Recent developments in distributed ledger technology, artificial intelligence, and decision-making agents hold the promise of radically transforming market infrastructures. Indeed, the emergence of Autonomous Market Infrastructure (AMI)âan open, fully automated, and decentralized set of market-related functionalitiesâis widely anticipated. Such infrastructures, serving agents capable of fully autonomous behavior, would enable fully automated trading strategies. Moreover, as AMI-based solutions require minimal human intervention, they could be implemented at a fraction of existing costs. This should bolster competition and democratization, as AMI is accessible to everyone and establishes a level playing field.
Autonomous platforms for fintech, decentralized finance, and digital civil infrastructures are at the research frontier. Delivering on their promise requires a foundational approach. Future research and development directions are organised by core architectural principles, enabling technologies, major challenges and risks, methods for development and evaluation, and governance models. Autonomous economic interaction and decision-making are principally guided by policy goals. Independence from human involvement cannot be guaranteed, especially when external agents fulfil custodial roles, but risk can be mitigated by solidifying the foundations. The term âautonomous platformâ constitutes a composite of economic theory and systems design. Platforms support economic interactions enabled by information and communication technologyâin particular, the Internet. Their distinctive feature is an architecture composed of services provided by multiple stakeholders. Platform engineering is a design discipline that seeks to deliver the hoped-for benefits, including lower costs, greater selection, and novel business models, while mitigating risks such as fraud and the abuse of market power. The promise of autonomy stems from the deployment of becoming-type, human-compliant purpose design in an effective oversized-modular architecture and begins with the fulfilment of core architectural principlesâan autonomous, modular, and composable layer for economic interaction and decision-making.
Cryptocurrencies have started gaining ground as investment vehicles. Cryptocurrencies exhibit characteristics that differentiate them from traditional financial assets. In 2009, Bitcoin (BTC), the first digital currency, was launched. In 2021, the Securities and Exchange Commission (SEC) approved ProShares Bitcoin Strategy (BITO), the first U.S. Bitcoin futures exchange-traded fund (ETF). In 2024, SEC gave final approval for spot Ether (ETH) ETFs to start trading, further legitimizing the asset class. Although cryptocurrencies share many features of alternative assets, they are hindered by high volatility and regulatory uncertainties. Extant literature studies cryptocurrencies as alternative investments from various perspectives. Using market data, this empirical paper aims to contribute to the literature by studying the extent to which cryptocurrencies improve the risk-return profile of a diversified portfolio. Specifically, we do so by examining the economic impact of including Bitcoin for a passive investor investing in the U.S. Stock market index (S&P 500 index).
The Paris Agreement of 2015 has prompted countries to accelerate their efforts to become carbon neutrality efforts, which meant reducing CO2 emissions to virtually zero. Limiting global warming to less than 1.5°C by 2050 rely on technologies that remove CO2 from the atmosphere faster than humans release it. This implies that CO2 will be removed at a rate of 1-30 gigaton per year by 2050. Carbon Capture Storage / Sequestration (CCS) and Utilization (CCU) are concepts and technologies that collect emitted CO2 store it permanently underground, or recycle it as energy or chemicals for use in manufacturing and other economic activities. CCS and CCU have been discussed globally, but have not reached local and practical levels. Currently planned large-scale CCS requires significant government investment and new technological developments for capture, transport, and storage / sequestration, therefore implementation is expected to start in the second half of 2030 towards the 2050 goal. The need to start acting now where possible rather than waiting for the distant future, makes it important to implement CCS and CCU on a small scale and build towards future scale-up as an immediate solution. This study proposes a support method and system to help companies that emit large amounts of CO2 such as power plants, cement, petrochemicals, and steel industries, to decide how to treat their CO2 emissions in the context of decarbonization. In this study, a Simple, Measurable, Attainable, Relative, and Time-Bound (SMART) decision support method and Direct Air Capture Location and Cost Simulator (DLCS) system were developed to provide a solution to the Negative Emission 5W1H âWhat, Who, Which, When, Why, and Howâ from the perspective of a company that emits CO2. A prototype model with parameter settings was proposed based on knowledge gained from practical experience. The functionality of the SMART method and DLCS system was confirmed by applying sample data from the actual data of the âTokyo Regionâ as a Proof of Concept (PoC). In this PoC, characteristics of direct air capture which is a critical technology for negative emissions, were verified. The core of the SMART and DLCS model entails combinatorial optimization, distance calculation, cost estimation, and market projection including constraint solution.
