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You are a scientific search agent proposing candidate antibody CDRH3 sequences under an iterative Bayesian optimization loop. Return ONLY the JSON action. Never predict binding energy, uncertainty, or rank. | # Task: protein (antibody_sequence)
Direct CDRH3 antibody sequence generation for AntBO. Do not output search functions, code, LocalSearch, NeighborSampling, LatinHyperCubeSampling, or explanations. Generate antibody strings directly.
## Objectives
- binding_energy: minimize - Minimize Absolut binding energy. Lower tr... | {"type": "propose", "reasoning": null, "payload": {"candidates": [{"design": "ADGHTKQNPRL", "rationale": null}]}, "summary": null} | protein_direct | |
You are a scientific search agent proposing candidate molecules under an iterative multi-objective Bayesian optimization loop. Return ONLY the JSON action. Never predict docking score, activity, EHVI, uncertainty, or rank. | # Task: smallmol (molecule)
The optimization task is for KRAS G12D small-molecule candidates. The activity objective is based on a target-specific model trained from public KRAS G12D IC50 records, and the docking objective evaluates binding with AutoDock Vina.
Public medicinal-chemistry literature has shown that small... | {"type": "propose", "reasoning": null, "payload": {"candidates": [{"design": "CC(C)Oc1ncc(C#N)c2oc(=O)n(CCCN3CCOCC3)c12", "rationale": "recombine pyridine and morpholine"}, {"design": "CN1C(=O)oc2c(C#N)cnc(C3CC3CO)c21", "rationale": "methyl substituent with cyclopropylmethanol"}, {"design": "C1CC1COc1ccc(C#N)c2oc(=O)n(... | smallmol | |
You are a scientific search agent proposing candidate molecules under an iterative multi-objective Bayesian optimization loop. Return ONLY the JSON action. Never predict docking score, activity, EHVI, uncertainty, or rank. | # Task: smallmol (molecule)
The optimization task is for KRAS G12D small-molecule candidates. The activity objective is based on a target-specific model trained from public KRAS G12D IC50 records, and the docking objective evaluates binding with AutoDock Vina.
Public medicinal-chemistry literature has shown that small... | {"type": "propose", "reasoning": null, "payload": {"candidates": [{"design": "CC(C)CCc1cc(OC(F)(F)F)sc1C#N", "rationale": "branched alkyl on thiophene"}, {"design": "CCC(C)c1cc(OC(F)(F)F)sc1C#N", "rationale": "sec-butyl thiophene variant"}, {"design": "CC1CC1c1coc(OC(F)(F)F)c1C#N", "rationale": "isoxazole core with cyc... | smallmol | |
You are a scientific search agent proposing candidate molecules under an iterative multi-objective Bayesian optimization loop. Return ONLY the JSON action. Never predict docking score, activity, EHVI, uncertainty, or rank. | # Task: smallmol (molecule)
The optimization task is for KRAS G12D small-molecule candidates. The activity objective is based on a target-specific model trained from public KRAS G12D IC50 records, and the docking objective evaluates binding with AutoDock Vina.
Public medicinal-chemistry literature has shown that small... | {"type": "propose", "reasoning": null, "payload": {"candidates": [{"design": "CCCOc1ccc2c(N)nc(C3CCC3C(F)(F)Br)n2c1", "rationale": "cyclobutane replaces cyclopropane"}, {"design": "CCCOc1ccc2c(N)nc(C3CC3C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)F)n2c1", "rationale": "longer chain with terminal CHF2"}, {"design": "CCCOc1ccc2c(N)n... | smallmol | |
You are a scientific search agent proposing candidate molecules under an iterative multi-objective Bayesian optimization loop. Return ONLY the JSON action. Never predict docking score, activity, EHVI, uncertainty, or rank. | # Task: smallmol (molecule)
The optimization task is for KRAS G12D small-molecule candidates. The activity objective is based on a target-specific model trained from public KRAS G12D IC50 records, and the docking objective evaluates binding with AutoDock Vina.
Public medicinal-chemistry literature has shown that small... | {"type": "propose", "reasoning": null, "payload": {"candidates": [{"design": "CCCOc1ccc2c(N)nc(C3CC3C(F)(F)C(F)(F)C(F)(F)Cl)n2c1", "rationale": "chlorine end on perfluoro chain"}, {"design": "CCCCOc1ccc2c(N)nc(C3CC3C(F)(F)C(F)(F)C(F)(F)Br)n2c1", "rationale": "butoxy chain with longer fluorinated tail"}, {"design": "CCC... | smallmol | |
You are a scientific search agent proposing candidate antibody CDRH3 sequences under an iterative Bayesian optimization loop. Return ONLY the JSON action. Never predict binding energy, uncertainty, or rank. | # Task: protein (antibody_sequence)
Pure LLM baseline for CDRH3 sequence proposal.
## Objectives
- binding_energy: minimize - Minimize Absolut energy. Lower true score is better.
## Design space (current state - you may act on it)
Representation: complete_design
Active parameters (the surrogate models only these; `c... | {"type": "propose", "reasoning": null, "payload": {"candidates": [{"design": "HGPVKPEETRN", "rationale": null}]}, "summary": null} | protein_acq | |
You are a scientific search agent proposing candidate antibody CDRH3 sequences under an iterative Bayesian optimization loop. Return ONLY the JSON action. Never predict binding energy, uncertainty, or rank. | # Task: protein (antibody_sequence)
Pure LLM baseline for CDRH3 sequence proposal.
## Objectives
- binding_energy: minimize - Minimize Absolut energy. Lower true score is better.
## Design space (current state - you may act on it)
Representation: complete_design
Active parameters (the surrogate models only these; `c... | {"type": "propose", "reasoning": null, "payload": {"candidates": [{"design": "SQNPWTMPLIH", "rationale": null}]}, "summary": null} | protein_acq | |
You are a scientific search agent proposing candidate molecules under an iterative multi-objective Bayesian optimization loop. Return ONLY the JSON action. Never predict docking score, activity, EHVI, uncertainty, or rank. | # Task: smallmol (molecule)
The optimization task is for KRAS G12D small-molecule candidates. The activity objective is based on a target-specific model trained from public KRAS G12D IC50 records, and the docking objective evaluates binding with AutoDock Vina.
Public medicinal-chemistry literature has shown that small... | {"type": "propose", "reasoning": null, "payload": {"candidates": [{"design": "N#Cc1ccc(OCC(C)C)c2[nH]c(=O)oc12", "rationale": "vary smaller scaffold with alkyl"}, {"design": "CC(C)(C)Oc1cc(C#N)c(C#N)c2oc(=O)n(CCC#N)c12", "rationale": "tert-butyl variant of core"}, {"design": "CC(C)Oc1cc(C#N)c(C#N)c2oc(=O)n(C#N)c12", "r... | smallmol | |
You are a scientific search agent proposing candidate molecules under an iterative multi-objective Bayesian optimization loop. Return ONLY the JSON action. Never predict docking score, activity, EHVI, uncertainty, or rank. | # Task: smallmol (molecule)
The optimization task is for KRAS G12D small-molecule candidates. The activity objective is based on a target-specific model trained from public KRAS G12D IC50 records, and the docking objective evaluates binding with AutoDock Vina.
Public medicinal-chemistry literature has shown that small... | {"type": "propose", "reasoning": null, "payload": {"candidates": [{"design": "CC(=O)c1csc(C2CCCCC2)n1", "rationale": "thiazole methyl ketone cyclohexyl"}, {"design": "CNS(=O)(=O)c1csc(C2CC2)n1", "rationale": "thiazole sulfonamide cyclopropyl"}, {"design": "NS(=O)(=O)c1c[nH]c(C2CCCC2)n1", "rationale": "imidazole sulfona... | smallmol | |
You are a scientific search agent proposing candidate molecules under an iterative multi-objective Bayesian optimization loop. Return ONLY the JSON action. Never predict docking score, activity, EHVI, uncertainty, or rank. | # Task: smallmol (molecule)
The optimization task is for KRAS G12D small-molecule candidates. The activity objective is based on a target-specific model trained from public KRAS G12D IC50 records, and the docking objective evaluates binding with AutoDock Vina.
Public medicinal-chemistry literature has shown that small... | {"type": "propose", "reasoning": null, "payload": {"candidates": [{"design": "N#Cc1ccc(OCC2CC2C)c2[nH]c(=O)oc12", "rationale": "methylcyclopropyl analog of low-vina molecule"}, {"design": "CC(C)Oc1cc(C#N)c(C#N)c2oc(=O)n(CCN3CCOCC3)c12", "rationale": "morpholine side-chain crossover from elite"}, {"design": "CC(C)Oc1cc(... | smallmol | |
You are a scientific search agent proposing candidate antibody CDRH3 sequences under an iterative Bayesian optimization loop. Return ONLY the JSON action. Never predict binding energy, uncertainty, or rank. | # Task: protein (antibody_sequence)
Pure LLM baseline for CDRH3 sequence proposal.
## Objectives
- binding_energy: minimize - Minimize Absolut energy. Lower true score is better.
## Design space (current state - you may act on it)
Representation: complete_design
Active parameters (the surrogate models only these; `c... | {"type": "propose", "reasoning": null, "payload": {"candidates": [{"design": "ANIFEEVLGRY", "rationale": null}]}, "summary": null} | protein_acq | |
You are a scientific search agent proposing candidate molecules under an iterative multi-objective Bayesian optimization loop. Return ONLY the JSON action. Never predict docking score, activity, EHVI, uncertainty, or rank. | # Task: smallmol (molecule)
The optimization task is for KRAS G12D small-molecule candidates. The activity objective is based on a target-specific model trained from public KRAS G12D IC50 records, and the docking objective evaluates binding with AutoDock Vina.
Public medicinal-chemistry literature has shown that small... | {"type": "propose", "reasoning": null, "payload": {"candidates": [{"design": "CC(C)c1c(Cl)sc(OC(F)(F)F)c1C#N", "rationale": "branched alkyl from high-activity parent"}, {"design": "CC1CC1c1cnc(OC(F)(F)F)cc1C#N", "rationale": "pyridine variant of low-vina scaffold"}, {"design": "CCCCCc1nc(OC(F)(F)F)sc1C#N", "rationale":... | smallmol | |
You are a scientific search agent proposing candidate antibody CDRH3 sequences under an iterative Bayesian optimization loop. Return ONLY the JSON action. Never predict binding energy, uncertainty, or rank. | # Task: protein (antibody_sequence)
Direct CDRH3 antibody sequence generation for AntBO. Do not output search functions, code, LocalSearch, NeighborSampling, LatinHyperCubeSampling, or explanations. Generate antibody strings directly.
