Search: “framework”

Federated Learning of Spiking Neural Networks under Heterogeneous Temporal Resolutions

model-releasesarxiv-cs-lg

arXiv:2605.15355v1 Announce Type: new Abstract: Spiking neural networks (SNNs) are biologically inspired energy-efficient models that use sparse binary spike-based communication between neurons, makin

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Research

From Guidelines to Guarantees: A Graph-Based Evaluation Harness for Domain-Specific Evaluation of LLMs

DGX agent

arXiv:2508.20810v3 Announce Type: replace Abstract: Rigorous evaluation of domain-specific language models requires benchmarks that are comprehensive, contamination-resistant, and maintainable. Static

researcharxiv-cs-ai

18 May 2026

FSCM: Frequency-Enhanced Spatial-Spectral Coupled Mamba for Infrared Hyperspectral Image Colorization

arXiv:2605.15880v1 Announce Type: cross Abstract: Thermal infrared imaging is robust to illumination variations and smoke interference, making it important for all-weather perception. However, the lac

Tutorials

Hypothesis-driven construction of mesoscopic dynamics

arXiv:2605.16211v1 Announce Type: new Abstract: Traditional scientific modeling typically begins with fixed, instance-wise effective equations and then carries out equation-specific analysis and compu

tutorialsarxiv-cs-lg

Learning Context-conditioned Gaussian Overbounds for Convolution-Based Uncertainty Propagation

arXiv:2605.15789v1 Announce Type: new Abstract: Uncertainty quantification is essential in safety-critical settings--from autonomous driving to aviation, finance, and health--where decisions must rely

safetyarxiv-cs-lg

Mecha-nudges for Machines

arXiv:2603.23433v2 Announce Type: replace Abstract: AI agents are becoming active decision-makers on the Internet. As they make decisions in the same environments as humans, the environments themselve

MIND: Decoupling Model-Induced Label Noise via Latent Manifold Disentanglement

arXiv:2605.16081v1 Announce Type: cross Abstract: The paradigm of learning from automatic annotations driven by pre-trained experts and Foundation Models dominates data-hungry applications. However, i

local-aiarxiv-cs-cv

Neural Policy Composition from Free Energy Minimization

arXiv:2512.04745v3 Announce Type: replace-cross Abstract: The ability to flexibly compose previously acquired skills to execute intelligent behaviors is a hallmark of natural intelligence. Such compos

local-aiarxiv-cs-ai

NIMO Controller: a self-driving laboratory orchestrator based on the Model Context Protocol

arXiv:2605.15227v1 Announce Type: new Abstract: Self-driving laboratories (SDLs) have attracted increasing attention as a means of accelerating scientific discovery; however, developing SDL software r

OmniVL-Guard: Towards Unified Vision-Language Forgery Detection and Grounding via Balanced RL

arXiv:2602.10687v3 Announce Type: replace-cross Abstract: Existing forgery detection methods are often limited to uni-modal or bi-modal settings, failing to handle the interleaved text, images, and vi

Online Algorithms for Repeated Optimal Stopping: Balancing Baseline Guarantees and Regret

arXiv:2511.04484v2 Announce Type: replace-cross Abstract: We study the repeated optimal stopping problem, in which the same optimal stopping instance with an unknown distribution is solved repeatedly

researcharxiv-cs-lg

Operator-Controlled 6G: From Connectivity Infrastructure to Guaranteed Digital Services

arXiv:2605.15553v1 Announce Type: cross Abstract: Sixth-generation mobile networks (6G) are approaching a structural inflection point. Five generations of vendor-led architectures have left operators

PACER: Acyclic Causal Discovery from Large-Scale Interventional Data

arXiv:2605.15353v1 Announce Type: cross Abstract: Inferring the structure of directed acyclic graphs (DAGs) from data is a central challenge in causal discovery, particularly in modern high-dimensiona

PRISM: Prompt Reliability via Iterative Simulation and Monitoring for Enterprise Conversational AI

arXiv:2605.15665v1 Announce Type: new Abstract: Deploying large language model (LLM)-driven conversational agents in enterprise settings requires prompts that are simultaneously correct at launch and

