Search: “evaluation”

Post-Training is About States, Not Tokens: A State Distribution View of SFT, RL, and On-Policy Distillation

arXiv:2605.22731v1 Announce Type: new Abstract: Large language model post-training methods such as supervised fine-tuning (SFT), reinforcement learning (RL), and distillation are often analyzed throug

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Research

Prototype-Guided Classification Sub-Task Decoupling Framework: Enhancing Generalization and Interpretability for Multivariate Time Series

DGX agent

arXiv:2605.22055v1 Announce Type: new Abstract: Time Series Classification (TSC) is a long-standing research problem that has gained increasing attention in recent years with the rapid growth of large

researcharxiv-cs-lg

23 May 2026

Reasoning through Verifiable Forecast Actions: Consistency-Grounded RL for Financial LLMs

arXiv:2605.21975v1 Announce Type: new Abstract: Financial markets are characterized by extreme non-stationarity, low signal-to-noise ratios, and strong dependence on external information such as news,

Regret-Based (epsilon,elta)-optimal Stopping Criteria for Bayesian Optimization

arXiv:2605.22561v1 Announce Type: new Abstract: Bayesian optimization (BO) is a widely used iterative black-box optimization method that utilizes Gaussian process (GP) surrogate models. In practice, B

Relational Linear Properties in Language Models: An Empirical Investigation

arXiv:2605.22532v1 Announce Type: new Abstract: Linear properties are ubiquitous in the representations of language models; however, testing them experimentally remains a challenging task. This work f

Rethinking Forward Processes for Score-Based Nonlinear Data Assimilation in High Dimensions

arXiv:2604.02889v2 Announce Type: replace-cross Abstract: Data assimilation is the process of estimating the state of a dynamical system over time by combining model predictions with measurements. Thi

Richer Bayesian Last Layers with Subsampled NTK Features

arXiv:2602.01279v2 Announce Type: replace Abstract: Bayesian Last Layers (BLLs) provide a convenient and computationally efficient way to estimate uncertainty in neural networks. However, they underes

Scalable On-Policy Reinforcement Learning via Adaptive Batch Scaling

arXiv:2605.21557v1 Announce Type: cross Abstract: Conventional wisdom holds that large-batch training is fundamentally incompatible with Reinforcement Learning (RL) - beyond a modest threshold, increa

SDPM: Survival Diffusion Probabilistic Model for Continuous-Time Survival Analysis

arXiv:2605.22776v1 Announce Type: new Abstract: Survival analysis aims to estimate a time-to-event distribution from data with censored observations. Many existing methods either impose structural ass

Self-Supervised ConvLSTM for Fermi Large Area Telescope Transient Detection

arXiv:2605.22112v1 Announce Type: cross Abstract: We present a framework for detecting transient gamma-ray phenomena in a controlled environment by combining end-to-end simulations of the Fermi-LAT sk

Sparse Orthogonal Parameters Tuning for Continual Learning

arXiv:2411.02813v3 Announce Type: replace Abstract: Continual learning methods based on pre-trained models (PTM) have recently gained attention which adapt to successive downstream tasks without catas

SWE-MiniSandbox: Container-Free Reinforcement Learning for Building Software Engineering Agents

arXiv:2602.11210v4 Announce Type: replace-cross Abstract: Reinforcement learning (RL) has become a key paradigm for training software engineering (SWE) agents, but existing pipelines typically rely on

Symbolic Density Estimation for Discrete Distributions

arXiv:2605.21813v1 Announce Type: new Abstract: Discrete probability laws underpin statistical modeling, yet the catalog of interpretable distributions has expanded only gradually through centuries of

Temporal Contrastive Transformer for Financial Crime Detection: Self-Supervised Sequence Embeddings via Predictive Contrastive Coding

arXiv:2605.21490v1 Announce Type: new Abstract: We introduce the Temporal Contrastive Transformer (TCT), a representation learning framework designed to capture contextual temporal dynamics in sequenc

applicationsarxiv-cs-lg

The Illusion of Reasoning: Exposing Evasive Data Contamination in LLMs via Zero-CoT Truncation

arXiv:2605.21856v1 Announce Type: new Abstract: Large language models (LLMs) have demonstrated impressive reasoning abilities across a wide range of tasks, but data contamination undermines the object

The Neural Compiler: Program-to-Network Translation for Hybrid Scientific Machine Learning

arXiv:2605.22498v1 Announce Type: new Abstract: Scientific machine learning often requires combining known physics with unknown parameters or correction terms learned from data. Existing approaches ei

The Signal in the Noise: OOD Detection Through Goodness-of-Fit Testing in Factorised Latent Spaces

arXiv:2605.22496v1 Announce Type: new Abstract: Deep generative models offer a natural foundation for out-of-distribution (OOD) detection, yet prior work has shown that their assigned likelihoods are

