Search: “benchmark”

Spatio-Temporal Similarity Volume Aggregation for Open-Vocabulary Action Recognition

arXiv:2605.23288v1 Announce Type: new Abstract: Recent Open-Vocabulary Action Recognition (OVAR) methods typically aggregate visual features into a global representation before computing text alignmen

safetyarxiv-cs-cv

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Spectral-inspired Operator Learning with Limited Data and Unknown Physics

DGX agent

arXiv:2505.21573v3 Announce Type: replace-cross Abstract: Learning PDE dynamics from limited data with unknown physics is challenging. Existing neural PDE solvers either require large datasets or rely

local-aiarxiv-cs-ai

25 May 2026

STAMBRIDGE: Spectral-Temporal Amplitude-aware Mid-Feature Bridge for EEG Visual Decoding

arXiv:2605.23137v1 Announce Type: cross Abstract: Electroencephalography (EEG) visual decoding remains challenging due to the modality gap between low-SNR neural signals and highly structured vision--

Suicide Risk Assessment from AI-powered Video Surveillance: An Interpretable Framework for Prevention in Metro Stations

arXiv:2605.22904v1 Announce Type: cross Abstract: Understanding and monitoring human behavior in metro stations play an important role in supporting suicide prevention efforts, where early identificat

researcharxiv-cs-ai

Applications

Super-Linear: A Lightweight Pretrained Mixture of Linear Experts for Time Series Forecasting

arXiv:2509.15105v3 Announce Type: replace Abstract: Time series forecasting (TSF) is critical in domains like energy, finance, healthcare, and logistics, requiring models that generalize across divers

applicationsarxiv-cs-lg

SyMerge: From Non-Interference to Synergistic Merging via Single-Layer Adaptation

arXiv:2412.19098v4 Announce Type: replace Abstract: Model merging combines independently trained models into a single multi-task model. However, most existing approaches focus primarily on avoiding ta

Agents

TABX: A High-Throughput Sandbox Battle Simulator for Multi-Agent Reinforcement Learning

arXiv:2602.01665v2 Announce Type: replace-cross Abstract: The design of environments plays a critical role in shaping the development and evaluation of cooperative multi-agent reinforcement learning (

agentsarxiv-cs-ai

The Misattribution Gap: When Memory Poisoning Looks Like Model Failure in Agentic AI Systems

model-releasesarxiv-cs-ai

arXiv:2605.22842v1 Announce Type: cross Abstract: Multi-agent AI pipelines typically assume that agent misconduct originates from model misalignment. We identify a structural failure in this assumptio

The Surprising Difficulty of Search in Model-Based Reinforcement Learning

model-releasesarxiv-cs-ai

arXiv:2601.21306v2 Announce Type: replace-cross Abstract: This paper investigates search in model-based reinforcement learning (RL). Conventional wisdom holds that long-term predictions and compoundin

Hardware

ThriftAttention: Selective Mixed Precision for Long-Context FP4 Attention

arXiv:2605.23081v1 Announce Type: new Abstract: Efficient attention algorithms are critical to mitigate the quadratic cost of attention in long-context workloads. Prior work utilises block-scaled quan

hardwarearxiv-cs-lg

Agents

Turning Adaptation into Assets: Cross-Domain Bridging for Online Vision-Language Navigation

arXiv:2605.23257v1 Announce Type: cross Abstract: Navigating under non-stationary environment shifts poses a critical challenge for a Vision-and-Language Navigation (VLN) agent deployed in the wild. Y

agentsarxiv-cs-cv

Using Ensemble Diffusion to Estimate Uncertainty for End-to-End Autonomous Driving

arXiv:2506.00560v2 Announce Type: replace-cross Abstract: End-to-end planning systems for autonomous driving are rapidly improving, especially in closed-loop simulation environments like CARLA. Many s

VI-CuRL: Stabilizing Verifier-Independent RL Reasoning via Confidence-Guided Variance Reduction

arXiv:2602.12579v2 Announce Type: replace-cross Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) has emerged as a dominant paradigm for enhancing Large Language Models (LLMs) reasoning,

safetyarxiv-cs-ai

VideoTemp-o3: Harmonizing Temporal Grounding and Video Understanding in Agentic Thinking-with-Videos

model-releasesarxiv-cs-ai

arXiv:2602.07801v4 Announce Type: replace-cross Abstract: In long-video understanding, conventional uniform frame sampling often fails to capture key visual evidence, leading to degraded performance a

VINS-120K: Ultra High-Resolution Image Editing with A Large-Scale Dataset

arXiv:2605.23518v1 Announce Type: new Abstract: Directly editing ultra-high-resolution (UHR) images is valuable but underexplored, primarily due to the lack of high-quality data and the challenge in m