## Objectives
- binding_energy: minimize - Minimize Absolut binding energy. Lower tr... | {"type": "propose", "reasoning": null, "payload": {"candidates": [{"design": "VCMTREWQKIA", "rationale": null}]}, "summary": null} | protein_direct | |
You are a scientific search agent proposing candidate antibody CDRH3 sequences under an iterative Bayesian optimization loop. Return ONLY the JSON action. Never predict binding energy, uncertainty, or rank. | # Task: protein (antibody_sequence)
Pure LLM baseline for CDRH3 sequence proposal.
## Objectives
- binding_energy: minimize - Minimize Absolut energy. Lower true score is better.
## Design space (current state - you may act on it)
Representation: complete_design
Active parameters (the surrogate models only these; `c... | {"type": "propose", "reasoning": null, "payload": {"candidates": [{"design": "CSQTDMFMVQW", "rationale": null}]}, "summary": null} | protein_acq | |
You are a scientific search agent proposing candidate molecules under an iterative multi-objective Bayesian optimization loop. Return ONLY the JSON action. Never predict docking score, activity, EHVI, uncertainty, or rank. | # Task: smallmol (molecule)
The optimization task is for KRAS G12D small-molecule candidates. The activity objective is based on a target-specific model trained from public KRAS G12D IC50 records, and the docking objective evaluates binding with AutoDock Vina.
Public medicinal-chemistry literature has shown that small... | {"type": "propose", "reasoning": null, "payload": {"candidates": [{"design": "CCC(C)Oc1ccc2c(N)nc(C3CC3C(F)(F)F)n2c1", "rationale": "sec-butoxy to vary alkoxy"}, {"design": "CCCOc1ccc2c(N)nc(C3CC3C(F)(F)C(F)(F)C(F)(F)F)n2c1", "rationale": "extend perfluoro chain"}, {"design": "CCCOc1ccc2c(N)nc(C3CC3C(F)(F)C(F)(F)OCC)n2... | smallmol | |
You are a scientific search agent proposing edits to a training program under an iterative model-based (Bayesian) optimization loop. You may either propose edits within the current design space or expand that space. Return ONLY the JSON action. Never predict objective values, surrogate mean/variance, acquisition, or ra... | # Task: nanogpt (training_program)
Autoresearch task context:
- `train.py` is the single editable file. It contains the full GPT model, optimizer, and training loop.
- Everything inside `train.py` is fair game: architecture, hyperparameters, optimizer details, batch size, schedule, and training-loop choices.
- The benc... | {"type": "expand_design_space", "reasoning": "Adding UNEMBEDDING_LR as a searchable feature to explore different learning rates for the language model head.", "payload": {"activate": "UNEMBEDDING_LR"}, "summary": null} | nanogpt | |
You are a scientific search agent proposing candidate molecules under an iterative multi-objective Bayesian optimization loop. Return ONLY the JSON action. Never predict docking score, activity, EHVI, uncertainty, or rank. | # Task: smallmol (molecule)
The optimization task is for KRAS G12D small-molecule candidates. The activity objective is based on a target-specific model trained from public KRAS G12D IC50 records, and the docking objective evaluates binding with AutoDock Vina.
Public medicinal-chemistry literature has shown that small... | {"type": "propose", "reasoning": null, "payload": {"candidates": [{"design": "CC(C)Oc1cc(C#N)c(C#N)c2oc(=O)n(CCCN(CC)CC)c12", "rationale": "diethylamino chain for basicity diversity"}, {"design": "CC(C)Oc1cc(C#N)c(C#N)c2sc(=O)n(CCCC#N)c12", "rationale": "sulfur core with butyronitrile chain"}, {"design": "CC(C)Oc1cc(Br... | smallmol | |
You are a scientific search agent proposing candidate molecules under an iterative multi-objective Bayesian optimization loop. Return ONLY the JSON action. Never predict docking score, activity, EHVI, uncertainty, or rank. | # Task: smallmol (molecule)
The optimization task is for KRAS G12C small-molecule candidates. The activity objective is based on a target-specific model trained from public KRAS G12C IC50 records, and the docking objective evaluates binding with AutoDock Vina.
Public medicinal-chemistry literature has shown that small... | {"type": "propose", "reasoning": null, "payload": {"candidates": [{"design": "CC(C)CCCOCCN(C)CCOCCS", "rationale": "thioether replaces ether"}, {"design": "CC(C)CCCOCCN(CC(N)=O)CCCN", "rationale": "elongated amine branch"}, {"design": "CC(C)CCCOCCN(C)CCOCCOCCOCCN", "rationale": "extended PEG chain"}, {"design": "CC(C)C... | smallmol | |
You are a scientific search agent proposing candidate antibody CDRH3 sequences under an iterative Bayesian optimization loop. Return ONLY the JSON action. Never predict binding energy, uncertainty, or rank. | # Task: protein (antibody_sequence)
Pure LLM baseline for CDRH3 sequence proposal.
## Objectives
- binding_energy: minimize - Minimize Absolut energy. Lower true score is better.
## Design space (current state - you may act on it)
Representation: complete_design
Active parameters (the surrogate models only these; `c... | {"type": "propose", "reasoning": null, "payload": {"candidates": [{"design": "IHMFSKWDLII", "rationale": null}]}, "summary": null} | protein_acq | |
You are a scientific search agent proposing candidate molecules under an iterative multi-objective Bayesian optimization loop. Return ONLY the JSON action. Never predict docking score, activity, EHVI, uncertainty, or rank. | # Task: smallmol (molecule)
The optimization task is for KRAS G12D small-molecule candidates. The activity objective is based on a target-specific model trained from public KRAS G12D IC50 records, and the docking objective evaluates binding with AutoDock Vina.
Public medicinal-chemistry literature has shown that small... | {"type": "propose", "reasoning": null, "payload": {"candidates": [{"design": "CC(C)COc1cc(N)c2c(N)nc(C3CC3C(F)(F)Br)n2c1", "rationale": "branching to improve solubility"}, {"design": "CCCCOc1cc(N)c2c(N)nc(C3CC3C(F)(F)C(F)(F)I)n2c1", "rationale": "introduce iodine for polarizability"}, {"design": "CCCCSc1cc(N)c2c(N)nc(C... | smallmol | |
You are a scientific search agent proposing candidate molecules under an iterative multi-objective Bayesian optimization loop. Return ONLY the JSON action. Never predict docking score, activity, EHVI, uncertainty, or rank. | # Task: smallmol (molecule)
The optimization task is for KRAS G12D small-molecule candidates. The activity objective is based on a target-specific model trained from public KRAS G12D IC50 records, and the docking objective evaluates binding with AutoDock Vina.
Public medicinal-chemistry literature has shown that small... | {"type": "propose", "reasoning": null, "payload": {"candidates": [{"design": "N#Cc1ccc(OCC2CC2)c2[nH]c(=O)sc12", "rationale": "core O to S replacement"}, {"design": "C1CCC1Oc1cc(C(F)(F)F)c(C#N)c2oc(=O)n(CCC#N)c12", "rationale": "isopropoxy to cyclobutoxy variant"}, {"design": "CC(C)Oc1ncc(C#N)c2oc(=O)n(CCCN3CCOCC3)c12"... | smallmol | |
You are a scientific search agent proposing edits to a training program under an iterative model-based (Bayesian) optimization loop. You may either propose edits within the current design space or expand that space. Return ONLY the JSON action. Never predict objective values, surrogate mean/variance, acquisition, or ra... | # Task: nanogpt (training_program)
Autoresearch task context:
- `train.py` is the single editable file. It contains the full GPT model, optimizer, and training loop.
- Everything inside `train.py` is fair game: architecture, hyperparameters, optimizer details, batch size, schedule, and training-loop choices.
- The benc... | {"type": "propose", "reasoning": "Reduce embedding learning rate to stabilize training. Increase weight decay to reduce overfitting.", "payload": {"candidates": [{"parent": "state_3986", "edits": [{"parameter": "EMBEDDING_LR", "edit_op": "set_numeric", "value": 0.6, "rationale": "Reduce embedding learning rate to stabi... | nanogpt | |
You are a scientific search agent proposing candidate antibody CDRH3 sequences under an iterative Bayesian optimization loop. Return ONLY the JSON action. Never predict binding energy, uncertainty, or rank. | # Task: protein (antibody_sequence)
Direct CDRH3 antibody sequence generation for AntBO. Do not output search functions, code, LocalSearch, NeighborSampling, LatinHyperCubeSampling, or explanations. Generate antibody strings directly.
## Objectives
- binding_energy: minimize - Minimize Absolut binding energy. Lower tr... | {"type": "propose", "reasoning": null, "payload": {"candidates": [{"design": "VYMCATNPLQH", "rationale": null}]}, "summary": null} | protein_direct | |
You are a scientific search agent proposing candidate molecules under an iterative multi-objective Bayesian optimization loop. Return ONLY the JSON action. Never predict docking score, activity, EHVI, uncertainty, or rank. | # Task: smallmol (molecule)
The optimization task is for KRAS G12D small-molecule candidates. The activity objective is based on a target-specific model trained from public KRAS G12D IC50 records, and the docking objective evaluates binding with AutoDock Vina.
Public medicinal-chemistry literature has shown that small... | {"type": "propose", "reasoning": null, "payload": {"candidates": [{"design": "CC(C)Oc1cc(C#N)c(C#N)c2oc(=O)n(CCCN3CCSCC3)c12", "rationale": "thiomorpholine variation of pareto"}, {"design": "CC(C)Oc1cc(Br)c(C#N)c2oc(=O)n(CCCN3CCOCC3)c12", "rationale": "bromo with morpholine chain"}, {"design": "N#Cc1ccc(OCC2CC2)c2n(C)c... | smallmol | |
You are a scientific search agent proposing candidate molecules under an iterative multi-objective Bayesian optimization loop. Return ONLY the JSON action. Never predict docking score, activity, EHVI, uncertainty, or rank. | # Task: smallmol (molecule)
The optimization task is for KRAS G12D small-molecule candidates. The activity objective is based on a target-specific model trained from public KRAS G12D IC50 records, and the docking objective evaluates binding with AutoDock Vina.
Public medicinal-chemistry literature has shown that small... | {"type": "propose", "reasoning": null, "payload": {"candidates": [{"design": "COc1ccc2c(N)ncnc2c1", "rationale": "quinazoline scaffold hop"}, {"design": "CCCOc1ccc2c(N)ncnc2c1", "rationale": "propoxy quinazoline variant"}, {"design": "COc1ccc2c(N)nc(N)n2c1", "rationale": "diamine quinazoline"}, {"design": "COc1cc2ncnc(... | smallmol | |
You are a scientific search agent proposing candidate antibody CDRH3 sequences under an iterative Bayesian optimization loop. Return ONLY the JSON action. Never predict binding energy, uncertainty, or rank. | # Task: protein (antibody_sequence)
Pure LLM baseline for CDRH3 sequence proposal.
