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Documents authored by Kiffer, Lucianna

Document
DOI: 10.4230/LIPIcs.SEA.2026.23
Exploiting Multi-Core Parallelism in Blockchain Validation and Construction

Authors: Arivarasan Karmegam, Lucianna Kiffer, and Antonio Fernández Anta

Published in: LIPIcs, Volume 371, 24th International Symposium on Experimental Algorithms (SEA 2026)

Abstract
Blockchain validators can reduce block processing time by exploiting multi-core CPUs, but deterministic execution must preserve a given total order while respecting transaction conflicts and per-block runtime limits. This paper systematically examines how validators can exploit multi-core parallelism during both block construction and execution without violating blockchain semantics. We formalize two validator-side optimization problems: (i) executing an already ordered block on p cores to minimize makespan while ensuring equivalence to sequential execution; and (ii) selecting and scheduling a subset of mempool transactions under a runtime limit B to maximize validator reward. For both, we develop exact Mixed-Integer Linear Programming (MILP) formulations that capture conflict, order, and capacity constraints, and propose fast deterministic heuristics that scale to realistic workloads. Using Ethereum mainnet traces and including a Solana-inspired declared-access baseline (Sol) for ordered-block scheduling and a simple reward-greedy baseline (RG) for block construction, we empirically quantify the trade-offs between optimality and runtime. MILPs quickly become intractable as heterogeneity or core count increases, whereas our heuristics run in milliseconds and achieve near-optimal quality. For ordered-block execution, heuristic makespans are typically within a few percent of the MILP solutions (and can even surpass the MILP incumbent when the solver times out), yielding up to 1.5 speedup with p = 2 and 2.3 speedup with p = 8 over sequential execution, despite tight ordering constraints. For block construction, the heuristic achieves 99-100% of the MILP optimum reward on homogeneous workloads, and 74-100% of an LP-relaxation upper bound on heterogeneous workloads, where exact optimization often times out. The resulting block-construction throughput scales close to linearly with p, reaching up to 7.9 speedup with p = 8 in our experiments. These results demonstrate that lightweight, conflict-aware scheduling and selection can unlock substantial parallelism in blockchain validation, bridging the gap between sequential execution and the true potential of multi-core hardware.
Cite as

Arivarasan Karmegam, Lucianna Kiffer, and Antonio Fernández Anta. Exploiting Multi-Core Parallelism in Blockchain Validation and Construction. In 24th International Symposium on Experimental Algorithms (SEA 2026). Leibniz International Proceedings in Informatics (LIPIcs), Volume 371, pp. 23:1-23:21, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026)

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@InProceedings{karmegam_et_al:LIPIcs.SEA.2026.23,
  author =	{Karmegam, Arivarasan and Kiffer, Lucianna and Fern\'{a}ndez Anta, Antonio},
  title =	{{Exploiting Multi-Core Parallelism in Blockchain Validation and Construction}},
  booktitle =	{24th International Symposium on Experimental Algorithms (SEA 2026)},
  pages =	{23:1--23:21},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-422-2},
  ISSN =	{1868-8969},
  year =	{2026},
  volume =	{371},
  editor =	{Aum\"{u}ller, Martin and Finocchi, Irene},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.SEA.2026.23},
  URN =		{urn:nbn:de:0030-drops-260271},
  doi =		{10.4230/LIPIcs.SEA.2026.23},
  annote =	{Keywords: Block construction, Block execution, Deterministic parallelism, Conflict-aware scheduling}
}
Document
DOI: 10.4230/LIPIcs.AFT.2025.27
Unravelling the Probabilistic Forest: Arbitrage in Prediction Markets

Authors: Oriol Saguillo, Vahid Ghafouri, Lucianna Kiffer, and Guillermo Suarez-Tangil

Published in: LIPIcs, Volume 354, 7th Conference on Advances in Financial Technologies (AFT 2025)

