Repository Analysis

ai4s-research/awesome-ai-for-science

A curated list of awesome AI tools, libraries, papers, datasets, and frameworks that accelerate scientific discovery — from physics and chemistry to biology, materials, and beyond.

51.3 Strong AI signal View on GitHub

Analysis Overview

This report presents the forensic synthetic code analysis of ai4s-research/awesome-ai-for-science with 1,767 GitHub stars. SynthScan v2.0 examined 1,179 lines of code across 2 source files, recording 23 pattern matches distributed across 2 syntactic categories. The overall adjusted score of 51.3 places this repository in the Strong AI signal band.

The scanner applied 160+ deterministic lexical heuristics, multi-line block detectors, abstract syntax tree depth profilers, and a cross-file Jaccard similarity matrix to construct a statistically normalised synthetic code estimate. All matches are individually weighted by severity coefficient and contextual multiplier before summation, and the resulting headline score is temporally discounted to account for the repository's development history relative to the commercial emergence of large language model coding tooling (November 2022 onward).

51.3
Adjusted Score
51.3
Raw Score
100%
Time Factor
2026-07-14
Last Push
1.8K
Stars
Language
1.2K
Lines of Code
2
Files
23
Pattern Hits
2026-07-14
Scan Date
1.00
HC Hit Rate

What These Metrics Mean

Adjusted Score
Primary synthetic code indicator. Raw score normalised per 1,000 lines of code and multiplied by the temporal discount factor. This is the definitive comparative metric — use it to rank repositories by AI authorship density.
Raw Score
The unmodified sum of all severity-weighted, context-multiplied pattern match scores before temporal discounting. Reflects the absolute signal strength independent of when the repository was last active.
Time Factor
The temporal discount multiplier (0–100%) applied to the raw score. Repositories last updated before ChatGPT's launch (Nov 2022) receive a 5% factor. Full signal is only assigned to repositories active in the post-adoption era (Jan 2024+).
Pattern Hits
Total count of individual pattern matches across all files and categories. A high hit count with a low score may indicate a very large codebase with isolated AI snippets; a low count with a high score indicates dense, concentrated AI signatures.
HC Hit Rate
High+Critical pattern hits per file, averaged across the repository. This orthogonal signal catches repositories where a few files are densely packed with high-severity AI tells — a strong indicator even when the normalised score appears moderate due to codebase size.
Lines of Code / Files
Total lines and files analysed. The scanner examines 94 file extensions. These denominators are used to normalise the score, enabling fair comparison between repositories of vastly different sizes.

Score History

This chart maps the temporal evolution of the adjusted synthetic code score across successive scan runs. An upward trajectory indicates ongoing incorporation of AI-generated code or expanding LLM-assisted scaffolding; a stable or declining trajectory may reflect active human refactoring, code removal, or the adoption of stricter authorship policies. The dashed secondary line (right axis) independently tracks total raw pattern hit count, which can diverge from the normalised score when codebase size changes significantly between scans.

Severity Breakdown

Classifies detected patterns by their diagnostic confidence and structural impact. CRITICAL patterns (coefficient 10) represent definitive synthetic signatures — hallucinated imports, explicit LLM attribution metadata — virtually never produced by human authors. HIGH (5) indicates strong structural tells such as cross-file repetition or cross-linguistic idioms. MEDIUM (2) covers recognisable conversational padding and AI-specific vocabulary. LOW (1) captures subtle indicators like tautological comments and generic boilerplate that require density to carry independent signal.

CRITICAL 0HIGH 2MEDIUM 21LOW 0

Directory Score Breakdown

This horizontal bar chart decomposes the repository's raw synthetic code score by top-level directory, allowing you to pinpoint precisely which modules or components carry the highest AI authorship density. Directories with disproportionately high scores relative to their size warrant targeted manual review: concentrated AI signatures often trace back to mass-generated configuration layers, auto-ported test suites, LLM-scaffolded boilerplate classes, or entire subsystems authored under heavy copilot assistance. Use this view to prioritise your human code-review effort.

Pattern Findings

The scanner identified 23 distinct pattern matches across 2 syntactic categories. Each entry below represents a discrete location in the source code where the engine recorded a statistically significant AI authorship indicator. Expand any category row to inspect the individual file paths, line numbers, code snippets, and the lexical context (CODE, COMMENT, or STRING) in which each match was detected.

