惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

U
Unit 42
V
V2EX
Martin Fowler
Martin Fowler
博客园 - Franky
P
Proofpoint News Feed
P
Palo Alto Networks Blog
H
Hackread – Cybersecurity News, Data Breaches, AI and More
B
Blog
The Register - Security
The Register - Security
Latest news
Latest news
S
Security @ Cisco Blogs
Simon Willison's Weblog
Simon Willison's Weblog
Recorded Future
Recorded Future
大猫的无限游戏
大猫的无限游戏
M
Microsoft Research Blog - Microsoft Research
Scott Helme
Scott Helme
T
Tailwind CSS Blog
cs.CV updates on arXiv.org
cs.CV updates on arXiv.org
Cyber Security Advisories - MS-ISAC
Cyber Security Advisories - MS-ISAC
Application and Cybersecurity Blog
Application and Cybersecurity Blog
T
True Tiger Recordings
有赞技术团队
有赞技术团队
I
Intezer
Cisco Talos Blog
Cisco Talos Blog
Hacker News - Newest:
Hacker News - Newest: "LLM"
The GitHub Blog
The GitHub Blog
cs.AI updates on arXiv.org
cs.AI updates on arXiv.org
T
Tenable Blog
博客园 - 叶小钗
Hugging Face - Blog
Hugging Face - Blog
Hacker News: Ask HN
Hacker News: Ask HN
S
Security Archives - TechRepublic
F
Future of Privacy Forum
爱范儿
爱范儿
PCI Perspectives
PCI Perspectives
H
Help Net Security
让小产品的独立变现更简单 - ezindie.com
让小产品的独立变现更简单 - ezindie.com
T
The Blog of Author Tim Ferriss
MyScale Blog
MyScale Blog
N
Netflix TechBlog - Medium
罗磊的独立博客
Apple Machine Learning Research
Apple Machine Learning Research
MongoDB | Blog
MongoDB | Blog
Security Latest
Security Latest
美团技术团队
博客园 - 三生石上(FineUI控件)
S
Schneier on Security
量子位
C
CERT Recently Published Vulnerability Notes
SecWiki News
SecWiki News