The rapid expansion of the blockchain gaming sector, projected to reach a $268.8 billion valuation by 2025 1 , has been severely compromised by the proliferation of automated Sybil attacks and bot-driven economic manipulation. Traditional anti-bot measures, such as CAPTCHAs and behavioural analytics, are increasingly circumvented by advanced AI-driven scripts. This paper proposes a novel Context-Aware Reputation-Identity Hybrid (CRIH) framework that integrates biometric-backed Proof-of-Personhood (PoP) with decentralized reputation metrics. By leveraging World IDâs hardware-oracle verification and recursive Zero-Knowledge Proofs (ZKPs), the CRIH framework enables thrustless identity portability across Layer 2 (World Chain) and Layer 3 (Mythos Chain) architectures. We demonstrate that this tiered, risk-sensitive approach significantly reduces bot-driven inflation while preserving player privacy and minimizing onboarding friction.
Verifying that a compiled binary originates from its claimed source code is a fundamental security requirement, called source code provenance. Achieving verifiable source code provenance in practice remains challenging. The most popular technique, called reproducible builds, requires difficult matching and reexecution of build toolchains and environments. We propose a novel approach to verifiable provenance based on compiling software with zero-knowledge virtual machines (zkVMs). By executing a compiler within a zkVM, our system produces both the compiled output and a cryptographic proof attesting that the compilation was performed on the claimed source code with the claimed compiler. We implement a proof-of-concept implementation using the RISC Zero zkVM and the ChibiCC C compiler, and evaluate it on 200 synthetic programs as well as 31 OpenSSL and 21 libsodium source files. Our results show that zk-compilation is applicable to real-world software and provides strong security guarantees: all adversarial tests targeting compiler substitution, source tampering, output manipulation, and replay attacks are successfully blocked.
This is an extended appendix for an unpublished paper. It covers the use of a framework defined in that paper to prove the zero-knowledge of a few zero-knowledge proofs. The first example, covering 3-colourability, is justified and explained. The second, covering boolean circuit satisfiability, is simply given.
This paper proposes an AI-based trading framework that integrates supervised price forecasting with reinforcement learning (RL)-based decision-making. The objective is to enhance both profitability and risk management in cryptocurrency trading by equipping RL agents with forward-looking market information and risk-aware incentives. The proposed methodology follows a two-stage design. First, a univariate long short-term memory (LSTM) model generates 72 bitcoin price forecasts. These predictions are used to compute future technical indicators, which are combined with current market indicators to construct an enriched, forward-looking state representation. Second, an RL agent is trained in this environment using a novel long-term reward function that incorporates transaction costs, drawdown penalties, volatility penalties, and delayed rewards to promote stable and sustainable trading behavior. Four state-of-the-art RL algorithms (PPO, SAC, TD3, and A2C) are systematically evaluated over randomized 180-day episodes using hourly bitcoin data. The results demonstrate that the proposed agent consistently outperforms conventional buy-and-hold and moving average crossover strategies, achieving an average profit ratio of 32% and a Sharpe ratio of 1.34. These findings highlight the novelty and effectiveness of combining mid-term price forecasts, enriched technical states, and risk-aware RL training for robust cryptocurrency trading.
A Long Short-Term Memory (LSTM) neural network trained on hourly ETH/USDT market data from the Binance exchange is used in this study to examine short-term Ethereum price behavior. The proposed model emphasizes learning temporal dependencies and momentum-driven structures rather than relying on conventional linear forecasting assumptions, acknowledging the highly nonlinear and noise-dominated nature of cryptocurrency markets. The daily high price of Ethereum is selected as the target variable in the forecasting task, which is defined as a univariate regression problem. To ensure realistic predictive assessment, model performance is evaluated using a strictly out-of-sample testing methodology. Empirical findings demonstrate that the LSTM model achieves a strong statistical fit despite significant market volatility. The obtained resultsâRMSE of 127.33, MAE of 98.76, MSE of 16,213.76, MAPE of 2.73%, and an R² of 0.96âindicate that a substantial portion of short-term price volatility is effectively captured by the nonlinear architecture. Even in a noise-dominated market, the low MAPE and high coefficient of determination suggest robust predictive alignment. Forecasts over the next five days reveal a recurring short-term directional pattern accompanied by widening prediction intervals, which reflect increasing uncertainty as the forecast horizon extends. This pattern underscores the intrinsic difficulty of achieving accurate price-level forecasts in highly volatile cryptocurrency markets. Overall, when applied to short-term cryptocurrency price dynamics, the results indicate that LSTM models are well-suited for capturing trend persistence and regime-related signals, affirming their usefulness as risk-aware decision-support tools rather than deterministic forecasting systems.