TimeGuard: Channel-wise Pool Training for Backdoor Defense in Time Series Forecasting

arXiv:2605.22365v1 Announce Type: cross Abstract: Time Series Forecasting (TSF) plays a critical role across many domains, yet it is vulnerable to backdoor attacks. However, backdoor defenses tailored

UNAD+: An Explainable Hybrid Framework for Unknown Network Attack Detection

arXiv:2605.22621v1 Announce Type: cross Abstract: The detection of previously unseen network attacks remains a major challenge for intrusion detection systems. Although supervised learning methods oft

Uncertainty quantification for Markov chain induced martingales with application to temporal difference learning

arXiv:2502.13822v3 Announce Type: replace-cross Abstract: We establish novel and general high-dimensional concentration inequalities and Berry-Esseen bounds for vector-valued martingales induced by Ma

Visibility nowcasting in South Korea: a machine learning approach to class imbalance and distribution shift

arXiv:2605.21507v1 Announce Type: cross Abstract: Atmospheric visibility is a critical variable for transportation safety and air quality management, however, accurate prediction remains challenging d

Hardware

WarmServe: Enabling One-for-Many GPU Prewarming for Multi-LLM Serving

arXiv:2512.09472v2 Announce Type: replace-cross Abstract: Deploying multiple models within shared GPU clusters is a key strategy to improve resource efficiency in large language model (LLM) serving. E

hardwarearxiv-cs-lg

3D LULC classification using multispectral LiDAR and deep learning: current and prospective schemes

model-releasesarxiv-cs-cv

arXiv:2605.22328v1 Announce Type: new Abstract: Land Use Land Cover (LULC) classification is essential for national 3D mapping, geospatial analysis, and sustainable planning. Multispectral (MS) LiDAR

4D Radar Semantic Segmentation of People in Field Conditions Using Temporal Multi-View Networks

arXiv:2404.05307v2 Announce Type: replace Abstract: Reliable people detection is crucial for the safe autonomy of mobile robots and heavy vehicles, both on roads and in industrial settings like mining

applicationsarxiv-cs-cv

Accelerated Test-Time Scaling with Model-Free Speculative Sampling

arXiv:2506.04708v3 Announce Type: replace Abstract: Language models have demonstrated remarkable capabilities in reasoning tasks through test-time scaling techniques like best-of-N sampling and tree s

researcharxiv-cs-cl

AgentCo-op: Retrieval-Based Synthesis of Interoperable Multi-Agent Workflows

model-releasesarxiv-cs-ai

arXiv:2605.20425v1 Announce Type: new Abstract: Designing multi-agent workflows is especially difficult in open-ended scientific settings where tasks lack curated training sets, reliable scalar evalua

AlignPose: Generalizable 6D Pose Estimation via Multi-view Feature-metric Alignment

model-releasesarxiv-cs-cv

arXiv:2512.20538v2 Announce Type: replace Abstract: Single-view RGB model-based object pose estimation methods achieve strong generalization but are fundamentally limited by depth ambiguity, clutter,

Amplifying, Not Learning: Fine-Tuned AI Text Detectors Amplify a Pretrained Direction

arXiv:2605.21653v1 Announce Type: cross Abstract: AI text detectors amplify a pretrained typicality axis; they do not construct an AI-vs-human boundary. On raw encoders before any task supervision, pr

safetyarxiv-cs-cl

Agents

An Entity Linking Agent for Question Answering

arXiv:2508.03865v4 Announce Type: replace Abstract: Some Question Answering (QA) systems rely on knowledge bases (KBs) to provide accurate answers. Entity Linking (EL) plays a critical role in linking

agentsarxiv-cs-cl

An Evidence Hierarchy for Bayesian Object Classification via OSINT-Aided Heterogeneous Sensor Fusion

arXiv:2605.22259v1 Announce Type: cross Abstract: Heterogeneous sensor fusion is vital for detecting, localizing, and classifying CBRNE threats. However, individual sensors are often only capable of d

Analytical and Experimental Force Analysis of a Soft Linear Pneumatic Actuator

arXiv:2605.21836v1 Announce Type: new Abstract: Soft sleeve actuators (SSAs) have recently been developed as a pneumatic actuation approach for wearable and assistive robotic systems. By integrating t

researcharxiv-cs-ro

Tutorials

AnyMo: Geometry-Aware Setup-Agnostic Modeling of Human Motion in the Wild

arXiv:2605.22715v1 Announce Type: cross Abstract: As wearable and mobile devices become increasingly embedded in daily life, they offer a practical way to continuously sense human motion in the wild.

