STARLI | F121–F130 / 300 Flagship 13 Fission · Volume II · AI-Guided Sensing and Uncertainty-Aware Microrobotic Control Author: 33 | Nonclinical Research Design Working Edition v1.1 / Gate Annotation v1.2 | English Zenodo Ten-Volume Overview This ten-volume STARLI research-design collection studies the interface between AI-based physical sensing, model uncertainty, quantum-sensor calibration and micro/nanorobotic feedback. The first five flagships address sensor-driven closed loops, uncertain control, signal/artifact separation, active learning and drift compensation. The next five examine causal state estimation, multimodal fusion, safety control, anomaly detection and model-predictive action. These are proposed, falsifiable laboratory designs, not claims of measured scientific breakthroughs. Ten English working-edition PDFs each contain 777 pages, 30 candidate mathematical plates, 82 chapters, 410 separately paginated studies, 200 Research Gates and 33 Phoenix Answer Embryos: 7,770 pages, 300 formula plates, 2,000 Gates and 330 Phoenix across the collection. Gate annotation revision v1.2 records prospective mechanisms, measurements, blinded countertests, rivals, stop rules and successor interfaces. Document structure and distinct wording do not themselves validate scientific independence. The Ten Research Directions F121 AI-Driven Nano-Quantum Sensing Closed-Loop Systems At The Limit — Evaluates selective risk of accepted sensing decisions (normalized loss) against a preregistered design-of-experiments baseline. An equal-budget blinded material challenge must distinguish a genuine closed-loop contribution from extra trials or unmetered external assistance. Acting outside the registered authority envelope is a stop condition. F122 Microrobotic Uncertainty-Aware Control At The Limit — Studies field-referenced calibration error (normalized error) and the ability to abstain under hidden out-of-distribution states. Equal-trial expert tuning is the declared comparator; undisclosed human intervention is an alternative explanation. High-confidence action on unsupported cases invalidates the proposed safety claim. F123 AI Biological Signal and Mechanical Artifact Separation At The Limit — Asks whether biological response can be discriminated from mechanical artifacts using a preregistered misattribution fraction. A frozen open-loop policy supplies the baseline; training-test leakage could create false evidence of autonomous separation. The raw measurement-to-action sequence must remain independently replayable. F124 Nano-Quantum System Active Learning At The Limit — Tests active experimental selection while directly recording the fraction of constraints breached during learning. Preregistered design of experiments is the strong baseline. Gains must survive identical trial and resource budgets; extra experiments or any action outside the authorization envelope constitute rejection grounds. F125 AI-Driven Quantum Sensor Drift Compensation At The Limit — Uses post-correction offset on a blinded sensor standard, measured in tesla, to evaluate proposed drift correction. Equal-trial expert tuning challenges any apparent gain. Hidden manual adjustment, changed standards and confident out-of-domain correction are rival or stopping mechanisms, rather than evidence of robust autonomous calibration. F126 Microrobotic Causal State Estimation At The Limit — Frames causal-state inference through counterfactual physical-state prediction error on withheld interventions (normalized error). A frozen open-loop policy and matched observations protect against claims created by training-test leakage. Reconstruction of each raw observation, inferred state and subsequent action is a mandatory evidence condition. F127 AI Multimodal Microscale Signal Fusion At The Limit — Studies held-out multimodal inference calibration error when separate microscale sensing channels are combined. Preregistered experimental design serves as the comparison; extra trials or untracked sensor budgets can mimic improvement. Hidden distribution shifts and action-authority boundaries test whether confidence remains physically warranted. F128 Nanorobotic Closed-Loop Safety Control At The Limit — Measures safe-set violation fraction under bounded feedback in authorized bench-scale nanorobotic tasks. Matched expert tuning provides a conventional comparator, with hidden human correction treated as a live rival. High-confidence actions outside the calibrated domain trigger an explicit safety stop. F129 AI Quantum Sensor Anomaly Detection At The Limit — Examines severity-weighted missed-anomaly risk under blinded sensor changes and unselected cases. A frozen open-loop policy