SILS: Strategic Influence on Liquidity Stability and Whale Detection in Concentrated-Liquidity DEXs
- URL: http://arxiv.org/abs/2507.19411v1
- Date: Fri, 25 Jul 2025 16:21:18 GMT
- Title: SILS: Strategic Influence on Liquidity Stability and Whale Detection in Concentrated-Liquidity DEXs
- Authors: Ali RajabiNekoo, Laleh Rasoul, Amirfarhad Farhadi, Azadeh Zamanifar,
- Abstract summary: SILS identifies impactful liquidity providers (LPs) in Concentrated Liquidity Market Makers (CLMMs)<n>This represents a paradigm shift from the static, volume-based analysis to a dynamic, impact-focused understanding.<n>The framework provides unprecedented transparency into the underlying liquidity structure and associated risks.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Traditional methods for identifying impactful liquidity providers (LPs) in Concentrated Liquidity Market Makers (CLMMs) rely on broad measures, such as nominal capital size or surface-level activity, which often lead to inaccurate risk analysis. The SILS framework offers a significantly more detailed approach, characterizing LPs not just as capital holders but as dynamic systemic agents whose actions directly impact market stability. This represents a fundamental paradigm shift from the static, volume-based analysis to a dynamic, impact-focused understanding. This advanced approach uses on-chain event logs and smart contract execution traces to compute Exponential Time-Weighted Liquidity (ETWL) profiles and apply unsupervised anomaly detection. Most importantly, it defines an LP's functional importance through the Liquidity Stability Impact Score (LSIS), a counterfactual metric that measures the potential degradation of the market if the LP withdraws. This combined approach provides a more detailed and realistic characterization of an LP's impact, moving beyond the binary and often misleading classifications used by existing methods. This impact-focused and comprehensive approach enables SILS to accurately identify high-impact LPs-including those missed by traditional methods and supports essential applications like a protective oracle layer and actionable trader signals, thereby significantly enhancing DeFi ecosystem. The framework provides unprecedented transparency into the underlying liquidity structure and associated risks, effectively reducing the common false positives and uncovering critical false negatives found in traditional models. Therefore, SILS provides an effective mechanism for proactive risk management, transforming how DeFi protocols safeguard their ecosystems against asymmetric liquidity behavior.
Related papers
- The Shadow Self: Intrinsic Value Misalignment in Large Language Model Agents [37.75212140218036]
We formalize the Loss-of-Control risk and identify the previously underexamined Intrinsic Value Misalignment (Intrinsic VM)<n>We then introduce IMPRESS, a scenario-driven framework for systematically assessing this risk.<n>We evaluate Intrinsic VM on 21 state-of-the-art LLM agents and find that it is a common and broadly observed safety risk across models.
arXiv Detail & Related papers (2026-01-24T07:09:50Z) - Prompt Injection Mitigation with Agentic AI, Nested Learning, and AI Sustainability via Semantic Caching [0.42970700836450487]
This paper builds on work that introduced a four-metric Total Injection Vulnerability Score (TIVS)<n>It investigates how defence effectiveness interacts with transparency in a HOPE-inspired Nested Learning architecture.<n> Experiments show that the system achieves secure responses with zero high-risk breaches.
arXiv Detail & Related papers (2026-01-19T16:10:11Z) - Robust Reinforcement Learning in Finance: Modeling Market Impact with Elliptic Uncertainty Sets [57.179679246370114]
In financial applications, reinforcement learning (RL) agents are commonly trained on historical data, where their actions do not influence prices.<n>During deployment, these agents trade in live markets where their own transactions can shift asset prices, a phenomenon known as market impact.<n>Traditional robust RL approaches address this model misspecification by optimizing the worst-case performance over a set of uncertainties.<n>We develop a novel class of elliptic uncertainty sets, enabling efficient and tractable robust policy evaluation.
