name stringlengths 18 61 | instruction stringlengths 4.62k 7.32k | description stringclasses 1
value | keywords listlengths 0 0 | schema_version stringclasses 1
value | config unknown | metadata unknown | files listlengths 69 178 | location stringlengths 24 34 |
|---|---|---|---|---|---|---|---|---|
Capacity-resolved knapsack audit | # Capacity-resolved knapsack audit
Implement `/app/solve.py`. It must compute the integer optimum, fractional optimum, and normalized integrality gap for every integer capacity of each supplied instance. All weights, profits and capacities are dimensionless. A positive gap can occur even when the integer solution is o... | [] | {
"task": {
"id": "capacity_knapsack_audit",
"name": "Capacity-resolved knapsack audit",
"version": "1.0"
},
"verifier": {
"timeout_sec": 900
},
"execution": {
"schema": "scientific_execution/v1",
"instruction_sha256": "1e802518efa11ddaa824da07d6341c5534fa276caae4aaa00f3b32b868a77c55",... | {} | [
"README.md",
"environment/Dockerfile",
"environment/RUNTIME.md",
"environment/public/audit_feedback.py",
"environment/public/inputs/instances.json",
"environment/public/inputs/public.json",
"environment/public/public_reference.json",
"environment/public/solve.py",
"environment/public/validate.py",
... | tasks/cs-ds-2512-21195v2 | ||
Sparse Markov prefix codec | # Sparse context-dependent prefix codec
Implement `/app/codec.py`, a CPU-only codec for bounded directed graph-walk corpora. No external data are needed. Use the Python CLI
```
python /app/codec.py --input REQUEST.json --output RESULT.json
```
The input's `mode` is either `fit-encode` or `decode`. Create any missing... | [] | {
"task": {
"id": "arxiv_2512_21320v2_idea0",
"name": "Sparse Markov prefix codec",
"version": "1.0"
},
"verifier": {
"timeout_sec": 900
},
"execution": {
"schema": "scientific_execution/v1",
"instruction_sha256": "f531e7323cf12e871db9023364caaf0745b112a95e51e08cc9f43b108cb70806",
... | {} | [
"README.md",
"environment/Dockerfile",
"environment/RUNTIME.md",
"environment/public/feedback_core.py",
"environment/public/feedback_example.py",
"environment/public/inputs/public/answers.json",
"environment/public/inputs/public/decode.json",
"environment/public/inputs/public/fit.json",
"environment... | tasks/cs-ds-2512-21320v2 | ||
Certified minimax marginal noise | # Certified minimax Gaussian covariances for marginal tables
Implement `/app/solve.py`. Run it as `python /app/solve.py --inputs INPUT.json --outputs OUTPUT.json`. Both arguments are required. Create a missing output parent. Inputs normally live under `/app/inputs` and outputs under `/app/outputs`.
## Workloads and o... | [] | {
"task": {
"id": "gaussian_marginal_certificates",
"name": "Certified minimax marginal noise"
},
"verifier": {
"timeout_sec": 200000
},
"execution": {
"schema": "scientific_execution/v1",
"instruction_sha256": "6455fa001a6edd9e0efb226e060fc24c3e9c96009aca94a5f6198f39edfc5fb4",
"execut... | {} | [
"README.md",
"environment/Dockerfile",
"environment/RUNTIME.md",
"environment/public/certificate.py",
"environment/public/inputs/calibration.json",
"environment/public/inputs/public.json",
"environment/public/validate.py",
"environment/runtime/wheels/mpmath-1.3.0-py3-none-any.whl",
"environment/runt... | tasks/cs-ds-2512-21499v1 | ||
Global directed-energy audit | # Global directed-energy audit
Implement `/app/outputs/audit.py`. This is a CPU-only scientific file task. All quantities are dimensionless.