is the declared baseline; training-test leakage can produce misleading anomaly scores. The audit must preserve raw measurements, false negatives and the complete alert-to-action record. F130 Microrobotic Model-Predictive Control At The Limit — Uses independently measured prediction error, in metres, over a frozen control horizon to test microrobotic model-predictive control. Preregistered experimental design is the comparator. Equal budgets and hidden shifts must separate predictive value from extra experimentation; crossing the physical authority envelope is a hard stop. Scope, Falsifiability and Evidence Boundaries All ten monographs are prospective, nonclinical research designs using traceable materials, calibrated sensors, authorized ex-vivo specimens, simulations or closed-bench reference platforms. No experimental result, clinical outcome, quantum advantage, independently established safety benefit or completed peer review is asserted. Equal-budget baselines, withheld shifts, blind references, failed-run denominators, original measurement records and hard stop rules are required to evaluate proposed gains. The source batch reports a complete PDF-structure check but only partial physics, semantic distinctness and DOI claim-mapping review; its full-page visual review remains incomplete. Selected Established Methodological References Degen, Reinhard & Cappellaro (2017) · Quantum sensing · DOI 10.1103/RevModPhys.89.035002 | Zhao et al. (2026) · Control barrier functions under uncertainty · DOI 10.1109/TCYB.2025.3633800 | Autonomous, multiproperty-driven molecular discovery (2023) · DOI 10.1126/science.adi1407 These references are listed in the supplied evidence register as established methodological context, not experimental proof of the STARLI proposals. Individual source-to-claim DOI links and physical derivations still require independent review. Join the STARLI Research Community Explore more than 100 interdisciplinary research themes and follow the evolving STARLI Research Gate community: https://zenodo.org/communities/starli-research-gate/ We do not need to know which door future generations will choose to open. We only hope that when they open it, they will find a little light to guide their way.

BibTeX

@article{allodium:10.5281/zenodo.23915302,
  title = {STARLI | F121–F130 / 300 Flagship 13 Fission · Volume II · AI-Guided Sensing and Uncertainty-Aware Microrobotic Control Author: 33  |  Nonclinical Research Design Working Edition v1.1 / Gate Annotation v1.2  |  English Zenodo Ten-Volume Overview This ten-volume STARLI research-design collection studies the interface between AI-based physical sensing, model uncertainty, quantum-sensor calibration and micro/nanorobotic feedback. The first five flagships address sensor-driven closed loops, uncertain control, signal/artifact separation, active learning and drift compensation. The next five examine causal state estimation, multimodal fusion, safety control, anomaly detection and model-predictive action. These are proposed, falsifiable laboratory designs, not claims of measured scientific breakthroughs. Ten English working-edition PDFs each contain 777 pages, 30 candidate mathematical plates, 82 chapters, 410 separately paginated studies, 200 Research Gates and 33 Phoenix Answer Embryos: 7,770 pages, 300 formula plates, 2,000 Gates and 330 Phoenix across the collection. Gate annotation revision v1.2 records prospective mechanisms, measurements, blinded countertests, rivals, stop rules and successor interfaces. Document structure and distinct wording do not themselves validate scientific independence. The Ten Research Directions F121  AI-Driven Nano-Quantum Sensing Closed-Loop Systems At The Limit — Evaluates selective risk of accepted sensing decisions (normalized loss) against a preregistered design-of-experiments baseline. An equal-budget blinded material challenge must distinguish a genuine closed-loop contribution from extra trials or unmetered external assistance. Acting outside the registered authority envelope is a stop condition. F122  Microrobotic Uncertainty-Aware Control At The Limit — Studies field-referenced calibration error (normalized error) and the ability to abstain under hidden out-of-distribution states. Equal-trial expert tuning is the declared comparator; undisclosed human intervention is an alternative explanation. High-confidence action on unsupported cases invalidates the proposed safety claim. F123  AI Biological Signal and Mechanical Artifact Separation At The Limit — Asks whether biological response can be discriminated from mechanical artifacts using a preregistered misattribution fraction. A frozen open-loop policy supplies