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@@ -365,6 +365,7 @@ A curated list of insanely awesome libraries, packages and resources for Quants
 - [ml-quant-trading](https://github.com/initial-d/ml-quant-trading) - `Python` - PyTorch research stack for multi-factor analysis, bias correction, portfolio optimization, and reproducible backtesting.
 - [QuantGPT](https://github.com/Miasyster/QuantGPT) - `Python` - Agent-driven A-share factor research engine with 8 MCP tools covering hypothesis design, backtesting, scoring, and anti-overfit detection.
 - [quant-lab-alpha](https://github.com/husainm97/quant-lab-alpha) - `Python` - Open-source investment analytics platform bridging academic research and retail finance.
+- [Perception-XAlpha Lite](https://github.com/xuxingjiankr-cpu/perception-xalpha-lite) - `Python` - Backtest-overfitting audit for factor research: CSCV probability of backtest overfitting, deflated Sharpe against the declared trial count, White's Reality Check, point-in-time universe membership and disclosure-date alignment. Ships a worked example in which 24 pure-noise series produce a 1.11 Sharpe and the audit says so.
 - [covFactorModel](https://github.com/dppalomar/covFactorModel) - `R` - Covariance matrix estimation via factor models.
 - [FactorAnalytics](https://github.com/braverock/FactorAnalytics) - `R` - The FactorAnalytics package contains fitting and analysis methods for the three main types of factor models used in conjunction with portfolio construction, optimization and risk management, namely fundamental factor models, time series factor models and statistical factor models.
 - [Expected Returns](https://github.com/JustinMShea/ExpectedReturns) - `R` - Solutions for enhancing portfolio diversification and replications of seminal papers with R, most of which are discussed in one of the best investment references of the recent decade, Expected Returns: An Investors Guide to Harvesting Market Rewards by Antti Ilmanen.