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Received — 29 October 2025 ⏭ Favourites

Test-Time Tuned Language Models Enable End-to-end De Novo Molecular Structure Generation from MS/MS Spectra

✇cs.AI, q-bio.NC updates on arXiv.org
By: Laura Mismetti · Marvin Alberts · Andreas Krause · Mara Graziani
29 October 2025 at 12:00
arXiv:2510.23746v1 Announce Type: new Abstract: Tandem Mass Spectrometry enables the identification of unknown compounds in crucial fields such as metabolomics, natural product discovery and environmental analysis. However, current methods rely on database matching from previously observed molecules, or on multi-step pipelines that require intermediate fragment or fingerprint prediction. This makes finding the correct molecule highly challenging, particularly for compounds absent from reference databases. We introduce a framework that, by leveraging test-time tuning, enhances the learning of a pre-trained transformer model to address this gap, enabling end-to-end de novo molecular structure generation directly from the tandem mass spectra and molecular formulae, bypassing manual annotations and intermediate steps. We surpass the de-facto state-of-the-art approach DiffMS on two popular benchmarks NPLIB1 and MassSpecGym by 100% and 20%, respectively. Test-time tuning on experimental spectra allows the model to dynamically adapt to novel spectra, and the relative performance gain over conventional fine-tuning is of 62% on MassSpecGym. When predictions deviate from the ground truth, the generated molecular candidates remain structurally accurate, providing valuable guidance for human interpretation and more reliable identification.

Generative AI for Healthcare: Fundamentals, Challenges, and Perspectives

✇cs.AI, q-bio.NC updates on arXiv.org
By: Gang Chen · Changshuo Liu · Gene Anne Ooi · Marcus Tan · Zhongle Xie · Jianwei Yin · James Wei Luen Yip · Wenqiao Zhang · Jiaqi Zhu · Beng Chin Ooi
29 October 2025 at 12:00
arXiv:2510.24551v1 Announce Type: new Abstract: Generative Artificial Intelligence (GenAI) is taking the world by storm. It promises transformative opportunities for advancing and disrupting existing practices, including healthcare. From large language models (LLMs) for clinical note synthesis and conversational assistance to multimodal systems that integrate medical imaging, electronic health records, and genomic data for decision support, GenAI is transforming the practice of medicine and the delivery of healthcare, such as diagnosis and personalized treatments, with great potential in reducing the cognitive burden on clinicians, thereby improving overall healthcare delivery. However, GenAI deployment in healthcare requires an in-depth understanding of healthcare tasks and what can and cannot be achieved. In this paper, we propose a data-centric paradigm in the design and deployment of GenAI systems for healthcare. Specifically, we reposition the data life cycle by making the medical data ecosystem as the foundational substrate for generative healthcare systems. This ecosystem is designed to sustainably support the integration, representation, and retrieval of diverse medical data and knowledge. With effective and efficient data processing pipelines, such as semantic vector search and contextual querying, it enables GenAI-powered operations for upstream model components and downstream clinical applications. Ultimately, it not only supplies foundation models with high-quality, multimodal data for large-scale pretraining and domain-specific fine-tuning, but also serves as a knowledge retrieval backend to support task-specific inference via the agentic layer. The ecosystem enables the deployment of GenAI for high-quality and effective healthcare delivery.

Integrating Genomics into Multimodal EHR Foundation Models

✇cs.AI, q-bio.NC updates on arXiv.org
By: Jonathan Amar · Edward Liu · Alessandra Breschi · Liangliang Zhang · Pouya Kheradpour · Sylvia Li · Lisa Soleymani Lehmann · Alessandro Giulianelli · Matt Edwards · Yugang Jia · David Nola · Raghav Mani · Pankaj Vats · Jesse Tetreault · T. J. Chen · Cory Y. McLean
29 October 2025 at 12:00
arXiv:2510.23639v1 Announce Type: cross Abstract: This paper introduces an innovative Electronic Health Record (EHR) foundation model that integrates Polygenic Risk Scores (PRS) as a foundational data modality, moving beyond traditional EHR-only approaches to build more holistic health profiles. Leveraging the extensive and diverse data from the All of Us (AoU) Research Program, this multimodal framework aims to learn complex relationships between clinical data and genetic predispositions. The methodology extends advancements in generative AI to the EHR foundation model space, enhancing predictive capabilities and interpretability. Evaluation on AoU data demonstrates the model's predictive value for the onset of various conditions, particularly Type 2 Diabetes (T2D), and illustrates the interplay between PRS and EHR data. The work also explores transfer learning for custom classification tasks, showcasing the architecture's versatility and efficiency. This approach is pivotal for unlocking new insights into disease prediction, proactive health management, risk stratification, and personalized treatment strategies, laying the groundwork for more personalized, equitable, and actionable real-world evidence generation in healthcare.

