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Derify/ChemRanker-alpha-sim

Name: ChemRanker-alpha-sim
Rating: 2.5 (26 reviews)

This Cross Encoder is finetuned from Derify/ModChemBERT-IR-BASE using hard-negative triplets derived from Derify/pubchem_10m_genmol_similarity. Positive SMILES pairs are first filtered by quality and similarity constraints, then reduced to one strongest positive target per anchor molecule to create a high-signal training set for reranking. The model computes relevance scores for pairs of SMILES strings, enabling SMILES reranking and molecular semantic search.

For this variant, the positive selection objective is pure similarity ranking where each anchor keeps the highest-similarity candidate after filtering, rather than using a QED+similarity composite score. The quality stage uses strict inequality filtering (QED > 0.85, similarity > 0.5, with similarity also bounded below 1.0), and then keeps the top-scoring pair per anchor molecule.

Hard negatives are mined with Sentence Transformers using Derify/ChemMRL-beta as the teacher model and a TopK-PercPos-style margin setting based on NV-Retriever, with relative_margin=0.05 and max_negative_score_threshold = pos_score * percentage_margin. Training uses triplet-format samples with 5 mined negatives per anchor-positive pair and optimizes a multiple-negatives ranking objective, while reranking evaluation uses n-tuple samples with 30 mined negatives per query.

Model Details

Model Description

Model Type: Cross Encoder
Base model: Derify/ModChemBERT-IR-BASE
Maximum Sequence Length: 512 tokens
Number of Output Labels: 1 label
Training Dataset:
- Derify/pubchem_10m_genmol_similarity Mined Hard Negatives
License: apache-2.0

Model Sources

Documentation: Sentence Transformers Documentation
Documentation: Cross Encoder Documentation
Repository: Sentence Transformers on GitHub
Hugging Face: Cross Encoders on Hugging Face

Usage

Direct Usage (Sentence Transformers)

First install the Transformers and Sentence Transformers libraries:

bash

pip install -U "transformers>=4.57.1,<5.0.0"
pip install -U sentence-transformers

Then you can load this model and run inference.

python

from sentence_transformers import CrossEncoder

# Download from the 🤗 Hub
model = CrossEncoder("Derify/ChemRanker-alpha-sim")
# Get scores for pairs of texts
pairs = [
    ['c1snnc1C[NH2+]Cc1cc2c(s1)CCC2', 'c1snnc1CCC[NH2+]Cc1cc2c(s1)CCC2'],
    ['c1sc2c(c1-c1nc(C3CCOC3)no1)CCCC2', 'O=CCc1noc(-c2csc3c2CCCC3)n1'],
    ['c1sc(C[NH2+]C2CC2)nc1C[NH+]1CCN2CCCC2C1', 'FC(F)[NH2+]Cc1nc(C[NH+]2CCN3CCCC3C2)cs1'],
    ['c1sc(CC[NH+]2CCOCC2)nc1C[NH2+]C1CC1', 'CCc1nc(C[NH2+]C2CC2)cs1'],
    ['c1sc(CC2CCC[NH2+]2)nc1C1CCCO1', 'c1sc(CC2CCC[NH2+]2)nc1C1CCCC1'],
]
scores = model.predict(pairs)
print(scores.shape)
# (5,)

# Or rank different texts based on similarity to a single text
ranks = model.rank(
    'c1snnc1C[NH2+]Cc1cc2c(s1)CCC2',
    [
        'c1snnc1CCC[NH2+]Cc1cc2c(s1)CCC2',
        'O=CCc1noc(-c2csc3c2CCCC3)n1',
        'FC(F)[NH2+]Cc1nc(C[NH+]2CCN3CCCC3C2)cs1',
        'CCc1nc(C[NH2+]C2CC2)cs1',
        'c1sc(CC2CCC[NH2+]2)nc1C1CCCC1',
    ]
)
# [{'corpus_id': ..., 'score': ...}, {'corpus_id': ..., 'score': ...}, ...]