the baseline; training-test leakage could create false evidence of autonomous separation. The raw measurement-to-action sequence must remain independently replayable. F124  Nano-Quantum System Active Learning At The Limit — Tests active experimental selection while directly recording the fraction of constraints breached during learning. Preregistered design of experiments is the strong baseline. Gains must survive identical trial and resource budgets; extra experiments or any action outside the authorization envelope constitute rejection grounds. F125  AI-Driven Quantum Sensor Drift Compensation At The Limit — Uses post-correction offset on a blinded sensor standard, measured in tesla, to evaluate proposed drift correction. Equal-trial expert tuning challenges any apparent gain. Hidden manual adjustment, changed standards and confident out-of-domain correction are rival or stopping mechanisms, rather than evidence of robust autonomous calibration. F126  Microrobotic Causal State Estimation At The Limit — Frames causal-state inference through counterfactual physical-state prediction error on withheld interventions (normalized error). A frozen open-loop policy and matched observations protect against claims created by training-test leakage. Reconstruction of each raw observation, inferred state and subsequent action is a mandatory evidence condition. F127  AI Multimodal Microscale Signal Fusion At The Limit — Studies held-out multimodal inference calibration error when separate microscale sensing channels are combined. Preregistered experimental design serves as the comparison; extra trials or untracked sensor budgets can mimic improvement. Hidden distribution shifts and action-authority boundaries test whether confidence remains physically warranted. F128  Nanorobotic Closed-Loop Safety Control At The Limit — Measures safe-set violation fraction under bounded feedback in authorized bench-scale nanorobotic tasks. Matched expert tuning provides a conventional comparator, with hidden human correction treated as a live rival. High-confidence actions outside the calibrated domain trigger an explicit safety stop. F129  AI Quantum Sensor Anomaly Detection At The Limit — Examines severity-weighted missed-anomaly risk under blinded sensor changes and unselected cases. A frozen open-loop policy is the declared baseline; training-test leakage can produce misleading anomaly scores. The audit must preserve raw measurements, false negatives and the complete alert-to-action record. F130  Microrobotic Model-Predictive Control At The Limit — Uses independently measured prediction error, in metres, over a frozen control horizon to test microrobotic model-predictive control. Preregistered experimental design is the comparator. Equal budgets and hidden shifts must separate predictive value from extra experimentation; crossing the physical authority envelope is a hard stop. Scope, Falsifiability and Evidence Boundaries All ten monographs are prospective, nonclinical research designs using traceable materials, calibrated sensors, authorized ex-vivo specimens, simulations or closed-bench reference platforms. No experimental result, clinical outcome, quantum advantage, independently established safety benefit or completed peer review is asserted. Equal-budget baselines, withheld shifts, blind references, failed-run denominators, original measurement records and hard stop rules are required to evaluate proposed gains. The source batch reports a complete PDF-structure check but only partial physics, semantic distinctness and DOI claim-mapping review; its full-page visual review remains incomplete. Selected Established Methodological References Degen, Reinhard \& Cappellaro (2017) · Quantum sensing · DOI 10.1103/RevModPhys.89.035002   |   Zhao et al. (2026) · Control barrier functions under uncertainty · DOI 10.1109/TCYB.2025.3633800   |   Autonomous, multiproperty-driven molecular discovery (2023) · DOI 10.1126/science.adi1407 These references are listed in the supplied evidence register as established methodological context, not experimental proof of the STARLI proposals. Individual source-to-claim DOI links and physical derivations still require independent review. Join the STARLI Research Community Explore more than 100 interdisciplinary research themes and follow the evolving STARLI Research Gate community: https://zenodo.org/communities/starli-research-gate/ We do not need to know which door future generations will choose to open. We only hope that when they open it, they will find a little light to guide their way.},
  author = {33},
  year = {2026},
  doi = {10.5281/zenodo.23915302},
  url = {https://doi.org/10.5281/zenodo.23915302}