## Objectives
- binding_energy: minimize - Minimize Absolut energy. Lower true score is better.
## Design space (current state - you may act on it)
Representation: complete_design
Active parameters (the surrogate models only these; `c... | {"type": "propose", "reasoning": null, "payload": {"candidates": [{"design": "ARAICMRLPEK", "rationale": null}]}, "summary": null} | protein_acq | |
You are a scientific search agent proposing candidate molecules under an iterative multi-objective Bayesian optimization loop. Return ONLY the JSON action. Never predict docking score, activity, EHVI, uncertainty, or rank. | # Task: smallmol (molecule)
The optimization task is for KRAS G12C small-molecule candidates. The activity objective is based on a target-specific model trained from public KRAS G12C IC50 records, and the docking objective evaluates binding with AutoDock Vina.
Public medicinal-chemistry literature has shown that small... | {"type": "propose", "reasoning": null, "payload": {"candidates": [{"design": "CC(C)CCCOCCN(CCOCCN=C(N)N)CCS(=O)(=O)N1CCOCC1", "rationale": "Vary ring in sulfonamide cap"}, {"design": "CC(C)CCCOCCN(CCOCCN=C(N)N)CCOCCS(C)(=O)=O", "rationale": "Swap sulfonamide to methylsulfone"}, {"design": "CC(C)CCCOCCN(CCS(N)(=O)=O)CCS... | smallmol | |
You are a scientific search agent proposing candidate molecules under an iterative multi-objective Bayesian optimization loop. Return ONLY the JSON action. Never predict docking score, activity, EHVI, uncertainty, or rank. | # Task: smallmol (molecule)
The optimization task is for KRAS G12D small-molecule candidates. The activity objective is based on a target-specific model trained from public KRAS G12D IC50 records, and the docking objective evaluates binding with AutoDock Vina.
Public medicinal-chemistry literature has shown that small... | {"type": "propose", "reasoning": null, "payload": {"candidates": [{"design": "CC1COC1c1ccc(OC(F)(F)F)cc1C#N", "rationale": "Replace cyclopropyl with oxetane"}, {"design": "CC1CC1c1cc(OC(F)(F)F)oc1C#N", "rationale": "Thiophene to furan heterocycle swap"}, {"design": "CC1CC1c1ccc(OC(F)(F)F)cc1C(=O)N", "rationale": "Nitri... | smallmol | |
You are a scientific search agent proposing candidate molecules under an iterative multi-objective Bayesian optimization loop. Return ONLY the JSON action. Never predict docking score, activity, EHVI, uncertainty, or rank. | # Task: smallmol (molecule)
The optimization task is for KRAS G12D small-molecule candidates. The activity objective is based on a target-specific model trained from public KRAS G12D IC50 records, and the docking objective evaluates binding with AutoDock Vina.
Public medicinal-chemistry literature has shown that small... | {"type": "propose", "reasoning": null, "payload": {"candidates": [{"design": "NC(=O)Nc1n[nH]c(C2CCCCC2)n1", "rationale": "urea on pyrazole with cyclohexyl"}, {"design": "COC(=O)Nc1noc(C2CC2)n1", "rationale": "carbamate on isoxazole with cyclopropyl"}, {"design": "CC(=O)Nc1noc(C2CCC2)n1", "rationale": "amide on isoxazol... | smallmol | |
You are a scientific search agent proposing candidate molecules under an iterative multi-objective Bayesian optimization loop. Return ONLY the JSON action. Never predict docking score, activity, EHVI, uncertainty, or rank. | # Task: smallmol (molecule)
The optimization task is for KRAS G12D small-molecule candidates. The activity objective is based on a target-specific model trained from public KRAS G12D IC50 records, and the docking objective evaluates binding with AutoDock Vina.
Public medicinal-chemistry literature has shown that small... | {"type": "propose", "reasoning": null, "payload": {"candidates": [{"design": "FC(F)(F)Oc1ccc(-n2noc2)c(C(F)(F)F)c1", "rationale": "heterocycle swap from thiophene to oxadiazole"}, {"design": "FC(F)(F)Oc1ccc(-n2cnc3cccnc32)c(C(F)(F)F)c1", "rationale": "positional isomer of balanced elite"}, {"design": "FC(F)(F)Oc1ccc(-n... | smallmol | |
You are a scientific search agent proposing candidate molecules under an iterative multi-objective Bayesian optimization loop. Return ONLY the JSON action. Never predict docking score, activity, EHVI, uncertainty, or rank. | # Task: smallmol (molecule)
The optimization task is for KRAS G12C small-molecule candidates. The activity objective is based on a target-specific model trained from public KRAS G12C IC50 records, and the docking objective evaluates binding with AutoDock Vina.
Public medicinal-chemistry literature has shown that small... | {"type": "propose", "reasoning": null, "payload": {"candidates": [{"design": "CC(C)CCCOCCN1CCOCC1", "rationale": "add morpholine ring for rigidity"}, {"design": "CC(C)CCCOCCN(C)C(C)=O", "rationale": "replace amide with N-methyl acetamide"}, {"design": "CC(C)CCCOCCN(C)CCCO", "rationale": "extend chain with terminal alco... | smallmol | |
You are a scientific search agent proposing candidate antibody CDRH3 sequences under an iterative Bayesian optimization loop. Return ONLY the JSON action. Never predict binding energy, uncertainty, or rank. | # Task: protein (antibody_sequence)
Direct CDRH3 antibody sequence generation for AntBO. Do not output search functions, code, LocalSearch, NeighborSampling, LatinHyperCubeSampling, or explanations. Generate antibody strings directly.
## Objectives
- binding_energy: minimize - Minimize Absolut binding energy. Lower tr... | {"type": "propose", "reasoning": null, "payload": {"candidates": [{"design": "ADGHTKQNPRW", "rationale": null}]}, "summary": null} | protein_direct | |
You are a scientific search agent proposing candidate antibody CDRH3 sequences under an iterative Bayesian optimization loop. Return ONLY the JSON action. Never predict binding energy, uncertainty, or rank. | # Task: protein (antibody_sequence)
Pure LLM baseline for CDRH3 sequence proposal.
## Objectives
- binding_energy: minimize - Minimize Absolut energy. Lower true score is better.
## Design space (current state - you may act on it)
Representation: complete_design
Active parameters (the surrogate models only these; `c... | {"type": "propose", "reasoning": null, "payload": {"candidates": [{"design": "AQKDYAGKNPW", "rationale": null}]}, "summary": null} | protein_acq | |
You are a scientific search agent proposing candidate antibody CDRH3 sequences under an iterative Bayesian optimization loop. Return ONLY the JSON action. Never predict binding energy, uncertainty, or rank. | # Task: protein (antibody_sequence)
Pure LLM baseline for CDRH3 sequence proposal.
## Objectives
- binding_energy: minimize - Minimize Absolut energy. Lower true score is better.
## Design space (current state - you may act on it)
Representation: complete_design
Active parameters (the surrogate models only these; `c... | {"type": "propose", "reasoning": null, "payload": {"candidates": [{"design": "ICLSYIRDKTL", "rationale": null}]}, "summary": null} | protein_acq | |
You are a scientific search agent proposing candidate molecules under an iterative multi-objective Bayesian optimization loop. Return ONLY the JSON action. Never predict docking score, activity, EHVI, uncertainty, or rank. | # Task: smallmol (molecule)
The optimization task is for KRAS G12C small-molecule candidates. The activity objective is based on a target-specific model trained from public KRAS G12C IC50 records, and the docking objective evaluates binding with AutoDock Vina.
Public medicinal-chemistry literature has shown that small... | {"type": "propose", "reasoning": null, "payload": {"candidates": [{"design": "CC(C)CCCOCCN(CCO)CCS(N)(=O)=O", "rationale": "replace terminal amide with alcohol"}, {"design": "CC(C)CCC(=O)N(CCOCCN)CCOCC(N)=O", "rationale": "replace ether with carbonyl linker"}, {"design": "CC(C)CCCOCCN(CCOCCS(N)(=O)=O)CCO", "rationale":... | smallmol | |
You are a scientific search agent proposing candidate molecules under an iterative multi-objective Bayesian optimization loop. Return ONLY the JSON action. Never predict docking score, activity, EHVI, uncertainty, or rank. | # Task: smallmol (molecule)
The optimization task is for KRAS G12D small-molecule candidates. The activity objective is based on a target-specific model trained from public KRAS G12D IC50 records, and the docking objective evaluates binding with AutoDock Vina.
Public medicinal-chemistry literature has shown that small... | {"type": "propose", "reasoning": null, "payload": {"candidates": [{"design": "CCCOc1ccc2c(N)nc(C3CCC3C(F)(F)Br)n2c1", "rationale": "cyclobutyl ring variant"}, {"design": "CCCOc1ccc2c(N)nc(C3CC3C(F)(F)C(F)(F)C(F)(F)C(F)(F)F)n2c1", "rationale": "longer fluorinated tail, fluorine end"}, {"design": "CCCCCOc1ccc2c(N)nc(C3CC... | smallmol | |
You are a scientific search agent proposing edits to a training program under an iterative model-based (Bayesian) optimization loop. You may either propose edits within the current design space or expand that space. Return ONLY the JSON action. Never predict objective values, surrogate mean/variance, acquisition, or ra... | # Task: nanogpt (training_program)
Autoresearch task context:
- `train.py` is the single editable file. It contains the full GPT model, optimizer, and training loop.
- Everything inside `train.py` is fair game: architecture, hyperparameters, optimizer details, batch size, schedule, and training-loop choices.
- The benc... | {"type": "propose", "reasoning": "Increase embedding learning rate to potentially improve token embedding convergence. Reduce weight decay to allow more flexibility in matrix parameter updates.", "payload": {"candidates": [{"parent": "state_4533", "edits": [{"parameter": "EMBEDDING_LR", "edit_op": "set_numeric", "value... | nanogpt | |
You are a scientific search agent proposing candidate molecules under an iterative multi-objective Bayesian optimization loop. Return ONLY the JSON action. Never predict docking score, activity, EHVI, uncertainty, or rank. | # Task: smallmol (molecule)
The optimization task is for KRAS G12D small-molecule candidates. The activity objective is based on a target-specific model trained from public KRAS G12D IC50 records, and the docking objective evaluates binding with AutoDock Vina.