Evaluation

Metrics

Cross Encoder Reranking

Evaluated with CrossEncoderRerankingEvaluator with these parameters:
json
1{ 2 "at_k": 10 3}

Metric	Value
map	0.4323
mrr@10	0.6975
ndcg@10	0.7034

Training Details

Training Dataset

GenMol Similarity Hard Negatives

Dataset: GenMol Similarity Hard Negatives
Size: 3,269,544 training samples
Columns: smiles_a, smiles_b, and negative

Approximate statistics based on the first 1000 samples:

	smiles_a	smiles_b	negative
type	string	string	string
details	min: 19 characters mean: 33.64 characters max: 65 characters	min: 20 characters mean: 34.16 characters max: 54 characters	min: 19 characters mean: 33.28 characters max: 57 characters

Samples:

smiles_a	smiles_b	negative
`c1sc2cc3c(cc2c1CC[NH2+]C1CC1)OCCO3`	`FC(F)(F)[NH2+]CCc1csc2cc3c(cc12)OCCO3`	`[NH3+]CCCc1cc2c(cc1C1CC1)OCO2`
`c1sc2cc3c(cc2c1CC[NH2+]C1CC1)OCCO3`	`FC(F)(F)[NH2+]CCc1csc2cc3c(cc12)OCCO3`	`COc1cc2c(cc1C[NH2+]C1CCC1)OCO2`
`c1sc2cc3c(cc2c1CC[NH2+]C1CC1)OCCO3`	`FC(F)(F)[NH2+]CCc1csc2cc3c(cc12)OCCO3`	`O=c1[nH]c2cc3c(cc2cc1CNC1CCCCC1)OCCO3`

Loss: MultipleNegativesRankingLoss with these parameters:
json
1{ 2 "scale": 10.0, 3 "num_negatives": 4, 4 "activation_fn": "torch.nn.modules.activation.Sigmoid" 5}

Evaluation Dataset

GenMol Similarity Hard Negatives

Dataset: GenMol Similarity Hard Negatives
Size: 165,968 evaluation samples
Columns: smiles_a, smiles_b, negative_1, negative_2, negative_3, negative_4, negative_5, negative_6, negative_7, negative_8, negative_9, negative_10, negative_11, negative_12, negative_13, negative_14, negative_15, negative_16, negative_17, negative_18, negative_19, negative_20, negative_21, negative_22, negative_23, negative_24, negative_25, negative_26, negative_27, negative_28, negative_29, and negative_30

Approximate statistics based on the first 1000 samples:

	smiles_a	smiles_b	negative_1	negative_2	negative_3	negative_4	negative_5	negative_6	negative_7	negative_8	negative_9	negative_10	negative_11	negative_12	negative_13	negative_14	negative_15	negative_16	negative_17	negative_18	negative_19	negative_20	negative_21	negative_22	negative_23	negative_24	negative_25	negative_26	negative_27	negative_28	negative_29	negative_30
type	string	string	string	string	string	string	string	string	string	string	string	string	string	string	string	string	string	string	string	string	string	string	string	string	string	string	string	string	string	string	string	string
details	min: 17 characters mean: 37.57 characters max: 96 characters	min: 14 characters mean: 34.46 characters max: 70 characters	min: 16 characters mean: 35.94 characters max: 77 characters	min: 12 characters mean: 35.1 characters max: 77 characters	min: 14 characters mean: 35.09 characters max: 81 characters	min: 17 characters mean: 35.38 characters max: 74 characters	min: 17 characters mean: 35.17 characters max: 70 characters	min: 14 characters mean: 35.25 characters max: 84 characters	min: 16 characters mean: 35.2 characters max: 77 characters	min: 13 characters mean: 35.05 characters max: 80 characters	min: 11 characters mean: 35.25 characters max: 90 characters	min: 11 characters mean: 35.23 characters max: 74 characters	min: 12 characters mean: 34.88 characters max: 60 characters	min: 14 characters mean: 35.42 characters max: 66 characters	min: 13 characters mean: 35.36 characters max: 69 characters	min: 13 characters mean: 34.81 characters max: 77 characters	min: 10 characters mean: 35.12 characters max: 77 characters	min: 17 characters mean: 35.05 characters max: 69 characters	min: 14 characters mean: 35.47 characters max: 72 characters	min: 14 characters mean: 35.12 characters max: 65 characters	min: 18 characters mean: 35.44 characters max: 72 characters	min: 14 characters mean: 35.0 characters max: 64 characters	min: 18 characters mean: 35.79 characters max: 81 characters	min: 17 characters mean: 35.43 characters max: 67 characters	min: 14 characters mean: 35.76 characters max: 68 characters	min: 14 characters mean: 35.29 characters max: 62 characters	min: 17 characters mean: 35.42 characters max: 66 characters	min: 16 characters mean: 35.31 characters max: 83 characters	min: 18 characters mean: 35.64 characters max: 77 characters	min: 18 characters mean: 35.47 characters max: 77 characters	min: 11 characters mean: 35.23 characters max: 65 characters	min: 16 characters mean: 35.26 characters max: 77 characters