}

RIS

TY  - JOUR
TI  - STARLI | F121–F130 / 300 Flagship 13 Fission · Volume II · AI-Guided Sensing and Uncertainty-Aware Microrobotic Control Author: 33  |  Nonclinical Research Design Working Edition v1.1 / Gate Annotation v1.2  |  English Zenodo Ten-Volume Overview This ten-volume STARLI research-design collection studies the interface between AI-based physical sensing, model uncertainty, quantum-sensor calibration and micro/nanorobotic feedback. The first five flagships address sensor-driven closed loops, uncertain control, signal/artifact separation, active learning and drift compensation. The next five examine causal state estimation, multimodal fusion, safety control, anomaly detection and model-predictive action. These are proposed, falsifiable laboratory designs, not claims of measured scientific breakthroughs. Ten English working-edition PDFs each contain 777 pages, 30 candidate mathematical plates, 82 chapters, 410 separately paginated studies, 200 Research Gates and 33 Phoenix Answer Embryos: 7,770 pages, 300 formula plates, 2,000 Gates and 330 Phoenix across the collection. Gate annotation revision v1.2 records prospective mechanisms, measurements, blinded countertests, rivals, stop rules and successor interfaces. Document structure and distinct wording do not themselves validate scientific independence. The Ten Research Directions F121  AI-Driven Nano-Quantum Sensing Closed-Loop Systems At The Limit — Evaluates selective risk of accepted sensing decisions (normalized loss) against a preregistered design-of-experiments baseline. An equal-budget blinded material challenge must distinguish a genuine closed-loop contribution from extra trials or unmetered external assistance. Acting outside the registered authority envelope is a stop condition. F122  Microrobotic Uncertainty-Aware Control At The Limit — Studies field-referenced calibration error (normalized error) and the ability to abstain under hidden out-of-distribution states. Equal-trial expert tuning is the declared comparator; undisclosed human intervention is an alternative explanation. High-confidence action on unsupported cases invalidates the proposed safety claim. F123  AI Biological Signal and Mechanical Artifact Separation At The Limit — Asks whether biological response can be discriminated from mechanical artifacts using a preregistered misattribution fraction. A frozen open-loop policy supplies the baseline; training-test leakage could create false evidence of autonomous separation. The raw measurement-to-action sequence must remain independently replayable. F124  Nano-Quantum System Active Learning At The Limit — Tests active experimental selection while directly recording the fraction of constraints breached during learning. Preregistered design of experiments is the strong baseline. Gains must survive identical trial and resource budgets; extra experiments or any action outside the authorization envelope constitute rejection grounds. F125  AI-Driven Quantum Sensor Drift Compensation At The Limit — Uses post-correction offset on a blinded sensor standard, measured in tesla, to evaluate proposed drift correction. Equal-trial expert tuning challenges any apparent gain. Hidden manual adjustment, changed standards and confident out-of-domain correction are rival or stopping mechanisms, rather than evidence of robust autonomous calibration. F126  Microrobotic Causal State Estimation At The Limit — Frames causal-state inference through counterfactual physical-state prediction error on withheld interventions (normalized error). A frozen open-loop policy and matched observations protect against claims created by training-test leakage. Reconstruction of each raw observation, inferred state and subsequent action is a mandatory evidence condition. F127  AI Multimodal Microscale Signal Fusion At The Limit — Studies held-out multimodal inference calibration error when separate microscale sensing channels are combined. Preregistered experimental design serves as the comparison; extra trials or untracked sensor budgets can mimic improvement. Hidden distribution shifts and action-authority boundaries test whether confidence remains physically warranted. F128  Nanorobotic Closed-Loop Safety Control At The Limit — Measures safe-set violation fraction under bounded feedback in authorized bench-scale nanorobotic tasks. Matched expert tuning provides a conventional comparator, with hidden human correction treated as a live rival. High-confidence actions outside the calibrated domain trigger an explicit safety stop. F129  AI Quantum Sensor Anomaly Detection At The Limit — Examines severity-weighted missed-anomaly risk under blinded sensor changes and