of surgical tools is critical for automa- tion, robotic proprioception, and safe interaction with the tissue ope

Preserve-and-Compose Training for Composed Image Retrieval

arXiv:2609.31202v1 Announce Type: new Abstract: Composed image retrieval (CIR) aims to retrieve images that satisfy a user-specified modification while preserving relevant visual content from a refere

ProCAP: Probabilistic Cross-Attentive Prompt Learning for Vision-Language Models

arXiv:2609.30434v1 Announce Type: new Abstract: Pre-trained vision-language models such as CLIP can recognize new categories via prompting, but they often struggle when labeled data are scarce or the

Quadruped Obstacle Avoidance and Footstep Planning with Distributed Low-cost Time-of-Flight Sensors

arXiv:2609.31008v1 Announce Type: new Abstract: Quadruped robots typically rely on depth cameras and LiDAR sensors to map their local environment. However, these sensors have limited close-range cover

researcharxiv-cs-ro

Quantum Diffusion Models for Medical Image Analysis

arXiv:2609.31070v1 Announce Type: cross Abstract: Quantum Machine Learning is a novel field of research aimed at devising machine learning approaches exploiting principles of quantum mechanics, such a

Query-Conditioned Prototype Adaptation for Cross-Domain Few-Shot Learning: Single-Query Inference, Controlled Comparisons, and Failure Modes

model-releasesarxiv-cs-cv

arXiv:2609.30769v1 Announce Type: new Abstract: Cross-domain few-shot learning requires adapting a classifier to a new visual domain from very few labelled examples without target-time parameter updat

RAZOR: Pruning Replaceable Experts in LLMs

arXiv:2609.30465v1 Announce Type: cross Abstract: Mixture-of-experts (MoE) models activate few experts per token but store the full expert pool. Expert pruning reduces this storage burden; at a fixed

Recursive Self-Improvement via On-Policy Distillation for Reasoning

arXiv:2609.30652v1 Announce Type: new Abstract: On-policy distillation (OPD) trains a student model by having it generate trajectories, then matching its next-token predictions with an external teache

safetyarxiv-cs-cl

Reliability-Regulated Trajectory Optimization for Progressive COLMAP-Free 3D Gaussian Splatting

arXiv:2609.30865v1 Announce Type: new Abstract: COLMAP-free 3D Gaussian Splatting (3DGS) bypasses computationally expensive structure-from-motion (SfM) pipelines, yet progressive camera pose tracking

RoboMonitor: Label-Efficient Runtime Monitoring of Robot Task Execution via Predictive Representation Learning

model-releasesarxiv-cs-ro

arXiv:2609.30715v1 Announce Type: new Abstract: Learned robot policies produce actions, but their outputs alone do not establish whether execution is progressing as intended. Robot execution monitorin

Robust Successor Features

arXiv:2609.31016v1 Announce Type: new Abstract: Generalization in Reinforcement Learning (RL) refers to the ability to execute close-to-optimal policies in unseen tasks after the agent has been traine

safetyarxiv-cs-lg

Scaffold: Support Graph Theory Based Sparsification for Graph Neural Networks

arXiv:2609.31466v1 Announce Type: new Abstract: Graph neural networks (GNNs) rely on message passing over graph edges, making their computational and memory costs strongly dependent on graph density.

researcharxiv-cs-lg

Self-Play Search Distillation for Large Language Model Reasoning

arXiv:2609.30936v1 Announce Type: new Abstract: Improving reasoning abilities in Large Language Models (LLMs) requires high-quality data that exposes difficult decisions, competing alternatives, and t

researcharxiv-cs-ai

Self-Supervised Representation Learning: From Spectral Foundation Models to Auroral Emission Spectra

arXiv:2609.31206v1 Announce Type: new Abstract: Auroral spectrographs such as the Auroral Spectrograph In Skibotn (ASIS) record hundreds of thousands of emission spectra, but only a few hundred can be

Skip the Talk, Re-Focus on Vision: Latent Reasoning for Reasoning Segmentation in Multimodal Large Language Models

arXiv:2609.30783v1 Announce Type: cross Abstract: Reasoning segmentation aims to interpret implicit textual queries and enable fine-grained visual perception, which is critical for applications such a

Tutorials

Spatial Information Bottleneck for Interpretable Visual Recognition

arXiv:2511.09239v2 Announce Type: replace Abstract: Deep neural networks typically learn spatially entangled representations that conflate discriminative foreground features with spurious background c

tutorialsarxiv-cs-cv

SPO: Discovering Adaptive Large Neighborhood Search Operators via Stackelberg Program Optimization

arXiv:2609.31179v1 Announce Type: new Abstract: Large neighborhood search (LNS) relies critically on destroy and repair operators, whose effectiveness depends on both adaptation to the evolving LNS st