Liquidation of collateral are the primary safeguard for solvency of lending protocols in decentralized finance. However, the mechanics of liquidations expose these protocols to predatory price manipulations and other forms of Maximal Extractable Value (MEV). In this paper, we characterize the optimal liquidation strategy, via a dynamic program, from the perspective of a profit-maximizing liquidator when the spot oracle is given by a Constant Product Market Maker (CPMM). We explicitly model Oracle Extractable Value (OEV) where liquidators manipulate the CPMM with sandwich attacks to trigger profitable liquidation events. We derive closed-form liquidation bounds and prove that CPMM transaction fees act as a critical security parameter. Crucially, we demonstrate that fees do not merely reduce attacker profits, but can make such manipulations unprofitable for an attacker. Our findings suggest that CPMM transaction fees serve a dual purpose: compensating liquidity providers and endogenously hardening CPMM oracles against manipulation without the latency of time-weighted averages or medianization.
We present Viturka, a blockchain architecture that replaces wasteful proof-of-work mining with productive federated learning. The core innovation is Proof of Credibility (PoC): a consensus mechanism where block production probability is determined by accumulated reputation from validated AI contributions rather than computational hash power or financial stake. Viturka leverages recent breakthroughs in Zero-Knowledge Machine Learning (ZKML) to achieve cryptographic verification of model training. Validators generate zero-knowledge proofs attesting to correct training execution, enabling instant on-chain verification without trusted intermediaries or statistical consensus mechanisms. By integrating frameworks like EZKL and Lagrange's DeepProve with GPU-accelerated proving via the Icicle library, validation that previously required hours of recomputation now produces mathematical proofs verifiable in milliseconds. Participants earn credibility by contributing quality training data or validating others' contributions. Only the top 10 highest-credibility validators can participate in validation rounds, with mandatory cooldown periods ensuring rotation. The system uses a temporal commit-reveal scheme for data contributions combined with ZK proofs for validationâfake contributions result in permanent bans, while fraudulent validation is mathematically impossible. This creates infrastructure for training AI models on distributed data without central coordination, with economic incentives aligned toward data quality rather than raw computation. Applications range from commercially valuable use cases like DeFi credit scoringâwhich could unlock over $100B in overcollateralized capitalâto public-good AI for rare diseases, minority languages, and environmental monitoring.
SATHISHKUMAR RANGANATHAN, Muralindran Mariappan, M. Karthigayan
Swarm robotics is an emerging field capable of accomplishing complex tasks through collective behaviour. However, it continues to face persistent challenges in secure communication, decentralized decision-making, and scalability. To operate effectively in resource-constrained environments, swarm networks require a decentralized mechanism that is secure, fast, and efficient. Although many studies have explored the use of blockchain technology for swarm robotics, existing blockchain consensus algorithms such as Proof of Work (PoW), Proof of Stake (PoS), and their variants remain unsuitable due to high computational complexity and risk of stake centralization. To address these challenges, we introduce the blockchain-based Rotational Leadership Role (RLR) consensus algorithm, a voting-based consensus re-engineered from the Raft approach, together with Decentralized Task Authorization and Validation (DeTAV), a token-based mechanism for context-aware task validation. This design ensures efficiency, security, and scalability in swarm robotics and drone systems. RLR is lightweight and well suited to operate within the limited computing resources of small robots or aerial drones. To validate its performance, a custom-built robotic simulator was developed as part of this research. Experiments conducted with up to 70 concurrent robots demonstrated that RLR consumed under 90 MB Random Access Memory (RAM) and 12% Central Processing Unit (CPU), whereas PoW required 460 MB RAM and 27% CPU with a minimum difficulty level of 21, reflecting an 80% reduction in memory usage and a 55% reduction in CPU consumption. Scalability tests with 4 to 70 robots further revealed RLRâs scalability with an average of 78% higher throughput, 47% lower election latency, and 34% lower consensus latency. Additionally, under the simulated attack scenarios and assuming uncompromised cryptographic keys, DeTAVâs context-based validation consistently achieved 100% success in detecting and isolating Byzantine nodes, while reducing Quality of Detection (QoD) time by 67%. Collectively, these results confirm that RLR with DeTAV effectively meets the efficiency, security, and scalability requirements of swarm robotic and drone networks.