unselected cases. A frozen open-loop policy is the declared baseline; training-test leakage can produce misleading anomaly scores. The audit must preserve raw measurements, false negatives and the complete alert-to-action record. F130  Microrobotic Model-Predictive Control At The Limit — Uses independently measured prediction error, in metres, over a frozen control horizon to test microrobotic model-predictive control. Preregistered experimental design is the comparator. Equal budgets and hidden shifts must separate predictive value from extra experimentation; crossing the physical authority envelope is a hard stop. Scope, Falsifiability and Evidence Boundaries All ten monographs are prospective, nonclinical research designs using traceable materials, calibrated sensors, authorized ex-vivo specimens, simulations or closed-bench reference platforms. No experimental result, clinical outcome, quantum advantage, independently established safety benefit or completed peer review is asserted. Equal-budget baselines, withheld shifts, blind references, failed-run denominators, original measurement records and hard stop rules are required to evaluate proposed gains. The source batch reports a complete PDF-structure check but only partial physics, semantic distinctness and DOI claim-mapping review; its full-page visual review remains incomplete. Selected Established Methodological References Degen, Reinhard & Cappellaro (2017) · Quantum sensing · DOI 10.1103/RevModPhys.89.035002   |   Zhao et al. (2026) · Control barrier functions under uncertainty · DOI 10.1109/TCYB.2025.3633800   |   Autonomous, multiproperty-driven molecular discovery (2023) · DOI 10.1126/science.adi1407 These references are listed in the supplied evidence register as established methodological context, not experimental proof of the STARLI proposals. Individual source-to-claim DOI links and physical derivations still require independent review. Join the STARLI Research Community Explore more than 100 interdisciplinary research themes and follow the evolving STARLI Research Gate community: https://zenodo.org/communities/starli-research-gate/ We do not need to know which door future generations will choose to open. We only hope that when they open it, they will find a little light to guide their way.
AU  - 33
PY  - 2026
DO  - 10.5281/zenodo.23915302
UR  - https://doi.org/10.5281/zenodo.23915302
ER  - 

APA

33 (2026). STARLI | F121–F130 / 300 Flagship 13 Fission · Volume II · AI-Guided Sensing and Uncertainty-Aware Microrobotic Control Author: 33 | Nonclinical Research Design Working Edition v1.1 / Gate Annotation v1.2 | English Zenodo Ten-Volume Overview This ten-volume STARLI research-design collection studies the interface between AI-based physical sensing, model uncertainty, quantum-sensor calibration and micro/nanorobotic feedback. The first five flagships address sensor-driven closed loops, uncertain control, signal/artifact separation, active learning and drift compensation. The next five examine causal state estimation, multimodal fusion, safety control, anomaly detection and model-predictive action. These are proposed, falsifiable laboratory designs, not claims of measured scientific breakthroughs. Ten English working-edition PDFs each contain 777 pages, 30 candidate mathematical plates, 82 chapters, 410 separately paginated studies, 200 Research Gates and 33 Phoenix Answer Embryos: 7,770 pages, 300 formula plates, 2,000 Gates and 330 Phoenix across the collection. Gate annotation revision v1.2 records prospective mechanisms, measurements, blinded countertests, rivals, stop rules and successor interfaces. Document structure and distinct wording do not themselves validate scientific independence. The Ten Research Directions F121 AI-Driven Nano-Quantum Sensing Closed-Loop Systems At The Limit — Evaluates selective risk of accepted sensing decisions (normalized loss) against a preregistered design-of-experiments baseline. An equal-budget blinded material challenge must distinguish a genuine closed-loop contribution from extra trials or unmetered external assistance. Acting outside the registered authority envelope is a stop condition. F122 Microrobotic Uncertainty-Aware Control At The Limit — Studies field-referenced calibration error (normalized error) and the ability to abstain under hidden out-of-distribution states. Equal-trial expert tuning is the declared comparator; undisclosed human intervention is an alternative explanation. High-confidence action on unsupported cases invalidates the proposed safety claim. F123 AI Biological Signal and Mechanical Artifact Separation At The Limit — Asks whether biological response can be discriminated from mechanical