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Quantitative coronary calcification analysis for prediction of myocardial ischemia using non-contrast CT calcium scoring

arXiv:2605.21745v1 Announce Type: new Abstract: Non-contrast computed tomography calcium scoring (CTCS) is widely recognized as an effective tool for cardiovascular risk stratification. This study aim

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Human DGX agent

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RADAR: Defending RAG Dynamically against Retrieval Corruption

DGX agent

arXiv:2605.22041v1 Announce Type: cross Abstract: While RAG systems are increasingly deployed in dynamic web search, temporal volatility amplifies their vulnerability to adversarial attacks. Existing

researcharxiv-cs-lg

23 May 2026

[Re] FairDICE: A Fair Tradeoff in Multi-objective Offline RL

arXiv:2603.03454v2 Announce Type: replace Abstract: Offline Reinforcement Learning (RL) is an emerging field of RL in which policies are learned solely from demonstrations. Within offline RL, some env

Hardware

Reading Task Failure Off the Activations: A Sparse-Feature Audit of GPT-2 Small on Indirect Object Identification

arXiv:2605.22719v1 Announce Type: new Abstract: We report a small, reproducible audit of which sparse-autoencoder (SAE) features of GPT-2 small fire differently on failed versus successful trials of t

hardwarearxiv-cs-lg

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

Reinforced Graph of Thoughts: RL-Driven Adaptive Prompting for LLMs

arXiv:2605.22195v1 Announce Type: new Abstract: Graph of Thoughts (GoT), a generalized form of recent prompting paradigms for large language models (LLMs), has been shown to be useful for elaborate pr

Reinforcement learning for ion shuttling on trapped-ion quantum computers

arXiv:2605.22463v1 Announce Type: cross Abstract: Scalable trapped-ion quantum computing is commonly realized with modular chips that feature distinct zones with specific functionalities, such as stor

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

Local Ai

Reliable Wireless Indoor Localization via Cross-Validated Prediction-Powered Calibration

arXiv:2507.20268v3 Announce Type: replace Abstract: Wireless indoor localization using predictive models with received signal strength information (RSSI) requires proper calibration for reliable posit

local-aiarxiv-cs-lg

Local Ai

Remember to be Curious: Episodic Context and Persistent Worlds for 3D Exploration

arXiv:2605.22814v1 Announce Type: new Abstract: Exploration is a prerequisite for learning useful behaviors in sparse-reward, long-horizon tasks, particularly within 3D environments. Curiosity-driven

local-aiarxiv-cs-lg

Representation Gap: Explaining the Unreasonable Effectiveness of Neural Networks from a Geometric Perspective

arXiv:2605.21692v1 Announce Type: new Abstract: Characterizing precisely the asymptotic generalization error of neural networks using parameters that can be estimated efficiently is a crucial problem

Local Ai

Represented Is Not Computed: A Causal Test of Candidate Algorithmic Intermediates in a Transformer

arXiv:2605.22488v1 Announce Type: new Abstract: Structured prompts require integrating components according to task-relevant relations. How a network implements this integration is often hard to judge

local-aiarxiv-cs-lg

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

Revisiting Regularized Policy Optimization for Stable and Efficient Reinforcement Learning in Two-Player Games

arXiv:2602.10894v2 Announce Type: replace Abstract: Two-player games such as board games have long been used as traditional benchmarks for reinforcement learning. This work revisits a policy optimizat

Revisiting Robustness for LLM Safety Alignment via Selective Geometry Control

arXiv:2602.07340v2 Announce Type: replace Abstract: Safety alignment of large language models remains brittle under domain shift and noisy preference supervision. Most existing robust alignment method

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

Applications

Riemannian geometry meets fMRI: the advantages of modeling correlation manifolds and eigenvector subspaces

arXiv:2605.22334v1 Announce Type: new Abstract: Correlation matrices are fundamental summaries of functional brain networks, yet standard analyses often treat entries independently, ignoring the curve

applicationsarxiv-cs-lg

RobustSpeechFlow: Learning Robust Text-to-Speech Trajectories via Augmentation-based Contrastive Flow Matching

arXiv:2605.22083v1 Announce Type: cross Abstract: While flow-matching text-to-speech (TTS) achieves strong zero-shot speaker similarity and naturalness, it remains susceptible to content fidelity issu

Rule-State Inference (RSI): A Bayesian Framework for Compliance Monitoring in Rule-Governed Domains

arXiv:2603.21610v2 Announce Type: replace Abstract: Compliance monitoring in rule-governed domains (tax administration, clinical protocol adherence, environmental regulation) faces three structural ob

Same Architecture, Different Capacity: Optimizer-Induced Spectral Scaling Laws

arXiv:2605.21803v1 Announce Type: new Abstract: Scaling laws have made language-model performance predictable from model size, data, and compute, but they typically treat the optimizer as a fixed trai

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

SCI-Defense: Defending Manipulation Attacks from Generative Engine Optimization

arXiv:2605.21948v1 Announce Type: new Abstract: LLM-based ranking systems are vulnerable to Generative Engine Optimization (GEO) attacks, where adversaries inject semantic signals into product descrip