Closing Gaps: An Imputation Analysis of ICU Vital Signs

✇cs.AI, q-bio.NC updates on arXiv.org
By: Alisher Turubayev · Anna Shopova · Fabian Lange · Mahmut Kamalak · Paul Mattes · Victoria Ayvasky · Bert Arnrich · Bjarne Pfitzner · Robin P. van de Water
29 October 2025 at 12:00
arXiv:2510.24217v1 Announce Type: cross Abstract: As more Intensive Care Unit (ICU) data becomes available, the interest in developing clinical prediction models to improve healthcare protocols increases. However, the lack of data quality still hinders clinical prediction using Machine Learning (ML). Many vital sign measurements, such as heart rate, contain sizeable missing segments, leaving gaps in the data that could negatively impact prediction performance. Previous works have introduced numerous time-series imputation techniques. Nevertheless, more comprehensive work is needed to compare a representative set of methods for imputing ICU vital signs and determine the best practice. In reality, ad-hoc imputation techniques that could decrease prediction accuracy, like zero imputation, are still used. In this work, we compare established imputation techniques to guide researchers in improving the performance of clinical prediction models by selecting the most accurate imputation technique. We introduce an extensible and reusable benchmark with currently 15 imputation and 4 amputation methods, created for benchmarking on major ICU datasets. We hope to provide a comparative basis and facilitate further ML development to bring more models into clinical practice.

Dynamic Monitoring of Recurrent Ovarian Cancer Using Serial ctDNA: A Real-World Case Series

✇MRD
By: Eric Rios-Doria · Jonathan B Reichel · Marc R Radke · Enna Manhardt · Mayumi Rubin-Saika · Christina Lockwood · Elizabeth M Swisher · Kalyan Banda
28 October 2025 at 18:00

Curr Oncol. 2025 Oct 21;32(10):585. doi: 10.3390/curroncol32100585.

ABSTRACT

Recurrent ovarian cancer (OC) is challenging to detect early using current methods like CA-125 and imaging. Circulating tumor DNA (ctDNA) may improve disease monitoring. Here, we assess the real-world clinical utility of serial ctDNA analyses in patients with recurrent OC. We analyzed serial plasma samples (N = 23) from six patients with recurrent OC using a tumor-informed next-generation sequencing assay targeting 68 cancer-related genes developed at the University of Washington. ctDNA variant allele frequencies (VAFs) were correlated with CA-125 levels, radiographic findings, and clinical outcomes. ctDNA levels generally reflected clinical status, accurately mirroring disease progression and therapeutic response. In one patient, rising ctDNA preceded clinical recurrence by four months, despite normal CA-125 and imaging, highlighting its potential advantage. Conversely, some patients exhibited clinical progression with undetectable ctDNA, indicating limitations in assay sensitivity, biological factors, or metastatic sites (e.g., brain metastases). ctDNA and CA-125 showed complementary value in most cases, suggesting potential combined use in clinical monitoring. Our findings demonstrate that ctDNA is a promising biomarker to complement existing monitoring approaches for recurrent OC. In some cases, capable of predicting relapse and treatment response ahead of current clinical indicators. However, identified discordances underscore technical and biological challenges that warrant further investigation. Larger prospective studies are necessary to refine ctDNA's clinical utility and integration into personalized OC care.

