<scp>Deeper</scp>: A shared liquidity decentralized exchange design for low trading volume tokens to enhance average liquidity
Bibliographic record
Abstract
Abstract This paper presents Deeper , a design for a decentralized exchange that enhances liquidity via reserve sharing. By doing this, it addresses the problem of shallow liquidity in low trading volume token pairs. Shallow liquidity impairs the functioning of on‐chain markets by creating room for unwanted phenomena such as high slippage and sandwich attacks. Deeper solves this by allowing liquidity providers of multiple trading pairs against a common token to share liquidity. This is achieved by creating a common reserve pool for the shared token that is accessible by each trading pair. Independent from the shared liquidity, providers are free to add liquidity to individual token pairs without any restriction. The trading between one token pair does not affect the price of other token pairs even though the reserve of the shared token changes. The proposed design is an extension of concentrated liquidity automated market maker DEXs that is simple enough to be implemented on smart contracts. This is demonstrated by providing a template for a hook‐based smart contract that adds our custom functionality to Uniswap V4 . Experiments on historical prices show that for a batch consisting of eight trading pairs, Deeper enhances liquidity by over 2.6– . The enhancement in liquidity can be increased further by increasing the participating tokens in the shared pool. While providing shared liquidity, liquidity providers should be cautious of certain risks and pitfalls, which are described. Overall, Deeper enables the creation of fair markets for low trading volume token pairs.
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How this classification was reachedexpand
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 | 0.002 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".