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At least 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Toward Trustworthy Autonomous Science: A Two-Year Community Roadmap

One year ago, the AISLE roadmap argued that autonomous laboratories operated as isolated islands and proposed a grassroots network organized around five critical dimensions. The field has since moved faster than that roadmap anticipated: multi-agent systems have produced experimentally validated hypotheses, self-driving laboratories have grown more interoperable and orchestrated, reasoning-trained and domain foundation models have raised the capability ceiling, and the Genesis Mission has placed autonomous experimentation at the center of U.S. federal science strategy, with industry emerging as a primary actor. Progress has met a sobering counter-current, including a corrected flagship discovery result, benchmarks showing that agents which rival experts on closed-ended questions still complete only a fraction of open-ended research, and fabricated citations surfacing at leading venues. We read this as the defining tension of the field: producing a candidate discovery is no longer the hard part, but verifying it is, and this asymmetry now limits autonomous science more than raw model capability. Accordingly, we update the roadmap around seven dimensions, revisiting the original five and elevating two former cross-cutting concerns, trust, verification, and reproducibility, and safety, security, and governance, to first-class status. We assess the original milestones (M1 through M14) as achieved, partially achieved, reframed, or open, add four new milestones (M15 through M18) for the elevated dimensions, and scope the path forward to a two-year horizon, with the first year concentrating on interfaces, protocol adoption, and the scaffolding of verification, and the second targeting federation, zero-trust coordination, and governance. Throughout, we position the grassroots network as the interoperability fabric that lets national programs, international initiatives, and commercial platforms connect rather than re-silo.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2026

Toward Trustworthy Detectors Detector Characterization for SuperCDMS SNOLAB Commissioning

SuperCDMS SNOLAB is a next generation direct detection experiment searching for low mass dark matter using cryogenic germanium and silicon detectors operated at millikelvin temperatures. As commissioning begins, establishing stable, predictable detector response to energy deposits is a prerequisite for future physics analysis. This work studies detector stability and calibration for four SuperCDMS SNOLAB detectors, det 7 and det 15 (germanium) and det 11 and det 14 (silicon), using Barium-133 calibration data (356 keV gamma ray reference) and low background data (ambient radiation, no external source). The Ba-133 data show no distinct line at the expected energy, and the low background data show a baseline that drifts and oscillates rather than remaining flat, consistently across multiple channels, suggesting a shared, detector wide cause. These observations point to the cryogenic support system as the likely source, since small temperature fluctuations could couple into the temperature sensitive detectors, informing the ongoing commissioning effort.

O'Hanlon, Vika [Skidmore Coll.]↗
NASA NTRS1994

The trustworthy digital camera: Restoring credibility to the photographic image

The increasing sophistication of computers has made digital manipulation of photographic images, as well as other digitally-recorded artifacts such as audio and video, incredibly easy to perform and increasingly difficult to detect. Today, every picture appearing in newspapers and magazines has been digitally altered to some degree, with the severity varying from the trivial (cleaning up 'noise' and removing distracting backgrounds) to the point of deception (articles of clothing removed, heads attached to other people's bodies, and the complete rearrangement of city skylines). As the power, flexibility, and ubiquity of image-altering computers continues to increase, the well-known adage that 'the photography doesn't lie' will continue to become an anachronism. A solution to this problem comes from a concept called digital signatures, which incorporates modern cryptographic techniques to authenticate electronic mail messages. 'Authenticate' in this case means one can be sure that the message has not been altered, and that the sender's identity has not been forged. The technique can serve not only to authenticate images, but also to help the photographer retain and enforce copyright protection when the concept of 'electronic original' is no longer meaningful.

Friedman, Gary L.↗
NASA NTRS2016

Traveler: Trustworthy Autonomy

This presentation discusses the FAA belief that this research may hold the key to enabling safe autonomous operation of vehicles.

complex systems↗
NASA NTRS1993

The Trustworthy Digital Camera: Restoring Credibility to the Photographic Image

The increasing sophistication of computers has made digital manipulation of photographic images (as well as other digitally-recorded artifacts, such as sound and video) incredibly easy to perform and, as time goes on, increasingly difficult to detect. Today, every picture appearing in newspapers and magazines has been digitally altered to some degree, with the severity varying from the trivial (cleaning up "noise" and removing distracting backgrounds) to the point of deception (articles of clothing removed, heads attached to other people's bodies, the complete rearrangement of city skylines). As the power, flexibility and ubiquity of image-altering computers continues to increase, the well-known adage that "the photograph doesn't lie" will continue to become an anachronism. A solution to this problem comes from the proposed Digital Signature Standard (DSS), which incorporates modern cryptographic techniques to authenticate electronic mail messages...

Friedman, Gary L.↗
NASA NTRSDate not supplied

Trustworthy Machine Learning for Damage Identification in Composites

A challenging opportunity in structural health monitoring of composite materials is using machine learning (ML) methods to classify acoustic emissions (AE) according to the damage mechanism that emitted the signal. Although a wide variety of ML frameworks have been developed, there is a distinct lack of ground truth datasets which has precluded any direct assessment of their accuracy. Here, we present a novel ground truth dataset gathered on simplified unidirectional SiC/SiC composite structures. Herein, AE is collected from minicomposites which are loaded to targeted percentages of the ultimate tensile stress. These minicomposites are then volumetrically imaged with XCT and individual damage events, along with the mechanism, are correlated to AE. We explore the signal features that allow for mechanism discrimination, along with the feasibility of both unsupervised and supervised frameworks for use in the online monitoring of composite structures.

Machine learning, acoustic emission, ceramic matri↗
NASA NTRS2021

ATTRACTOR: Autonomy Teaming and TRAjectories for Complex Trusted Operational Reliability

Autonomous systems (AS) are crucial to realizing the vision of new, complex transportation modes, such as advanced air mobility (AAM) and urban air mobility (UAM). A chief barrier to induction of AS into aviation is insufficient understanding of AS reliability in time-critical and safety-critical environments—an obstacle to certification. ATTRACTOR is aimed at building a basis for certification of classes of autonomous cyber-physical-human systems (CPHS) via establishing metrics and models of trustworthiness and trust in multi-agent team interactions, analyzable trajectories, explainability of computational algorithms (explainable artificial intelligence, or XAI), and persistent modeling and simulation, in the context of missions planning and operation. By “building a basis for certification,” we mean acquiring an understanding of when a system is trustworthy and developing computable means to estimate trustworthiness and trust in order to eventually inform functional requirements that contribute to certification. The outcomes are applicable not just to aviation but to all domains that rely on autonomous systems.

Autonomy↗
DOE OSTI2025

Scalable workflow for evaluating and optimizing large language models

This work describes the improved workflow for evaluating open-source large language models (LLMs) for trustworthiness. The workflow facilitates the acquisition of LLMs, the generation of LLM responses, and the evaluation of the responses for their trustworthiness. As a use case, the workflow is employed to evaluate dense, quantized, and pruned Meta Llama3.1 LLMs for their truthfulness. The outcome of the project could set the stage for understanding and developing trustworthy models in the future projects.

97 MATHEMATICS AND COMPUTING↗