Search: calibrate - ai.jp.net

Research Paper #Cardiovascular Modeling, Pediatric PAH, Disease Progression 🔬 ResearchAnalyzed: Jan 3, 2026 06:13

Computational Modeling for Pediatric PAH Progression

Published:Dec 31, 2025 18:27

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1 min read

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ArXiv

Analysis

This paper is significant because it applies computational modeling to a rare and understudied pediatric disease, Pulmonary Arterial Hypertension (PAH). The use of patient-specific models calibrated with longitudinal data allows for non-invasive monitoring of disease progression and could potentially inform treatment strategies. The development of an automated calibration process is also a key contribution, making the modeling process more efficient.

Key Takeaways

•Developed patient-specific cardiovascular models for pediatric PAH patients.
•Used longitudinal MRI and catheterization data for model calibration.
•Automated calibration process significantly reduced calibration time.
•Model-derived metrics correlated with clinical indicators of disease severity.
•Computational modeling offers a promising tool for monitoring and informing treatment strategies for pediatric PAH.

Reference

“Model-derived metrics such as arterial stiffness, pulse wave velocity, resistance, and compliance were found to align with clinical indicators of disease severity and progression.”

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Research Paper #Large Language Models (LLMs) for Code Generation 🔬 ResearchAnalyzed: Jan 3, 2026 09:21

Localized Uncertainty for Code LLMs

Published:Dec 31, 2025 02:00

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1 min read

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ArXiv

Analysis

This paper addresses the critical issue of LLM output reliability in code generation. By providing methods to identify potentially problematic code segments, it directly supports the practical use of LLMs in software development. The focus on calibrated uncertainty is crucial for enabling developers to trust and effectively edit LLM-generated code. The comparison of white-box and black-box approaches offers valuable insights into different strategies for achieving this goal. The paper's contribution lies in its practical approach to improving the usability and trustworthiness of LLMs for code generation, which is a significant step towards more reliable AI-assisted software development.

Key Takeaways

•Proposes techniques to localize potentially misaligned code generated by LLMs.
•Introduces a dataset of "Minimal Intent Aligning Patches" for evaluation.
•Compares white-box and black-box approaches for uncertainty calibration.
•Demonstrates that a small supervisor model can effectively estimate edited lines.
•Discusses generalizability and connections to AI oversight and control.

Reference

“Probes with a small supervisor model can achieve low calibration error and Brier Skill Score of approx 0.2 estimating edited lines on code generated by models many orders of magnitude larger.”

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Research Paper #Data Integration, Statistical Modeling, Heterogeneous Data 🔬 ResearchAnalyzed: Jan 3, 2026 15:37

Data Integration Framework for Heterogeneous Sources

Published:Dec 30, 2025 16:50

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1 min read

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ArXiv

Analysis

This paper addresses a crucial problem in data science: integrating data from diverse sources, especially when dealing with summary-level data and relaxing the assumption of random sampling. The proposed method's ability to estimate sampling weights and calibrate equations is significant for obtaining unbiased parameter estimates in complex scenarios. The application to cancer registry data highlights the practical relevance.

Key Takeaways

•Proposes a novel statistical framework for integrating summary-level data with heterogeneous data sources.
•Leverages auxiliary information to estimate study-specific sampling weights.
•Calibrates estimating equations to obtain full model parameters.
•Evaluated through simulations and applied to real-world cancer registry data.

Reference

“The proposed approach estimates study-specific sampling weights using auxiliary information and calibrates the estimating equations to obtain the full set of model parameters.”

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Research Paper #Graph Theory, Topology, AI 🔬 ResearchAnalyzed: Jan 3, 2026 17:15

Topological Spatial Graph Reduction

Published:Dec 30, 2025 16:27

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1 min read

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ArXiv

Analysis

This paper addresses the important problem of simplifying spatial graphs while preserving their topological structure. This is crucial for applications where the spatial relationships and overall structure are essential, such as in transportation networks or molecular modeling. The use of topological descriptors, specifically persistent diagrams, is a novel approach to guide the graph reduction process. The parameter-free nature and equivariance properties are significant advantages, making the method robust and applicable to various spatial graph types. The evaluation on both synthetic and real-world datasets further validates the practical relevance of the proposed approach.