Samples:

smiles_a	smiles_b	negative_1	negative_2	negative_3	negative_4	negative_5	negative_6	negative_7	negative_8	negative_9	negative_10	negative_11	negative_12	negative_13	negative_14	negative_15	negative_16	negative_17	negative_18	negative_19	negative_20	negative_21	negative_22	negative_23	negative_24	negative_25	negative_26	negative_27	negative_28	negative_29	negative_30
`c1snnc1C[NH2+]Cc1cc2c(s1)CCC2`	`c1snnc1CCC[NH2+]Cc1cc2c(s1)CCC2`	`c1snnc1CCC[NH2+]Cc1cc2c(s1)CCC2`	`Cn1cc(C[NH2+]Cc2cc3c(s2)CCC3)nn1`	`Cn1cc(CC[NH2+]Cc2cc3c(s2)CCC3)nn1`	`Cc1cc(C[NH2+]Cc2csnn2)sc1C`	`NC(=O)c1csc(C[NH2+]Cc2cc3c(s2)CCC3)c1`	`Cc1cc(CC[NH2+]Cc2csnn2)sc1C`	`Ic1ccc(C[NH2+]Cc2cc3c(s2)CCC3)o1`	`Cc1cc(C[NH2+]CCCCc2cc3c(s2)CCC3)c(C)s1`	`c1ccc(C[NH2+]Cc2cc3c(s2)CCC3)cc1`	`c1ncc(C[NH2+]Cc2csnn2)s1`	`FC(F)c1csc(C[NH2+]Cc2cc3c(s2)CCC3)c1`	`c1c(C[NH2+]CC2CC2)sc2c1CSCC2`	`N#Cc1cc(F)cc(C[NH2+]Cc2cc3c(s2)CCC3)c1`	`c1cc(C[NH2+]Cc2nc3c(s2)CCC3)no1`	`CCc1ccc(C[NH2+]Cc2csnn2)s1`	`NCc1csc(NCc2cc3c(s2)CCC3)n1`	`C[object Object]Cc1nnc(-c2cc3c(s2)CCCC3)o1`	`Fc1cc(C[NH2+]Cc2cc3c(s2)CCC3)ccc1Br`	`FC(F)(F)C[NH2+]Cc1cc2c(s1)CCSC2`	`c1cc(C[NH2+]Cc2cc3c(s2)CCC3)c[nH]1`	`Cc1cc(C)c(CC[NH2+]Cc2cc3c(s2)CCC3)c(C)c1`	`Oc1ccc(C[NH2+]Cc2cc3c(s2)CCC3)cc1Br`	`O=C([O-])c1ccc(CC[NH2+]Cc2cc3c(s2)CCC3)s1`	`c1c(C[NH2+]CC2CCCC2)sc2c1CCC2`	`O=C([O-])c1ccc(C[NH2+]Cc2cc3c(s2)CCC3)s1`	`COc1cc(C)cc(C[NH2+]Cc2cc3c(s2)CCC3)c1`	`PSc1ccc(C[NH2+]Cc2csnn2)s1`	`CCc1cnc(C[NH2+]Cc2csnn2)s1`	`Clc1cc(C[NH2+]Cc2cc3c(s2)CCC3)ccc1Br`	`c1c(C[NH2+]CC2CC2)sc2c1CCCCC2`
`c1sc2c(c1-c1nc(C3CCOC3)no1)CCCC2`	`O=CCc1noc(-c2csc3c2CCCC3)n1`	`Nc1sc2c(c1-c1nc(C3CCOC3)no1)CCCC2`	`Nc1sc2c(c1-c1nc(C3CCC3)no1)CCCC2`	`c1c(-c2nc(C3CCCNC3)no2)sc2c1CCCCCC2`	`Nc1sccc1-c1nc(C2CCCOC2)no1`	`Nc1sc2c(c1-c1nc(C3CCCO3)no1)CCCC2`	`Cc1csc(-c2nc(C3CCOCC3)no2)c1N`	`Cc1oc2c(c1-c1nc(C3CCOC3)no1)C(=O)CCC2`	`c1c(-c2nc(C3C[NH2+]CCO3)no2)sc2c1CCCCC2`	`O=C([O-])Nc1sc2c(c1-c1nc(C3CC3)no1)CCCC2`	`c1cc2c(s1)CCCC2c1nc(C2CC2)no1`	