More Like this

1. Ethereum Tokens: Simulating Native Tokens in Ethereum

   https://doi.org/10.1007/978-3-031-07689-3_29  

   Andrews, John; Ciampi, Michele; Zikas, Vassilis (July 2022, CSCML 2022)
2. Oblivious Digital Tokens

   Liskij, M; Ding, X; Tsudik, G; Basin, D (August 2025, Usenix Security Symposium)
3. Optimal RANDAO Manipulation in Ethereum

   https://doi.org/10.4230/LIPICS.AFT.2024.10  

   Alpturer, Kaya; Weinberg, S Matthew (January 2024, Schloss Dagstuhl – Leibniz-Zentrum für Informatik)

   Böhme, Rainer; Kiffer, Lucianna (Ed.)

   It is well-known that RANDAO manipulation is possible in Ethereum if an adversary controls the proposers assigned to the last slots in an epoch. We provide a methodology to compute, for any fraction α of stake owned by an adversary, the maximum fraction f(α) of rounds that a strategic adversary can propose. We further implement our methodology and compute f(⋅) for all α. For example, we conclude that an optimal strategic participant with 5% of the stake can propose a 5.048% fraction of rounds, 10% of the stake can propose a 10.19% fraction of rounds, and 20% of the stake can propose a 20.68% fraction of rounds. 

   more » « less
4. Characterizing Ethereum Address Poisoning Attack

   https://doi.org/10.1145/3658644.3690277  

   Guan, Shixuan; Li, Kai (December 2024, ACM CCS 2024)
5. Improving Pathfinding with Anchoring Tokens

   Zhang, Huaqing; Liu, Bingbin; Kim, Juno; Risteski, Andrej (July 2025, ICML 2025 Workshop on Methods and Opportunities at Small Scale (MOSS))

   Planning is a critical aspect of multi-step reasoning, yet it remains challenging for large language models (LLMs). In this work, we use pathfinding in graphs as a sandbox for understanding and improving the planning abilities of LLMs. Our results show that while conventional autoregressive training generalizes poorly, an anchoring strategy, whereby a model first predicts a small subset of intermediate nodes along the path, significantly improves the path finding performance. We confirm these gains on two families of graphs with markedly different structures and provide preliminary heuristics for selecting effective anchor nodes, offering guidance for more realistic settings. 

   more » « less