Datasets:

prometheus-eval
/

into-the-unknown

paper string	group string	title string	domain string	venue string	citations int64	lab string	original_paper string	follow_up_direction string	task_spec string	paper_pdf string	paper_md string
P1	A	Hierarchical modeling of molecular energies using a deep neural network	Computational chemistry	J. Chem. Phys.	428	LANL	https://arxiv.org/abs/1710.00017	Use HIP-NN's hierarchy-breakdown signal to identify failure motifs and calibrate uncertainty estimates that transfer across conformations.	benchmark/paper1/task_spec.md	benchmark/paper1/paper.pdf	benchmark/paper1/paper.md
P2	A	Extending the accuracy of the SNAP interatomic potential form	Materials science	J. Chem. Phys.	311	SNL	https://arxiv.org/abs/1711.11131	Replace SNAP bispectrum components with a learned descriptor and test its accuracy, data efficiency, and cross-material transfer.	benchmark/paper2/task_spec.md	benchmark/paper2/paper.pdf	benchmark/paper2/paper.md
P3	A	Convolutional Dictionary Learning: A Comparative Review and New Algorithms	Computational imaging	IEEE TCI	282	LANL	https://arxiv.org/abs/1709.02893	Unify CDL dictionary-update algorithms within one multichannel derivation framework and use it to reveal a new method and equivalence.	benchmark/paper3/task_spec.md	benchmark/paper3/paper.pdf	benchmark/paper3/paper.md
P4	A	A Kriging-based approach to autonomous experimentation with applications to X-ray scattering	Scientific ML	Sci. Rep.	136	LBNL	https://doi.org/10.1038/s41598-019-48114-3	Develop an acquisition strategy that reconstructs synthetic functions faster than SMART and gSMART.	benchmark/paper4/task_spec.md	benchmark/paper4/paper.pdf	benchmark/paper4/paper.md
P5	A	Adaptive variational quantum dynamics simulations	Quantum	PRX Quantum	203	Ames	https://arxiv.org/abs/2011.00622	Make AVQDS compatible with fixed nearest-neighbor qubit topology while measuring the resulting accuracy and resource overhead.	benchmark/paper5/task_spec.md	benchmark/paper5/paper.pdf	benchmark/paper5/paper.md
P6	A	Simulations of relativistic-quantum plasmas using real-time lattice scalar QED	Plasma physics	Phys. Rev. E	32	PPPL	https://arxiv.org/abs/1802.00524	Stress-test variational lattice scalar QED in new valid and fundamentally invalid regimes, then diagnose and repair its failures.	benchmark/paper6/task_spec.md	benchmark/paper6/paper.pdf	benchmark/paper6/paper.md
P7	A	The non-Riemannian nature of perceptual color space	Color science	PNAS	51	LANL	https://doi.org/10.1073/pnas.2119753119	Test whether perceptual diminishing returns are organized by cone channels rather than global scaling or generic anisotropy.	benchmark/paper7/task_spec.md	benchmark/paper7/paper.pdf	benchmark/paper7/paper.md
P8	A	Parameterized Physics-informed Neural Networks for Parameterized PDEs	Scientific ML	ICML	113	null	https://arxiv.org/abs/2408.09446	Extend P²INN into a problem-specification encoder that generalizes jointly across coefficients, initial conditions, and boundary conditions.	benchmark/paper8/task_spec.md	benchmark/paper8/paper.pdf	benchmark/paper8/paper.md
P9	B	An efficient explicit implementation of a near-optimal quantum algorithm for simulating linear dissipative differential equations	Quantum	J. Comput. Phys.	6	LLNL	https://arxiv.org/abs/2501.11146	Improve the mathematical LCHS algorithm by substituting a kernel with better asymptotic resource scaling.	benchmark/paper9/task_spec.md	benchmark/paper9/paper.pdf	benchmark/paper9/paper.md
P10	B	Efficient Berry Phase Calculation via Adaptive Variational Quantum Computing Approach	Quantum	APL Quantum	5	Ames	https://arxiv.org/abs/2506.19150	Determine how much realistic hardware noise the AVQDS Berry-phase method can tolerate before its topological output fails.	benchmark/paper10/task_spec.md	benchmark/paper10/paper.pdf	benchmark/paper10/paper.md
P11	B	Projected Hessian Learning: Fast Curvature Supervision for Accurate Machine-Learning Interatomic Potentials	Computational chemistry	Unpublished	3	LANL	https://arxiv.org/abs/2603.04523	Reduce the stochastic-probe requirement of Projected Hessian Learning without sacrificing estimator or downstream model quality.	benchmark/paper11/task_spec.md	benchmark/paper11/paper.pdf	benchmark/paper11/paper.md
P12	B	gp2Scale: Compactly-Supported Non-Stationary Kernels and Distributed Computing for Exact Gaussian Processes on 10 Million Data Points	Scientific ML	ICML	2	LBNL	https://arxiv.org/abs/2512.06143	Apply exact large-scale Gaussian processes to a new domain using a domain-specific kernel or sparsity construction.	benchmark/paper12/task_spec.md	benchmark/paper12/paper.pdf	benchmark/paper12/paper.md
P13	B	Particle-in-cell simulations of laser crossbeam energy transfer via magnetized ion-acoustic wave	Plasma physics	Physics	2	LLNL	https://arxiv.org/abs/2511.03090	Map the boundary where Vlasov theory ceases to predict MagCBET PIC behavior and identify the first invalid assumption.	benchmark/paper13/task_spec.md	benchmark/paper13/paper.pdf	benchmark/paper13/paper.md
P14	B	Simulating plasma wave propagation on a superconducting quantum chip	Quantum	Phys. Rev. App.	1	LLNL	https://arxiv.org/abs/2507.09479	Add ZZ interactions to the spin-chain model and test whether it quantitatively reproduces nonlinear plasma dynamics.	benchmark/paper14/task_spec.md	benchmark/paper14/paper.pdf	benchmark/paper14/paper.md
P15	B	Beyond Classical Molecular Dynamics with Layered Interatomic Potentials	Materials science	Unpublished	0	SNL	https://www.researchsquare.com/article/rs-9077430/v1	Replace the Sommerfeld potential's heuristic sigmoid with a physically principled switching function.	benchmark/paper15/task_spec.md	benchmark/paper15/paper.pdf	benchmark/paper15/paper.md