`CC(=O)N1CCCC(c2noc(-c3cc4c(s3)CCCCCC4)n2)C1`	`Cc1cc(-c2nc([C@@H]3CCOC3)no2)c(N)s1`	`c1cc2c(nc1-c1noc(C3CCCOC3)n1)CCCC2`	`Nc1sccc1-c1nc(C2CCCC2)no1`	`c1cc2c(nc1-c1noc(C3CCOCC3)n1)CCCC2`	`[NH3+]C(c1noc(-c2cc3c(s2)CCCC3)n1)C1CC1`	`c1cc2c(c(-c3nc(C4CCOCC4)no3)c1)CCCN2`	`c1c(-c2nc(C3CC3)no2)nn2c1CCCC2`	`CN1CC(c2noc(-c3cc4c(s3)CCCC4)n2)CC1=O`	`Oc1c(-c2nc(C3CCC(F)(F)C3)no2)ccc2c1CCCC2`	`O=CCc1noc(-c2csc3c2CCCC3)n1`	`Cc1cc(=O)c(-c2noc(C3CCCOC3)n2)c2n1CCC2`	`O=C([O-])CNc1sc2c(c1-c1nc(C3CC3)no1)CCCC2`	`c1cc(-c2noc(C3CCCOC3)n2)cs1`	`Cn1nc(-c2nc(C3CCCO3)no2)c2c1CCCC2`	`O=C(Nc1sc2c(c1-c1nc(C3CC3)no1)COCC2)C1=CCCCC1`	`Cc1cscc1-c1noc(C2CCOCC2)n1`	`CC1(C)CCCc2sc(N)c(-c3nc(C4CC4)no3)c21`	`Clc1cc2c(c(-c3nc(C4CCOC4)no3)c1)OCC2`	`Nc1sc2c(c1-c1nnc(C3CC3)o1)CCCC2`
`c1sc(C[NH2+]C2CC2)nc1C[NH+]1CCN2CCCC2C1`	`FC(F)[NH2+]Cc1nc(C[NH+]2CCN3CCCC3C2)cs1`	`FC(F)[NH2+]Cc1nc(C[NH+]2CCN3CCCC3C2)cs1`	`CC(C)[NH2+]Cc1nc(C[NH+]2CCC3CCCCC3C2)cs1`	`CN1C2CCC1C[object Object]CC2`	`Nc1nc(CC[NH+]2CCCN3CCCC3C2)cs1`	`NCc1nc(C[NH+]2CCCC3CCCCC32)cs1`	`CC1C[object Object]CCN1C`	`Oc1csc(CN2CCCC3C[NH2+]CC32)n1`	`CCc1nc(C[NH+]2CCCC3CCCCC32)cs1`	`C[NH2+]Cc1csc(N2CC[NH+]3CCCC3C2)n1`	`[NH3+]Cc1nc(C[NH+]2CCC3CCCCC32)cs1`	`CC1CN2CCCCC2C[NH+]1Cc1csc(CC[NH3+])n1`	`CCCc1nc(CN2CCCC2C2CCC[NH2+]2)cs1`	`ClCCc1nc(CN2CCCC2C2CCC[NH2+]2)cs1`	`c1cc(C[NH2+]C2CC2)c(C[NH+]2CCN3CCCCC3C2)o1`	`O=C(Cc1nc(CCl)cs1)N1CCC[NH+]2CCCC2C1`	`CC[NH2+]Cc1csc(N2CCC3C(CCC[NH+]3C)C2)n1`	`c1sc(C[NH2+]C2CC2)nc1C[NH+]1CCCCC1`	`[NH3+]Cc1nc(C[NH+]2CCCC2C2CCCC2)cs1`	`Cc1csc(C[NH+]2CCC3C[NH2+]CC3C2)n1`	`c1cc(C[NH+]2CCCN3CCCC3C2)nc(C2CC2)n1`	`Cc1ccsc1C[NH2+]CCN1CCN2CCCC2C1`	`c1sc(C[NH2+]C2CCCC2)nc1C[NH+]1CCCCC1`	`Brc1csc(C[NH2+]CCN2CCN3CCCCC3C2)c1`	`Cc1nc(CCC[NH2+]C2CCN3CCCCC23)cs1`	`CCOC(=O)c1nc(CN2CC3CCC[NH2+]C3C2)cs1`	`CCCC(=O)c1nc(CN2CC3CCC[NH2+]C3C2)cs1`	`CC(C)(C)c1csc(CN2CCC[NH2+]C(C3CC3)C2)n1`	`COCc1nc(CN2CCC([NH3+])C2)cs1`	`CCC[NH2+]Cc1nc(C[NH+]2CC3CCC2C3)cs1`	`CCC1CN2CCCC2C[NH+]1CCc1csc(C)n1`