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-orthogonalizing attractor neural networks emerging from the free energy principle

arXiv:2505.22749v2 Announce Type: replace-cross Abstract: Attractor dynamics are a hallmark of many complex systems, including the brain. Understanding how such self-organizing dynamics emerge from fi

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

SepsisAI Orchestrator: A Containerized and Scalable Platform for Deploying AI Models and Real-Time Monitoring in Early Sepsis Detection

arXiv:2605.22331v1 Announce Type: new Abstract: Despite strong predictive results in the clinical machine learning literature, the translation of these models into bedside use remains limited by syste

SeqLoRA: Bilevel Orthogonal Adaptation for Continual Multi-Concept Generation

arXiv:2605.22743v1 Announce Type: new Abstract: Parameter-efficient fine-tuning enables fast personalization of text-to-image diffusion models, but composing multiple custom concepts remains challengi

Short-Term-to-Long-Term Memory Transfer for Knowledge Graphs under Partial Observability

arXiv:2605.22142v1 Announce Type: new Abstract: Reinforcement learning under partial observability requires deciding what information to retain, yet most memory-based approaches do not explicitly mode

Agents

Skill Weaving: Efficient LLM Improvement via Modular Skillpacks

arXiv:2605.22205v1 Announce Type: cross Abstract: Large language models increasingly require specialization across diverse domains, yet existing approaches struggle to balance multi-domain capacities

agentsarxiv-cs-lg

Soft Bayesian Context Tree Models for Real-Valued Time Series

arXiv:2601.11079v2 Announce Type: replace Abstract: This paper proposes the soft Bayesian context tree model (Soft-BCT), which is a novel BCT model for real-valued time series. The Soft-BCT considers

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

Spectra as Language: Large Language Models for Scalable Stellar Parameter and Abundance Inference

arXiv:2605.22162v1 Announce Type: cross Abstract: Stellar spectra encode key information on the physical properties and chemical compositions of stars. Accurate stellar parameter determination is esse

SPECTRA: Spectral Domain-Aware Graph Generation for Imbalanced Molecular Property Regression

arXiv:2511.04838v2 Announce Type: replace Abstract: Molecular property regression struggles with cases in chemically relevant target ranges that are underrepresented in datasets. Standard average erro

Stabilising Explainability Fragility in Cybersecurity AI: The Impact and Mitigation of Multicollinearity in Public Benchmark Datasets

arXiv:2605.22529v1 Announce Type: new Abstract: This paper investigates a unexplored yet impactful vulnerability in AI explainability used in intrusion detection (IDS): multicollinearity-induced insta

Support-aware offline policy selection for advertising marketplaces

arXiv:2605.21736v1 Announce Type: cross Abstract: Logged advertising auctions make offline reserve-price evaluation attractive but risky. Replay tables can identify policies with large apparent yield

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

Tabular foundation models for robust calibration of near-infrared chemical sensing data

arXiv:2605.21544v1 Announce Type: new Abstract: Near-infrared spectroscopy is increasingly used as a rapid, non-destructive chemical sensing technology for the analysis of food, pharmaceutical, biolog

Tailoring Teaching to Aptitude: Direction-Adaptive Self-Distillation for LLM Reasoning

arXiv:2605.22263v1 Announce Type: new Abstract: On-policy self-distillation (OPSD) is an emerging LLM post-training paradigm in which the model serves as its own teacher: conditioned on privileged inf

Target-Aligned Bellman Backup for Cross-domain Offline Reinforcement Learning

arXiv:2605.22376v1 Announce Type: new Abstract: Cross-domain offline reinforcement learning (CDRL) aims to improve policy learning in a target domain by leveraging data collected from a source domain.

TBP-mHC: full expressivity for manifold-constrained hyper connections through transportation polytopes

arXiv:2605.21724v1 Announce Type: new Abstract: Hyper-Connections (HC) improve residual networks by introducing learnable mixing across multiple residual streams, but unconstrained mixing leads to tra

Applications

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

Ternary Decision Trees with Locally-Adaptive Uncertainty Zones

arXiv:2605.22740v1 Announce Type: new Abstract: Decision trees partition the feature space using hard binary thresholds, assigning identical confidence to instances far from a decision boundary and to

The Attribution Impossibility: No Feature Ranking Is Faithful, Stable, and Complete Under Collinearity

arXiv:2605.21492v1 Announce Type: new Abstract: No feature ranking can be simultaneously faithful, stable, and complete when features are collinear. For collinear pairs, ranking reduces to a coin flip

The Distillation Game: Adaptive Attacks & Efficient Defenses

arXiv:2605.22737v1 Announce Type: new Abstract: Distillation attacks create a deployment trade-off for model providers: the same outputs that make a model more useful can also make it easier to imitat

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 Matching Principle: A Geometric Theory of Loss Functions for Nuisance-Robust Representation Learning

arXiv:2605.22800v1 Announce Type: new Abstract: Robustness, domain adaptation, photometric and occlusion invariance, compositional generalisation, temporal robustness, alignment safety, and classical