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arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

169,051 papers · 148 categories

Trend · papers per month

count% of corpus
0111 · Aug 201819922001200920182026
2 results for “minification”
Context2Name: A Deep Learning-Based Approach to Infer Natural Variable Names from Usage Contextscs.SE

Context2Name predicts natural names from minified code, improving code readability.

problem Minified code makes it hard to understand natural names in JavaScript.
method Combines static analysis and neural networks to predict natural names.
result Successfully predicts 47.5% of all minified identifiers in real-world code.
minificationnatural namesJavaScriptdeep learningcode readability
2018-08-31abs ↗pdf ↗
FedSight AI: Multi-Agent System Architecture for Federal Funds Target Rate Predictionq-fin.GN

FedSight AI predicts federal funds rate using LLMs and multi-agent reasoning.

problem Predicting Federal Open Market Committee's decisions on federal funds rate.
method Multi-agent framework with large language models, structured and unstructured inputs, and CoD extension for efficient reasoning.
result Achieved 93.75% accuracy and 93.33% stability in predicting FOMC outcomes.
federal funds rateFOMCmulti-agent systemlarge language modelsChain-of-Draft
2025-12-05abs ↗pdf ↗