PMID:41149505 | PMC:PMC12563156 | DOI:10.3390/curroncol32100585

Tongyi DeepResearch Technical Report

✇cs.AI, q-bio.NC updates on arXiv.org
By: Tongyi DeepResearch Team · Baixuan Li · Bo Zhang · Dingchu Zhang · Fei Huang · Guangyu Li · Guoxin Chen · Huifeng Yin · Jialong Wu · Jingren Zhou · Kuan Li · Liangcai Su · Litu Ou · Liwen Zhang · Pengjun Xie · Rui Ye · Wenbiao Yin · Xinmiao Yu · Xinyu Wang · Xixi Wu · Xuanzhong Chen · Yida Zhao · Zhen Zhang · Zhengwei Tao · Zhongwang Zhang · Zile Qiao · Chenxi Wang · Donglei Yu · Gang Fu · Haiyang Shen · Jiayin Yang · Jun Lin · Junkai Zhang · Kui Zeng · Li Yang · Hailong Yin · Maojia Song · Ming Yan · Peng Xia · Qian Xiao · Rui Min · Ruixue Ding · Runnan Fang · Shaowei Chen · Shen Huang · Shihang Wang · Shihao Cai · Weizhou Shen · Xiaobin Wang · Xin Guan · Xinyu Geng · Yingcheng Shi · Yuning Wu · Zhuo Chen · Zijian Li · Yong Jiang
29 October 2025 at 12:00
arXiv:2510.24701v1 Announce Type: cross Abstract: We present Tongyi DeepResearch, an agentic large language model, which is specifically designed for long-horizon, deep information-seeking research tasks. To incentivize autonomous deep research agency, Tongyi DeepResearch is developed through an end-to-end training framework that combines agentic mid-training and agentic post-training, enabling scalable reasoning and information seeking across complex tasks. We design a highly scalable data synthesis pipeline that is fully automatic, without relying on costly human annotation, and empowers all training stages. By constructing customized environments for each stage, our system enables stable and consistent interactions throughout. Tongyi DeepResearch, featuring 30.5 billion total parameters, with only 3.3 billion activated per token, achieves state-of-the-art performance across a range of agentic deep research benchmarks, including Humanity's Last Exam, BrowseComp, BrowseComp-ZH, WebWalkerQA, xbench-DeepSearch, FRAMES and xbench-DeepSearch-2510. We open-source the model, framework, and complete solutions to empower the community.

Agent Data Protocol: Unifying Datasets for Diverse, Effective Fine-tuning of LLM Agents

✇cs.AI, q-bio.NC updates on arXiv.org
By: Yueqi Song · Ketan Ramaneti · Zaid Sheikh · Ziru Chen · Boyu Gou · Tianbao Xie · Yiheng Xu · Danyang Zhang · Apurva Gandhi · Fan Yang · Joseph Liu · Tianyue Ou · Zhihao Yuan · Frank Xu · Shuyan Zhou · Xingyao Wang · Xiang Yue · Tao Yu · Huan Sun · Yu Su · Graham Neubig
29 October 2025 at 12:00
arXiv:2510.24702v1 Announce Type: cross Abstract: Public research results on large-scale supervised finetuning of AI agents remain relatively rare, since the collection of agent training data presents unique challenges. In this work, we argue that the bottleneck is not a lack of underlying data sources, but that a large variety of data is fragmented across heterogeneous formats, tools, and interfaces. To this end, we introduce the agent data protocol (ADP), a light-weight representation language that serves as an "interlingua" between agent datasets in diverse formats and unified agent training pipelines downstream. The design of ADP is expressive enough to capture a large variety of tasks, including API/tool use, browsing, coding, software engineering, and general agentic workflows, while remaining simple to parse and train on without engineering at a per-dataset level. In experiments, we unified a broad collection of 13 existing agent training datasets into ADP format, and converted the standardized ADP data into training-ready formats for multiple agent frameworks. We performed SFT on these data, and demonstrated an average performance gain of ~20% over corresponding base models, and delivers state-of-the-art or near-SOTA performance on standard coding, browsing, tool use, and research benchmarks, without domain-specific tuning. All code and data are released publicly, in the hope that ADP could help lower the barrier to standardized, scalable, and reproducible agent training.

The Role of Omentin in Gastrointestinal Cancer: Diagnostic, Prognostic, and Therapeutic Perspectives

✇Omics in Gastric
By: Adam Mylonakis · Maximos Frountzas · Irene Lidoriki · Alexandros Kozadinos · Maria Evangelia Koloutsou · Angeliki Margoni · Areti Kalfoutzou · Dimitrios Theodorou · Konstantinos G Toutouzas · Dimitrios Schizas
28 October 2025 at 18:00

Metabolites. 2025 Sep 30;15(10):649. doi: 10.3390/metabo15100649.