Public medicinal-chemistry literature has shown that small... | {"type": "propose", "reasoning": null, "payload": {"candidates": [{"design": "CC(C)Oc1ccc(C#N)c2[nH]c(=O)n(C)c12", "rationale": "imidazolone core variation"}, {"design": "COc1ccc(C#N)c2oc(=O)n(CCC#N)c12", "rationale": "methoxy substituent exploration"}, {"design": "CC(C)Oc1ccc(C#N)c2oc(=O)n(CCN(C)C)c12", "rationale": "... | smallmol | |
You are a scientific search agent proposing edits to a training program under an iterative model-based (Bayesian) optimization loop. You may either propose edits within the current design space or expand that space. Return ONLY the JSON action. Never predict objective values, surrogate mean/variance, acquisition, or ra... | # Task: nanogpt (training_program)
Autoresearch task context:
- `train.py` is the single editable file. It contains the full GPT model, optimizer, and training loop.
- Everything inside `train.py` is fair game: architecture, hyperparameters, optimizer details, batch size, schedule, and training-loop choices.
- The benc... | {"type": "expand_design_space", "reasoning": "The value domains of HEAD_DIM and WINDOW_PATTERN have been exhaustively enumerated (all 18 combinations evaluated), with a range of 0.000196, which does not exceed the noise floor (~2e-04), indicating that the current active feature set can no longer explain the remaining v... | nanogpt | |
You are a scientific search agent proposing candidate molecules under an iterative multi-objective Bayesian optimization loop. Return ONLY the JSON action. Never predict docking score, activity, EHVI, uncertainty, or rank. | # Task: smallmol (molecule)
The optimization task is for KRAS G12C small-molecule candidates. The activity objective is based on a target-specific model trained from public KRAS G12C IC50 records, and the docking objective evaluates binding with AutoDock Vina.
Public medicinal-chemistry literature has shown that small... | {"type": "propose", "reasoning": null, "payload": {"candidates": [{"design": "CC(C)CCCOCCN(CCS(N)(=O)=O)CC(N)=O", "rationale": "crossover of top low vina and high activity"}, {"design": "CC(C)CCCOCCN(CCOCCS(=O)(=O)N)CCS(C)(=O)=O", "rationale": "sulfonamide variant of balanced elite"}, {"design": "CC(C)CCCOCCOCCN(CCS(C)... | smallmol | |
You are a scientific search agent proposing candidate molecules under an iterative multi-objective Bayesian optimization loop. Return ONLY the JSON action. Never predict docking score, activity, EHVI, uncertainty, or rank. | # Task: smallmol (molecule)
The optimization task is for KRAS G12C small-molecule candidates. The activity objective is based on a target-specific model trained from public KRAS G12C IC50 records, and the docking objective evaluates binding with AutoDock Vina.
Public medicinal-chemistry literature has shown that small... | {"type": "propose", "reasoning": null, "payload": {"candidates": [{"design": "CC(C)CCCOCCN1CCN(C)C1=O", "rationale": "cyclic amide variant from pareto"}, {"design": "CC(C)CCCOCCN(CCS)CC(N)=O", "rationale": "thiol extension of polar dense"}, {"design": "CC(C)CCCOCCNCC(=O)N", "rationale": "primary amide instead of acid"}... | smallmol | |
You are a scientific search agent proposing candidate molecules under an iterative multi-objective Bayesian optimization loop. Return ONLY the JSON action. Never predict docking score, activity, EHVI, uncertainty, or rank. | # Task: smallmol (molecule)
The optimization task is for KRAS G12D small-molecule candidates. The activity objective is based on a target-specific model trained from public KRAS G12D IC50 records, and the docking objective evaluates binding with AutoDock Vina.
Public medicinal-chemistry literature has shown that small... | {"type": "propose", "reasoning": null, "payload": {"candidates": [{"design": "CS(=O)(=O)c1noc(C2CCCCC2)n1", "rationale": "sulfone replaces cyano on cyclohexane"}, {"design": "N#Cc1noc(C2CCNCC2)n1", "rationale": "piperidine variant of top activity parent"}, {"design": "CCNC(=O)c1noc(C2CCCCC2)n1", "rationale": "ethylcarb... | smallmol | |
You are a scientific search agent proposing candidate molecules under an iterative multi-objective Bayesian optimization loop. Return ONLY the JSON action. Never predict docking score, activity, EHVI, uncertainty, or rank. | # Task: smallmol (molecule)
The optimization task is for KRAS G12D small-molecule candidates. The activity objective is based on a target-specific model trained from public KRAS G12D IC50 records, and the docking objective evaluates binding with AutoDock Vina.
Public medicinal-chemistry literature has shown that small... | {"type": "propose", "reasoning": null, "payload": {"candidates": [{"design": "CCOc1cc(N)c2c(N)nc(C3CC3C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)F)n2c1", "rationale": "crossover: short alkoxy with long fluoroalkyl"}, {"design": "CCCOc1cc(N)c2c(N)nc(C3CC3C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)F)n2c1", "rationale": "vary alkoxy leng... | smallmol | |
You are a scientific search agent proposing candidate molecules under an iterative multi-objective Bayesian optimization loop. Return ONLY the JSON action. Never predict docking score, activity, EHVI, uncertainty, or rank. | # Task: smallmol (molecule)
The optimization task is for KRAS G12D small-molecule candidates. The activity objective is based on a target-specific model trained from public KRAS G12D IC50 records, and the docking objective evaluates binding with AutoDock Vina.
Public medicinal-chemistry literature has shown that small... | {"type": "propose", "reasoning": null, "payload": {"candidates": [{"design": "FC(F)(F)Oc1ccc(N2CC(C(F)(F)F)OC2)cc1C(F)(F)F", "rationale": "morpholine CF3 variant of high-activity parent"}, {"design": "FC(F)(F)Oc1ccc(N2CCC(C(F)(F)F)C2)c(C#N)c1", "rationale": "replace CF3 with cyano on activity parent"}, {"design": "O=S(... | smallmol | |
You are a scientific search agent proposing candidate molecules under an iterative multi-objective Bayesian optimization loop. Return ONLY the JSON action. Never predict docking score, activity, EHVI, uncertainty, or rank. | # Task: smallmol (molecule)
The optimization task is for KRAS G12D small-molecule candidates. The activity objective is based on a target-specific model trained from public KRAS G12D IC50 records, and the docking objective evaluates binding with AutoDock Vina.
Public medicinal-chemistry literature has shown that small... | {"type": "propose", "reasoning": null, "payload": {"candidates": [{"design": "CC(C)Cc1ccc(OC(F)(F)F)cc1C#N", "rationale": "isobutyl on benzene scaffold"}, {"design": "CC(C)Cc1cc(OC(F)(F)F)ccc1C#N", "rationale": "isobutyl meta-benzene"}, {"design": "CCCCc1c(Br)sc(OC(F)(F)F)c1C#N", "rationale": "bromo instead of chloro"}... | smallmol | |
You are a scientific search agent proposing candidate molecules under an iterative multi-objective Bayesian optimization loop. Return ONLY the JSON action. Never predict docking score, activity, EHVI, uncertainty, or rank. | # Task: smallmol (molecule)
The optimization task is for KRAS G12D small-molecule candidates. The activity objective is based on a target-specific model trained from public KRAS G12D IC50 records, and the docking objective evaluates binding with AutoDock Vina.
Public medicinal-chemistry literature has shown that small... | {"type": "propose", "reasoning": null, "payload": {"candidates": [{"design": "N#Cc1cc(OC(F)(F)F)ccn1", "rationale": "Pyridine core with cyano and OCF3"}, {"design": "O=S(=O)(N)c1ccc(C(F)(F)F)cc1", "rationale": "Sulfonamide group on CF3 benzene"}, {"design": "N#Cc1c(OC(F)(F)F)cccc1", "rationale": "Ortho cyano OCF3 benze... | smallmol | |
You are a scientific search agent proposing candidate molecules under an iterative multi-objective Bayesian optimization loop. Return ONLY the JSON action. Never predict docking score, activity, EHVI, uncertainty, or rank. | # Task: smallmol (molecule)
The optimization task is for KRAS G12D small-molecule candidates. The activity objective is based on a target-specific model trained from public KRAS G12D IC50 records, and the docking objective evaluates binding with AutoDock Vina.
Public medicinal-chemistry literature has shown that small... | {"type": "propose", "reasoning": null, "payload": {"candidates": [{"design": "CC(C)Oc1cc(F)c(C#N)c2oc(=O)n(CCO)c12", "rationale": "shorter polar chain on fluoro"}, {"design": "CC(C)Oc1cc(C#N)c(C#N)c2sc(=O)n(CCN3CCOCC3)c12", "rationale": "thiazole morpholine shorter linker"}, {"design": "CC(C)Oc1cc(Br)c(C#N)c2sc(=O)n(CC... | smallmol | |
You are a scientific search agent proposing candidate molecules under an iterative multi-objective Bayesian optimization loop. Return ONLY the JSON action. Never predict docking score, activity, EHVI, uncertainty, or rank. | # Task: smallmol (molecule)
The optimization task is for KRAS G12D small-molecule candidates. The activity objective is based on a target-specific model trained from public KRAS G12D IC50 records, and the docking objective evaluates binding with AutoDock Vina.
Public medicinal-chemistry literature has shown that small... | {"type": "propose", "reasoning": null, "payload": {"candidates": [{"design": "COc1ccc2c(N)ncc2n1", "rationale": "rearrange methoxy position"}, {"design": "Nc1ncc2c(=O)ccc2n1", "rationale": "introduce carbonyl in triazine"}, {"design": "NC1=NC2=C(C=CC=C2)C=C1", "rationale": "extend ring fusion"}, {"design": "COc1cccc2c(... | smallmol | |
You are a scientific search agent proposing candidate molecules under an iterative multi-objective Bayesian optimization loop. Return ONLY the JSON action. Never predict docking score, activity, EHVI, uncertainty, or rank. | # Task: smallmol (molecule)
The optimization task is for KRAS G12D small-molecule candidates. The activity objective is based on a target-specific model trained from public KRAS G12D IC50 records, and the docking objective evaluates binding with AutoDock Vina.
Public medicinal-chemistry literature has shown that small... | {"type": "propose", "reasoning": null, "payload": {"candidates": [{"design": "N#Cc1noc(C2CC2C(F)(F)F)n1", "rationale": "explore trifluoromethyl cyclopropyl"}, {"design": "N#Cc1noc(C2CCC2Cl)n1", "rationale": "chloro on cyclobutyl"}, {"design": "CS(=O)(=O)c1noc(C2CC2)n1", "rationale": "methylsulfonyl on small ring"}, {"d... | smallmol | |
You are a scientific search agent proposing candidate molecules under an iterative multi-objective Bayesian optimization loop. Return ONLY the JSON action. Never predict docking score, activity, EHVI, uncertainty, or rank. | # Task: smallmol (molecule)
The optimization task is for KRAS G12D small-molecule candidates. The activity objective is based on a target-specific model trained from public KRAS G12D IC50 records, and the docking objective evaluates binding with AutoDock Vina.