## Input and interface
Run `python /app/outputs/audit.py --input INPUT --output OUTPUT`. With no arguments use `/app/inputs/pairs.json` and `/app/outputs/results.json`. Create m... | [] | {
"task": {
"id": "directed_energy_audit",
"name": "Global directed-energy audit"
},
"verifier": {
"timeout_sec": 1800
},
"execution": {
"schema": "scientific_execution/v1",
"instruction_sha256": "59663358b941b76feff89e244ac934bb921eb8fee8e7699557036e1ad11a5ef0",
"execution_disclosure"... | {} | [
"README.md",
"environment/Dockerfile",
"environment/RUNTIME.md",
"environment/public/check_output.py",
"environment/public/inputs/pairs.json",
"environment/public/inputs/public.json",
"environment/public/outputs/audit.py",
"environment/public/public_margins.json",
"environment/public/science.py",
... | tasks/cs-ds-2512-21671v2 | ||
Compile shared-frontier binary tiles | # Compile shared-frontier binary tiles
Implement a CPU-executable compiler for 128 independent eight-bit masked population counts. This is an algebraic lowering problem, not a GPU implementation or performance benchmark. No graph data or accelerator is needed.
## Interface
Create `/app/solution.py`. It must run as:
... | [] | {
"task": {
"id": "shared_frontier_tiles",
"name": "Compile shared-frontier binary tiles",
"version": "1.0"
},
"verifier": {
"timeout_sec": 900
},
"execution": {
"schema": "scientific_execution/v1",
"instruction_sha256": "22000061c949bb79ad637951ac7a566136ddf5532e19711198acffff0f63bff1... | {} | [
"README.md",
"environment/Dockerfile",
"environment/RUNTIME.md",
"environment/public/cert_io.py",
"environment/public/evaluate.py",
"environment/public/inputs/masks.json",
"environment/public/prepare_public.py",
"environment/public/solution.py",
"environment/runtime/wheels/mpmath-1.3.0-py3-none-any.... | tasks/cs-ds-2512-21967v1 | ||
Exact signed bilinear circuit compilation | # Compile exact signed bilinear circuits
Implement `/app/compiler.py`. Invocation:
```
python /app/compiler.py --input INPUT_JSON --output OUTPUT_JSON
```
Create the output parent directory if it does not exist. Explicit arguments are required; there are no no-argument defaults. A public example is `/app/inputs/publ... | [] | {
"task": {
"id": "arxiv_2512_21980v1_idea0",
"name": "Exact signed bilinear circuit compilation"
},
"verifier": {
"timeout_sec": 900
},
"execution": {
"schema": "scientific_execution/v1",
"instruction_sha256": "932450d42901787396816d185be98b746eee4aac2a9e292c34d4eb2c7b6375b9",
"execut... | {} | [
"README.md",
"environment/Dockerfile",
"environment/RUNTIME.md",
"environment/public/inputs/public.json",
"environment/public/validate.py",
"environment/runtime/wheels/mpmath-1.3.0-py3-none-any.whl",
"environment/runtime/wheels/numpy-2.2.6-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl",
"... | tasks/cs-ds-2512-21980v1 | ||
cs-ds-2512-22475v1 | # Maximum-weight connected colorful subsets
Implement `/app/solve.py` as a Python command-line program. For each colored, weighted, simple undirected graph, find a maximum-weight connected set containing exactly one vertex of every color, or correctly report infeasibility.
## Mathematical target
Vertices are labeled... | [] | {
"task": {
"id": "connected_colorful_subset",
"title": "Exact connected colorful subset optimization"
},
"verifier": {
"timeout_sec": 900
},
"execution": {
"schema": "scientific_execution/v1",
"instruction_sha256": "26c10357378a5662ad284fea7bf8dd56cb2059290f529f2cf6c3e134fc311fbc",
"e... | {} | [
"README.md",
"environment/Dockerfile",
"environment/RUNTIME.md",
"environment/public/inputs/public.json",
"environment/public/public_values.json",
"environment/public/validate.py",
"environment/runtime/wheels/mpmath-1.3.0-py3-none-any.whl",
"environment/runtime/wheels/numpy-2.2.6-cp311-cp311-manylinux... | tasks/cs-ds-2512-22475v1 | ||
cs-ds-2512-22803v2 | # Finite-size rank-one spin covariance diagnostic
Implement `/app/solve.py`. This diagnostic compares an exact finite spin covariance with a saddle-point heuristic; no equality or asymptotic error bound for the heuristic is assumed.
