Toxicity-Competitiveness Trade-off in Concentrated Liquidity Provision
Bibliographic record
Abstract
Decentralized exchanges (DEXs) adopt automated market makers (AMM) as an alternative to the traditional limit-order book (LOB), which is too costly to implement with blockchain technology. To protect liquidity providers (LPs) against toxic trades by arbitrageurs, Uniswap v3, the leading DEX, has introduced a concentrated liquidity mechanism that allows LPs to restrict the price range accepting trades. We define a liquidity provision game of this setting and characterize the optimal strategy and equilibrium liquidity allocation. We demonstrate that LP profits consist of the competitive and non-competitive parts. Crucially, the non-competitive components arise from toxic trades by arbitrageurs. Consequently, liquidity provision involves a toxicity-competitiveness tradeoff as opposed to the literature understanding them as two independent factors. By incorporating this tradeoff, we derive a novel guideline and implications for liquidity provision in the decentralized financial market.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".