Search: “evaluation”

Hybrid Quantum-Classical Corrective Diffusion Modeling for Meteorological Downscaling

arXiv:2605.23403v1 Announce Type: new Abstract: Statistical downscaling is a crucial component of the weather modeling field, where high-resolution outputs must be reconstructed from coarse-resolution

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Safety

Infra-Bayesian Reinforcement Learning Agents Outperform Classical RL For Worst-Case Robustness

DGX agent

arXiv:2605.23146v1 Announce Type: cross Abstract: Classical reinforcement learning assumes the agent interacts with a fixed environment whose behavior does not depend on the agent's policy. This assum

safetyarxiv-cs-ai

25 May 2026

IntentionNav: A Benchmark for Intent-Driven Object Navigation from Implicit Human Instruction

model-releasesarxiv-cs-cv

arXiv:2605.23187v1 Announce Type: new Abstract: Existing object navigation benchmarks usually tell an embodied agent which object category to find, such as microwave or chair. Human-facing embodied AI

Interactive Query Answering on Knowledge Graphs with Soft Entity Constraints

arXiv:2508.13663v5 Announce Type: replace Abstract: Methods for query answering over incomplete knowledge graphs retrieve entities that are likely to be answers, which is particularly useful when such

applicationsarxiv-cs-ai

Is a Document Educational or Just Wikipedia-Style? -- Pitfalls of Classifier-Based Quality Filtering

arXiv:2605.23721v1 Announce Type: new Abstract: Classifier-based Quality Filtering has recently emerged as a fundamental technique in constructing pre-training corpora. The ability to deploy a single

applicationsarxiv-cs-cl

Is Capability a Liability? More Capable Language Models Make Worse Forecasts When It Matters Most

arXiv:2605.22672v2 Announce Type: replace Abstract: We document inverse scaling in LLMs on forecasting problems whose underlying time series exhibit superlinear growth and tail risk of regime change,

Is TabPFN the Silver Bullet for Insurance Pricing?

arXiv:2605.22892v1 Announce Type: cross Abstract: Modelling claim frequency and severity for non-life insurance pricing predominantly relies on generalised linear models, with gradient-boosted machine

Tutorials

Its very limiting that a big set of very hard problems that we have just lying around are Erdos problems. Don’t get me wrong, they are quite…

tutorialsethan-mollick--x

Its very limiting that a big set of very hard problems that we have just lying around are Erdos problems. Don’t get me wrong, they are quite cool, but we really need hard problems repositories for man

Joint Target-Less Intrinsic and Extrinsic Camera-LiDAR Calibration using Deep Point Correspondences

arXiv:2605.23397v1 Announce Type: new Abstract: Accurate camera-LiDAR calibration is a prerequisite for robust multi-modal perception in robotics. Recent target-less approaches based on deep point cor

researcharxiv-cs-cv

JointHRRP-Net: A Statistically Constrained Decoupling Network for Joint Target and Jamming Recognition in Composite Jamming

arXiv:2605.22857v1 Announce Type: cross Abstract: High-resolution range profile (HRRP)-based radar automatic target recognition suffers from severe performance degradation in composite jamming environ

KAPLAN: Kolmogorov-Arnold Prognostic Learnable Activation Networks for Survival Analysis

arXiv:2605.23082v1 Announce Type: cross Abstract: Survival analysis aims to model how covariates and time jointly shape the time-to-event distribution under right censoring. Classical methods such as

Tutorials

Learning partially observed systems with neural Hamiltonian ordinary differential equations

arXiv:2605.23510v1 Announce Type: new Abstract: When learning dynamical systems from data, embedding physical structure can constrain the solution space and improve generalization, but many physics-in

tutorialsarxiv-cs-lg

Tutorials

Less Effort, Shorter Proofs: Reinforcement Learning for Security Protocol Analysis in Tamarin

arXiv:2605.23643v1 Announce Type: cross Abstract: Tools like Tamarin and ProVerif have achieved notable success in analyzing and verifying complex real-world protocols such as EMV, 5G, and WPA2, even

tutorialsarxiv-cs-lg

LFRAG: Layout-oriented Fine-grained Retrieval-Augmented Generation on Multimodal Document Understanding

arXiv:2605.22829v1 Announce Type: cross Abstract: Multimodal Retrieval-Augmented Generation (RAG) has emerged as an effective paradigm for enhancing Large Language Models (LLMs) with external knowledg

Lipschitz Optimization for Formal Verification of Homographies

arXiv:2605.23203v1 Announce Type: cross Abstract: The adoption of vision neural networks in regulated industries requires formal robustness guarantees, especially in safety-critical domains such as he