tutorialsarxiv-cs-cl

Assisted Counterspeech Writing at the Crossroads of Hate Speech and Misinformation

arXiv:2605.22435v1 Announce Type: new Abstract: Hate speech and misinformation frequently co-occur online, amplifying prejudice and polarization. Given their scale, using Large Language Models (LLMs)

researcharxiv-cs-cl

Local Ai

AtomicMotion: Learning Human Motion From Different Human Parts

arXiv:2605.22631v1 Announce Type: new Abstract: Accurately reconstructing full-body poses from sparse head and hand trajectories is a foundational challenge for immersive AR/VR telepresence. Current m

local-aiarxiv-cs-cv

Attacking the Spike: On the Transferability and Security of Spiking Neural Networks to Adversarial Examples

arXiv:2209.03358v5 Announce Type: replace-cross Abstract: Spiking neural networks (SNNs) have attracted much attention for their high energy efficiency and recent advances in classification performanc

Local Ai

Auction-Consensus Algorithm with Learned Bidding Scheme for Multi-Robot Systems

arXiv:2605.21932v1 Announce Type: new Abstract: Multi-Robot Task Allocation (MRTA) is a central challenge in decentralized multi-agent systems, where teams of robots must cooperatively assign and exec

local-aiarxiv-cs-ro

AVI-HT: Adaptive Vision-IMU Fusion for 3D Hand Tracking

arXiv:2605.21714v1 Announce Type: new Abstract: We present AVI-HT, an adaptive visual-IMU fusion approach for tracking 3D hand poses by jointly modeling the egocentric image with on-glove 6-DoF IMU si

applicationsarxiv-cs-cv

Beyond Acoustic Emotion Recognition: Multimodal Pathos Analysis in Political Speech Using LLM-Based and Acoustic Emotion Models

arXiv:2605.22732v1 Announce Type: cross Abstract: We investigate whether acoustic emotion recognition models can serve as proxies for the Pathos dimension in political speech analysis, as operationali

Beyond Euclidean Proximity: Repairing Latent World Models with Horizon-Matched Trajectory Reachability Metrics

model-releasesarxiv-cs-ro

arXiv:2605.22164v1 Announce Type: cross Abstract: Latent world models can contain the state needed for control, yet their terminal-cost interface can expose the planner to the wrong decision-relevant

Blind Spots in the Guard: How Domain-Camouflaged Injection Attacks Evade Detection in Multi-Agent LLM Systems

arXiv:2605.22001v1 Announce Type: cross Abstract: Injection detectors deployed to protect LLM agents are calibrated on static, template-based payloads that announce themselves as override directives.

Agents

Causal Past Logic for Runtime Verification of Distributed LLM Agent Workflows

arXiv:2605.20923v1 Announce Type: cross Abstract: Distributed LLM agent workflows should not be monitored as if they produced a single sequential log. In an asynchronous execution, a decision can only

agentsarxiv-cs-ai

Code Researcher: Deep Research Agent for Large Systems Code and Commit History

model-releasesarxiv-cs-ai

arXiv:2506.11060v2 Announce Type: replace-cross Abstract: Large Language Model (LLM)-based coding agents have shown promising results on coding benchmarks, but their effectiveness on systems code rema

Comparing LLM and Fine-Tuned Model Performance on NVDRS Circumstance Extraction with Varying Prompt Complexity

arXiv:2605.21845v1 Announce Type: new Abstract: Suicide is a leading cause of death in the United States, and understanding the circumstances that precede it requires extracting structured information

ConvNeXt-FD: A Fractal-Based Deep Model for Robust Biomedical Image Segmentation

arXiv:2605.22002v1 Announce Type: new Abstract: Biomedical image segmentation is a critical task in medical diagnosis and treatment planning, enabling precise delineation of anatomical structures and

CoRMA: Contrastive RMA for Contact-Rich Meta-Adaptation

model-releasesarxiv-cs-ro

arXiv:2605.22082v1 Announce Type: new Abstract: We present CoRMA(Contrastive Robotic Motor Adaptation), a context-based meta-adaptation framework that modifies RMA for force-dominant assembly. CoRMA r

Cross-Lingual Consensus: Aligning Multilingual Cultural Knowledge via Multilingual Self-Consistency

arXiv:2605.22137v1 Announce Type: new Abstract: Although Large Language Models (LLMs) demonstrate strong capabilities across various tasks, they exhibit significant performance discrepancies across la

CryoNet: A Deep Learning Framework for Multi-Modal Debris-Covered Glacier Mapping. A Case Study of the Poiqu Basin, Central Himalaya

arXiv:2605.21527v1 Announce Type: cross Abstract: Glaciers play a critical role as freshwater reserves and indicators of climate change, yet their automatic delineation, especially for debris-covered

applicationsarxiv-cs-cv

D3Seg: Dependency-Aware Diffusion for Brain Tumor Segmentation with Missing Modalities

arXiv:2605.22249v1 Announce Type: new Abstract: Accurate brain tumor segmentation using multiparametric MRI is critical for effective treatment planning. However, in clinical settings, complete acquis