## Model and targets
All quantities are dimensionless. A case has integer `n` with 2 ... | [] | {
"task": {
"id": "arxiv_2512_22803v2_idea0",
"title": "Finite-size rank-one covariance diagnostic"
},
"verifier": {
"timeout_sec": 900
},
"execution": {
"schema": "scientific_execution/v1",
"instruction_sha256": "ded1c031be86e323cde871e6dd656206e642cb4d16ea9e55971c3325d77edc39",
"exec... | {} | [
"README.md",
"environment/Dockerfile",
"environment/RUNTIME.md",
"environment/public/check_public.py",
"environment/public/inputs/cases.json",
"environment/public/inputs/public.json",
"environment/public/public_reference.json",
"environment/public/science_core.py",
"environment/public/validate.py",
... | tasks/cs-ds-2512-22803v2 | ||
cs-ds-2512-22975v1 | # Minimum interval-modular partitions
Implement `/app/solve.py`, a CPU-only command-line program that exactly minimizes the number of interval-module blocks of each input graph. No asymptotic running-time claim is graded.
## Mathematics
A graph is simple, undirected, unweighted and dimensionless. Its vertices are th... | [] | {
"task": {
"id": "interval_modular_partitions",
"title": "Minimum interval-modular partitions"
},
"verifier": {
"timeout_sec": 2400
},
"execution": {
"schema": "scientific_execution/v1",
"instruction_sha256": "2c02fd2d44d0582c3dd968ff3d7741bf4cee8532bb7044b87804925fa92d95b3",
"executi... | {} | [
"README.md",
"environment/Dockerfile",
"environment/RUNTIME.md",
"environment/public/format_io.py",
"environment/public/inputs/graphs.json",
"environment/public/inputs/public.json",
"environment/public/validate.py",
"environment/runtime/wheels/mpmath-1.3.0-py3-none-any.whl",
"environment/runtime/whe... | tasks/cs-ds-2512-22975v1 | ||
Exact permutation compatibility rectangles | # Exact rectangles in permutation compatibility restrictions
Implement `/app/solve.py`, a CPU-only exact solver for the following finite combinatorial optimization problem. All quantities are dimensionless integers. No floating-point approximation or tolerance is used.
## Mathematical target
A permutation of order `... | [] | {
"task": {
"id": "permutation_rectangles",
"name": "Exact permutation compatibility rectangles"
},
"verifier": {
"timeout_sec": 1800
},
"execution": {
"schema": "scientific_execution/v1",
"instruction_sha256": "e6b87183a09760e70d73fa3d787766101adc27a85cbcbeb74c09e419840b6dda",
"execut... | {} | [
"README.md",
"environment/Dockerfile",
"environment/RUNTIME.md",
"environment/public/inputs/cases.json",
"environment/public/inputs/public.json",
"environment/public/public_reference.json",
"environment/public/validate.py",
"environment/runtime/wheels/mpmath-1.3.0-py3-none-any.whl",
"environment/run... | tasks/cs-ds-2512-23121v1 | ||
Exact active-weight visibility | # Exact active-weight visibility
Implement `/app/evaluate.py`, a CPU-only exact evaluator of a supplied visibility-weight state. This is an integration task, not a search for guard positions.
## Geometry and target
An outer counterclockwise simple polygonal ring and zero, one, or two clockwise simple hole rings defi... | [] | {
"task": {
"id": "exact_active_visibility",
"name": "Exact active-weight visibility",
"version": "1.0"
},
"verifier": {
"timeout_sec": 3600
},
"execution": {
"schema": "scientific_execution/v1",
"instruction_sha256": "0e3b1451bd8cf0d636840da032935f6170928c35bfbe35b16e5b56f2a845020a",
... | {} | [
"README.md",
"environment/Dockerfile",
"environment/RUNTIME.md",
"environment/public/evaluate.py",
"environment/public/inputs/cases.json",
"environment/public/inputs/public.json",
"environment/public/validate.py",
"environment/runtime/wheels/mpmath-1.3.0-py3-none-any.whl",
"environment/runtime/wheel... | tasks/cs-ds-2512-23297v2 | ||
cs-ds-2512-23468v1 | # Exact minimum-cut isolation profiles
Implement `/app/solve.py` using ordinary CPU computation. The task is to recover complete tied cut families, not just one minimum cut. No efficiency claim beyond the execution budget is required.
