First 15 rows of train
| paperstring | groupstring | titlestring | domainstring | venuestring | citationsint64 | labstring | original_paperstring | follow_up_directionstring | task_specstring | paper_pdfstring | paper_mdstring |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 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 | 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 |