ABSTRACT

Background/Objectives: Omentin, also known as intelectin-1, is a secreted adipokine with anti-inflammatory, insulin-sensitizing, and immune-modulatory functions, primarily expressed in visceral adipose tissue. While omentin has been associated with favorable metabolic outcomes, its role in cancer pathogenesis appears context-dependent and remains poorly understood. This review investigates the biological functions, expression patterns, and clinical relevance of omentin across gastrointestinal malignancies. Methods: A comprehensive review of the literature was conducted using PubMed, Scopus, and Web of Science up to August 2025 to evaluate the role of omentin in gastrointestinal cancers. Both preclinical and clinical studies evaluating omentin, its analogues and omentin-enhancing agents in gastric, colorectal, hepatic, pancreatic, and esophageal cancers were included. Results: Omentin exhibits anti-proliferative, anti-inflammatory, and anti-angiogenic effects within the tumor microenvironment in several GI malignancies. However, evidence also indicates a dual role. High intratumoral omentin expression correlates with improved prognosis in colorectal, gastric, and hepatic cancers; in contrast, elevated circulating levels-particularly in colorectal and pancreatic cancers-have been paradoxically associated with increased cancer risk and poor outcomes. Mechanistically, omentin modulates PI3K/Akt, NF-κB, AMPK, and oxidative stress pathways, and interacts with TMEM207. However, most available studies are small-scale and heterogeneous, with methodological inconsistencies and limited multi-omics integration, leaving major knowledge gaps. Conclusions: This review highlights omentin's distinct systemic and local roles across GI cancers, underscoring its translational implications. Omentin emerges as a promising but context-dependent biomarker and therapeutic target, with future research needed to address heterogeneity, standardize assays, and validate its clinical utility in large-scale prospective studies.

PMID:41149627 | PMC:PMC12566161 | DOI:10.3390/metabo15100649

Navigating Cancer Complexity: Integrative Multi-Omics Methodologies for Clinical Insights

✇(Multiomics OR Omics) AND (Pancreatic)
By: Martina Catalano · Alberto D'Angelo · Francesco De Logu · Romina Nassini · Daniele Generali · Giandomenico Roviello
28 October 2025 at 18:00

Clin Med Insights Oncol. 2025 Oct 21;19:11795549251384582. doi: 10.1177/11795549251384582. eCollection 2025.

ABSTRACT

Recent advancements in cancer multi-omics have transformed our understanding of cancer biology by integrating genomics, transcriptomics, proteomics, and metabolomics. These integrative approaches have led to the identification of novel biomarkers and therapeutic targets, offering deeper insights into the molecular intricacies of various cancers, including breast, lung, gastric, pancreatic, and glioblastoma. Despite these advances, challenges remain, such as the integration of disparate data types and the interpretation of complex biological interactions. However, developments in proteogenomics and mass spectrometry have enhanced the correlation between molecular profiles and clinical features, refining the prediction of therapeutic responses. Future research in cancer drug discovery is poised to benefit from multi-omics approaches, improving the precision and efficacy of personalized therapies. By developing integrative network-based models, researchers aim to address challenges related to heterogeneity, reproducibility, and data interpretation. A standardized framework for multi-omics data integration could revolutionize cancer research, optimizing the identification of novel drug targets and enhancing our understanding of cancer biology. This complete approach holds the promise of advancing personalized therapies by fully characterizing the molecular landscape of cancer, ultimately improving patient outcomes through more effective and targeted treatment strategies. This narrative review underscores the potential of multi-omics approaches to transform cancer research and improve patient outcomes through more precise and effective treatments.