VisAnalog: A Diagnostic Suite for Visual Concept Transfer on Natural Images

arXiv:2605.23141v1 Announce Type: new Abstract: A useful test of visual concept learning is not just whether a model can recognize a concept in a single image, but whether it can preserve and manipula

What Linear Probes Miss: Multi-View Probing for Weight-Space Learning

arXiv:2605.23410v1 Announce Type: cross Abstract: The explosive growth of open-source model repositories has created a Model Jungle, where checkpoints are frequently shared without adequate documentat

When Do LLMs Reason? A Dynamical Systems View via Entropy Phase Transitions

arXiv:2605.22873v1 Announce Type: cross Abstract: Chain-of-thought (CoT) reasoning has become the default strategy for enhancing LLM capabilities, yet its application raises a fundamental question: wh

researcharxiv-cs-ai

When Symptoms Are Not Enough: Evidence-Weighting Patterns in Large Language Model Psychiatric Screening

model-releasesarxiv-cs-cl

arXiv:2605.23148v1 Announce Type: new Abstract: As demand for mental health care outpaces clinician-delivered assessment, scalable screening tools are increasingly needed. Large language models (LLMs)

1. Agreed w @scaling01 that Mythos appears to be better GPT 5.5 on many metrics. 2. Mythos is definitely a major wakeup call wrt security, a…

model-releasesgary-marcus--x

1. Agreed w @scaling01 that Mythos appears to be better GPT 5.5 on many metrics. 2. Mythos is definitely a major wakeup call wrt security, and will pose problems for real-world systems that aren’t wel

A2QTGN: Adaptive Amplitude Quantum-Integrated Temporal Graph Network for Dynamic Link Prediction

arXiv:2605.21916v1 Announce Type: cross Abstract: Dynamic link prediction is important for modeling evolving interactions in complex systems, including social, communication, financial, and transporta

Adaptive RBF-KAN: A Comparative Evaluation of Dynamic Shape Parameters in Kolmogorov-Arnold Networks

arXiv:2605.21534v1 Announce Type: cross Abstract: Kolmogorov-Arnold Networks (KANs) approximate multivariate functions using learnable univariate edge functions, typically parameterized by B-spline ba

Algorithm Design and Stronger Guarantees for the Improving Multi-Armed Bandits Problem

arXiv:2511.10619v2 Announce Type: replace Abstract: The improving multi-armed bandits problem is a formal model for allocating effort under uncertainty, motivated by scenarios such as investing resear

Alike Parts: A Feature-Informed Approach to Local and Global Prototype Explanations

arXiv:2605.21646v1 Announce Type: new Abstract: Prototype-based explanations offer an intuitive, example-based approach to support the interpretability of machine learning black box classifiers but of

An Improved Adaptive PID Optimizer with Enhanced Convergence and Stability for Deep Learning

arXiv:2605.21968v1 Announce Type: new Abstract: Optimization is essential in deep learning. The foundational method upon which most optimizers are built is momentum-based stochastic gradient descent.

ARC-STAR: Auditable Post-Hoc Correction for PDE Foundation Models

arXiv:2605.22222v1 Announce Type: new Abstract: Partial differential equation (PDE) foundation models are pretrained networks that forecast how physical fields like velocity and pressure evolve from a

ASSEMBLAGE-DEEPHISTORY: A Cross-Build Binary Dataset with Temporal Coverage

arXiv:2605.21615v1 Announce Type: cross Abstract: Existing binary corpora typically capture only one or two axes of binary variation: they either provide cross-compiler builds without a temporal axis,

Atom-anchored LLMs speak Chemistry: A Retrosynthesis Demonstration

arXiv:2510.16590v2 Announce Type: replace Abstract: Applications of machine learning in chemistry are often limited by the scarcity and expense of labeled data, restricting traditional supervised meth

Automatic Contextual Audio Denoising

arXiv:2605.22262v1 Announce Type: cross Abstract: Audio context determines which sound components and sources are relevant and which can be perceived as irrelevant (noise) by listeners. For example, t

CASE-NET: Deep Spatio-Temporal Representation Learning via Causal Attention and Channel Recalibration for Multivariate Time Series Classification

arXiv:2605.22043v1 Announce Type: new Abstract: Multivariate time series (MTS) classification is foundational to pervasive computing and financial analysis, yet existing multi-scale paradigms are ofte

CausalGuard: Conformal Inference under Graph Uncertainty

arXiv:2605.21928v1 Announce Type: new Abstract: Estimating treatment effects from observational data requires choosing an adjustment set, but valid adjustment depends on an unknown causal graph. Graph