Reading the findings table: The Severity column indicates the diagnostic confidence level (CRITICAL / HIGH / MEDIUM / LOW). The Context column identifies whether the match occurred inside executable code, an inline comment, or a string literal — comment-context matches receive a ×1.5 weight because LLMs systematically over-annotate. The ⚡ bolt icon marks clustered matches: three or more patterns within a 10-line window, each receiving an additional ×1.5 density multiplier as dense clusters constitute far stronger evidence of synthetic authorship than isolated hits.

Modern AI Meta-Vocabulary21 hits · 48 pts
SeverityFileLineSnippetContext
MEDIUMREADME.md180### Scientific Literature RAG & AnalysisCOMMENT
MEDIUMREADME.md195- [OpenBioMed](https://github.com/PharMolix/OpenBioMed) - Open-source biomedical AI platform integrating multimodal founCODE
MEDIUMREADME.md198- [BioMCP](https://github.com/genomoncology/biomcp) - Biomedical Model Context Protocol (MCP) server unifying literatureCODE
MEDIUMREADME.md199- [MATLAB Agentic Toolkit](https://github.com/matlab/matlab-agentic-toolkit) - Official MathWorks toolkit connecting AI CODE
MEDIUMREADME.md200- [BioNeMo Agent Toolkit (NVIDIA)](https://github.com/NVIDIA-BioNeMo/bionemo-agent-toolkit) - Turn any AI agent into a lCODE
MEDIUMREADME.md259- [AIDE (WecoAI, arXiv 2025)](https://github.com/WecoAI/aideml) - LLM-driven machine learning engineering agent using agCODE
MEDIUMREADME.md270- [POPPER](https://github.com/snap-stanford/POPPER) - Automated hypothesis testing with agentic sequential falsificationCODE
MEDIUMREADME.md274- [PantheonOS (Stanford, 2025)](https://github.com/aristoteleo/PantheonOS) - Evolvable and privacy-preserving multi-agenCODE
MEDIUMREADME.md277- [SR-Scientist (ICLR 2026)](https://github.com/GAIR-NLP/SR-Scientist) - Scientific equation discovery with agentic AI, CODE
MEDIUMREADME.md75- [DeepAnalyze](https://github.com/ruc-datalab/DeepAnalyze) - First agentic LLM for autonomous data science with end-to-CODE
MEDIUMREADME.md85- [GDM Science Skills](https://github.com/google-deepmind/science-skills) - Google DeepMind's official collection of ageCODE
MEDIUMREADME.md181- [PaperQA2](https://github.com/future-house/paper-qa) - High-accuracy RAG for scientific PDFs with citation support, agCODE
MEDIUMREADME.md240- [EvoMaster (SJTU SAI, arXiv 2026)](https://github.com/sjtu-sai-agents/EvoMaster) - Foundational auto-research agent frCODE
MEDIUMREADME.md243- [The AI Scientist v2 (SakanaAI)](https://github.com/SakanaAI/AI-Scientist-v2) - Official implementation of the second-CODE
MEDIUMREADME.md305- [Get Physics Done (PSI)](https://github.com/psi-oss/get-physics-done) - First open-source agentic AI physicist turningCODE
MEDIUMREADME.md307- [Zephyrus (ICLR 2026)](https://github.com/Rose-STL-Lab/Zephyrus) - First agentic framework for weather science, pairinCODE
MEDIUMREADME.md389- [From AI for Science to Agentic Science: A Survey on Autonomous Scientific Discovery](https://arxiv.org/abs/2508.14111CODE
MEDIUMREADME.md423- [The AI Scientist-v2: Workshop-Level Automated Scientific Discovery via Agentic Tree Search](https://arxiv.org/abs/250CODE
MEDIUMREADME.md443- [DeepAnalyze: Agentic Large Language Models for Autonomous Data Science](https://arxiv.org/abs/2510.16872) (2025.10) -CODE
MEDIUMREADME.md519- [REINVENT](https://github.com/MolecularAI/Reinvent) - Industrial-grade reinforcement-learning-based generative platforCODE
MEDIUMREADME.md644- [MedAgentGym](https://github.com/wshi83/MedAgentGym) - Scalable agentic training environment for code-centric reasoninCODE
Synthetic Comment Markers2 hits · 12 pts
SeverityFileLineSnippetContext
HIGHREADME.md267- [Robin](https://github.com/Future-House/robin) - FutureHouse's end-to-end scientific discovery multi-agent system orchCODE
HIGHREADME.md588- [OpenCRISPR](https://github.com/profluent-ai/opencrispr) - First open-source AI-generated gene editing systems developCODE