Guilhem Repetto, Nojan Sheybani, Gabrielle De Micheli, Farinaz Koushanfar
Privacy concerns in machine learning systems have grown significantly with the increasing reliance on sensitive user data for training large-scale models. This paper introduces a novel framework combining Probably Approximately Correct (PAC) Privacy with zero-knowledge proofs (ZKPs) to provide verifiable privacy guarantees in trustless computing environments. Our approach addresses the limitations of traditional privacy-preserving techniques by enabling users to verify both the correctness of computations and the proper application of privacy-preserving noise, particularly in cloud-based systems. We leverage non-interactive ZKP schemes to generate proofs that attest to the correct implementation of PAC privacy mechanisms while maintaining the confidentiality of proprietary systems. Our results demonstrate the feasibility of achieving verifiable PAC privacy in outsourced computation, offering a practical solution for maintaining trust in privacy-preserving machine learning and database systems while ensuring computational integrity.
This paper introduces DMind-3, a sovereign Edge-Local-Cloud intelligence stack designed to secure irreversible financial execution in Web3 environments against adversarial risks and strict latency constraints. While existing cloud-centric assistants compromise privacy and fail under network congestion, and purely local solutions lack global ecosystem context, DMind-3 resolves these tensions by decomposing capability into three cooperating layers: a deterministic signing-time intent firewall at the edge, a private high-fidelity reasoning engine on user hardware, and a policy-governed global context synthesizer in the cloud. We propose policy-driven selective offloading to route computation based on privacy sensitivity and uncertainty, supported by two novel training objectives: Hierarchical Predictive Synthesis (HPS) for fusing time-varying macro signals, and Contrastive Chain-of-Correction Supervised Fine-Tuning (C$^3$-SFT) to enhance local verification reliability. Extensive evaluations demonstrate that DMind-3 achieves a 93.7% multi-turn success rate in protocol-constrained tasks and superior domain reasoning compared to general-purpose baselines, providing a scalable framework where safety is bound to the edge execution primitive while maintaining sovereignty over sensitive user intent.
The increasing deployment of agentic artificial intelligence (AI) systems and decentralized digital infrastructures has challenged traditional assumptions about organizational administration, control, and governance. While AI has advanced task-level optimization and decision support, administrative functions such as coordination, compliance, and accountability remain largely centralized and dependent on humans. This paper introduces Autonomous Administrative Intelligence (AAI), a governance-aware AI capability that enables autonomous agents to execute and adapt administrative decisions within strategically defined constraints and decentralized governance mechanisms. Building on the Strategic Decentralized ResilienceâAI (SDRT-AI) framework, the study develops a layered architecture and operational flow integrating agentic decision-making, governance-aware learning, and protocol-based validation. The proposed framework explains how strategic intent, organizational capabilities, and decentralized trust jointly enable scalable administrative autonomy while preserving accountability and control. By reframing administration as an AI-mediated governance process, this paper extends research on agentic AI and contributes to administrative science by providing a conceptual foundation for the design and governance of autonomous administrative systems in decentralized organizations.
We introduce FlashChain, a decentralized framework that integrates IO-aware attention mechanismsâespecially FlashAttentionâinto scalable, trustless AI systems. As Transformer-based models become foundational to Web3 infrastructure (e.g., DAOs, decentralized search, autonomous agents), their quadratic compute and memory bottlenecks present critical challenges. FlashChain adapts block-sparse FlashAttention into a modular architecture optimized for multi-node, low-bandwidth environments typical of blockchain and edge networks. We propose a hybrid protocol combining attention kernel optimization with zero-knowledge verifiability, enabling real-time, trustless AI inference across distributed nodes. Benchmarks show 3â5Ă speedups and up to 30Ă gas savings per inference compared to baseline on-chain models.