ChronoVAE-HOPE: Beyond Attention -- A Next-Generation VAE Foundation Model for Specialized Time Series Classification

arXiv:2605.22684v1 Announce Type: new Abstract: Time Series Foundation Models (TSFMs) have become a new component of the state-of-the-art in general time series forecasting. However, adapting them to

Tutorials

Conditional Entropy of Heat Diffusion on Temporal Networks

arXiv:2605.21514v1 Announce Type: cross Abstract: Many complex systems can be modeled by temporal networks, whose organization often evolves through distinct structural phases. Detecting the change po

tutorialsarxiv-cs-lg

DecepChain: Inducing Deceptive Reasoning in Large Language Models

arXiv:2510.00319v2 Announce Type: replace Abstract: Large Language Models (LLMs) have been demonstrating strong reasoning capability with their chain-of-thoughts (CoT), which are routinely used by hum

Decision-Aware Quadratic ReLU Replacement for HE-Friendly Inference

arXiv:2605.22237v1 Announce Type: cross Abstract: Fully homomorphic encryption (FHE) supports only additions and multiplications, so FHE-only neural-network inference typically replaces ReLU with poly

Do Deep Ensembles Actually Capture Uncertainty in Graph Neural Networks?

arXiv:2605.22593v1 Announce Type: new Abstract: While deep ensembles are widely considered to be the default method for uncertainty quantification in deep learning, their effectiveness for graph-struc

Do Not Trust The Auctioneer: Learning to Bid in Feedback-Manipulated Auctions

arXiv:2605.22438v1 Announce Type: cross Abstract: Shilling is the use of artificial bids to make competition appear stronger and push prices upward. We study repeated first-price auctions in which shi

EmoTrack: Robust Depression Tracking from Counseling Transcripts across Session Regimes

arXiv:2605.22286v1 Announce Type: new Abstract: Text-based counseling is an important interface for AI mental-health support, where transcripts may be used to monitor depression severity and flag sess

Engineering Hybrid Physics-Informed Neural Networks for Next-Generation Electricity Systems: A State-of-the-Art Review

arXiv:2605.21903v1 Announce Type: cross Abstract: The integration of machine learning with domain-specific physics is transforming the design, monitoring, and control of electricity systems, where dat

Applications

Geometry-Induced Diffusion on Graphs: A Learnable Weighted Laplacian for Spectral GNNs

arXiv:2602.18141v2 Announce Type: replace Abstract: Long-range graph tasks are challenging for Graph Neural Networks (GNNs): global mechanisms such as attention or rewiring schemes can be computationa

applicationsarxiv-cs-lg

Graph neural network explanations reveal a topological signature of disease-associated hubs in biological networks

arXiv:2605.21502v1 Announce Type: cross Abstract: Graph neural networks (GNNs) are increasingly used to model biological systems, yet the reliability of post-hoc explanation methods for recovering mea

GraphFlow: A Graph-Based Workflow Management for Efficient LLM-Agent Serving

arXiv:2605.22566v1 Announce Type: new Abstract: Large Language Model (LLM)-based agents demonstrate strong reasoning and execution capabilities on complex tasks when guided by structured instructions,

Harnesses for Inference-Time Alignment over Execution Trajectories

arXiv:2605.21516v1 Announce Type: new Abstract: Harness engineering has emerged as an important inference-time technique for large language model (LLM) agents, aiming to improve long-term performance

Heterogeneous Agent Collaborative Reinforcement Learning

arXiv:2603.02604v2 Announce Type: replace Abstract: We introduce Heterogeneous Agent Collaborative Reinforcement Learning (HACRL), a new Reinforcement Learning from Verifiable Reward (RLVR) problem th

Hybrid Kolmogorov-Arnold Network and XGBoost Framework for Week-Ahead Price Forecasting in Australia's National Electricity Market

arXiv:2605.22387v1 Announce Type: new Abstract: Accurate electricity price forecasting (EPF) is essential for market participants to support operational planning and risk management, yet remains chall

Integrable Elasticity via Neural Demand Potentials

arXiv:2605.22820v1 Announce Type: new Abstract: We propose the Integrable Context-Dependent Demand Network (ICDN), a demand-first neural model for multiproduct retail demand. The model learns log-dema

Interpreting and Steering State-Space Models via Activation Subspace Bottlenecks

arXiv:2602.22719v2 Announce Type: replace Abstract: State-space models (SSMs) have emerged as an efficient strategy for building powerful language models, avoiding the quadratic complexity of computin

Kernel-Based Safe Exploration in Deep Reinforcement Learning

arXiv:2605.22207v1 Announce Type: cross Abstract: Safety has been a major concern when deploying deep reinforcement learning algorithms in the real world. A promising direction that ensures that the l