arXiv Detail & Related papers (2025-10-22T18:22:25Z) - Anchoring Refusal Direction: Mitigating Safety Risks in Tuning via Projection Constraint [52.878820730054365]
Instruction Fine-Tuning (IFT) has been widely adopted as an effective post-training strategy to enhance abilities of Large Language Models (LLMs)<n>Recent research into the internal mechanisms of LLMs has identified the refusal direction (r-direction) in the hidden states, which plays a pivotal role in governing refusal behavior.<n>To mitigate such drift, our proposed ProCon method introduces a projection-constrained loss term that regularizes the projection magnitude of each training sample's hidden state onto the r-direction.
arXiv Detail & Related papers (2025-09-08T15:24:33Z) - Personality as a Probe for LLM Evaluation: Method Trade-offs and Downstream Effects [0.6087817758152709]
We present a systematic study of personality control using the Big Five traits.<n>Trait-level analysis shows openness as uniquely challenging, agreeableness as most resistant to ICL.<n>Experiments on Gemma-2-2B-IT and LLaMA-3-8B-Instruct reveal clear trade-offs.
arXiv Detail & Related papers (2025-09-05T04:19:15Z) - HyPV-LEAD: Proactive Early-Warning of Cryptocurrency Anomalies through Data-Driven Structural-Temporal Modeling [0.0]
Abnormal cryptocurrency transactions pose escalating risks to financial integrity.<n>Existing approaches are predominantly model-centric and post hoc.<n>This paper introduces HyPV-LEAD, a data-driven early-warning framework.
arXiv Detail & Related papers (2025-09-03T12:23:38Z) - A Dynamical Systems Framework for Reinforcement Learning Safety and Robustness Verification [1.104960878651584]
This paper introduces a novel framework that addresses the lack of formal methods for verifying the robustness and safety of learned policies.<n>By leveraging tools from dynamical systems theory, we identify and visualize Lagrangian Coherent Structures (LCS) that act as the hidden "skeleton" governing the system's behavior.<n>We show that this framework provides a comprehensive and interpretable assessment of policy behavior, successfully identifying critical flaws in policies that appear successful based on reward alone.
arXiv Detail & Related papers (2025-08-21T14:00:26Z) - Preliminary Investigation into Uncertainty-Aware Attack Stage Classification [81.28215542218724]
This work addresses the problem of attack stage inference under uncertainty.<n>We propose a classification approach based on Evidential Deep Learning (EDL), which models predictive uncertainty by outputting parameters of a Dirichlet distribution over possible stages.<n>Preliminary experiments in a simulated environment demonstrate that the proposed model can accurately infer the stage of an attack with confidence.
arXiv Detail & Related papers (2025-08-01T06:58:00Z) - Circumventing Safety Alignment in Large Language Models Through Embedding Space Toxicity Attenuation [13.971909819796762]
Large Language Models (LLMs) have achieved remarkable success across domains such as healthcare, education, and cybersecurity.<n>Embedding space poisoning is a subtle attack vector where adversaries manipulate the internal semantic representations of input data to bypass safety alignment mechanisms.<n>We propose ETTA, a novel framework that identifies and attenuates toxicity-sensitive dimensions in embedding space via linear transformations.
arXiv Detail & Related papers (2025-07-08T03:01:00Z) - UProp: Investigating the Uncertainty Propagation of LLMs in Multi-Step Agentic Decision-Making [47.64013151246807]
Large Language Models (LLMs) are integrated into safety-critical applications involving sequential decision-making.<n>Existing LLM Uncertainty Quantification (UQ) methods are primarily designed for single-turn question-answering formats.<n>We introduce a principled, information-theoretic framework that decomposes LLM sequential decision uncertainty into two parts.
arXiv Detail & Related papers (2025-06-20T18:34:04Z) - NDCG-Consistent Softmax Approximation with Accelerated Convergence [67.10365329542365]
We propose novel loss formulations that align directly with ranking metrics.<n>We integrate the proposed RG losses with the highly efficient Alternating Least Squares (ALS) optimization method.<n> Empirical evaluations on real-world datasets demonstrate that our approach achieves comparable or superior ranking performance.