Key Takeaways

•Proposes a novel approach for spatial graph reduction.
•Employs topological descriptors (persistent diagrams) to guide the reduction.
•The method is parameter-free and equivariant.
•Demonstrates effectiveness on both synthetic and real-world data.

Reference

“The coarsening is realized by collapsing short edges. In order to capture the topological information required to calibrate the reduction level, we adapt the construction of classical topological descriptors made for point clouds (the so-called persistent diagrams) to spatial graphs.”

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Paper #LLM Reliability 🔬 ResearchAnalyzed: Jan 3, 2026 17:04

Composite Score for LLM Reliability

Published:Dec 30, 2025 08:07

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1 min read

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ArXiv

Analysis

This paper addresses a critical issue in the deployment of Large Language Models (LLMs): their reliability. It moves beyond simply evaluating accuracy and tackles the crucial aspects of calibration, robustness, and uncertainty quantification. The introduction of the Composite Reliability Score (CRS) provides a unified framework for assessing these aspects, offering a more comprehensive and interpretable metric than existing fragmented evaluations. This is particularly important as LLMs are increasingly used in high-stakes domains.

Key Takeaways

•Introduces the Composite Reliability Score (CRS) as a unified metric for LLM reliability.
•Integrates calibration, robustness, and uncertainty quantification.
•Evaluates ten open-source LLMs across five QA datasets.
•CRS provides stable model rankings and reveals hidden failure modes.
•Highlights the importance of balancing accuracy, robustness, and calibrated uncertainty for dependable LLMs.

Reference

“The Composite Reliability Score (CRS) delivers stable model rankings, uncovers hidden failure modes missed by single metrics, and highlights that the most dependable systems balance accuracy, robustness, and calibrated uncertainty.”

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research #forecasting 🔬 ResearchAnalyzed: Jan 4, 2026 06:48

Calibrated Multi-Level Quantile Forecasting

Published:Dec 29, 2025 18:25

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1 min read

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ArXiv

Analysis

This article likely presents a new method or improvement in the field of forecasting, specifically focusing on quantile forecasting. The term "calibrated" suggests an emphasis on the accuracy and reliability of the predictions. The multi-level aspect implies the model considers different levels or granularities of data. The source, ArXiv, indicates this is a research paper.

Key Takeaways

•Focuses on quantile forecasting, a method for predicting the distribution of a variable.
•Employs a "calibrated" approach, suggesting improved accuracy and reliability.
•Utilizes a multi-level structure, potentially allowing for more nuanced predictions.
•Published on ArXiv, indicating a research paper.

Reference

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Research Paper #Sensorimotor Synchronization, Cognitive Science, Human Movement 🔬 ResearchAnalyzed: Jan 3, 2026 18:31

Dynamical Incompatibilities in Finger Tapping

Published:Dec 29, 2025 18:14

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1 min read

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ArXiv

Analysis

This paper addresses a fundamental contradiction in the study of sensorimotor synchronization using paced finger tapping. It highlights that responses to different types of period perturbations (step changes vs. phase shifts) are dynamically incompatible when presented in separate experiments, leading to contradictory results in the literature. The key finding is that the temporal context of the experiment recalibrates the error-correction mechanism, making responses to different perturbation types compatible only when presented randomly within the same experiment. This has implications for how we design and interpret finger-tapping experiments and model the underlying cognitive processes.

Key Takeaways

•Different period perturbation types (step changes and phase shifts) in paced finger tapping experiments can lead to dynamically incompatible responses.
•Temporal context recalibrates the error-correction mechanism, influencing responses.
•Responses are compatible only when different perturbation types are presented randomly within the same experiment.
•This understanding helps improve experimental design and data interpretation in sensorimotor synchronization research.