Propagation latency is inherent to any distributed network, including blockchains. Typically, blockchain protocols provide a timing buffer for block propagation across the network. In leader-based blockchains, the leader -- block proposer -- is known in advance for each slot. A fast (or low-latency) proposer may delay the block proposal in anticipation of more rewards from the transactions that would otherwise be included in the subsequent block. Deploying such a strategy by manipulating the timing is known as timing games. It increases the risk of missed blocks due to reduced time for other nodes to vote on the block, affecting the overall efficiency of the blockchain. Moreover, proposers who play timing games essentially appropriate MEV (additional rewards over transaction fees and the block reward) that would otherwise accrue to the next block, making it unfair to subsequent block proposers. We propose a double-block proposal mechanism, 2-Prop, to curtail timing games. 2-Prop selects two proposers per slot to propose blocks and confirms one of them. We design a reward-sharing policy for proposers based on how quickly their blocks propagate to avoid strategic deviations. In the induced game, which we call the Latency Game, we show that it is a Nash Equilibrium for the proposers to propose the block without delay under homogeneous network settings. Under heterogeneous network settings, we study many configurations, and our analysis shows that a faster proposer would prefer not to delay unless the other proposer is extremely slow. Thus, we show the efficacy of 2-Prop in mitigating the effect of timing games.
Marwa Mouallem, Lorenz Breidenbach, Ittay Eyal, Ari Juels
Smart contracts are stateful programs deployed on blockchains; they secure over a trillion dollars in transaction value per year. High-stakes smart contracts often rely on timely alerts about external events, but prior work has not analyzed their resilience to an attacker suppressing alerts via bribery. We formalize this challenge in a cryptoeconomic setting as the \emph{alerting problem}, giving rise to a game between a bribing adversary and~$n$ rational participants, who pay a penalty if they are caught deviating from the protocol. We establish a quadratic, i.e.,~$O(n^2)$, upper bound, whereas a straightforward alerting protocol only achieves~$O(n)$ bribery cost. We present a \emph{simultaneous game} that asymptotically achieves the quadratic upper bound and thus asymptotically-optimal bribery resistance. We then present two protocols that implement our simultaneous game: The first leverages a strong network synchrony assumption. The second relaxes this strong assumption and instead takes advantage of trusted hardware and blockchain proof-of-publication to establish a timed commitment scheme. These two protocols are constant-time but incur a linear storage overhead on the blockchain. We analyze a third, \emph{sequential alerting} protocol that optimistically incurs no on-chain storage overhead, at the expense of~$O(n)$ worst-case execution time. All three protocols achieve asymptotically-optimal bribery costs, but with different resource and performance tradeoffs. Together, they illuminate a rich design space for practical solutions to the alerting problem.
The Internet of Things (IoT) security landscape requires the architectural solutions that can address the technical and operational challenges across the heterogeneous environments. The IoT systems operate in different conditions, and security issues continue to increase. This paper presents the comprehensive security framework for IoT that should integrate the Trusted Execution Environments (TEEs) with the semantic middleware and blockchain technologies. The work provides a systematic analysis of the architectural patterns based on more than twenty recent research works and the existing standards, and it proposes a layered security architecture. The architecture includes the hardware rooted trust at peripheral level, the zero trust principles at network level, and the semantic security mechanisms at application level. The framework focuses on practical implementation aspects such as the performance overhead, interoperability requirements, and the compliance with new regulations, which are very important for the real IoT deployments. The paper reports quantitative metrics which include the cryptographic performance on Cortex-M class microcontrollers with the detection accuracy rates and the energy consumption values. The proposed architecture shows that cross-layer security integration can provide defense in depth while it still satisfies the constraints of resource-limited IoT environments. The discussion highlights open challenges and the future research directions for the IoT security architectures that include the post-quantum migration, secure federated model exchange and the automated compliance verification.
Many tracking companies collect user data and sell it to data markets and advertisers. While they claim to protect user privacy by anonymizing the data, our research reveals that significant privacy risks persist even with anonymized data. Attackers can exploit this data to identify users' accounts on other websites and perform targeted identity alignment. In this paper, we propose an effective identity alignment scheme for accurately identifying targeted users. We develop a data collector to obtain the necessary datasets, an algorithm for identity alignment, and, based on this, construct two types of de-anonymization attacks: the \textit{passive attack}, which analyzes tracker data to align identities, and the \textit{active attack}, which induces users to interact online, leading to higher success rates. Furthermore, we introduce, for the first time, a novel evaluation framework for online tracking-based identity alignment. We investigate the key factors influencing the effectiveness of identity alignment. Additionally, we provide an independent assessment of our generated dataset and present a fully functional system prototype applied to a cryptocurrency use case.