cs.LG updates on arXiv.org

Frequency-Domain Regularized Adversarial Alignment for Transferable Attacks against Closed-Source MLLMs ChronoMedicalWorld: A Medical World Model for Learning Patient Trajectories from Longitudinal Care Data Alike Parts: A Feature-Informed Approach to Local and Global Prototype Explanations Quantitative coronary calcification analysis for prediction of myocardial ischemia using non-contrast CT calcium scoring Local Covariate Selection for Average Causal Effect Estimation without Pretreatment and Causal Sufficiency Assumptions No Epoch Like the Present: Robust Climate Emulation Requires Out-of-Distribution Generalisation PeakFocus: Bridging Peak Localization and Intensity Regression via a Unified Multi-Scale Framework for Electricity Load Forecasting Graph neural network explanations reveal a topological signature of disease-associated hubs in biological networks Beyond Euclidean Proximity: Repairing Latent World Models with Horizon-Matched Trajectory Reachability Metrics Optimal Guarantees for Auditing Rényi Differentially Private Machine Learning How Many Different Outputs Can a Transformer Generate? AutoMCU: Feasibility-First MCU Neural Network Customization via LLM-based Multi-Agent Systems How Sparsity Allocation Shapes Label-Free Post-Pruning Recoverability OPPO: Bayesian Value Recursion for Token-Level Credit Assignment in LLM Reasoning Reasoning through Verifiable Forecast Actions: Consistency-Grounded RL for Financial LLMs The Illusion of Reasoning: Exposing Evasive Data Contamination in LLMs via Zero-CoT Truncation DualOptim+: Bridging Shared and Decoupled Optimizer States for Better Machine Unlearning in Large Language Models Aerodynamic force reconstruction using physics-informed Gaussian processes Machine learning prediction of obstructive coronary artery disease using opportunistic coronary calcium and epicardial fat assessments from CT calcium scoring scans Decomposing Ensemble Spread in Lorenz '96 With Learned Stochastic Parameterizations From Betting to Empirical Bernstein LIL TONIC: Token-Centric Semantic Communication for Task-Oriented Wireless Systems Embedding-Based Federated Learning with Runtime Governance for Iron Deficiency Prediction Discovering Entity-Conditioned Lag Heterogeneity: A Lag-Gated Neural Audit Framework for Panel Time Series Holomorphic Neural ODEs with Kolmogorov-Arnold Networks for Interpretable Discovery of Complex Dynamics On-Policy Consistency Training Improves LLM Safety with Minimal Capability Degradation Can Transformers Learn to Verify During Backtracking Search? An Improved Adaptive PID Optimizer with Enhanced Convergence and Stability for Deep Learning Measuring Cross-Modal Synergy: A Benchmark for VLM Explainability MMD-Balls as Credal Sets: A PAC-Bayesian Framework for Epistemic Uncertainty in Test-Time Adaptation When to Switch, Not Just What: Transition Quality Prediction in Clash Royale CausalGuard: Conformal Inference under Graph Uncertainty Calibration, Uncertainty Communication, and Deployment Readiness in CKD Risk Prediction: A Framework Evaluation Study AgForce Enables Antigen-conditioned Generative Antibody Design TBP-mHC: full expressivity for manifold-constrained hyper connections through transportation polytopes Equilibrium Propagation and Hamiltonian Inference in the Diffusive Fitzhugh-Nagumo Model Provable Joint Decontamination for Benchmarking Multiple Large Language Models Models Can Model, But Can't Bind: Structured Grounding in Text-to-Optimization Memory-R2: Fair Credit Assignment for Long-Horizon Memory-Augmented LLM Agents CASE-NET: Deep Spatio-Temporal Representation Learning via Causal Attention and Channel Recalibration for Multivariate Time Series Classification Engineering Hybrid Physics-Informed Neural Networks for Next-Generation Electricity Systems: A State-of-the-Art Review Visibility nowcasting in South Korea: a machine learning approach to class imbalance and distribution shift From Sequential Nodes to GPU Batches: Parallel Branch and Bound for Optimal $k$-Sparse GLMs One LR Doesn't Fit All: Heavy-Tail Guided Layerwise Learning Rates for LLMs PEARL: Unbiased Percentile Estimation via Contrastive Learning for Industrial-Scale Livestream Recommendation Same Architecture, Different Capacity: Optimizer-Induced Spectral Scaling Laws Prototype-Guided Classification Sub-Task Decoupling Framework: Enhancing Generalization and Interpretability for Multivariate Time Series ARC-STAR: Auditable Post-Hoc Correction for PDE Foundation Models Three Costs of Amortizing Gaussian Process Inference with Neural Processes Symbolic Density Estimation for Discrete Distributions Detecting Atypical Clients in Federated Learning via Representation-Level Divergence ECPO: Evidence-Coupled Policy Optimization for Evidence-Certified Candidate Ranking Noise Schedule Design for Diffusion Models: An Optimal Control Perspective Temporal Contrastive Transformer for Financial Crime Detection: Self-Supervised Sequence Embeddings via Predictive Contrastive Coding Expectation Consistency Loss: Rethink Confidence Calibration under Covariate Shift SCI-Defense: Defending Manipulation Attacks from Generative Engine Optimization A Reproducible Log-Driven AutoML Framework for Interpretable Pipeline Optimization in Healthcare Risk Prediction Scalable On-Policy Reinforcement Learning via Adaptive Batch Scaling Leveraging Self-Paced Curriculum Learning for Enhanced Modality Balance in Multimodal Conversational Emotion Recognition Can Breath Biomarkers Causally Influence Blood Glucose? Investigating VOC-Mediated Modulation in Diabetes Adaptive Measurement Allocation for Learning Kernelized SVMs Under Noisy Observations Harnesses for Inference-Time Alignment over Execution Trajectories Tabular foundation models for robust calibration of near-infrared chemical sensing data ConTact: Contact-First Antibody CDR Design via Explicit Interface Reasoning Beyond Single Slot: Joint Optimization for Multi-Slot Guaranteed Display Advertising Support-aware offline policy selection for advertising marketplaces Objective-Induced Bias and Search Dynamics in Multiobjective Unsupervised Feature Selection Beyond Scalar Objectives: Expert-Feedback-Driven Autonomous Experimentation for Scientific Discovery at the Nanoscale Toward Understanding Adversarial Distillation: Why Robust Teachers Fail Dropout Universality: Scaling Laws and Optimal Scheduling at the Edge-of-Chaos Position: The Time for Sampling Is Now! Charting a New Course for Bayesian Deep Learning On the Sample Complexity of Discounted Reinforcement Learning with Optimized Certainty Equivalents IKNO: Infinite-order Kernel Neural Operators Provable Robustness against Backdoor Attacks via the Primal-Dual Perspective on Differential Privacy Short-Term-to-Long-Term Memory Transfer for Knowledge Graphs under Partial Observability I-SAFE: Wasserstein Coherence Metrics for Structural Auditing of Scientific AI Models stable-worldmodel: A Platform for Reproducible World Modeling Research and Evaluation LABO: LLM-Accelerated Bayesian Optimization through Broad Exploration and Selective Experimentation When Are Teacher Tokens Reliable? Position-Weighted On-Policy Self-Distillation for Reasoning Bandit Convex Optimization with Gradient Prediction Adaptivity Reinforced Graph of Thoughts: RL-Driven Adaptive Prompting for LLMs The Attribution Impossibility: No Feature Ranking Is Faithful, Stable, and Complete Under Collinearity What are the Right Symmetries for Formal Theorem Proving? Protein Thoughts: Interpretable Reasoning with Tree of Thoughts and Embedding-Space Flow Matching for Protein-Protein Interaction Discovery Long-term Fairness with Selective Labels Explainable AI for Data-Driven Design of High-Dimensional Predictive Studies SepsisAI Orchestrator: A Containerized and Scalable Platform for Deploying AI Models and Real-Time Monitoring in Early Sepsis Detection Dynamic Mixture of Latent Memories for Self-Evolving Agents Thermodynamic Irreversibility of Training Algorithms One-Way Policy Optimization for Self-Evolving LLMs Manifold-Guided Attention Steering EmoTrack: Robust Depression Tracking from Counseling Transcripts across Session Regimes Algebraic Machine Learning for Small-to-Medium Datasets Is Competitive against Strong Standard Baselines Predicting Performance of Symbolic and Prompt Programs with Examples Ex-GraphRAG: Interpretable Evidence Routing for Graph-Augmented LLMs $\textit{BlockFormer}$ : Transformer-based inference from interaction maps Correcting Class Imbalance in Prior-Data Fitted Networks for Tabular Classification Representation Gap: Explaining the Unreasonable Effectiveness of Neural Networks from a Geometric Perspective VeriScale: Adversarial Test-Suite Scaling for Verifiable Code Generation Tailoring Teaching to Aptitude: Direction-Adaptive Self-Distillation for LLM Reasoning
Spectra as Language: Large Language Models for Scalable Stellar Parameter and Abundance Inference
Hai-Ling Lu, · 2026-05-23 · via cs.LG updates on arXiv.org

