Computing at the speed of trading (keynote)
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
When the word trading comes up in conversation, most people think first of the stock markets and frantic traders of movie and television. In reality many of the largest deals are done over-the-counter and so expose the parties to the contract to each other’s financial circumstances. This raises the question: what is my potential future exposure (PFE) if “the other guy” – the counterparty – defaults? Sophisticated measures like PFE and the related CVA allow firms to monitor their exposure to others, to limit it, and to ensure their capital will support the deals it makes. This analysis involves forecasting deal values far into the future, examining legal agreements between the two firms, and evaluating the deal itself. Algorithms in this domain use thousands of scenarios as well as complex aggregation and pricing techniques, all across hundreds of future time points to produce actionable risk metrics. In this talk I’ll discuss some of the complexities of the problem, how it can be broken down into efficient computational chunks and delve into our recent experiments with parallelizing the aggregation across scenarios and time points using OpenMP to enhance real-time performance.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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