PMID:41147019 | PMC:PMC12553891 | DOI:10.1177/11795549251384582

Understanding AI Trustworthiness: A Scoping Review of AIES & FAccT Articles

✇cs.AI, q-bio.NC updates on arXiv.org
By: Siddharth Mehrotra · Jin Huang · Xuelong Fu · Roel Dobbe · Clara I. S\'anchez · Maarten de Rijke
29 October 2025 at 12:00
arXiv:2510.21293v2 Announce Type: replace Abstract: Background: Trustworthy AI serves as a foundational pillar for two major AI ethics conferences: AIES and FAccT. However, current research often adopts techno-centric approaches, focusing primarily on technical attributes such as reliability, robustness, and fairness, while overlooking the sociotechnical dimensions critical to understanding AI trustworthiness in real-world contexts. Objectives: This scoping review aims to examine how the AIES and FAccT communities conceptualize, measure, and validate AI trustworthiness, identifying major gaps and opportunities for advancing a holistic understanding of trustworthy AI systems. Methods: We conduct a scoping review of AIES and FAccT conference proceedings to date, systematically analyzing how trustworthiness is defined, operationalized, and applied across different research domains. Our analysis focuses on conceptualization approaches, measurement methods, verification and validation techniques, application areas, and underlying values. Results: While significant progress has been made in defining technical attributes such as transparency, accountability, and robustness, our findings reveal critical gaps. Current research often predominantly emphasizes technical precision at the expense of social and ethical considerations. The sociotechnical nature of AI systems remains less explored and trustworthiness emerges as a contested concept shaped by those with the power to define it. Conclusions: An interdisciplinary approach combining technical rigor with social, cultural, and institutional considerations is essential for advancing trustworthy AI. We propose actionable measures for the AI ethics community to adopt holistic frameworks that genuinely address the complex interplay between AI systems and society, ultimately promoting responsible technological development that benefits all stakeholders.

Navigating Cancer Complexity: Integrative Multi-Omics Methodologies for Clinical Insights

✇(Multiomics OR Omics) AND (Lung OR gastric OR Hepatocellular)
By: Martina Catalano · Alberto D'Angelo · Francesco De Logu · Romina Nassini · Daniele Generali · Giandomenico Roviello
28 October 2025 at 18:00

Clin Med Insights Oncol. 2025 Oct 21;19:11795549251384582. doi: 10.1177/11795549251384582. eCollection 2025.

ABSTRACT

Recent advancements in cancer multi-omics have transformed our understanding of cancer biology by integrating genomics, transcriptomics, proteomics, and metabolomics. These integrative approaches have led to the identification of novel biomarkers and therapeutic targets, offering deeper insights into the molecular intricacies of various cancers, including breast, lung, gastric, pancreatic, and glioblastoma. Despite these advances, challenges remain, such as the integration of disparate data types and the interpretation of complex biological interactions. However, developments in proteogenomics and mass spectrometry have enhanced the correlation between molecular profiles and clinical features, refining the prediction of therapeutic responses. Future research in cancer drug discovery is poised to benefit from multi-omics approaches, improving the precision and efficacy of personalized therapies. By developing integrative network-based models, researchers aim to address challenges related to heterogeneity, reproducibility, and data interpretation. A standardized framework for multi-omics data integration could revolutionize cancer research, optimizing the identification of novel drug targets and enhancing our understanding of cancer biology. This complete approach holds the promise of advancing personalized therapies by fully characterizing the molecular landscape of cancer, ultimately improving patient outcomes through more effective and targeted treatment strategies. This narrative review underscores the potential of multi-omics approaches to transform cancer research and improve patient outcomes through more precise and effective treatments.

PMID:41147019 | PMC:PMC12553891 | DOI:10.1177/11795549251384582

Benchmarking AI Models in Software Engineering: A Review, Search Tool, and Unified Approach for Elevating Benchmark Quality

✇cs.AI, q-bio.NC updates on arXiv.org
By: Roham Koohestani, Philippe de Bekker, Beg\"um Ko\c{c}, Maliheh Izadi
29 October 2025 at 12:00
arXiv:2503.05860v2 Announce Type: replace-cross Abstract: Benchmarks are essential for unified evaluation and reproducibility. The rapid rise of Artificial Intelligence for Software Engineering (AI4SE) has produced numerous benchmarks for tasks such as code generation and bug repair. However, this proliferation has led to major challenges: (1) fragmented knowledge across tasks, (2) difficulty in selecting contextually relevant benchmarks, (3) lack of standardization in benchmark creation, and (4) flaws that limit utility. Addressing these requires a dual approach: systematically mapping existing benchmarks for informed selection and defining unified guidelines for robust, adaptable benchmark development. We conduct a review of 247 studies, identifying 273 AI4SE benchmarks since 2014. We categorize them, analyze limitations, and expose gaps in current practices. Building on these insights, we introduce BenchScout, an extensible semantic search tool for locating suitable benchmarks. BenchScout employs automated clustering with contextual embeddings of benchmark-related studies, followed by dimensionality reduction. In a user study with 22 participants, BenchScout achieved usability, effectiveness, and intuitiveness scores of 4.5, 4.0, and 4.1 out of 5. To improve benchmarking standards, we propose BenchFrame, a unified framework for enhancing benchmark quality. Applying BenchFrame to HumanEval yielded HumanEvalNext, featuring corrected errors, improved language conversion, higher test coverage, and greater difficulty. Evaluating 10 state-of-the-art code models on HumanEval, HumanEvalPlus, and HumanEvalNext revealed average pass-at-1 drops of 31.22% and 19.94%, respectively, underscoring the need for continuous benchmark refinement. We further examine BenchFrame's scalability through an agentic pipeline and confirm its generalizability on the MBPP dataset. All review data, user study materials, and enhanced benchmarks are publicly released.