LLM Code Smells: A Taxonomy and Detection Approach

arXiv:2605.22976v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly integrated into software systems for diverse purposes, due to their versatility, flexibility, and abilit

researcharxiv-cs-ai

LLM Sparsity Prior for Robust Feature Selection

arXiv:2605.23102v1 Announce Type: cross Abstract: Large language models (LLMs) offer a scalable mechanism to elicit domain-informed prior information for high-dimensional variable selection. However,

LQ-rPPG: A Label-Quantized Coarse-to-Fine Learning Framework for Remote Physiological Measurement

model-releasesarxiv-cs-cv

arXiv:2605.23174v1 Announce Type: new Abstract: Remote photoplethysmography (rPPG) enables non-contact measurement of physiological signals from facial videos, offering strong potential for remote hea

MadEvolve: Evolutionary Optimization of Trading Systems with Large Language Models

arXiv:2605.23007v1 Announce Type: cross Abstract: We explore the application of LLM-driven algorithm optimization to several common tasks in quantitative finance. MadEvolve, a general-purpose algorith

MaMa: A Game-Theoretic Approach for Designing Safe Agentic Systems

arXiv:2602.04431v2 Announce Type: replace Abstract: LLM-based multi-agent systems have demonstrated impressive capabilities, but they also introduce significant safety risks when individual agents fai

safetyarxiv-cs-lg

MedExpMem: Adapting Experience Memory for Differential Diagnosis

arXiv:2605.22872v1 Announce Type: cross Abstract: Experienced physicians develop diagnostic expertise through clinical practice, acquiring not only disease knowledge but also the ability to differenti

Mediative Fuzzy Logic: From Type-1 Foundations to Type-2, Type-3 and Quantum Extensions

arXiv:2605.22900v1 Announce Type: new Abstract: Mediative Fuzzy Logic was conceived as a practical scheme for reconciling hesitant or conflicting assessments in fuzzy control and decision-making. Howe

MedSAE: Dissecting MedCLIP Representations with Sparse Autoencoders

arXiv:2510.26411v2 Announce Type: replace Abstract: Artificial intelligence in healthcare requires models that are accurate and interpretable. We advance mechanistic interpretability in medical vision

applicationsarxiv-cs-ai

Agents

MemAudit: Post-hoc Auditing of Poisoned Agent Memory via Causal Attribution and Structural Anomaly Detection

arXiv:2605.23723v1 Announce Type: new Abstract: Large language model agents increasingly rely on persistent memory to store past interactions, retrieve relevant demonstrations, and improve long-horizo

agentsarxiv-cs-ai

Agents

Multi-Floor Exploration for Ground Robots via an Incremental Reachable Graph and Structural Priors

arXiv:2605.23350v1 Announce Type: new Abstract: Autonomous exploration of multi-floor buildings remains challenging for ground robots because conventional 2D and 2.5D maps cannot represent overlapping

agentsarxiv-cs-ro

Multilingual Knowledge Transfer under Data Constraints via Lexical Interventions

arXiv:2605.23885v1 Announce Type: new Abstract: Cross-lingual knowledge transfer is critical for building high-performing multilingual language models for languages with insufficient training data. Wh

researcharxiv-cs-cl

Multilingual Steering by Design: Multilingual Sparse Autoencoders and Principled Layer Selection

model-releasesarxiv-cs-cl

arXiv:2605.23036v1 Announce Type: new Abstract: Sparse autoencoders (SAEs) enable feature-level mechanistic interpretability and activation steering in large language models (LLMs), but SAE-based lang

Multimodal Distribution Matching for Vision-Language Dataset Distillation

arXiv:2605.23482v1 Announce Type: cross Abstract: Dataset distillation compresses large training sets into compact synthetic datasets while preserving downstream performance. As modern systems increas

Near-Optimal Private Linear Regression via Iterative Hessian Mixing

arXiv:2601.07545v2 Announce Type: replace Abstract: We study differentially private ordinary least squares (DP-OLS) with bounded data (X,Y) via sketching-based mechanisms. While Gaussian sketching app

Agents

NeuroWeaver: An Autonomous Evolutionary Agent for Exploring the Programmatic Space of EEG Analysis Pipelines

arXiv:2602.13473v2 Announce Type: replace Abstract: Although foundation models have demonstrated remarkable success in general domains, the application of these models to electroencephalography (EEG)

agentsarxiv-cs-ai

Not Too Generative, Not Too Discriminative: The Human Alignment Sweet Spot

arXiv:2605.23819v1 Announce Type: cross Abstract: A central question in computational vision is whether human-like visual representations are better explained by discriminative or generative learning.