Into the Unknown

Dataset Summary

Into the Unknown tests whether AI agents can build on a scientist's work and write a follow-up paper. Each of its 15 tasks gives an agent a published (or recently submitted) paper and a follow-up research direction proposed by the paper's own author; the agent must carry out the research and write a follow-up paper that is honest, complete, and meets the scientist's bar.

This dataset holds the 15 tasks and everything nine agents produced on them: 135 submissions, with each agent's paper, full trajectory, and the files it wrote. Code, rubrics and graders are on GitHub; the paper is coming soon.

Supported Tasks and Leaderboards

The task is open-ended research: given the paper and the direction, produce the follow-up paper. The leaderboard shows every agent's results and every rubric verdict, and you can submit your agent's paper to be evaluated.

Subsets

subset	content
`benchmark/`	The 15 tasks: `paper<i>/task_spec.md`, the original paper (`paper.pdf`, its text `paper.md`, figures `images/`) and `index.csv`. A task's code and data are downloaded by `prepare_tasks.py` in the GitHub repository.
`outcomes/`	The papers the agents wrote, `<agent>/paper<i>.pdf` (the `proposal/report.pdf` the judge read): 131 PDFs, since four GLM-5.3 runs produced none.
`trajectories/`	Each run's `<agent>/paper<i>.json` (run metadata and every agent event), plus the harnesses' own logs (`.harness_logs.tar`; DeepSeek-V4-Pro's full conversation is here) and the runners' console logs (`.runner_logs.tar`).
`file_systems/`	Each submission's files as the judge saw them: `proposal/` and the rest of the working directory (`workdir_outside_proposal/`), without environments, caches or the final transcript. `view.tar.part-*` with a SHA-256 `manifest.json`; nine runs add a `supplement.tar` of files the judge's copy lacked.

Agents (<agent>): opus-4.8, opus-5, opus-5.5, fable-5, sol-5.6, astra-6, deepseek-v4-pro, kimi-k3, glm-5.3.

Dataset Structure

An example of a benchmark datum is as follows:

paper: (str) - The task id, P1 to P15.
group: (str) - A: published 2024 or earlier; B: submitted 2026.
title: (str) - The original paper's title.
domain, venue, lab: (str) - Its field, venue and the laboratory of its authors.
citations: (int) - Citations as of September 14, 2026.
original_paper: (str) - A link to the original paper.
follow_up_direction: (str) - The research direction the paper's author proposed.
task_spec, paper_pdf, paper_md: (str) - Paths to the task specification and the original paper in this dataset.

outcomes/index.csv and trajectories/index.csv list one row per submission (agent, paper, file, and run metadata).

Evaluation

Run an agent and judge its submission with the GitHub repository:

bash src/solver/run.sh <agent> <paper_id>
python src/grader/run_codex_judge.py prepare --agents <agent>     # then rescope, launch

To unpack one submission's files:

huggingface-cli download prometheus-eval/into-the-unknown --repo-type dataset \
    --include "file_systems/astra-6/paper3/*" --local-dir .
cd file_systems/astra-6/paper3 && cat view.tar.part-* > view.tar && tar -xf view.tar

A few notes specific to this dataset:

outcomes/fable-5/paper8.pdf has a real person's name, affiliation and e-mail blacked out on its title page; every other PDF is byte-identical to the submission.
Files written on the compute cluster can contain absolute paths of the cluster's file system.
In file_systems/opus-5.5/paper12, two R help pages (qsreg.html, spam.html) differ only in case from other pages; the archive has the originals, the others are in supplement.tar, and manifest.json lists both under checked_against_judge_record.sha256_mismatch.

License

Task specifications, rubrics and documentation are released under the Apache-2.0 license of the GitHub repository. The original papers in benchmark/ remain under their authors' and publishers' terms and are included for research use; agent outputs may contain third-party code and data that keep their own licenses.

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