DeFi '25: 5th ACM Workshop on Decentralized Finance and Security
Bibliographic record
Abstract
Decentralized Finance (DeFi) has undergone significant expansion, evolving from a niche market into a complex alternative financial ecosystem. This burgeoning landscape now encompasses a diverse array of financial services, including decentralized exchanges, lending and borrowing platforms, stablecoins, derivatives, yield optimization services, prediction markets, and privacy-enhancing technologies such as token mixers. While the total value locked in DeFi protocols—estimated at approximately 77 billion USD—underscores its increasing significance, it simultaneously highlights the critical necessity for robust security measures. This workshop aims to address the pressing security challenges in the maturing DeFi space by convening leading experts from the fields of cryptography, game theory, economics, and cybersecurity. Our primary objective is to foster interdisciplinary dialogue and showcase cutting-edge research that rigorously examines the current state of DeFi security and charts a comprehensive path forward. The anticipated outcomes include a prioritized research agenda, new collaborative initiatives bridging theoretical advancements with practical implementations, and a strategic roadmap for enhancing security in the rapidly evolving DeFi ecosystem. This year's program features a keynote talk by Prof. Vassilis Zikas, two invited talks by the winners of the Best DeFi Paper Award (theoretical research track and applied research track), and four presentations of accepted original papers, showcasing both fundamental advances and real-world applications.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.063 | 0.020 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".