Loss: MultipleNegativesRankingLoss with these parameters:
json
1{ 2 "scale": 10.0, 3 "num_negatives": 4, 4 "activation_fn": "torch.nn.modules.activation.Sigmoid" 5}

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: epoch
per_device_train_batch_size: 256
per_device_eval_batch_size: 256
torch_empty_cache_steps: 1000
learning_rate: 3e-05
weight_decay: 1e-05
max_grad_norm: None
lr_scheduler_type: warmup_stable_decay
lr_scheduler_kwargs: {'num_decay_steps': 6385, 'warmup_type': 'linear', 'decay_type': '1-sqrt'}
warmup_steps: 6385
seed: 12
data_seed: 24681357
bf16: True
bf16_full_eval: True
tf32: True
dataloader_num_workers: 8
dataloader_prefetch_factor: 2
load_best_model_at_end: True
optim: stable_adamw
optim_args: decouple_lr=True,max_lr=3e-05
dataloader_persistent_workers: True
resume_from_checkpoint: False
gradient_checkpointing: True
torch_compile: True
torch_compile_backend: inductor
torch_compile_mode: max-autotune
eval_on_start: True
batch_sampler: no_duplicates

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: epoch
prediction_loss_only: True
per_device_train_batch_size: 256
per_device_eval_batch_size: 256
per_gpu_train_batch_size: None
per_gpu_eval_batch_size: None
gradient_accumulation_steps: 1
eval_accumulation_steps: None
torch_empty_cache_steps: 1000
learning_rate: 3e-05
weight_decay: 1e-05
adam_beta1: 0.9
adam_beta2: 0.999
adam_epsilon: 1e-08
max_grad_norm: None
num_train_epochs: 3
max_steps: -1
lr_scheduler_type: warmup_stable_decay
lr_scheduler_kwargs: {'num_decay_steps': 6385, 'warmup_type': 'linear', 'decay_type': '1-sqrt'}
warmup_ratio: 0.0
warmup_steps: 6385
log_level: passive
log_level_replica: warning
log_on_each_node: True
logging_nan_inf_filter: True
save_safetensors: True
save_on_each_node: False
save_only_model: False
restore_callback_states_from_checkpoint: False
no_cuda: False
use_cpu: False
use_mps_device: False
seed: 12
data_seed: 24681357
jit_mode_eval: False
bf16: True
fp16: False
fp16_opt_level: O1
half_precision_backend: auto
bf16_full_eval: True
fp16_full_eval: False
tf32: True
local_rank: 0
ddp_backend: None
tpu_num_cores: None
tpu_metrics_debug: False
debug: []
dataloader_drop_last: True
dataloader_num_workers: 8
dataloader_prefetch_factor: 2
past_index: -1
disable_tqdm: False
remove_unused_columns: True
label_names: None
load_best_model_at_end: True
ignore_data_skip: False
fsdp: []
fsdp_min_num_params: 0
fsdp_config: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
fsdp_transformer_layer_cls_to_wrap: None
accelerator_config: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
parallelism_config: None
deepspeed: None
label_smoothing_factor: 0.0
optim: stable_adamw
optim_args: decouple_lr=True,max_lr=3e-05
adafactor: False
group_by_length: False
length_column_name: length
project: huggingface
trackio_space_id: trackio
ddp_find_unused_parameters: None
ddp_bucket_cap_mb: None
ddp_broadcast_buffers: False