Occlusion-Aware Physics-Semantic Keyframe Selection for Robust Video Editing

arXiv:2605.23192v1 Announce Type: new Abstract: Video editing has recently achieved remarkable progress with diffusion-based generative models, enabling diverse object-level manipulations from natural

researcharxiv-cs-cv

One Policy, Infinite NPCs: Persona-Traceable Shared RL Policies for Scalable Game Agents

arXiv:2605.23652v1 Announce Type: new Abstract: On a 300-persona life-simulation benchmark, pcsp achieves compositional zero-shot persona identification up to 17x above chance, Spearman rho approx 0.7

Ontological Knowledge Blocks: Executable Compliance and Profile-Based Validation for Trustworthy AI Systems

arXiv:2605.23297v1 Announce Type: new Abstract: AI-enabled services deployed in critical digital infrastructure are subject to governance obligations spanning transparency, accountability, fairness, a

Operator Learning for Reconstructing Flow Fields from Sparse Measurements: a Language Model Approach

model-releasesarxiv-cs-lg

arXiv:2605.23712v1 Announce Type: cross Abstract: Reconstructing flow fields from sparse measurements is a fundamental problem in fluid mechanics with broad implications for modeling, control, and des

PaP-NF: Probabilistic Long-Term Time Series Forecasting via Prefix-as-Prompt Reprogramming and Normalizing Flows

arXiv:2605.23219v1 Announce Type: cross Abstract: Time series forecasting plays a central role in many real-world applications and has been extensively studied. Most existing approaches rely on determ

applicationsarxiv-cs-ai

Philosophical Dispositions as Behavioral Constraints for AI-Assisted Code Review: An Empirical Study

arXiv:2605.23108v1 Announce Type: cross Abstract: AI-assisted code review tools typically operate as generic 'expert reviewer' agents, producing homogeneous findings regardless of the analysis type ne

PhotoFlow: Agentic 3D Virtual Photography Missions

arXiv:2605.23771v1 Announce Type: cross Abstract: Virtual photography asks an agent to enter a prepared 3D scene with no preselected camera pose or reference image, infer a suitable shot from scene in

PilotWiMAE: Pilot-Native Representation Learning for Wireless Channels

arXiv:2605.22856v1 Announce Type: cross Abstract: Channel foundation models assume access to fully observed channels, an assumption that fails in deployment. We introduce PilotWiMAE, a self-supervised

PoisonForge: Task-Level Targeted Poisoning Benchmark for Instruction-Tuned LLMs

arXiv:2605.23168v1 Announce Type: cross Abstract: When practitioners fine-tune LLMs on unvetted datasets, an adversary can exploit the data supply chain through task-level poisoning: inserting a small

Pooling and Semantic Shift: The Fundamental Challenges in Long Text Embedding and Retrieval

arXiv:2603.21437v2 Announce Type: replace Abstract: Transformer-based embedding models frequently exhibit geometric pathologies, such as anisotropy and length-induced representation collapse, which ca

researcharxiv-cs-cl

Push Your Agent: Measuring and Enforcing Quantitative Goal Persistence in Long-Horizon LLM Agents

model-releasesarxiv-cs-lg

arXiv:2605.23574v1 Announce Type: new Abstract: Long-horizon language agents can make many plausible local tool calls yet fail to persist until a requested count is actually complete. We study this ga

RADAR: Relative Angular Divergence Across Representations

arXiv:2605.23028v1 Announce Type: cross Abstract: Machine learning methods rely on data. However, gathering suitable data can be challenging due to availability constraints, cost, or the need for doma

researcharxiv-cs-cl

ReCoVer: Resilient LLM Pre-Training System via Fault-Tolerant Collective and Versatile Workload

arXiv:2605.11215v2 Announce Type: replace-cross Abstract: Pre-training large language models on massive GPU clusters has made hardware faults routine rather than rare, driving the need for resilient t

Recursive Block-Diagonal Coupling for Resource-Efficient Training of Vision Models

model-releasesarxiv-cs-cv

arXiv:2605.23656v1 Announce Type: new Abstract: Training high-capacity vision models from scratch requires substantial computational resources. To improve training efficiency of a wide target model, e

Reflex: Reinforcement Learning with Reflection Symmetry Exploitation in State-Based Continuous Control

arXiv:2605.23415v1 Announce Type: cross Abstract: Reinforcement learning has long struggled with poor sample efficiency. One promising approach to mitigate this problem is leveraging group-invariant M

Relevant Walk Search for Explaining Graph Neural Networks

arXiv:2605.23673v1 Announce Type: new Abstract: Graph Neural Networks (GNNs) have become important machine learning tools for graph analysis, and its explainability is crucial for safety, fairness, an

safetyarxiv-cs-lg

Rethinking Transfer Learning for Industrial Inspection: DINOv3 vs. ImageNet Pretraining Across RGB and X-ray Tasks

arXiv:2605.23472v1 Announce Type: new Abstract: Vision foundation models pretrained on web-scale data have recently shown strong transfer capabilities on many downstream tasks, but their effectiveness

researcharxiv-cs-cv