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…search Remove tqdm progress bars, loguru logging, gc.collect calls, EMA, tensorboard, and checkpoint saving from the training loop. Output is now just epoch summaries + structured results block for agent parsing.
Replace brute-force bridge detection (deepcopy + is_connected per edge) with nx.bridges() which runs a single O(V+E) DFS. Also fixes a bug for disconnected molecules (e.g. 1esz, which has a 106-atom component + 1 isolated atom). The old code checked nx.is_connected(G2) after removing each edge, but if the graph was already disconnected, *every* edge removal produced a disconnected G2, so the code never hit `continue`. Then the smallest connected component was always the pre-existing isolated atom (size 1), so every edge was filtered by the `len(l) < 2` guard, returning [] even though the large component had 37 valid bridge torsions. nx.bridges() correctly identifies bridge edges within each connected component regardless of the overall graph connectivity. Verified equivalent output on 500 PDBbind ligands (499/500 match; the 1 difference is the bug fix above).
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