Reference

“Responses to different perturbation types are dynamically incompatible when they occur in separate experiments... On the other hand, if both perturbation types are presented at random during the same experiment then the responses are compatible with each other and can be construed as produced by a unique underlying mechanism.”

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Research Paper #Materials Science, AI, XANES Spectroscopy 🔬 ResearchAnalyzed: Jan 3, 2026 18:48

AI-Driven XANES Prediction: Universal and Experiment-Calibrated

Published:Dec 29, 2025 13:12

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1 min read

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ArXiv

Analysis

This paper addresses the limitations of current XANES simulation methods by developing an AI model for faster and more accurate prediction. The key innovation is the use of a crystal graph neural network pre-trained on simulated data and then calibrated with experimental data. This approach allows for universal prediction across multiple elements and significantly improves the accuracy of the predictions, especially when compared to experimental data. The work is significant because it provides a more efficient and reliable method for analyzing XANES spectra, which is crucial for materials characterization, particularly in areas like battery research.

Key Takeaways

•Developed an AI model for XANES prediction using a crystal graph neural network.
•The model is pre-trained on simulated data and calibrated with experimental data.
•Achieves universal XANES prediction across 48 elements.
•Significantly reduces edge energy misalignment error after calibration.
•Provides a faster and more accurate method for XANES analysis.

Reference

“The method demonstrated in this work opens up a new way to achieve fast, universal, and experiment-calibrated XANES prediction.”

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Research Paper #Uncertainty Quantification, Regression, Machine Learning 🔬 ResearchAnalyzed: Jan 3, 2026 18:49

Calibrating Uncertainty in Regression Models

Published:Dec 29, 2025 13:02

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1 min read

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ArXiv

Analysis

This paper addresses a crucial aspect of machine learning: uncertainty quantification. It focuses on improving the reliability of predictions from multivariate statistical regression models (like PLS and PCR) by calibrating their uncertainty. This is important because it allows users to understand the confidence in the model's outputs, which is critical for scientific applications and decision-making. The use of conformal inference is a notable approach.

Key Takeaways

•Proposes a method to calibrate uncertainty in multivariate statistical regression models.
•Method is inspired by conformal inference.
•Tested on both traditional and kernelized versions of PLS and PCR.
•Demonstrated on synthetic and real-world datasets (NIR and hyperspectral data).
•Achieves accurate prediction intervals, matching the desired confidence level.

Reference

“The model was able to successfully identify the uncertain regions in the simulated data and match the magnitude of the uncertainty. In real-case scenarios, the optimised model was not overconfident nor underconfident when estimating from test data: for example, for a 95% prediction interval, 95% of the true observations were inside the prediction interval.”

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Research Paper #Large Language Models, Conformal Prediction, Uncertainty Quantification 🔬 ResearchAnalyzed: Jan 3, 2026 16:22

Conformal Prediction for LLM Next-Token Prediction

Published:Dec 27, 2025 19:08

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1 min read

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ArXiv

Analysis

This paper addresses the critical need for uncertainty quantification in large language models (LLMs), particularly in high-stakes applications. It highlights the limitations of standard softmax probabilities and proposes a novel approach, Vocabulary-Aware Conformal Prediction (VACP), to improve the informativeness of prediction sets while maintaining coverage guarantees. The core contribution lies in balancing coverage accuracy with prediction set efficiency, a crucial aspect for practical deployment. The paper's focus on a practical problem and the demonstration of significant improvements in set size make it valuable.

Key Takeaways

•Addresses the problem of poorly calibrated probabilities in LLMs.
•Proposes Vocabulary-Aware Conformal Prediction (VACP) to improve prediction set efficiency.
•Demonstrates significant reduction in prediction set size while maintaining coverage guarantees.
•Provides a practical solution for uncertainty quantification in LLMs.

Reference

“VACP achieves 89.7 percent empirical coverage (90 percent target) while reducing the mean prediction set size from 847 tokens to 4.3 tokens -- a 197x improvement in efficiency.”