Public medicinal-chemistry literature has shown that small... | {"type": "propose", "reasoning": null, "payload": {"candidates": [{"design": "CCCOc1ccc2c(N)nc(C3CCO3)n2c1", "rationale": "replace cyclopropyl with oxetane"}, {"design": "CCCOc1ccc2c(N)nc(C3CCS3)n2c1", "rationale": "replace oxygen with sulfur"}, {"design": "CCCOc1ccc2c(N)nc(C3CC3F)n2c1", "rationale": "add fluorine to c... | smallmol | |
You are a scientific search agent proposing edits to a training program under an iterative model-based (Bayesian) optimization loop. You may either propose edits within the current design space or expand that space. Return ONLY the JSON action. Never predict objective values, surrogate mean/variance, acquisition, or ra... | # Task: nanogpt (training_program)
Autoresearch task context:
- `train.py` is the single editable file. It contains the full GPT model, optimizer, and training loop.
- Everything inside `train.py` is fair game: architecture, hyperparameters, optimizer details, batch size, schedule, and training-loop choices.
- The benc... | {"type": "propose", "reasoning": "Try a different sliding window pattern to potentially improve context handling. Reduce head dimension to see if it improves training efficiency and final performance.", "payload": {"candidates": [{"parent": "state_6684", "edits": [{"parameter": "WINDOW_PATTERN", "edit_op": "set_choice"... | nanogpt | |
You are a scientific search agent proposing candidate molecules under an iterative multi-objective Bayesian optimization loop. Return ONLY the JSON action. Never predict docking score, activity, EHVI, uncertainty, or rank. | # Task: smallmol (molecule)
The optimization task is for KRAS G12C small-molecule candidates. The activity objective is based on a target-specific model trained from public KRAS G12C IC50 records, and the docking objective evaluates binding with AutoDock Vina.
Public medicinal-chemistry literature has shown that small... | {"type": "propose", "reasoning": null, "payload": {"candidates": [{"design": "CC(C)CCCOCCOCCNC(=O)CN", "rationale": "amide variation from Pareto parent"}, {"design": "CC(C)COCCOCCN(C)C(=O)CCN", "rationale": "extend linker, replace amide"}, {"design": "CC(C)CCCOCCOCCN(C)C(=O)CS", "rationale": "introduce thioether for di... | smallmol | |
You are a scientific search agent proposing candidate antibody CDRH3 sequences under an iterative Bayesian optimization loop. Return ONLY the JSON action. Never predict binding energy, uncertainty, or rank. | # Task: protein (antibody_sequence)
Pure LLM baseline for CDRH3 sequence proposal.
## Objectives
- binding_energy: minimize - Minimize Absolut energy. Lower true score is better.
## Design space (current state - you may act on it)
Representation: complete_design
Active parameters (the surrogate models only these; `c... | {"type": "propose", "reasoning": null, "payload": {"candidates": [{"design": "GHFNWKPQQVL", "rationale": null}]}, "summary": null} | protein_acq | |
You are a scientific search agent proposing edits to a training program under an iterative model-based (Bayesian) optimization loop. You may either propose edits within the current design space or expand that space. Return ONLY the JSON action. Never predict objective values, surrogate mean/variance, acquisition, or ra... | # Task: nanogpt (training_program)
Autoresearch task context:
- `train.py` is the single editable file. It contains the full GPT model, optimizer, and training loop.
- Everything inside `train.py` is fair game: architecture, hyperparameters, optimizer details, batch size, schedule, and training-loop choices.
- The benc... | {"type": "propose", "reasoning": "Reduce batch size to allow for more frequent updates and potentially better generalization. Increase embedding learning rate to allow for faster adaptation of token embeddings.", "payload": {"candidates": [{"parent": "state_4762", "edits": [{"parameter": "TOTAL_BATCH_SIZE", "edit_op": ... | nanogpt | |
You are a scientific search agent proposing edits to a training program under an iterative model-based (Bayesian) optimization loop. You may either propose edits within the current design space or expand that space. Return ONLY the JSON action. Never predict objective values, surrogate mean/variance, acquisition, or ra... | # Task: nanogpt (training_program)
Autoresearch task context:
- `train.py` is the single editable file. It contains the full GPT model, optimizer, and training loop.
- Everything inside `train.py` is fair game: architecture, hyperparameters, optimizer details, batch size, schedule, and training-loop choices.
- The benc... | {"type": "propose", "reasoning": "Increase model width to potentially capture more complex patterns. Increase batch size to improve gradient estimates and training stability.", "payload": {"candidates": [{"parent": "state_1210", "edits": [{"parameter": "ASPECT_RATIO", "edit_op": "set_numeric", "value": 128, "rationale"... | nanogpt | |
You are a scientific search agent proposing candidate antibody CDRH3 sequences under an iterative Bayesian optimization loop. Return ONLY the JSON action. Never predict binding energy, uncertainty, or rank. | # Task: protein (antibody_sequence)
Direct CDRH3 antibody sequence generation for AntBO. Do not output search functions, code, LocalSearch, NeighborSampling, LatinHyperCubeSampling, or explanations. Generate antibody strings directly.
## Objectives
- binding_energy: minimize - Minimize Absolut binding energy. Lower tr... | {"type": "propose", "reasoning": null, "payload": {"candidates": [{"design": "YMVSDLSLPRL", "rationale": null}]}, "summary": null} | protein_direct | |
You are a scientific search agent proposing candidate antibody CDRH3 sequences under an iterative Bayesian optimization loop. Return ONLY the JSON action. Never predict binding energy, uncertainty, or rank. | # Task: protein (antibody_sequence)
Pure LLM baseline for CDRH3 sequence proposal.
## Objectives
- binding_energy: minimize - Minimize Absolut energy. Lower true score is better.
## Design space (current state - you may act on it)
Representation: complete_design
Active parameters (the surrogate models only these; `c... | {"type": "propose", "reasoning": null, "payload": {"candidates": [{"design": "IWDEIRWKSSI", "rationale": null}]}, "summary": null} | protein_acq | |
You are a scientific search agent proposing candidate molecules under an iterative multi-objective Bayesian optimization loop. Return ONLY the JSON action. Never predict docking score, activity, EHVI, uncertainty, or rank. | # Task: smallmol (molecule)
The optimization task is for KRAS G12D small-molecule candidates. The activity objective is based on a target-specific model trained from public KRAS G12D IC50 records, and the docking objective evaluates binding with AutoDock Vina.
Public medicinal-chemistry literature has shown that small... | {"type": "propose", "reasoning": null, "payload": {"candidates": [{"design": "CC1CC1c1cc(OC(F)(F)F)oc1C#N", "rationale": "Combine cyclopropyl with furan for polar diversity"}, {"design": "CC1CCC1c1cc(OC(F)(F)F)oc1C#N", "rationale": "Cyclobutyl-furan variant to probe ring size"}, {"design": "CC(C)c1cc(OC(F)(F)F)sc1C#N",... | smallmol | |
You are a scientific search agent proposing candidate antibody CDRH3 sequences under an iterative Bayesian optimization loop. Return ONLY the JSON action. Never predict binding energy, uncertainty, or rank. | # Task: protein (antibody_sequence)
Direct CDRH3 antibody sequence generation for AntBO. Do not output search functions, code, LocalSearch, NeighborSampling, LatinHyperCubeSampling, or explanations. Generate antibody strings directly.
## Objectives
- binding_energy: minimize - Minimize Absolut binding energy. Lower tr... | {"type": "propose", "reasoning": null, "payload": {"candidates": [{"design": "YCPWLKQADGA", "rationale": null}]}, "summary": null} | protein_direct | |
You are a scientific search agent proposing candidate molecules under an iterative multi-objective Bayesian optimization loop. Return ONLY the JSON action. Never predict docking score, activity, EHVI, uncertainty, or rank. | # Task: smallmol (molecule)
The optimization task is for KRAS G12C small-molecule candidates. The activity objective is based on a target-specific model trained from public KRAS G12C IC50 records, and the docking objective evaluates binding with AutoDock Vina.
Public medicinal-chemistry literature has shown that small... | {"type": "propose", "reasoning": null, "payload": {"candidates": [{"design": "CC(C)CCCOCCN(CCOCCN(C)CCO)CCS(N)(=O)=O", "rationale": "extend polar chain from Pareto parent"}, {"design": "CC(C)CCCOCCN(CCOCCOC(C)=O)CCS(C)(=O)=O", "rationale": "ester variant of top_high_activity"}, {"design": "CC(C)CCCOCCN(CCS(N)(=O)=O)CCO... | smallmol | |
You are a scientific search agent proposing candidate molecules under an iterative multi-objective Bayesian optimization loop. Return ONLY the JSON action. Never predict docking score, activity, EHVI, uncertainty, or rank. | # Task: smallmol (molecule)
The optimization task is for KRAS G12D small-molecule candidates. The activity objective is based on a target-specific model trained from public KRAS G12D IC50 records, and the docking objective evaluates binding with AutoDock Vina.
Public medicinal-chemistry literature has shown that small... | {"type": "propose", "reasoning": null, "payload": {"candidates": [{"design": "CC(=O)Nc1ccccc1", "rationale": "N-acetyl aniline variant"}, {"design": "NC(=O)c1ccc2ccccc2c1", "rationale": "larger aromatic extension"}, {"design": "NC(=O)c1cccc(Cl)c1", "rationale": "meta chloro benzamide"}, {"design": "O=C(Nc1ccccc1)c1cccc... | smallmol | |
You are a scientific search agent proposing candidate antibody CDRH3 sequences under an iterative Bayesian optimization loop. Return ONLY the JSON action. Never predict binding energy, uncertainty, or rank. | # Task: protein (antibody_sequence)
Pure LLM baseline for CDRH3 sequence proposal.
## Objectives
- binding_energy: minimize - Minimize Absolut energy. Lower true score is better.
## Design space (current state - you may act on it)
Representation: complete_design
Active parameters (the surrogate models only these; `c... | {"type": "propose", "reasoning": null, "payload": {"candidates": [{"design": "GHKDEWNPWRL", "rationale": null}]}, "summary": null} | protein_acq | |
You are a scientific search agent proposing candidate antibody CDRH3 sequences under an iterative Bayesian optimization loop. Return ONLY the JSON action. Never predict binding energy, uncertainty, or rank. | # Task: protein (antibody_sequence)
Pure LLM baseline for CDRH3 sequence proposal.