ST-pRRTC: Parallel Space-Time RRT-C with Adaptive Goal-Time Forests

arXiv:2609.30533v1 Announce Type: new Abstract: We propose ST-pRRTC, a GPU-parallel space- time RRT-Connect motion planner for problems with known obstacle trajectories and unspecified arrival time. S

safetyarxiv-cs-ro

Stale-Document Poisoning: When Outdated Retrieval Overrides Correct Model Answers

arXiv:2609.31342v1 Announce Type: new Abstract: Retrieval-augmented generation (RAG) is often used to address outdated knowledge by providing external evidence. But retrieval helps only when that evid

Stepwise Intrinsic Rewards for Reasoning in Large Language Models

arXiv:2602.01034v2 Announce Type: replace Abstract: Reinforcement learning (RL) has become a widely used paradigm for improving the reasoning abilities of large language models (LLMs) and Vision-langu

Strategic Self-Consistency

arXiv:2609.30352v1 Announce Type: cross Abstract: Self-consistency has become a popular technique for enhancing the reasoning abilities of large language models by generating multiple reasoning paths

Structured Reasoning Agentic Framework for Interpretable Critical View of Safety Assessment

model-releasesarxiv-cs-cv

arXiv:2609.31524v1 Announce Type: new Abstract: Surgical scene understanding is critical for computer-assisted intervention, yet laparoscopic cholecystectomy remains challenged by the complex anatomy

T-RoPE: Time-Aware Rotary Position Embedding for Sequential Recommendation

arXiv:2609.30576v1 Announce Type: new Abstract: Large-scale recommenders increasingly adopt the sequential generative recipe behind large language models, bringing the Transformer into recommendation

The Plot Twist: Jailbreaking Unified Multimodal Models with a Three-Act NarrativeAttack

arXiv:2509.26473v2 Announce Type: replace Abstract: Unified Multimodal Understanding and Generation Models (UMMs) increasingly combine visual understanding and image generation within a single interac

The Right Information Extraction Pipeline Depends on the Document: Accuracy-Energy Trade-offs for Small, Local Models

arXiv:2609.31341v1 Announce Type: new Abstract: Whether an information extraction pipeline should process page images or parsed text depends on the document, and the answer flips across the layout spe

Local Ai

The Shape of Events: Edge-Based Inductive Biases via Cross-Domain Distillation

arXiv:2609.30478v1 Announce Type: new Abstract: Convolutional neural networks trained on ImageNet are known to exhibit a strong preference for local high-frequency texture, an inductive bias that tran

local-aiarxiv-cs-cv

Agents

ToolSearcher: Optimizing Tool Selection at Scale via Reinforcement Learning

arXiv:2609.30906v1 Announce Type: new Abstract: Large language models (LLMs) excel at natural language processing but struggle to interact with external environments. Tool learning provides a promisin

agentsarxiv-cs-cl

Understanding the Role of Prompt Template in Knowledge Distillation for Safety Alignment

arXiv:2609.30802v1 Announce Type: new Abstract: Prior research has demonstrated that the choice of prompt template during Supervised Fine-Tuning (SFT) significantly impacts the robustness of safety al

VFM-UDA++: Improving Network Architectures and Data Strategies for Unsupervised Domain Adaptive Semantic Segmentation

model-releasesarxiv-cs-cv

arXiv:2503.10685v3 Announce Type: replace Abstract: Unsupervised Domain Adaptation (UDA) enables strong generalization from a labeled source domain to an unlabeled target domain, often with limited da

WALT: Learning World-Model-Aligned Latent Trajectories for Autonomous Driving

arXiv:2609.30436v1 Announce Type: cross Abstract: Driving world models learn rich predictive representations of the surrounding environment from visual observations, yet accurate visual prediction doe

safetyarxiv-cs-cv

Agents

When Is a Multi-Agent Code Judge Actually Grounded? Two Label-Free Measurements, and a Judge That Declines to Guess

arXiv:2609.30328v1 Announce Type: new Abstract: When one language model judges whether another's code is correct, it does not report the absence of evidence. It returns a confident verdict with reason

agentsarxiv-cs-ai

When the Preconditioning Exponent Turns Negative: Learning-Rate Coupling and Cross-Environment Generalization

arXiv:2609.30271v1 Announce Type: new Abstract: Adaptive optimizers are commonly parameterized by a fixed power of the second-moment estimate. Existing partially adaptive methods study exponents betwe

A Corpus of Real Scam- and Spam-Call Conversations from an Active Voice-Agent Honeypot

arXiv:2609.29528v1 Announce Type: cross Abstract: Real conversations between fraudsters and their targets are among the most informative artifacts for studying telephone scams, yet also the scarcest:

25 Sept 2026

A Manifold-Aware Topic Modeling Approach via Rank-Based Prototypes