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Research Paper #EEG Analysis, Machine Learning, Neurological Disorders 🔬 ResearchAnalyzed: Jan 3, 2026 19:47

Multi-Disorder EEG Classification Benchmarks

Published:Dec 27, 2025 17:11

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1 min read

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ArXiv

Analysis

This paper addresses the critical need for automated EEG analysis across multiple neurological disorders, moving beyond isolated diagnostic problems. It establishes realistic performance baselines and demonstrates the effectiveness of sensitivity-prioritized machine learning for scalable EEG screening and triage. The focus on clinically relevant disorders and the use of a large, heterogeneous dataset are significant strengths.

Key Takeaways

•Establishes benchmarks for multi-disorder EEG classification.
•Demonstrates the effectiveness of sensitivity-prioritized machine learning.
•Provides evidence for scalable EEG screening and triage.
•Uses a large, heterogeneous clinical EEG dataset.

Reference

“Sensitivity-oriented modeling achieves recall exceeding 80% for the majority of disorder categories.”

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Research Paper #Bayesian Inference, Variational Bayes, Uncertainty Quantification 🔬 ResearchAnalyzed: Jan 3, 2026 19:47

Trustworthy Variational Bayes for Reliable Uncertainty Quantification

Published:Dec 27, 2025 17:09

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1 min read

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ArXiv

Analysis

This paper addresses a critical limitation of Variational Bayes (VB), a popular method for Bayesian inference: its unreliable uncertainty quantification (UQ). The authors propose Trustworthy Variational Bayes (TVB), a method to recalibrate VB's UQ, ensuring more accurate and reliable uncertainty estimates. This is significant because accurate UQ is crucial for the practical application of Bayesian methods, especially in safety-critical domains. The paper's contribution lies in providing a theoretical guarantee for the calibrated credible intervals and introducing practical methods for efficient implementation, including the "TVB table" for parallelization and flexible parameter selection. The focus on addressing undercoverage issues and achieving nominal frequentist coverage is a key strength.

Key Takeaways

•Addresses the problem of unreliable uncertainty quantification in Variational Bayes.
•Proposes Trustworthy Variational Bayes (TVB) to recalibrate UQ.
•Provides theoretical guarantees for calibrated credible intervals.
•Introduces the "TVB table" for efficient implementation and parallelization.
•Demonstrates improved performance over standard VB in numerical experiments.

Reference

“The paper introduces "Trustworthy Variational Bayes (TVB), a method to recalibrate the UQ of broad classes of VB procedures... Our approach follows a bend-to-mend strategy: we intentionally misspecify the likelihood to correct VB's flawed UQ.”

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Paper #AI in Healthcare 🔬 ResearchAnalyzed: Jan 3, 2026 16:36

MMCTOP: Multimodal AI for Clinical Trial Outcome Prediction

Published:Dec 26, 2025 06:56

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1 min read

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ArXiv

Analysis

This paper introduces MMCTOP, a novel framework for predicting clinical trial outcomes by integrating diverse biomedical data types. The use of schema-guided textualization, modality-aware representation learning, and a Mixture-of-Experts (SMoE) architecture is a significant contribution to the field. The focus on interpretability and calibrated probabilities is crucial for real-world applications in healthcare. The consistent performance improvements over baselines and the ablation studies demonstrating the impact of key components highlight the framework's effectiveness.

Reference

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Research #llm 🔬 ResearchAnalyzed: Jan 4, 2026 08:50

Universal Adversarial Suffixes Using Calibrated Gumbel-Softmax Relaxation

Published:Dec 9, 2025 00:03

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1 min read

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ArXiv

Analysis

This article likely presents a novel approach to generating adversarial suffixes for large language models (LLMs). The use of Gumbel-Softmax relaxation suggests an attempt to make the suffix generation process more robust and potentially more effective at fooling the models. The term "calibrated" implies an effort to improve the reliability and predictability of the adversarial attacks. The source being ArXiv indicates this is a research paper, likely detailing the methodology, experiments, and results.

Key Takeaways

•Focuses on adversarial attacks against LLMs.
•Employs Gumbel-Softmax relaxation for suffix generation.
•Aims to improve the robustness and effectiveness of attacks.
•Likely a research paper detailing a new method.