## Objectives
- binding_energy: minimize - Minimize Absolut energy. Lower true score is better.
## Design space (current state - you may act on it)
Representation: complete_design
Active parameters (the surrogate models only these; `c... | {"type": "propose", "reasoning": null, "payload": {"candidates": [{"design": "KENLIPRHTLW", "rationale": null}]}, "summary": null} | protein_acq | |
You are a scientific search agent proposing edits to a training program under an iterative model-based (Bayesian) optimization loop. You may either propose edits within the current design space or expand that space. Return ONLY the JSON action. Never predict objective values, surrogate mean/variance, acquisition, or ra... | # Task: nanogpt (training_program)
Autoresearch task context:
- `train.py` is the single editable file. It contains the full GPT model, optimizer, and training loop.
- Everything inside `train.py` is fair game: architecture, hyperparameters, optimizer details, batch size, schedule, and training-loop choices.
- The benc... | {"type": "propose", "reasoning": "Try a uniform short window pattern to see if it improves performance. Reduce head dimension to potentially improve training dynamics.", "payload": {"candidates": [{"parent": "state_3154", "edits": [{"parameter": "WINDOW_PATTERN", "edit_op": "set_choice", "value": "SSSS", "rationale": "... | nanogpt | |
You are a scientific search agent proposing edits to a training program under an iterative model-based (Bayesian) optimization loop. You may either propose edits within the current design space or expand that space. Return ONLY the JSON action. Never predict objective values, surrogate mean/variance, acquisition, or ra... | # Task: nanogpt (training_program)
Autoresearch task context:
- `train.py` is the single editable file. It contains the full GPT model, optimizer, and training loop.
- Everything inside `train.py` is fair game: architecture, hyperparameters, optimizer details, batch size, schedule, and training-loop choices.
- The benc... | {"type": "propose", "reasoning": "EMBEDDING_LR, HEAD_DIM have been frozen (value domain exhausted), select ASPECT_RATIO for editing among the remaining active parameters.", "payload": {"candidates": [{"parent": "state_2110", "edits": [{"parameter": "ASPECT_RATIO", "edit_op": "set_numeric", "value": 128, "rationale": "I... | nanogpt | |
You are a scientific search agent proposing candidate antibody CDRH3 sequences under an iterative Bayesian optimization loop. Return ONLY the JSON action. Never predict binding energy, uncertainty, or rank. | # Task: protein (antibody_sequence)
Pure LLM baseline for CDRH3 sequence proposal.
## Objectives
- binding_energy: minimize - Minimize Absolut energy. Lower true score is better.
## Design space (current state - you may act on it)
Representation: complete_design
Active parameters (the surrogate models only these; `c... | {"type": "propose", "reasoning": null, "payload": {"candidates": [{"design": "CQGRGWSLDGS", "rationale": null}]}, "summary": null} | protein_acq | |
You are a scientific search agent proposing edits to a training program under an iterative model-based (Bayesian) optimization loop. You may either propose edits within the current design space or expand that space. Return ONLY the JSON action. Never predict objective values, surrogate mean/variance, acquisition, or ra... | # Task: nanogpt (training_program)
Autoresearch task context:
- `train.py` is the single editable file. It contains the full GPT model, optimizer, and training loop.
- Everything inside `train.py` is fair game: architecture, hyperparameters, optimizer details, batch size, schedule, and training-loop choices.
- The benc... | {"type": "expand_design_space", "reasoning": "The value ranges of HEAD_DIM and WINDOW_PATTERN have been exhaustively enumerated (all 18 combinations evaluated), with a range of 0.000191, which does not exceed the noise floor (~2e-04), indicating that the current active feature set can no longer explain the remaining va... | nanogpt | |
You are a scientific search agent proposing candidate molecules under an iterative multi-objective Bayesian optimization loop. Return ONLY the JSON action. Never predict docking score, activity, EHVI, uncertainty, or rank. | # Task: smallmol (molecule)
The optimization task is for KRAS G12D small-molecule candidates. The activity objective is based on a target-specific model trained from public KRAS G12D IC50 records, and the docking objective evaluates binding with AutoDock Vina.
Public medicinal-chemistry literature has shown that small... | {"type": "propose", "reasoning": null, "payload": {"candidates": [{"design": "FC(F)(F)Sc1ccc(-n2ccnc2)c(C(F)(F)F)c1", "rationale": "sulfur analog of OCF3 parent"}, {"design": "FC(F)(F)Oc1cc(-n2nc(C(F)(F)F)cc2C(F)(F)F)cc1", "rationale": "pyrazole with extra CF3"}, {"design": "O=C(Nc1ccc(OC(F)(F)F)cc1C(F)(F)F)c1cccnc1", ... | smallmol | |
You are a scientific search agent proposing candidate molecules under an iterative multi-objective Bayesian optimization loop. Return ONLY the JSON action. Never predict docking score, activity, EHVI, uncertainty, or rank. | # Task: smallmol (molecule)
The optimization task is for KRAS G12D small-molecule candidates. The activity objective is based on a target-specific model trained from public KRAS G12D IC50 records, and the docking objective evaluates binding with AutoDock Vina.
Public medicinal-chemistry literature has shown that small... | {"type": "propose", "reasoning": null, "payload": {"candidates": [{"design": "C=CCOc1ccc2c(N)nc(C3CC3)n2c1", "rationale": "allyloxy chain variation"}, {"design": "FCCOc1ccc2c(N)nc(C3CC3)n2c1", "rationale": "fluoroethoxy substituent"}, {"design": "CCCOc1ccc2c(N)nnc(C3CC3)n2c1", "rationale": "triazolopyridine core"}, {"d... | smallmol | |
You are a scientific search agent proposing edits to a training program under an iterative model-based (Bayesian) optimization loop. You may either propose edits within the current design space or expand that space. Return ONLY the JSON action. Never predict objective values, surrogate mean/variance, acquisition, or ra... | # Task: nanogpt (training_program)
Autoresearch task context:
- `train.py` is the single editable file. It contains the full GPT model, optimizer, and training loop.
- Everything inside `train.py` is fair game: architecture, hyperparameters, optimizer details, batch size, schedule, and training-loop choices.
- The benc... | {"type": "propose", "reasoning": "Increase head dimension to boost model capacity. Increase embedding learning rate to improve token representation learning.", "payload": {"candidates": [{"parent": "state_5809", "edits": [{"parameter": "HEAD_DIM", "edit_op": "set_choice", "value": 128, "rationale": "Increase head dimen... | nanogpt | |
You are a scientific search agent proposing candidate molecules under an iterative multi-objective Bayesian optimization loop. Return ONLY the JSON action. Never predict docking score, activity, EHVI, uncertainty, or rank. | # Task: smallmol (molecule)
The optimization task is for KRAS G12D small-molecule candidates. The activity objective is based on a target-specific model trained from public KRAS G12D IC50 records, and the docking objective evaluates binding with AutoDock Vina.
Public medicinal-chemistry literature has shown that small... | {"type": "propose", "reasoning": null, "payload": {"candidates": [{"design": "CC(C)Oc1cc(C#N)c(C#N)c2sc(=O)n(CCCN3CCN(C)CC3)c12", "rationale": "sulfur analog with N-methylpiperazine"}, {"design": "CC(C)Oc1cc(F)c(C#N)c2oc(=O)n(CCN3CCOCC3)c12", "rationale": "fluorine variant with ethyl morpholine"}, {"design": "CC(C)Oc1c... | smallmol | |
You are a scientific search agent proposing candidate antibody CDRH3 sequences under an iterative Bayesian optimization loop. Return ONLY the JSON action. Never predict binding energy, uncertainty, or rank. | # Task: protein (antibody_sequence)
Pure LLM baseline for CDRH3 sequence proposal.
## Objectives
- binding_energy: minimize - Minimize Absolut energy. Lower true score is better.
## Design space (current state - you may act on it)
Representation: complete_design
Active parameters (the surrogate models only these; `c... | {"type": "propose", "reasoning": null, "payload": {"candidates": [{"design": "CLWRVLKGVGF", "rationale": null}]}, "summary": null} | protein_acq | |
You are a scientific search agent proposing candidate molecules under an iterative multi-objective Bayesian optimization loop. Return ONLY the JSON action. Never predict docking score, activity, EHVI, uncertainty, or rank. | # Task: smallmol (molecule)
The optimization task is for KRAS G12D small-molecule candidates. The activity objective is based on a target-specific model trained from public KRAS G12D IC50 records, and the docking objective evaluates binding with AutoDock Vina.
Public medicinal-chemistry literature has shown that small... | {"type": "propose", "reasoning": null, "payload": {"candidates": [{"design": "c1ccc2c(c1)ncn2-c1ccccc1", "rationale": "benzimidazole core, vary heteroatom"}, {"design": "c1ccc2c(c1)oc1ccccc12", "rationale": "oxygen analog of dibenzothiophene"}, {"design": "CNc1ccc(-c2cnc3ccccc3n2)cc1", "rationale": "methylamino substit... | smallmol | |
You are a scientific search agent proposing edits to a training program under an iterative model-based (Bayesian) optimization loop. You may either propose edits within the current design space or expand that space. Return ONLY the JSON action. Never predict objective values, surrogate mean/variance, acquisition, or ra... | # Task: nanogpt (training_program)
Autoresearch task context:
- `train.py` is the single editable file. It contains the full GPT model, optimizer, and training loop.
- Everything inside `train.py` is fair game: architecture, hyperparameters, optimizer details, batch size, schedule, and training-loop choices.
- The benc... | {"type": "propose", "reasoning": "Increase embedding learning rate based on recent evaluations showing improved performance. Reduce weight decay to allow more flexible parameter updates.", "payload": {"candidates": [{"parent": "state_1609", "edits": [{"parameter": "EMBEDDING_LR", "edit_op": "set_numeric", "value": 0.8,... | nanogpt | |
You are a scientific search agent proposing candidate molecules under an iterative multi-objective Bayesian optimization loop. Return ONLY the JSON action. Never predict docking score, activity, EHVI, uncertainty, or rank. | # Task: smallmol (molecule)
The optimization task is for KRAS G12C small-molecule candidates. The activity objective is based on a target-specific model trained from public KRAS G12C IC50 records, and the docking objective evaluates binding with AutoDock Vina.
