d5fadc36773f0a6e03ae542aac79a3747abd47f77028ab304cb878dc2beb3ec1  .gitattributes
d980b376af380689d7e60e8c86d2ca9b96a09215337a59670430a90df3eadaf1  CITATION.cff
cfc7749b96f63bd31c3c42b5c471bf756814053e847c10f3eb003417bc523d30  LICENSE
a7afc45c75c5ba304cf2bfcd9061e0832f28cb3a42cc29fea2c1284d6def2964  NOTICE
5d7a0c27387950ac75b835844c2092f59e0ab13071a5b7009f151267827233ed  README.md
e5465da8a0115cb226fc36e640ef534e761f1adec88c1b3345c33d57c483a443  TECHNICAL_REPORT.md
39d3e713e98dc32ad4b663d58e0ffff719d91550d08545076d5094d804ce5e05  adapter_config.json
e0e6655a16a10f28cbce898e564640bf6a4a64ca84bb2014a1da3a60c5f11eda  adapter_model.safetensors
1449b4e04afd787b8594d36bc522c28dbbafd5d9bf40ad0973bb20f3954e84c7  assets/benchmark-overview.png
50c7c38b901aababb16ccaf599bcb95a36a2ad9bfd41bfcf8210bff46b0f8631  assets/dot-loom-conductor-cover.png
d8b632da85953ed75b11f9e77d998ad01e89e042a14abd8f3990fc5fc2442b68  assets/dot-mark.png
4d5dfe31eea6fcd2951805c4fc2999962659521dd89b90e3c9cc6f90ce27eeb4  assets/family-generalization.png
e72de3a667dbc2ed166224d6297b863e452d02340766c1c03852dcbd4ac32b10  assets/training-dynamics.png
433ffcf387183f580ce03978f4d2dff0d518416f67083c481c5073dcf3d7d31d  evidence/checkpoint-selection.json
ced5263c61c6732af7da8fb7c03a227e71ff3f129b54b3a1965ede6c1ba0d33a  evidence/deterministic_scored.summary.json
dbe6682d157932edeafddf20303c0f0e4380bf467af7934e1070a4862519ec3f  evidence/gpu-telemetry.summary.json
3224792c3e208ef3be7b78425f7dadc0bcfbbb8b1f97c6a88b7700e460392440  evidence/paired-statistics.json
0f5f6f57b420180594a632a75da532bfb3b2966e8e9793dcfb6cb8f6dea5910f  evidence/trained_guarded_scored.summary.json
4307ca4ba613457cabf8bc2f1e300a0fdb23eacc3e9fbc6ee7884e37bcc47c95  evidence/training_receipt.json
c8b607e2988837d3fd5bf6818c4fdb413a17f7249657125c2cb7c300d8bc063d  inference_example.py
af7b401610495b77d64793597cb756b5466577af71b9fd5197e181b08dd1a2c9  requirements.txt