## Input and command
Run `python /app/solve.py --input INPUT --output OUTPUT`. Bot... | [] | {
"task": {
"id": "arxiv_2512_23468v1_idea0",
"title": "Exact cut isolation profiles"
},
"verifier": {
"timeout_sec": 1200
},
"execution": {
"schema": "scientific_execution/v1",
"instruction_sha256": "6f99fc8ccdbe3375a442a9ad8ee8c5af0852334012735679891675a9b672161b",
"execution_disclos... | {} | [
"README.md",
"environment/Dockerfile",
"environment/RUNTIME.md",
"environment/public/inputs/public.json",
"environment/public/profile_format.py",
"environment/public/public_table.json",
"environment/public/validate.py",
"environment/runtime/wheels/mpmath-1.3.0-py3-none-any.whl",
"environment/runtime... | tasks/cs-ds-2512-23468v1 | ||
Exact finite sparse-projection distortion laws | # Exact finite sparse-projection distortion laws
Implement `/app/solution.py`, a command-line program computing exact finite probability distributions. Use only CPU computation; all algorithms and standard libraries are permitted.
## Scientific target
Each case has dimensionless integers `k`, `s`, and a coefficient v... | [] | {
"task": {
"id": "finite_sparse_projection_histograms",
"name": "Exact finite sparse-projection distortion laws"
},
"verifier": {
"timeout_sec": 1800
},
"execution": {
"schema": "scientific_execution/v1",
"instruction_sha256": "9fc3f36d78d76ae4a7cf0e4c6903d72c87691ab84c8d91a80c43e29acf453... | {} | [
"README.md",
"environment/Dockerfile",
"environment/RUNTIME.md",
"environment/public/format_rules.py",
"environment/public/inputs/cases.json",
"environment/public/inputs/public_cases.json",
"environment/public/public_table.json",
"environment/public/validate.py",
"environment/runtime/wheels/mpmath-1... | tasks/cs-ds-2512-23756v1 | ||
Stochastic diffusion optimal control | # Stochastic diffusion optimal control
Implement `/app/solve.py`. Solve the finite-dimensional, dimensionless optimization problem below. Any mathematically equivalent numerical algorithm is allowed; there is no speed score.
## Input and CLI
Run `python /app/solve.py --input INPUT.json --output OUTPUT.json`. Both ar... | [] | {
"task_id": "stochastic_diffusion_control",
"version": "1.0",
"task": {
"name": "Stochastic diffusion optimal control",
"category": "scientific-computing"
},
"verifier": {
"timeout_sec": 1800
},
"execution": {
"schema": "scientific_execution/v1",
"instruction_sha256": "cf0d3efe4c654e6... | {} | [
"README.md",
"environment/Dockerfile",
"environment/RUNTIME.md",
"environment/public/feedback_core.py",
"environment/public/feedback_data/analytic.json",
"environment/public/feedback_data/coupled.json",
"environment/public/feedback_one.py",
"environment/public/inputs/public/analytic.json",
"environm... | tasks/math-na-2512-23804v2 | ||
Conforming space-time wave projection | # Space-time wave least-squares projection
Implement `/app/solver.py`. This is a finite-dimensional approximation task, not a claim about approximation of all weak wave solutions. Use any numerical method that satisfies the acceptance rule.
## Mathematical target
All quantities are dimensionless. On (0,1), the orthon... | [] | {
"task": {
"id": "wave_graph_projection",
"name": "Conforming space-time wave projection"
},
"verifier": {
"timeout_sec": 1800
},
"execution": {
"schema": "scientific_execution/v1",
"instruction_sha256": "55c94125a30b938a0be9b5b52f2f1df063198bf007ef49965e0995a3008a4fff",
"execution_di... | {} | [
"README.md",
"environment/Dockerfile",
"environment/RUNTIME.md",
"environment/public/inputs/cases.json",
"environment/public/inputs/public.json",
"environment/public/solver.py",
"environment/public/validation/check.py",
"environment/public/validation/metrics.py",
"environment/public/validation/publi... | tasks/math-na-2512-23807v2 | ||
math-na-2512-23814v1 | # Complex-affine stability lower envelopes
Implement `/app/outputs/scm.py`, a CPU Python program evaluating a constrained Rayleigh-coordinate relaxation. NumPy and SciPy are available. All quantities are dimensionless.