RanSOM: Second-Order Momentum with Randomized Scaling for Constrained and Unconstrained Optimization

arXiv:2602.06824v2 Announce Type: replace-cross Abstract: Momentum methods, such as Polyak's Heavy Ball, are the standard for training deep networks but suffer from curvature-induced bias in stochasti

safetyarxiv-cs-lg

Applications

RealRep: Generalized SDR-to-HDR Conversion via Attribute-Disentangled Representation Learning

arXiv:2505.07322v4 Announce Type: replace Abstract: High-Dynamic-Range Wide-Color-Gamut (HDR-WCG) technology is becoming increasingly widespread, driving a growing need for converting Standard Dynamic

applicationsarxiv-cs-cv

Res^2CLIP: Few-Shot Generalist Anomaly Detection with Residual-to-Residual Alignment

arXiv:2605.16171v1 Announce Type: new Abstract: Few-shot Generalist Anomaly Detection requires models to generalize to novel categories without retraining, posing significant challenges in real-world

safetyarxiv-cs-cv

Applications

Reweighting free energy profiles between universal machine learning interatomic potentials for fast consensus building

arXiv:2605.15630v1 Announce Type: cross Abstract: Free energy profiles serve as a fundamental bridge between microscopic atomic fluctuations and macroscopic thermodynamic observables. Estimating the f

applicationsarxiv-cs-lg

See Before You Code: Learning Visual Priors for Spatially Aware Educational Animation Generation

arXiv:2605.15585v1 Announce Type: new Abstract: Large language models can generate executable code for educational animations, but the resulting renders often exhibit visual defects, including element

local-aiarxiv-cs-ai

SFHand: Learning Embodied Manipulation by Streaming Egocentric 3D Hand Forecasting

arXiv:2511.18127v2 Announce Type: replace Abstract: Real-time 3D hand forecasting is a critical component for fluid human-computer interaction in applications like AR and assistive robotics. However,

researcharxiv-cs-cv

SOLAR: Self-supervised Joint Learning for Symmetric Multimodal Retrieval

model-releasesarxiv-cs-cv

arXiv:2605.15868v1 Announce Type: new Abstract: In this work, we address the critical yet underexplored challenge of symmetric multimodal-to-multimodal (MM2MM) retrieval, where queries and contexts ar

Structure Abstraction and Generalization in a Hippocampal-Entorhinal Inspired World Model

arXiv:2605.15733v1 Announce Type: cross Abstract: Humans abstract experiences into structured representations to facilitate pattern inference and knowledge transfer. While the hippocampal-entorhinal (

Surrogate Neural Architecture Codesign Package (SNAC-Pack)

arXiv:2605.16138v1 Announce Type: cross Abstract: Neural architecture search (NAS) is a powerful approach for automating model design, but existing methods often optimize for accuracy alone or rely on

local-aiarxiv-cs-ai

Ti-iLSTM: A TinyDL Approach for Logic-Level Anomaly Detection in Industrial Water Treatment Systems

arXiv:2605.15874v1 Announce Type: new Abstract: Industrial Water Treatment Systems (IWTS) are safety critical cyber-physical infrastructures and due to increased connectivity, these systems are expose

local-aiarxiv-cs-lg

Tutorials

Towards Generalization of Block Attention via Automatic Segmentation and Block Distillation

arXiv:2605.15913v1 Announce Type: cross Abstract: Block attention, which processes the input as separate blocks that cannot attend to one another, offers significant potential to improve KV cache reus

tutorialsarxiv-cs-ai

Unified High-Probability Analysis of Stochastic Variance-Reduced Estimation

arXiv:2605.15388v1 Announce Type: new Abstract: Stochastic estimators are fundamental to large-scale optimization, where population quantities must be inferred from noisy oracle observations. Although

safetyarxiv-cs-lg

Unsupervised Domain Shift Detection with Interpretable Subspace Attribution

arXiv:2605.15920v1 Announce Type: cross Abstract: We developed a tool for detecting domain shifts, namely subtle differences in the probability distributions of datasets. We identify these shifts usin

researcharxiv-cs-lg

Verifiable Agentic Infrastructure: Proof-Derived Authorization for Sovereign AI Systems

arXiv:2605.15228v1 Announce Type: new Abstract: Modern cloud and enterprise systems rely on identity-centric authorization, assuming that callers possessing valid credentials are safe to execute comma

WeatherOcc3D: VLM-Assisted Adverse Weather Aware 3D Semantic Occupancy Prediction

model-releasesarxiv-cs-cv

arXiv:2605.16127v1 Announce Type: new Abstract: While multi-modal 3D semantic occupancy prediction typically enhances robustness by fusing camera and LiDAR inputs, its effectiveness is fundamentally c

When and Why Adversarial Training Improves PINNs: A Neural Tangent Kernel Perspective

arXiv:2605.15959v1 Announce Type: cross Abstract: Physics-informed neural networks (PINNs) are powerful surrogates for differential equations but are notoriously difficult to train due to spectral bia

A Mutual Information Lower Bound for Multimodal Regression Active Learning

arXiv:2605.14917v1 Announce Type: new Abstract: Active learning for continuous regression has lacked an acquisition function that targets epistemic uncertainty when the predictive distribution is mult

researcharxiv-cs-lg

A Problem-Oriented Taxonomy of Evaluation Metrics for Time Series Anomaly Detection

arXiv:2511.18739v2 Announce Type: replace Abstract: Time series anomaly detection is widely used in IoT and cyber-physical systems, yet its evaluation remains challenging due to diverse application ob

