{
    "comment": "HYP-36: Champion enhancement test. Hyp-32 (soft_800k_20ep, bs=512) is the current best at AUROC@50=0.7901 (+0.005 vs Hyp-14, +0.011 vs TCRdist) but the marginal lift over Hyp-14 says the 'more epochs' axis is essentially saturated. The remaining cheap knob inside the InBatchTcrdistSoftCollator pipeline is BATCH SIZE: doubling bs from 512 to 1024 doubles each anchor's pool of in-batch candidates (and the soft-positive set scales with that pool). This sharpens the contrastive signal per gradient step in a way that more epochs cannot. Single-variable change from Hyp-32: batch_size 512 -> 1024. Step count halves (827k/1024 ~= 808 steps/epoch * 20 = ~16,200 steps, comparable to Hyp-14's compute budget) but each step sees 4x more pairwise relations. Distinct save name 'SCEPTR6L_AC_soft_800k_20ep_bs1024' so we can compare directly against Hyp-32 in the same benchmark sweep.",
    "training_delegate": {
        "class": "ClTrainingDelegate",
        "initargs": {}
    },
    "model": {
        "name": "SCEPTR6L_AC_soft_800k_20ep_bs1024",
        "path_to_pretrained_state_dict": null,
        "token_embedder": {
            "class": "CdrSimpleEmbedder",
            "initargs": {}
        },
        "self_attention_stack": {
            "class": "ScaledSceptrEncoderWithProjection",
            "initargs": {
                "embedding_dim": 29,
                "num_layers": 6,
                "embed_dim": 128,
                "num_heads": 8,
                "dropout": 0.1
            }
        },
        "mlm_token_prediction_projector": {
            "class": "AminoAcidTokenProjector",
            "initargs": {
                "d_model": 128
            }
        },
        "vector_representation_delegate": {
            "class": "ClsVectorRepresentationDelegate",
            "initargs": {}
        },
        "trainable_model": {
            "class": "ClTrainableModel",
            "initargs": {}
        }
    },
    "data": {
        "training_data": {
            "dataset": {
                "class": "TcrDataset",
                "initargs": {}
            },
            "dataloader": {
                "class": "SingleDatasetDataLoader",
                "initargs": {
                    "batch_size": 1024,
                    "num_workers": 4
                }
            },
            "csv_paths": [
                "tcr_data/preprocessed/tanno/train.csv"
            ]
        },
        "validation_data": {
            "dataset": {
                "class": "TcrDataset",
                "initargs": {}
            },
            "dataloader": {
                "class": "SingleDatasetDataLoader",
                "initargs": {
                    "batch_size": 1024,
                    "num_workers": 4
                }
            },
            "csv_paths": [
                "tcr_data/preprocessed/tanno/test.csv"
            ]
        },
        "tokeniser": {
            "class": "CdrTokeniser",
            "initargs": {}
        },
        "batch_collator": {
            "class": "InBatchTcrdistSoftCollator",
            "initargs": {
                "frac_dropped_tokens": 0.2,
                "prob_drop_chain": 0.5,
                "training_csv_path": "tcr_data/preprocessed/tanno/train.csv",
                "tau_tcrdist": 50.0,
                "tcrdist_threshold": 200.0
            }
        }
    },
    "loss": {
        "cross_entropy_loss": {
            "class": "AdjustedCrossEntropyLoss",
            "initargs": {
                "label_smoothing": 0.1
            }
        },
        "contrastive_loss": {
            "class": "DotProductLoss",
            "initargs": {
                "temp": 0.05
            }
        }
    },
    "optimiser": {
        "initargs": {
            "n_warmup_steps": 2000,
            "d_model": 128
        }
    },
    "num_epochs": 20
}