## Mathematics
For three complex matrices, let `M[0] = i I` and
\(M(t)=\sum_{j=1}^3 t_j M_j\), with... | [] | {
"task": {
"id": "arxiv_2512_23814v1_idea0",
"title": "Complex-affine stability lower envelopes"
},
"verifier": {
"timeout_sec": 900
},
"execution": {
"schema": "scientific_execution/v1",
"instruction_sha256": "f13905cc758024e9067c12e8336f94068115c218a9edc19bcbfe3a83baa7ce4c",
"execut... | {} | [
"README.md",
"environment/Dockerfile",
"environment/RUNTIME.md",
"environment/public/inputs/check_scm.py",
"environment/public/inputs/public_optima.json",
"environment/public/inputs/scm.json",
"environment/public/outputs/scm.py",
"environment/runtime/wheels/mpmath-1.3.0-py3-none-any.whl",
"environme... | tasks/math-na-2512-23814v1 | ||
Certified max-affine quadratic prior recovery | # Recover a prior from a max-affine quadratic potential
Implement `/app/solve.py`. All quantities are dimensionless. For each query y, compute the conjugate and the recovered prior of
psi(z) = c ||z||_2^2 / 2 + max_i (b_i dot z + r_i),
C(y) = sup_z [y dot z - psi(z)],
J(y) = [C(y) - ||y||_2^2 / 2] / t.
The rows b_i ... | [] | {
"task": {
"id": "arxiv_2512_23829v3_idea0",
"name": "Certified max-affine quadratic prior recovery"
},
"verifier": {
"timeout_sec": 1800
},
"execution": {
"schema": "scientific_execution/v1",
"instruction_sha256": "41467cff7ba2a094086acd6c9879656bec00bbd3eff0f77c594fe7c09fe030be",
"e... | {} | [
"README.md",
"environment/Dockerfile",
"environment/RUNTIME.md",
"environment/certificate.py",
"environment/check.py",
"environment/public/certificate.py",
"environment/public/inputs/constants.json",
"environment/public/inputs/public.npz",
"environment/public/solve.py",
"environment/public/validat... | tasks/math-na-2512-23829v3 | ||
Planar reflected cell occupation moments | # Planar reflected nearest-site cell integrals
Implement `/app/outputs/solve.py` in Python. It must support
`python /app/outputs/solve.py --input INPUT --output OUTPUT` and, with no arguments, read `/app/inputs/cases.json` and write `/app/outputs/results.json`. Create missing output parent directories. No external dat... | [] | {
"task": {
"id": "planar_reflected_cells",
"name": "Planar reflected cell occupation moments"
},
"verifier": {
"timeout_sec": 600
},
"execution": {
"schema": "scientific_execution/v1",
"instruction_sha256": "4d43a88015416170ee2784cf1ee92265ea974b707aab71a260af65dd0d0a9117",
"execution... | {} | [
"README.md",
"environment/Dockerfile",
"environment/RUNTIME.md",
"environment/public/cases.json",
"environment/public/check_output.py",
"environment/public/check_public.py",
"environment/public/expected.json",
"environment/public/metrics.py",
"environment/runtime/wheels/mpmath-1.3.0-py3-none-any.whl... | tasks/math-na-2512-23843v1 | ||
Temperature-aware Gaussian-mixture drift and spatial Jacobian | # Temperature-aware Gaussian-mixture drift
Create `/app/outputs/solver.py`, a CPU Python program that evaluates a drift and its spatial Jacobian. All quantities are dimensionless. Implement the mathematical target, not a target-density score approximation.
## Mathematical target
For dimension d, positive mixture weig... | [] | {
"task_id": "temperature_mixture_drift",
"task": {
"name": "Temperature-aware Gaussian-mixture drift and spatial Jacobian"
},
"verifier": {
"timeout_sec": 1800
},
"execution": {
"schema": "scientific_execution/v1",
"instruction_sha256": "908610662d621e5e5797055b0e5bbe22de1491b0c0bdb2bd184d0... | {} | [
"README.md",
"environment/Dockerfile",
"environment/RUNTIME.md",
"environment/public/inputs/public.npz",
"environment/public/inputs/public_arrays.json",
"environment/public/outputs/solver.py",
"environment/public/validation/check.py",
"environment/public/validation/compare.py",
"environment/public/v... | tasks/math-na-2512-23965v2 | ||
Periodic quadratic-control Bellman trajectories | # Periodic quadratic-control Bellman trajectories
Implement `/app/solver.py` for a dimensionless two-dimensional, fully discrete control problem on the unit torus. Only values are requested: different optimal controls, including ties between periodic lifts, are equivalent.