Cryptomining poses significant security risks, yet traditional detection methods like blacklists and Deep Packet Inspection (DPI) are often ineffective against encrypted mining traffic and suffer from high false positive rates. In this paper, we propose a practical encrypted cryptomining traffic detection mechanism. It consists of a two-stage detection framework, which can effectively provide fine-grained detection results by machine learning and reduce false positives from classifiers through active probing. Our system achieves an F1-score of 0.99 and identifies specific cryptocurrencies with a 99.39\% accuracy rate. Extensive testing across various mining pools confirms the effectiveness of our approach, offering a more precise and reliable solution for identifying cryptomining activities.
Manaf Ahmed, Mohammed Adnan, Ali Matar, Faez Hlail Srayyih ¡ 7 authors
Predicting cryptocurrency price is challenging owing to high volatility, less historical data, and the impact of external parameters like news, public sentiment, and regulatory announcements. This challenge is tackled in this research by employing models of deep learning like Recurrent Neural Network (RNN), Bidirectional Long Short-Term Memory (BiLSTM) and Gated Recurrent Unit (GRU)âto predict Bitcoin's OHLC prices daily. Based on historical time-series data of Coin Codex, the research uses an autoencoder-based feature extraction method with five-day sliding window method for sequence generation. Hyperband optimization is used to tune hyperparameter of each model. The result shows that BiLSTM performs better than all the other models with minimum Mean Squared Error (MSE = 0.001183), Mean Absolute Error (MAE = 0.026090), and maximum R² score (0.980596) after optimization. The results emphasize the significance of deep learning in capturing nonlinear dynamics in time series of financial applications and bear testimony to the effectiveness of hyperparameter tuning in enhancing model accuracy. The study enhances the development of prediction tools for digital asset markets and enables more informed investment decisions.
The rapid evolution of digital technologies has fundamentally disrupted traditional arbitration processes, introducing new complexities and opportunities at the intersection of blockchain technology, smart contracts, and online arbitration. This study examines the legal and practical challenges arising from the integration of blockchain technology, smart contracts, and online arbitration. The research addresses the problem of adapting traditional dispute resolution frameworks to decentralized automated agreements that transcend national borders. Using doctrinal analysis and comparative review of legal sources, case studies, and real-world platforms, the study identifies key types of smart contracts and evaluates on-chain and off-chain arbitration models. Findings highlight both the efficiency and transparency offered by blockchain-based dispute resolution, as well as persistent issues such as jurisdictional uncertainty, enforceability, technical vulnerabilities, and privacy risks. The results underscore the need for clear legal standards, technical safeguards, and the adoption of advanced technologies. The study recommends promoting interoperability, specifying governing law in smart contracts, and leveraging AI and off-chain execution systems to enhance the robustness and adaptability of digital dispute resolution.
Slow Liquidity Drain (SLID) scams have recently emerged as a subtle and persistent threat within the decentralized finance (DeFi) environment. While prior studies have introduced heuristic and machine learning techniques for identifying SLID behaviors, deploying these methods in real-world industrial systems reveals substantial challenges. In particular, updated large-scale datasets collected from operational DeFi platforms show that SLID behaviors and their effective detection time-range evolve over time, rendering previously reported fixed thresholds unreliable for production use. This work presents a data-driven reassessment of SLID detection under contemporary DeFi conditions and demonstrates that the observation window required for reliable detection shifts as new data and new scam behaviors emerge. Building on these findings, we introduce an industry-oriented detection framework that decouples machine learning models from time-range selection and supports adaptive operation without retraining or feature redesign. Rather than proposing a single deployment strategy, we outline two practical operating modes: a slow-adaptive mode that prioritizes stability and auditability through periodic window updates, and a fast-adaptive mode that enables flexible sensitivity and tiered alerts for security-driven environments. Together, these designs translate empirical insights into concrete system architectures suitable for large-scale DeFi monitoring, bridging the gap between academic SLID detection research and production deployment requirements.