Tutorials

AaSP: Aliasing-aware Self-Supervised Pre-Training for Audio Spectrogram Transformers

arXiv:2512.03637v2 Announce Type: replace-cross Abstract: Transformer-based audio self-supervised learning (SSL) models commonly use spectrograms, vision-style Transformers, and masked modeling object

tutorialsarxiv-cs-lg

AgenticEval: Toward Agentic and Self-Evolving Safety Evaluation of Large Language Models

arXiv:2509.26100v2 Announce Type: replace Abstract: The rapid integration of Large Language Models (LLMs) into high-stakes domains necessitates reliable safety and compliance evaluation. However, exis

Hardware

An Amortized Efficiency Threshold for Comparing Neural and Heuristic Solvers in Combinatorial Optimization

arXiv:2605.14624v1 Announce Type: cross Abstract: A common critique of neural combinatorial-optimization solvers is that they are less energy-efficient than CPU metaheuristics, given the operational e

hardwarearxiv-cs-ai

Applications

An Interpretable Latency Model for Speculative Decoding in LLM Serving

arXiv:2605.15051v1 Announce Type: new Abstract: Speculative decoding (SD) accelerates large language model (LLM) inference by using a smaller draft model to propose multiple tokens that are verified b

applicationsarxiv-cs-lg

ArGEnT: Arbitrary Geometry-encoded Transformer for Operator Learning

arXiv:2602.11626v2 Announce Type: replace-cross Abstract: Learning solution operators for systems with complex, varying geometries and parametric physical settings is a central challenge in scientific

Auditing Agent Harness Safety

model-releasesarxiv-cs-cl

arXiv:2605.14271v1 Announce Type: new Abstract: LLM agents increasingly run inside execution harnesses that dispatch tools, allocate resources, and route messages between specialized components. Howev

Beyond Cosine Similarity: Zero-Initialized Residual Complex Projection for Aspect-Based Sentiment Analysis

arXiv:2603.28205v2 Announce Type: replace Abstract: Aspect-Based Sentiment Analysis (ABSA) faces critical challenges due to representation entanglement and false-negative collisions in real-valued emb

researcharxiv-cs-cl

Beyond the Final Answer: Evaluating the Reasoning Trajectories of Tool-Augmented Agents

arXiv:2510.02837v2 Announce Type: replace Abstract: Although recent tool-augmented benchmarks involve complex requests, evaluation remains limited to answer matching, neglecting critical trajectory as

Case-Based Calibration of Adaptive Reasoning and Execution for LLM Tool Use

arXiv:2605.15041v1 Announce Type: new Abstract: Tool use extends large language models beyond parametric knowledge, but reliable execution requires balancing appropriate reasoning depth with strict st

Composable Crystals: Controllable Materials Discovery via Concept Learning

arXiv:2605.14769v1 Announce Type: new Abstract: De novo crystal generation, a central task in materials discovery, aims to generate crystals that are simultaneously valid, stable, unique, and novel. E

local-aiarxiv-cs-lg

ContextFlow: Context-Aware Flow Matching For Trajectory Inference From Spatial Omics Data

arXiv:2510.02952v3 Announce Type: replace Abstract: Inferring trajectories from longitudinal spatially-resolved omics data is fundamental to understanding the dynamics of structural and functional tis

local-aiarxiv-cs-lg

Correctness-Aware Repository Filtering Under Maximum Effective Context Window Constraints

arXiv:2605.14362v1 Announce Type: cross Abstract: Context window efficiency is a practical constraint in large language model (LLM)-based developer tools. Paulsen [12] shows that all tested models deg

COTCAgent: Preventive Consultation via Probabilistic Chain-of-Thought Completion

arXiv:2605.15016v1 Announce Type: cross Abstract: As large language models empower healthcare, intelligent clinical decision support has developed rapidly. Longitudinal electronic health records (EHR)

CuSearch: Curriculum Rollout Sampling via Search Depth for Agentic RAG

arXiv:2605.11611v2 Announce Type: replace Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) has emerged as a promising paradigm for training agentic retrieval-augmented generation (RAG)

Dimension-Level Intent Fidelity Evaluation for Large Language Models: Evidence from Structured Prompt Ablation

arXiv:2605.14517v1 Announce Type: cross Abstract: Holistic evaluation scores capture overall output quality but do not distinguish whether a model reproduced the structural form of a user's request fr

Dual Ascent Diffusion for Inverse Problems

arXiv:2505.17353v2 Announce Type: replace-cross Abstract: Ill-posed inverse problems are fundamental in many domains, ranging from astrophysics to medical imaging. Emerging diffusion models provide a