Materials and Systems for Closed-Loop Optical Actuation with Integrated Sensing Pathways
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Closed-loop operation transforms optical platforms from passive emitters into adaptive systems capable of perceiving, deciding, and acting in real time. This work traces the progression of such systems across material, system, and artificial intelligence (AI)/network domains. At the material level, self-healing, phase-change, and shape-memory polymers exhibit intrinsic feedback-responsiveness, enabling autonomous mechanical and optical adaptation. System-level platforms integrate sensing, actuation, and feedback, ranging from wireless optogenetics and adaptive camouflage systems to on-skin haptic interfaces, demonstrating precise optical modulation and real-time environmental interaction. AI-assisted control further enhances these capabilities by extracting relevant signals from complex data and enabling predictive context-aware feedback. Looking forward, scalable manufacturing that supports large-area, high-density, and locally addressable architectures, combined with on-device AI for low-latency inference, will enable distributed, self-optimizing optical systems that seamlessly interact with users and their surroundings, bridging materials, machines, and intelligence.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it