Navigating Cancer Complexity: Integrative Multi-Omics Methodologies for Clinical Insights

✇Omics In Lung
By: Martina Catalano · Alberto D'Angelo · Francesco De Logu · Romina Nassini · Daniele Generali · Giandomenico Roviello
28 October 2025 at 18:00

Clin Med Insights Oncol. 2025 Oct 21;19:11795549251384582. doi: 10.1177/11795549251384582. eCollection 2025.

ABSTRACT

Recent advancements in cancer multi-omics have transformed our understanding of cancer biology by integrating genomics, transcriptomics, proteomics, and metabolomics. These integrative approaches have led to the identification of novel biomarkers and therapeutic targets, offering deeper insights into the molecular intricacies of various cancers, including breast, lung, gastric, pancreatic, and glioblastoma. Despite these advances, challenges remain, such as the integration of disparate data types and the interpretation of complex biological interactions. However, developments in proteogenomics and mass spectrometry have enhanced the correlation between molecular profiles and clinical features, refining the prediction of therapeutic responses. Future research in cancer drug discovery is poised to benefit from multi-omics approaches, improving the precision and efficacy of personalized therapies. By developing integrative network-based models, researchers aim to address challenges related to heterogeneity, reproducibility, and data interpretation. A standardized framework for multi-omics data integration could revolutionize cancer research, optimizing the identification of novel drug targets and enhancing our understanding of cancer biology. This complete approach holds the promise of advancing personalized therapies by fully characterizing the molecular landscape of cancer, ultimately improving patient outcomes through more effective and targeted treatment strategies. This narrative review underscores the potential of multi-omics approaches to transform cancer research and improve patient outcomes through more precise and effective treatments.

PMID:41147019 | PMC:PMC12553891 | DOI:10.1177/11795549251384582

Robustness is Important: Limitations of LLMs for Data Fitting

✇cs.AI, q-bio.NC updates on arXiv.org
By: Hejia Liu · Mochen Yang · Gediminas Adomavicius
29 October 2025 at 12:00
arXiv:2508.19563v3 Announce Type: replace-cross Abstract: Large Language Models (LLMs) are being applied in a wide array of settings, well beyond the typical language-oriented use cases. In particular, LLMs are increasingly used as a plug-and-play method for fitting data and generating predictions. Prior work has shown that LLMs, via in-context learning or supervised fine-tuning, can perform competitively with many tabular supervised learning techniques in terms of predictive performance. However, we identify a critical vulnerability of using LLMs for data fitting -- making changes to data representation that are completely irrelevant to the underlying learning task can drastically alter LLMs' predictions on the same data. For example, simply changing variable names can sway the size of prediction error by as much as 82% in certain settings. Such prediction sensitivity with respect to task-irrelevant variations manifests under both in-context learning and supervised fine-tuning, for both close-weight and open-weight general-purpose LLMs. Moreover, by examining the attention scores of an open-weight LLM, we discover a non-uniform attention pattern: training examples and variable names/values which happen to occupy certain positions in the prompt receive more attention when output tokens are generated, even though different positions are expected to receive roughly the same attention. This partially explains the sensitivity in the presence of task-irrelevant variations. We also consider a state-of-the-art tabular foundation model (TabPFN) trained specifically for data fitting. Despite being explicitly designed to achieve prediction robustness, TabPFN is still not immune to task-irrelevant variations. Overall, despite LLMs' impressive predictive capabilities, currently they lack even the basic level of robustness to be used as a principled data-fitting tool.
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