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
A major research challenge is presented by scalability of algorithms for solving decentralized POMDPs because of their double exponential worst-case complexity for finite horizon problems. First algorithms have only been able to solve very small instances on very small horizons. One exception is the Memory-Bounded Dynamic Programming algorithm – an approximation technique that has proved efficient in handling same sized problems but on large horizons. In this paper, we propose an online algorithm that also approximates larger instances of finite horizon DEC-POMDPs based on the Rollout algorithm. To evaluate the effectiveness of this approach, we compare the presented approach to a recently proposed algorithm called memory bounded dynamic programming. Experimental results show that despite the very high complexity of DEC-POMDPs, the combination of Rollout techniques and estimation techniques performs well and leads to a significant improvement of existing approximation techniques.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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