arXiv Detail & Related papers (2025-06-11T06:59:17Z) - Fundamental Safety-Capability Trade-offs in Fine-tuning Large Language Models [92.38300626647342]
Fine-tuning Large Language Models (LLMs) on some task-specific datasets has been a primary use of LLMs.<n>This paper presents a theoretical framework for understanding the interplay between safety and capability in two primary safety-aware LLM fine-tuning strategies.
arXiv Detail & Related papers (2025-03-24T20:41:57Z) - Improving DeFi Accessibility through Efficient Liquidity Provisioning with Deep Reinforcement Learning [0.3376269351435395]
This paper applies deep reinforcement learning (DRL) to optimize liquidity provision in a DeFi protocol.<n>By promoting more efficient liquidity management, this work aims to make DeFi markets more accessible and inclusive for a broader range of participants.
arXiv Detail & Related papers (2025-01-13T17:27:11Z) - Leveraging Ensemble-Based Semi-Supervised Learning for Illicit Account Detection in Ethereum DeFi Transactions [0.0]
Decentralized Finance (DeFi) has introduced significant security risks, including the proliferation of illicit accounts.<n>Traditional detection methods are limited by the scarcity of labeled data and the evolving tactics of malicious actors.<n>We propose a novel Self-Learning Ensemble-based Illicit account Detection framework to address these challenges.
arXiv Detail & Related papers (2024-12-03T12:03:13Z) - Enhancing Security in Federated Learning through Adaptive
Consensus-Based Model Update Validation [2.28438857884398]
This paper introduces an advanced approach for fortifying Federated Learning (FL) systems against label-flipping attacks.
We propose a consensus-based verification process integrated with an adaptive thresholding mechanism.
Our results indicate a significant mitigation of label-flipping attacks, bolstering the FL system's resilience.
arXiv Detail & Related papers (2024-03-05T20:54:56Z) - KIEval: A Knowledge-grounded Interactive Evaluation Framework for Large Language Models [53.84677081899392]
KIEval is a Knowledge-grounded Interactive Evaluation framework for large language models.
It incorporates an LLM-powered "interactor" role for the first time to accomplish a dynamic contamination-resilient evaluation.
Extensive experiments on seven leading LLMs across five datasets validate KIEval's effectiveness and generalization.
arXiv Detail & Related papers (2024-02-23T01:30:39Z) - INTAGS: Interactive Agent-Guided Simulation [4.04638613278729]
In many applications involving multi-agent system (MAS), it is imperative to test an experimental (Exp) autonomous agent in a high-fidelity simulator prior to its deployment to production.
We propose a metric to distinguish between real and synthetic multi-agent systems, which is evaluated through the live interaction between the Exp and BG agents.
We show that using INTAGS to calibrate the simulator can generate more realistic market data compared to the state-of-the-art conditional Wasserstein Generative Adversarial Network approach.
arXiv Detail & Related papers (2023-09-04T19:56:18Z) - Safety Margins for Reinforcement Learning [53.10194953873209]
We show how to leverage proxy criticality metrics to generate safety margins.
We evaluate our approach on learned policies from APE-X and A3C within an Atari environment.
arXiv Detail & Related papers (2023-07-25T16:49:54Z) - An Information-Theoretic Perspective on Variance-Invariance-Covariance Regularization [52.44068740462729]
We present an information-theoretic perspective on the VICReg objective.
We derive a generalization bound for VICReg, revealing its inherent advantages for downstream tasks.
We introduce a family of SSL methods derived from information-theoretic principles that outperform existing SSL techniques.
arXiv Detail & Related papers (2023-03-01T16:36:25Z) - Empirical Study of Market Impact Conditional on Order-Flow Imbalance [0.0]
We show that for small signed order-flows, the price impact grows linearly with increase in the order-flow imbalance.
We have, further, implemented a machine learning algorithm to forecast market impact given a signed order-flow.
Our findings suggest that machine learning models can be used in estimation of financial variables.
arXiv Detail & Related papers (2020-04-17T14:58:29Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.