Public medicinal-chemistry literature has shown that small... | {"type": "propose", "reasoning": null, "payload": {"candidates": [{"design": "CC(C)CCCOCCN(CCOCCS(=O)(=O)N)CCOCC(N)=O", "rationale": "methyl to NH2 on sulfonamide"}, {"design": "CC(C)CCCOCCN(CCOCCOC(N)=O)CCS(=O)(=O)N", "rationale": "methyl to NH2 on sulfonamide"}, {"design": "CC(C)CCCOCCN(CCS(C)(=O)=O)CCOCC(N)=O", "rat... | smallmol | |
You are a scientific search agent proposing candidate molecules under an iterative multi-objective Bayesian optimization loop. Return ONLY the JSON action. Never predict docking score, activity, EHVI, uncertainty, or rank. | # Task: smallmol (molecule)
The optimization task is for KRAS G12C small-molecule candidates. The activity objective is based on a target-specific model trained from public KRAS G12C IC50 records, and the docking objective evaluates binding with AutoDock Vina.
Public medicinal-chemistry literature has shown that small... | {"type": "propose", "reasoning": null, "payload": {"candidates": [{"design": "CC(C)CCCOCCN(C)CCOCC(=O)N", "rationale": "replace sulfonamide with amide"}, {"design": "CC(C)CCCOCCOCCN(C)CC(N)=O", "rationale": "shorten chain, add amide"}, {"design": "CC(C)CCCOCCN(CCOCCN)CCOCCS(N)(=O)=O", "rationale": "recombine sulfonamid... | smallmol | |
You are a scientific search agent proposing candidate molecules under an iterative multi-objective Bayesian optimization loop. Return ONLY the JSON action. Never predict docking score, activity, EHVI, uncertainty, or rank. | # Task: smallmol (molecule)
The optimization task is for KRAS G12D small-molecule candidates. The activity objective is based on a target-specific model trained from public KRAS G12D IC50 records, and the docking objective evaluates binding with AutoDock Vina.
Public medicinal-chemistry literature has shown that small... | {"type": "propose", "reasoning": null, "payload": {"candidates": [{"design": "CC(C)Oc1cc(N)c2c(N)nc(C3CC3C(F)(F)Br)n2c1", "rationale": "isopropoxy variant of short Br parent"}, {"design": "CSc1cc(N)c2c(N)nc(C3CC3C(F)(F)Br)n2c1", "rationale": "methylthio substitution for alkoxy"}, {"design": "CCCCOc1cc(N)c2c(N)nc(C3CC3C... | smallmol | |
You are a scientific search agent proposing candidate molecules under an iterative multi-objective Bayesian optimization loop. Return ONLY the JSON action. Never predict docking score, activity, EHVI, uncertainty, or rank. | # Task: smallmol (molecule)
The optimization task is for KRAS G12D small-molecule candidates. The activity objective is based on a target-specific model trained from public KRAS G12D IC50 records, and the docking objective evaluates binding with AutoDock Vina.
Public medicinal-chemistry literature has shown that small... | {"type": "propose", "reasoning": null, "payload": {"candidates": [{"design": "CCCCCOc1cc(N)c2c(N)nc(C3CC3C(F)(F)C(F)(F)C(F)(F)Cl)n2c1", "rationale": "vary fluorine chain length and halogen"}, {"design": "CCCCOc1cc(N)c2c(N)nc(C3CC3C(F)(F)C(F)(F)C(F)(F)F)n2c1", "rationale": "change terminal to CF3"}, {"design": "CCCCCOc1... | smallmol | |
You are a scientific search agent proposing candidate molecules under an iterative multi-objective Bayesian optimization loop. Return ONLY the JSON action. Never predict docking score, activity, EHVI, uncertainty, or rank. | # Task: smallmol (molecule)
The optimization task is for KRAS G12D small-molecule candidates. The activity objective is based on a target-specific model trained from public KRAS G12D IC50 records, and the docking objective evaluates binding with AutoDock Vina.
Public medicinal-chemistry literature has shown that small... | {"type": "propose", "reasoning": null, "payload": {"candidates": [{"design": "O=C(O)c1csc(C2CC2)n1", "rationale": "thiazole with cyclopropyl"}, {"design": "O=C(O)c1c[nH]c(C3CC3)n1", "rationale": "imidazole with cyclopropyl"}, {"design": "O=C(O)c1coc(C2CC2)n1", "rationale": "oxazole with cyclopropyl"}, {"design": "O=C(O... | smallmol | |
You are a scientific search agent proposing candidate antibody CDRH3 sequences under an iterative Bayesian optimization loop. Return ONLY the JSON action. Never predict binding energy, uncertainty, or rank. | # Task: protein (antibody_sequence)
Pure LLM baseline for CDRH3 sequence proposal.
## Objectives
- binding_energy: minimize - Minimize Absolut energy. Lower true score is better.
## Design space (current state - you may act on it)
Representation: complete_design
Active parameters (the surrogate models only these; `c... | {"type": "propose", "reasoning": null, "payload": {"candidates": [{"design": "MLWTAPPARGI", "rationale": null}]}, "summary": null} | protein_acq | |
You are a scientific search agent proposing edits to a training program under an iterative model-based (Bayesian) optimization loop. You may either propose edits within the current design space or expand that space. Return ONLY the JSON action. Never predict objective values, surrogate mean/variance, acquisition, or ra... | # Task: nanogpt (training_program)
Autoresearch task context:
- `train.py` is the single editable file. It contains the full GPT model, optimizer, and training loop.
- Everything inside `train.py` is fair game: architecture, hyperparameters, optimizer details, batch size, schedule, and training-loop choices.
- The benc... | {"type": "propose", "reasoning": "Increase embedding learning rate to potentially improve token representation learning. Slightly increase weight decay to reduce overfitting.", "payload": {"candidates": [{"parent": "state_2855", "edits": [{"parameter": "EMBEDDING_LR", "edit_op": "set_numeric", "value": 0.9, "rationale"... | nanogpt | |
You are a scientific search agent proposing candidate antibody CDRH3 sequences under an iterative Bayesian optimization loop. Return ONLY the JSON action. Never predict binding energy, uncertainty, or rank. | # Task: protein (antibody_sequence)
Pure LLM baseline for CDRH3 sequence proposal.
## Objectives
- binding_energy: minimize - Minimize Absolut energy. Lower true score is better.
## Design space (current state - you may act on it)
Representation: complete_design
Active parameters (the surrogate models only these; `c... | {"type": "propose", "reasoning": null, "payload": {"candidates": [{"design": "WDHGEEHRYVK", "rationale": null}]}, "summary": null} | protein_acq | |
You are a scientific search agent proposing candidate antibody CDRH3 sequences under an iterative Bayesian optimization loop. Return ONLY the JSON action. Never predict binding energy, uncertainty, or rank. | # Task: protein (antibody_sequence)
Pure LLM baseline for CDRH3 sequence proposal.
## Objectives
- binding_energy: minimize - Minimize Absolut energy. Lower true score is better.
## Design space (current state - you may act on it)
Representation: complete_design
Active parameters (the surrogate models only these; `c... | {"type": "propose", "reasoning": null, "payload": {"candidates": [{"design": "RLQRIMTNAQG", "rationale": null}]}, "summary": null} | protein_acq | |
You are a scientific search agent proposing candidate molecules under an iterative multi-objective Bayesian optimization loop. Return ONLY the JSON action. Never predict docking score, activity, EHVI, uncertainty, or rank. | # Task: smallmol (molecule)
The optimization task is for KRAS G12C small-molecule candidates. The activity objective is based on a target-specific model trained from public KRAS G12C IC50 records, and the docking objective evaluates binding with AutoDock Vina.
Public medicinal-chemistry literature has shown that small... | {"type": "propose", "reasoning": null, "payload": {"candidates": [{"design": "CC(C)CCCOCCN(C)CCSCCN", "rationale": "replace O with S for polarity change"}, {"design": "CC(C)CCCOCCOCCN1CCOCC1", "rationale": "introduce morpholine ring for rigidity"}, {"design": "CC(C)CCCOCCN(CC(N)=O)CCCO", "rationale": "vary terminal ami... | smallmol | |
You are a scientific search agent proposing candidate molecules under an iterative multi-objective Bayesian optimization loop. Return ONLY the JSON action. Never predict docking score, activity, EHVI, uncertainty, or rank. | # Task: smallmol (molecule)
The optimization task is for KRAS G12C small-molecule candidates. The activity objective is based on a target-specific model trained from public KRAS G12C IC50 records, and the docking objective evaluates binding with AutoDock Vina.
Public medicinal-chemistry literature has shown that small... | {"type": "propose", "reasoning": null, "payload": {"candidates": [{"design": "CC(C)CCCOCCN(CCOCCO)CC(N)=O", "rationale": "add hydroxyl chain variant"}, {"design": "CC(C)CCCOCCN(CCS(=O)(=O)CC)CCS(N)(=O)=O", "rationale": "replace sulfonamide with sulfone"}, {"design": "CC(C)CCCOCCN(CCOCC#N)CC(N)=O", "rationale": "introdu... | smallmol | |
You are a scientific search agent proposing edits to a training program under an iterative model-based (Bayesian) optimization loop. You may either propose edits within the current design space or expand that space. Return ONLY the JSON action. Never predict objective values, surrogate mean/variance, acquisition, or ra... | # Task: nanogpt (training_program)
Autoresearch task context:
- `train.py` is the single editable file. It contains the full GPT model, optimizer, and training loop.
- Everything inside `train.py` is fair game: architecture, hyperparameters, optimizer details, batch size, schedule, and training-loop choices.
- The benc... | {"type": "expand_design_space", "reasoning": "The value ranges of HEAD_DIM and WINDOW_PATTERN have been exhaustively enumerated (all 18 combinations evaluated), with a range of 0.000130, which does not exceed the noise floor (~2e-04), indicating that the current active feature set can no longer explain the remaining va... | nanogpt | |
You are a scientific search agent proposing candidate molecules under an iterative multi-objective Bayesian optimization loop. Return ONLY the JSON action. Never predict docking score, activity, EHVI, uncertainty, or rank. | # Task: smallmol (molecule)
The optimization task is for KRAS G12D small-molecule candidates. The activity objective is based on a target-specific model trained from public KRAS G12D IC50 records, and the docking objective evaluates binding with AutoDock Vina.
Public medicinal-chemistry literature has shown that small... | {"type": "propose", "reasoning": null, "payload": {"candidates": [{"design": "CC(C)Cc1c(C2CCCC2)sc(OC(F)(F)F)c1C#N", "rationale": "cyclopentyl variant of balanced elite"}, {"design": "CCCCc1c(C(C)C)sc(OC(F)(F)F)c1C#N", "rationale": "isopropyl instead of methyl"}, {"design": "CCCCc1c(Br)sc(OC(F)(F)F)c1C#N", "rationale":... | smallmol | |
You are a scientific search agent proposing candidate antibody CDRH3 sequences under an iterative Bayesian optimization loop. Return ONLY the JSON action. Never predict binding energy, uncertainty, or rank. | # Task: protein (antibody_sequence)
Pure LLM baseline for CDRH3 sequence proposal.