dataloader_pin_memory: True
dataloader_persistent_workers: True
skip_memory_metrics: True
use_legacy_prediction_loop: False
push_to_hub: False
resume_from_checkpoint: False
hub_model_id: None
hub_strategy: every_save
hub_private_repo: None
hub_always_push: False
hub_revision: None
gradient_checkpointing: True
gradient_checkpointing_kwargs: None
include_inputs_for_metrics: False
include_for_metrics: []
eval_do_concat_batches: True
fp16_backend: auto
push_to_hub_model_id: None
push_to_hub_organization: None
mp_parameters:
auto_find_batch_size: False
full_determinism: False
torchdynamo: None
ray_scope: last
ddp_timeout: 1800
torch_compile: True
torch_compile_backend: inductor
torch_compile_mode: max-autotune
include_tokens_per_second: False
include_num_input_tokens_seen: no
neftune_noise_alpha: None
optim_target_modules: None
batch_eval_metrics: False
eval_on_start: True
use_liger_kernel: False
liger_kernel_config: None
eval_use_gather_object: False
average_tokens_across_devices: True
prompts: None
batch_sampler: no_duplicates
multi_dataset_batch_sampler: proportional
router_mapping: {}
learning_rate_mapping: {}

Training Logs

Epoch	Step	Training Loss	Validation Loss	ndcg@10
1.0963	7000	0.0046	-	-
1.2529	8000	0.0043	-	-
1.4096	9000	0.0038	-	-
1.5662	10000	0.0035	-	-
1.7228	11000	0.0033	-	-
1.8794	12000	0.0031	-	-
2.0	12770	-	1.5814	0.6986
2.0360	13000	0.003	-	-
2.1926	14000	0.0027	-	-
2.3493	15000	0.0025	-	-
2.5059	16000	0.0025	-	-
2.6625	17000	0.0024	-	-
2.8191	18000	0.0024	-	-
2.9757	19000	0.0024	-	-
3.0	19155	-	1.5688	0.7034

The bold row denotes the saved checkpoint.

Environmental Impact

Carbon emissions were measured using CodeCarbon.

Energy Consumed: 12.236 kWh
Carbon Emitted: 2.512 kg of CO2
Hours Used: 19.958 hours

Training Hardware

On Cloud: No
GPU Model: 2 x NVIDIA GeForce RTX 3090
CPU Model: AMD Ryzen 7 3700X 8-Core Processor
RAM Size: 62.70 GB

Framework Versions

Python: 3.13.7
Sentence Transformers: 5.1.2
Transformers: 4.57.1
PyTorch: 2.9.0+cu128
Accelerate: 1.11.0
Datasets: 4.4.1
Tokenizers: 0.22.1

Citation

BibTeX

Sentence Transformers

bibtex

@inproceedings{reimers-2019-sentence-bert,
    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
    author = "Reimers, Nils and Gurevych, Iryna",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
    month = "11",
    year = "2019",
    publisher = "Association for Computational Linguistics",
    url = "https://arxiv.org/abs/1908.10084",
}

NV-Retriever

bibtex

@misc{moreira2025nvretrieverimprovingtextembedding,
      title={NV-Retriever: Improving text embedding models with effective hard-negative mining}, 
      author={Gabriel de Souza P. Moreira and Radek Osmulski and Mengyao Xu and Ronay Ak and Benedikt Schifferer and Even Oldridge},
      year={2025},
      eprint={2407.15831},
      archivePrefix={arXiv},
      primaryClass={cs.IR},
      url={https://arxiv.org/abs/2407.15831}, 
}