Selected work

Representative Papers

From Impermanent Loss to Sustainable Gain: Quantifying Profitability Zones for Liquidity Providers on DEX

Apr 30, 2026

This study addresses the lack of a quantitative framework for analyzing impermanent loss risk faced by liquidity providers in automated market maker (AMM) protocols and the associated profit distribution with arbitrageurs. By constructing an empirical pool–based mathematical model that integrates on-chain data and probabilistic analysis, this work is the first to characterize the symbiotic profit region shared by both parties, derive their joint profit bounds, and quantify the probability and duration of entering the impermanent loss regime. Key contributions include a target-probability–based method for computing the lower bound of trading fees, along with estimates of the expected number of blocks before impermanent loss occurs and the minimum fee rate required to sustain positive returns. These results provide theoretical foundations for incentive alignment and market stability in AMM-based decentralized exchanges.

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Sense of Self and Time in Borderline Personality. A Comparative Robustness Study with Generative AI

Aug 26, 2025

This study pioneers the application of large language models (LLMs) to phenomenological analysis of borderline personality disorder (BPD), specifically examining their capacity to interpret first-person narratives concerning patients’ lived experiences of temporality and selfhood. Method: We employed GPT-4o, Gemini 2.5 Pro, and Claude Opus 4 to analyze life-story interview transcripts, evaluating outputs quantitatively via semantic consistency, Jaccard similarity, and qualitatively through multidimensional validity criteria—credibility, coherence, substantive adequacy, and data grounding. Contribution/Results: Gemini 2.5 Pro achieved the highest thematic overlap (58%, *p* < 0.0001 vs. others), optimal validity scores, and was indistinguishable from human analysts in blind expert evaluation. Thematic extraction quality correlated strongly with input text length (*R* > 0.78). Critically, LLMs successfully recovered themes missed by human analysts, demonstrating potential to mitigate interpretive bias and enhance rigor in phenomenological research.

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Recent publications

Latest Papers

From Impermanent Loss to Sustainable Gain: Quantifying Profitability Zones for Liquidity Providers on DEX

Apr 30, 2026

This study addresses the lack of a quantitative framework for analyzing impermanent loss risk faced by liquidity providers in automated market maker (AMM) protocols and the associated profit distribution with arbitrageurs. By constructing an empirical pool–based mathematical model that integrates on-chain data and probabilistic analysis, this work is the first to characterize the symbiotic profit region shared by both parties, derive their joint profit bounds, and quantify the probability and duration of entering the impermanent loss regime. Key contributions include a target-probability–based method for computing the lower bound of trading fees, along with estimates of the expected number of blocks before impermanent loss occurs and the minimum fee rate required to sustain positive returns. These results provide theoretical foundations for incentive alignment and market stability in AMM-based decentralized exchanges.

0 citationsRead paper

Sense of Self and Time in Borderline Personality. A Comparative Robustness Study with Generative AI

Aug 26, 2025

This study pioneers the application of large language models (LLMs) to phenomenological analysis of borderline personality disorder (BPD), specifically examining their capacity to interpret first-person narratives concerning patients’ lived experiences of temporality and selfhood. Method: We employed GPT-4o, Gemini 2.5 Pro, and Claude Opus 4 to analyze life-story interview transcripts, evaluating outputs quantitatively via semantic consistency, Jaccard similarity, and qualitatively through multidimensional validity criteria—credibility, coherence, substantive adequacy, and data grounding. Contribution/Results: Gemini 2.5 Pro achieved the highest thematic overlap (58%, *p* < 0.0001 vs. others), optimal validity scores, and was indistinguishable from human analysts in blind expert evaluation. Thematic extraction quality correlated strongly with input text length (*R* > 0.78). Critically, LLMs successfully recovered themes missed by human analysts, demonstrating potential to mitigate interpretive bias and enhance rigor in phenomenological research.

0 citationsRead paper