View PDF HTML (experimental)

Abstract:Stellar spectra encode key information on the physical properties and chemical compositions of stars. Accurate stellar parameter determination is essential for addressing major questions such as galaxy and stellar evolution. Large-scale spectroscopic surveys have accumulated unprecedented spectral data. Traditional feature extraction or model-fitting approaches struggle with high-dimensional, massive datasets, limited generalization, and computational inefficiency. Recent advances in large language models demonstrate strong generalization and feature-learning in tasks like natural language processing, DNA/RNA sequence analysis, and protein/chemical parsing. Stellar spectra are continuous sequential signals, enabling the transfer of language models to stellar spectroscopy. Here, we propose a two-stage large language model framework for stellar parameter inference, achieving accurate estimation of effective temperature, surface gravity, metallicity, and abundances of ~20 chemical elements. Scaling-law analyses show systematic performance improvements with increasing data, providing a scalable framework for forthcoming large-scale surveys.
Subjects: Instrumentation and Methods for Astrophysics (astro-ph.IM); Solar and Stellar Astrophysics (astro-ph.SR); Machine Learning (cs.LG)
Cite as: arXiv:2605.22162 [astro-ph.IM]
  (or arXiv:2605.22162v1 [astro-ph.IM] for this version)
  https://doi.org/10.48550/arXiv.2605.22162

arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Hailing Lu [view email]
[v1] Thu, 21 May 2026 08:33:47 UTC (667 KB)