Reference

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Research #llm 🔬 ResearchAnalyzed: Jan 4, 2026 09:29

CHiQPM: Calibrated Hierarchical Interpretable Image Classification

Published:Nov 25, 2025 19:16

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1 min read

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ArXiv

Analysis

This article introduces a new approach to image classification, focusing on interpretability and calibration. The hierarchical aspect suggests a multi-level understanding of images. The use of 'calibrated' implies an attempt to improve the reliability of the model's predictions. Further analysis would require examining the specific methods and results presented in the ArXiv paper.

Key Takeaways

•Focuses on interpretable image classification.
•Employs a hierarchical approach for image understanding.
•Emphasizes calibrated predictions for improved reliability.

Reference

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Research #llm 🔬 ResearchAnalyzed: Jan 4, 2026 07:06

Beyond Component Strength: Synergistic Integration and Adaptive Calibration in Multi-Agent RAG Systems

Published:Nov 21, 2025 07:53

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1 min read

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ArXiv

Analysis

This article likely discusses the importance of how different components of a multi-agent Retrieval-Augmented Generation (RAG) system work together, rather than just the individual performance of each component. It probably emphasizes the need for these components to be integrated synergistically and calibrated adaptively to achieve optimal performance. The focus is on the system-level design and optimization of RAG systems.

Key Takeaways

Reference

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Research #AI Benchmarking 📝 BlogAnalyzed: Dec 29, 2025 18:31

ARC Prize v2 Launch: New Challenges for Advanced Reasoning Models

Published:Mar 24, 2025 20:26

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1 min read

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ML Street Talk Pod

Analysis

The article announces the launch of ARC Prize v2, a benchmark designed to evaluate advanced reasoning capabilities in AI models. The key improvement in v2 is the calibration of challenges to be solvable by humans while remaining difficult for state-of-the-art LLMs. This suggests a focus on adversarial selection to prevent models from exploiting shortcuts. The article highlights the negligible performance of current LLMs on this challenge, indicating a significant gap in reasoning abilities. The inclusion of a new research lab, Tufa AI Labs, as a sponsor, further emphasizes the ongoing research and development in the field of AGI and reasoning.

Key Takeaways

•ARC Prize v2 introduces new challenges designed to test advanced reasoning in AI models.
•The challenges are calibrated to be solvable by humans but difficult for current LLMs.
•The benchmark aims to push the boundaries of AI reasoning capabilities and identify areas for improvement.

Reference

“In version 2, the challenges have been calibrated with humans such that at least 2 humans could solve each task in a reasonable task, but also adversarially selected so that frontier reasoning models can't solve them.”

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Research #llm 📝 BlogAnalyzed: Dec 29, 2025 07:52

Building a Unified NLP Framework at LinkedIn with Huiji Gao - #481

Published:May 6, 2021 19:18

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1 min read

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Practical AI

Analysis

This article discusses an interview with Huiji Gao, a Senior Engineering Manager at LinkedIn, focusing on the development and implementation of NLP tools and systems. The primary focus is on DeText, an open-source framework for ranking, classification, and language generation models. The conversation explores the motivation behind DeText, its impact on LinkedIn's NLP landscape, and its practical applications within the company. The article also touches upon the relationship between DeText and LiBERT, a LinkedIn-specific version of BERT, and the engineering considerations for optimization and practical use of these tools. The interview provides insights into LinkedIn's approach to NLP and its open-source contributions.

Key Takeaways

•DeText is an open-source NLP framework developed at LinkedIn for ranking, classification, and language generation.
•LiBERT is a LinkedIn-specific version of BERT, trained and calibrated on LinkedIn data, used in conjunction with DeText.
•The article highlights the practical engineering considerations and optimization approaches used in deploying these NLP tools at LinkedIn.

Reference

“We dig into his interest in building NLP tools and systems, including a recent open-source project called DeText, a framework for generating models for ranking classification and language generation.”

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