artifacts using a preregistered misattribution fraction. A frozen open-loop policy supplies the baseline; training-test leakage could create false evidence of autonomous separation. The raw measurement-to-action sequence must remain independently replayable. F124 Nano-Quantum System Active Learning At The Limit — Tests active experimental selection while directly recording the fraction of constraints breached during learning. Preregistered design of experiments is the strong baseline. Gains must survive identical trial and resource budgets; extra experiments or any action outside the authorization envelope constitute rejection grounds. F125 AI-Driven Quantum Sensor Drift Compensation At The Limit — Uses post-correction offset on a blinded sensor standard, measured in tesla, to evaluate proposed drift correction. Equal-trial expert tuning challenges any apparent gain. Hidden manual adjustment, changed standards and confident out-of-domain correction are rival or stopping mechanisms, rather than evidence of robust autonomous calibration. F126 Microrobotic Causal State Estimation At The Limit — Frames causal-state inference through counterfactual physical-state prediction error on withheld interventions (normalized error). A frozen open-loop policy and matched observations protect against claims created by training-test leakage. Reconstruction of each raw observation, inferred state and subsequent action is a mandatory evidence condition. F127 AI Multimodal Microscale Signal Fusion At The Limit — Studies held-out multimodal inference calibration error when separate microscale sensing channels are combined. Preregistered experimental design serves as the comparison; extra trials or untracked sensor budgets can mimic improvement. Hidden distribution shifts and action-authority boundaries test whether confidence remains physically warranted. F128 Nanorobotic Closed-Loop Safety Control At The Limit — Measures safe-set violation fraction under bounded feedback in authorized bench-scale nanorobotic tasks. Matched expert tuning provides a conventional comparator, with hidden human correction treated as a live rival. High-confidence actions outside the calibrated domain trigger an explicit safety stop. F129 AI Quantum Sensor Anomaly Detection At The Limit — Examines severity-weighted missed-anomaly risk under blinded sensor changes and unselected cases. A frozen open-loop policy is the declared baseline; training-test leakage can produce misleading anomaly scores. The audit must preserve raw measurements, false negatives and the complete alert-to-action record. F130 Microrobotic Model-Predictive Control At The Limit — Uses independently measured prediction error, in metres, over a frozen control horizon to test microrobotic model-predictive control. Preregistered experimental design is the comparator. Equal budgets and hidden shifts must separate predictive value from extra experimentation; crossing the physical authority envelope is a hard stop. Scope, Falsifiability and Evidence Boundaries All ten monographs are prospective, nonclinical research designs using traceable materials, calibrated sensors, authorized ex-vivo specimens, simulations or closed-bench reference platforms. No experimental result, clinical outcome, quantum advantage, independently established safety benefit or completed peer review is asserted. Equal-budget baselines, withheld shifts, blind references, failed-run denominators, original measurement records and hard stop rules are required to evaluate proposed gains. The source batch reports a complete PDF-structure check but only partial physics, semantic distinctness and DOI claim-mapping review; its full-page visual review remains incomplete. Selected Established Methodological References Degen, Reinhard & Cappellaro (2017) · Quantum sensing · DOI 10.1103/RevModPhys.89.035002 | Zhao et al. (2026) · Control barrier functions under uncertainty · DOI 10.1109/TCYB.2025.3633800 | Autonomous, multiproperty-driven molecular discovery (2023) · DOI 10.1126/science.adi1407 These references are listed in the supplied evidence register as established methodological context, not experimental proof of the STARLI proposals. Individual source-to-claim DOI links and physical derivations still require independent review. Join the STARLI Research Community Explore more than 100 interdisciplinary research themes and follow the evolving STARLI Research Gate community: https://zenodo.org/communities/starli-research-gate/ We do not need to know which door future generations will choose to open. We only hope that when they open it, they will find a little light to guide their way.. https://doi.org/10.5281/zenodo.23915302