csim-ai

Neural-augmented Python code plagiarism detection for programming judges. Successor to csim (ANTLR4 parse-tree normalization + Tree Edit Distance), adding a contrastively fine-tuned bi-encoder for the structural/semantic plagiarism cases where pure TED similarity degrades. Scores are a fusion of both signals via a small GBDT, verified to beat Dolos on this project's own test data -- see docs/REPORT.md for the full methodology and results, docs/DEVELOPMENT.md for the phase-by-phase build log.

Task: plagiarism detection (did B derive from A?), not semantic clone detection (does B solve the same problem as A?). Two independent correct solutions to the same problem are a negative, not a positive.

Install

pip install csim-ai              # bi-encoder cosine similarity only (onnxruntime, no torch)
pip install csim-ai[ast,scorer]  # + csim TED signal + GBDT fusion -- the full hybrid score

Model weights aren't bundled in the package (the ONNX export is ~500MB) -- run csim-ai setup once after installing to download and cache them from Hugging Face Hub (edson-eddy/csim-ai). After that, both the CLI and the Scorer class auto-detect the cache and give the full hybrid score with no further flags or arguments.

pip install csim-ai[ast,scorer]
csim-ai setup
# bi-encoder cached at: ~/.cache/huggingface/hub/models--edson-eddy--csim-ai/...
# fusion model cached at: .../fusion_model.joblib

CLI

CLI shape follows csim's, not a from-scratch design: a single csim-ai command, an action positional, --path pointing at a directory compared exhaustively -- same pattern as csim {report,group,tree,view,info} --path DIR --lang ... --talg ..., since this tool has the same predecessor and audience.

csim-ai report --path submissions/
# b.py is similar to a.py with similarity index: 0.9998 (biencoder_cosine=1.0000, csim_ted=1.0000, fusion=0.9998)

csim-ai group --path submissions/ --threshold 0.9
# Group 1 (Average Similarity: 1.00):
# a.py
# c.py
# Unique Files (similarity below threshold):
# b.py

csim-ai info
# which optional backends (onnxruntime, tokenizers, huggingface_hub, csim, scikit-learn, torch) are available

report

Pairwise similarity report over every .py file in --path, all combinations.

group

Same comparison as report, but groups files into connected components by a similarity threshold instead of listing every pair.

Same flags as report, plus:

info

No comparison -- just reports which optional backends are importable (onnxruntime/tokenizers/huggingface_hub from the base install; csim/scikit-learn from [ast,scorer]; torch from [export]). Takes an optional --model-path to also check a directory for model.onnx/tokenizer.json.

setup

Not part of pip install . -- a separate step because the weights aren't bundled in the package.

For every report/group result, "similarity index" is the fusion score when available, else biencoder_cosine. csim_ted/ fusion come back as None (and are dropped from the report line) when csim/scikit-learn aren't installed, so a bare pip install csim-ai (no extras, no setup) still gives a usable bi-encoder-only score.

Python API

from csim_ai import Scorer

scorer = Scorer()                     # after `csim-ai setup`: full hybrid, cache auto-detected
scorer = Scorer(use_fusion=True)      # force-downloads the fusion model too if `setup` wasn't run yet
scorer = Scorer(
    "path/to/onnx_model",
    fusion_model_path="path/to/fusion_model.joblib",
)                                      # fully local, no network

scorer.score(code_a, code_b)
# {"biencoder_cosine": 0.987, "csim_ted": 0.83, "fusion": 0.978}

Scorer(model_path=None, fusion_model_path=None, use_fusion=False):

• model_path: directory with model.onnx/tokenizer.json. None (default) downloads from Hugging Face Hub, cached after first call.
• fusion_model_path: path to a fusion_model.joblib. None (default) auto-uses a fusion model already cached by a prior csim-ai setup or use_fusion=True call, without triggering a network request to check.
• use_fusion: if True and no fusion_model_path is given, force-downloads the fusion model from HF Hub instead of just checking the cache.

scorer.score(code_a: str, code_b: str) -> dict returns {"biencoder_cosine": float, "csim_ted": float | None, "fusion": float | None} -- csim_ted/fusion are None when csim/scikit-learn aren't installed or no fusion model is available.

Layout

src/csim_ai/       inference package -- ONNX bi-encoder + csim TED + GBDT fusion
tests/             pytest smoke tests for src/csim_ai
training/          dataset prep, synthetic plagiarism generation, training, eval, export tooling
docs/
  REPORT.md        project narrative: problem, methodology, results, limitations
  DEVELOPMENT.md   phase-by-phase build log: commands, exact numbers, bugs hit and fixed