Abstract
Polymarket is a prediction market platform where users can speculate on future events by trading shares tied to specific outcomes, known as conditions. Each market on Polymarket is associated with a set of one or more such conditions. To ensure proper market resolution, the condition set must be exhaustive - collectively accounting for all possible outcomes - and mutually exclusive - only one condition may resolve as true. Thus, the collective prices (probabilities) of all related outcomes (whether in a condition or market) should be $1, representing a combined probability of 1 of any outcome. Despite this design, Polymarket exhibits cases where dependent assets are mispriced, allowing for purchasing (or selling) a certain outcome for less than (or more than) $1, guaranteeing profit. This phenomenon, known as arbitrage, could enable sophisticated participants to exploit such inconsistencies. In this paper, we conduct an empirical arbitrage analysis on Polymarket data to answer three key questions: (Q1) What conditions give rise to arbitrage? (Q2) Does arbitrage actually occur on Polymarket?, and (Q3) Has anyone exploited these opportunities? A major challenge in analyzing arbitrage between related markets lies in the scalability of comparisons across a large number of markets and conditions, with a naive analysis requiring O(2^{n+m}) comparisons. To overcome this, we employ a heuristic-driven reduction strategy based on timeliness, topical similarity, and combinatorial relationships, further validated by expert input. Our study reveals two distinct forms of arbitrage on Polymarket: Market Rebalancing Arbitrage, which occurs within a single market or condition (intra-market), and Combinatorial Arbitrage, which spans across multiple markets (inter-market). We use on-chain historical order book data to analyze when these types of arbitrage opportunities have existed, and when they have been executed by users. We find a realized estimate of 40 million USD of profit extracted across both types of arbitrage during our measurement period.
Cite as

Oriol Saguillo, Vahid Ghafouri, Lucianna Kiffer, and Guillermo Suarez-Tangil. Unravelling the Probabilistic Forest: Arbitrage in Prediction Markets. In 7th Conference on Advances in Financial Technologies (AFT 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 354, pp. 27:1-27:24, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{saguillo_et_al:LIPIcs.AFT.2025.27,
  author =	{Saguillo, Oriol and Ghafouri, Vahid and Kiffer, Lucianna and Suarez-Tangil, Guillermo},
  title =	{{Unravelling the Probabilistic Forest: Arbitrage in Prediction Markets}},
  booktitle =	{7th Conference on Advances in Financial Technologies (AFT 2025)},
  pages =	{27:1--27:24},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-400-0},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{354},
  editor =	{Avarikioti, Zeta and Christin, Nicolas},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.AFT.2025.27},
  URN =		{urn:nbn:de:0030-drops-247468},
  doi =		{10.4230/LIPIcs.AFT.2025.27},
  annote =	{Keywords: Prediction Markets, Maximal Extractable Value, Large Language Models}
}
Document
Complete Volume
DOI: 10.4230/LIPIcs.AFT.2024
LIPIcs, Volume 316, AFT 2024, Complete Volume

Authors: Rainer Böhme and Lucianna Kiffer

Published in: LIPIcs, Volume 316, 6th Conference on Advances in Financial Technologies (AFT 2024)

Abstract
LIPIcs, Volume 316, AFT 2024, Complete Volume
Cite as

6th Conference on Advances in Financial Technologies (AFT 2024). Leibniz International Proceedings in Informatics (LIPIcs), Volume 316, pp. 1-704, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@Proceedings{bohme_et_al:LIPIcs.AFT.2024,
  title =	{{LIPIcs, Volume 316, AFT 2024, Complete Volume}},
  booktitle =	{6th Conference on Advances in Financial Technologies (AFT 2024)},
  pages =	{1--704},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-345-4},
  ISSN =	{1868-8969},
  year =	{2024},
  volume =	{316},
  editor =	{B\"{o}hme, Rainer and Kiffer, Lucianna},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.AFT.2024},
  URN =		{urn:nbn:de:0030-drops-209355},
  doi =		{10.4230/LIPIcs.AFT.2024},
  annote =	{Keywords: LIPIcs, Volume 316, AFT 2024, Complete Volume}
}
Document
Front Matter
DOI: 10.4230/LIPIcs.AFT.2024.0
Front Matter, Table of Contents, Preface, Conference Organization

Authors: Rainer Böhme and Lucianna Kiffer

Published in: LIPIcs, Volume 316, 6th Conference on Advances in Financial Technologies (AFT 2024)

Abstract
Front Matter, Table of Contents, Preface, Conference Organization
Cite as

6th Conference on Advances in Financial Technologies (AFT 2024). Leibniz International Proceedings in Informatics (LIPIcs), Volume 316, pp. 0:i-0:xxii, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@InProceedings{bohme_et_al:LIPIcs.AFT.2024.0,
  author =	{B\"{o}hme, Rainer and Kiffer, Lucianna},
  title =	{{Front Matter, Table of Contents, Preface, Conference Organization}},
  booktitle =	{6th Conference on Advances in Financial Technologies (AFT 2024)},
  pages =	{0:i--0:xxii},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-345-4},
  ISSN =	{1868-8969},
  year =	{2024},
  volume =	{316},
  editor =	{B\"{o}hme, Rainer and Kiffer, Lucianna},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.AFT.2024.0},
  URN =		{urn:nbn:de:0030-drops-209361},
  doi =		{10.4230/LIPIcs.AFT.2024.0},
  annote =	{Keywords: Front Matter, Table of Contents, Preface, Conference Organization}
}
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