## Objectives
- binding_energy: minimize - Minimize Absolut energy. Lower true score is better.
## Design space (current state - you may act on it)
Representation: complete_design
Active parameters (the surrogate models only these; `c... | {"type": "propose", "reasoning": null, "payload": {"candidates": [{"design": "WSQVPTTNIWR", "rationale": null}]}, "summary": null} | protein_acq | |
You are a scientific search agent proposing candidate molecules under an iterative multi-objective Bayesian optimization loop. Return ONLY the JSON action. Never predict docking score, activity, EHVI, uncertainty, or rank. | # Task: smallmol (molecule)
The optimization task is for KRAS G12D small-molecule candidates. The activity objective is based on a target-specific model trained from public KRAS G12D IC50 records, and the docking objective evaluates binding with AutoDock Vina.
Public medicinal-chemistry literature has shown that small... | {"type": "propose", "reasoning": null, "payload": {"candidates": [{"design": "NC(=O)c1ccc(C(=O)Nc2ccccc2)cc1", "rationale": "extend amide with aniline"}, {"design": "O=C(O)c1ccc2ccccc2c1", "rationale": "naphthoic acid analogue"}, {"design": "NC(=O)c1ccc(OC(F)(F)F)c(C(F)(F)F)c1", "rationale": "dual CF3 and OCF3 substitu... | smallmol | |
You are a scientific search agent proposing candidate antibody CDRH3 sequences under an iterative Bayesian optimization loop. Return ONLY the JSON action. Never predict binding energy, uncertainty, or rank. | # Task: protein (antibody_sequence)
Pure LLM baseline for CDRH3 sequence proposal.
## Objectives
- binding_energy: minimize - Minimize Absolut energy. Lower true score is better.
## Design space (current state - you may act on it)
Representation: complete_design
Active parameters (the surrogate models only these; `c... | {"type": "propose", "reasoning": null, "payload": {"candidates": [{"design": "DGRDRNNFKSS", "rationale": null}]}, "summary": null} | protein_acq | |
You are a scientific search agent proposing candidate molecules under an iterative multi-objective Bayesian optimization loop. Return ONLY the JSON action. Never predict docking score, activity, EHVI, uncertainty, or rank. | # Task: smallmol (molecule)
The optimization task is for KRAS G12D small-molecule candidates. The activity objective is based on a target-specific model trained from public KRAS G12D IC50 records, and the docking objective evaluates binding with AutoDock Vina.
Public medicinal-chemistry literature has shown that small... | {"type": "propose", "reasoning": null, "payload": {"candidates": [{"design": "CC(C)CCOc1cc(N)c2c(N)nc(C3CC3C(F)(F)Br)n2c1", "rationale": "branched alkoxy for diversity"}, {"design": "CCCCOc1cc(N)c2c(N)nc(C3CC3C(F)(F)I)n2c1", "rationale": "iodine substitution on tail"}, {"design": "CCCCCOc1cc(C)c(N)c2c(N)nc(C3CC3C(F)(F)... | smallmol | |
You are a scientific search agent proposing candidate molecules under an iterative multi-objective Bayesian optimization loop. Return ONLY the JSON action. Never predict docking score, activity, EHVI, uncertainty, or rank. | # Task: smallmol (molecule)
The optimization task is for KRAS G12D small-molecule candidates. The activity objective is based on a target-specific model trained from public KRAS G12D IC50 records, and the docking objective evaluates binding with AutoDock Vina.
Public medicinal-chemistry literature has shown that small... | {"type": "propose", "reasoning": null, "payload": {"candidates": [{"design": "O=C(O)c1c[nH]c(C2CCCC2)n1", "rationale": "imidazole with cyclopentyl"}, {"design": "O=C(O)c1c[nH]c(C2CCCCC2)n1", "rationale": "imidazole with cyclohexyl"}, {"design": "O=C(O)c1csc(C2CCCC2)n1", "rationale": "thiazole with cyclopentyl"}, {"desi... | smallmol | |
You are a scientific search agent proposing candidate molecules under an iterative multi-objective Bayesian optimization loop. Return ONLY the JSON action. Never predict docking score, activity, EHVI, uncertainty, or rank. | # Task: smallmol (molecule)
The optimization task is for KRAS G12D small-molecule candidates. The activity objective is based on a target-specific model trained from public KRAS G12D IC50 records, and the docking objective evaluates binding with AutoDock Vina.
Public medicinal-chemistry literature has shown that small... | {"type": "propose", "reasoning": null, "payload": {"candidates": [{"design": "CC(C)COc1ccc2c(N)nc(C3CC3)n2c1", "rationale": "crossover isobutoxy with cyclopropyl"}, {"design": "CCOc1ccc2c(N)nc(C3CC3Br)n2c1", "rationale": "shorter alkoxy on bromocyclopropyl"}, {"design": "CCCOc1ccc(F)c2c(N)nc(C3CN3)n2c1", "rationale": "... | smallmol | |
You are a scientific search agent proposing candidate antibody CDRH3 sequences under an iterative Bayesian optimization loop. Return ONLY the JSON action. Never predict binding energy, uncertainty, or rank. | # Task: protein (antibody_sequence)
Direct CDRH3 antibody sequence generation for AntBO. Do not output search functions, code, LocalSearch, NeighborSampling, LatinHyperCubeSampling, or explanations. Generate antibody strings directly.
## Objectives
- binding_energy: minimize - Minimize Absolut binding energy. Lower tr... | {"type": "propose", "reasoning": null, "payload": {"candidates": [{"design": "YSVPMWCDKTR", "rationale": null}]}, "summary": null} | protein_direct | |
You are a scientific search agent proposing edits to a training program under an iterative model-based (Bayesian) optimization loop. You may either propose edits within the current design space or expand that space. Return ONLY the JSON action. Never predict objective values, surrogate mean/variance, acquisition, or ra... | # Task: nanogpt (training_program)
Autoresearch task context:
- `train.py` is the single editable file. It contains the full GPT model, optimizer, and training loop.
- Everything inside `train.py` is fair game: architecture, hyperparameters, optimizer details, batch size, schedule, and training-loop choices.
- The benc... | {"type": "propose", "reasoning": "Reduce total batch size to potentially improve convergence and reduce memory pressure. Decrease head dimension to explore a different model configuration that might yield better performance.", "payload": {"candidates": [{"parent": "state_1505", "edits": [{"parameter": "TOTAL_BATCH_SIZE... | nanogpt | |
You are a scientific search agent proposing candidate molecules under an iterative multi-objective Bayesian optimization loop. Return ONLY the JSON action. Never predict docking score, activity, EHVI, uncertainty, or rank. | # Task: smallmol (molecule)
The optimization task is for KRAS G12D small-molecule candidates. The activity objective is based on a target-specific model trained from public KRAS G12D IC50 records, and the docking objective evaluates binding with AutoDock Vina.
Public medicinal-chemistry literature has shown that small... | {"type": "propose", "reasoning": null, "payload": {"candidates": [{"design": "CCCSc1ccc2c(N)nc(C3CC3C(F)(F)Br)n2c1", "rationale": "thioether replaces ether"}, {"design": "CCCOc1csc2c(N)nc(C3CC3C(F)(F)Br)n2c1", "rationale": "thiophene replaces benzene"}, {"design": "CC(C)COc1ccc2c(N)nc(C3CC3C(F)(F)Br)n2c1", "rationale":... | smallmol | |
You are a scientific search agent proposing candidate antibody CDRH3 sequences under an iterative Bayesian optimization loop. Return ONLY the JSON action. Never predict binding energy, uncertainty, or rank. | # Task: protein (antibody_sequence)
Pure LLM baseline for CDRH3 sequence proposal.
## Objectives
- binding_energy: minimize - Minimize Absolut energy. Lower true score is better.
## Design space (current state - you may act on it)
Representation: complete_design
Active parameters (the surrogate models only these; `c... | {"type": "propose", "reasoning": null, "payload": {"candidates": [{"design": "VVEWMIQMNPH", "rationale": null}]}, "summary": null} | protein_acq | |
You are a scientific search agent proposing candidate molecules under an iterative multi-objective Bayesian optimization loop. Return ONLY the JSON action. Never predict docking score, activity, EHVI, uncertainty, or rank. | # Task: smallmol (molecule)
The optimization task is for KRAS G12C small-molecule candidates. The activity objective is based on a target-specific model trained from public KRAS G12C IC50 records, and the docking objective evaluates binding with AutoDock Vina.
Public medicinal-chemistry literature has shown that small... | {"type": "propose", "reasoning": null, "payload": {"candidates": [{"design": "CC(C)CCCOCCN(CCS(C)(=O)=O)CCOCCS(N)(=O)=O", "rationale": "extend pareto with ether chain"}, {"design": "CC(C)CCCOCCN(CCS(N)(=O)=O)CCS(=O)(=O)NCCN", "rationale": "swap sulfonamide for ethylenediamine"}, {"design": "CC(C)CCCOCCOCCN(CCOCC(N)=O)C... | smallmol | |
You are a scientific search agent proposing candidate antibody CDRH3 sequences under an iterative Bayesian optimization loop. Return ONLY the JSON action. Never predict binding energy, uncertainty, or rank. | # Task: protein (antibody_sequence)
Pure LLM baseline for CDRH3 sequence proposal.
## Objectives
- binding_energy: minimize - Minimize Absolut energy. Lower true score is better.
## Design space (current state - you may act on it)
Representation: complete_design
Active parameters (the surrogate models only these; `c... | {"type": "propose", "reasoning": null, "payload": {"candidates": [{"design": "ESIGHIFQLWL", "rationale": null}]}, "summary": null} | protein_acq | |
You are a scientific search agent proposing edits to a training program under an iterative model-based (Bayesian) optimization loop. You may either propose edits within the current design space or expand that space. Return ONLY the JSON action. Never predict objective values, surrogate mean/variance, acquisition, or ra... | # Task: nanogpt (training_program)
Autoresearch task context:
- `train.py` is the single editable file. It contains the full GPT model, optimizer, and training loop.
- Everything inside `train.py` is fair game: architecture, hyperparameters, optimizer details, batch size, schedule, and training-loop choices.
- The benc... | {"type": "propose", "reasoning": "Increase model width to potentially improve performance. Increase model depth to potentially improve performance.", "payload": {"candidates": [{"parent": "state_7689", "edits": [{"parameter": "ASPECT_RATIO", "edit_op": "set_numeric", "value": 128, "rationale": "Increase model width to ... | nanogpt |
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