## Inputs and mathematical target
An NPZ fi... | [] | {
"task": {
"id": "periodic_quadratic_bellman",
"name": "Periodic quadratic-control Bellman trajectories"
},
"verifier": {
"timeout_sec": 2400
},
"execution": {
"schema": "scientific_execution/v1",
"instruction_sha256": "013ed4856baa402c6302b9b3b6f378b97b1901bb84b4cc397584dcabb8c87bad",
... | {} | [
"README.md",
"environment/Dockerfile",
"environment/RUNTIME.md",
"environment/public/acceptance.py",
"environment/public/feedback.py",
"environment/public/inputs/case.npz",
"environment/public/inputs/constant.json",
"environment/public/inputs/interior.json",
"environment/public/inputs/public/constan... | tasks/math-na-2512-24051v2 | ||
Nonlinear periodic staggered acoustic step | # One structure-preserving acoustic step
Build `/app/outputs/solve.py`, a Python program performing one dimensionless time step on a periodic Cartesian grid. Run it as
```
python /app/outputs/solve.py --input /app/inputs/case.npz --output /app/outputs/result.npz
```
Both flags are optional, with the defaults shown. ... | [] | {
"task": {
"id": "staggered_acoustic_step",
"name": "Nonlinear periodic staggered acoustic step"
},
"verifier": {
"timeout_sec": 2400
},
"execution": {
"schema": "scientific_execution/v1",
"instruction_sha256": "cf123a3713dd5bb98765a553c1f4e37dfef0b8b954a22ef2ed81231f5bf5dc59",
"execu... | {} | [
"README.md",
"environment/Dockerfile",
"environment/RUNTIME.md",
"environment/public/inputs/case.npz",
"environment/public/inputs/public/constant.json",
"environment/public/inputs/public/constant.npz",
"environment/public/inputs/public/small.json",
"environment/public/inputs/public/small.npz",
"envi... | tasks/math-na-2512-24127v1 | ||
math-na-2512-24401v1 | # Voxel elasticity actions and nodal Jacobi blocks
Implement `/app/outputs/kernel.py`, invoked as `python /app/outputs/kernel.py --input INPUT.json --output OUTPUT.json`. Create the output parent directory if absent. No no-argument mode is required. Inputs and outputs may be anywhere permitted by the execution environ... | [] | {
"task": {
"id": "voxel_elasticity_kernel",
"title": "Constrained voxel elasticity actions"
},
"verifier": {
"timeout_sec": 1200
},
"execution": {
"schema": "scientific_execution/v1",
"instruction_sha256": "b182af93a8131bbf12d11f5403ce9d8979d61bb5dba3afe90e86ef10dfe3fb82",
"execution_... | {} | [
"README.md",
"environment/Dockerfile",
"environment/RUNTIME.md",
"environment/public/compare.py",
"environment/public/expected.json",
"environment/public/public.json",
"environment/public/scientific.py",
"environment/public/validate.py",
"environment/runtime/wheels/mpmath-1.3.0-py3-none-any.whl",
... | tasks/math-na-2512-24401v1 | ||
math-na-2512-24405v3 | # Real tensor approximation with a shared rank budget
Implement `/app/solve.py`. For each case, return a real low-rank tensor approximation and a capacity certificate. All quantities are dimensionless. Use CPU computation only.
## Files and CLI
Run `python /app/solve.py --input INPUT.json --output OUTPUT.json`. Both... | [] | {
"task": {
"id": "tensor_rank_budget",
"title": "Real tensor approximation with conjugate-paired capacities"
},
"verifier": {
"timeout_sec": 900
},
"execution": {
"schema": "scientific_execution/v1",
"instruction_sha256": "a267899c2badd499e8ef704480efff44b3cdb3b2d761a67df08bef4c2bb8b99e",... | {} | [
"README.md",
"environment/Dockerfile",
"environment/RUNTIME.md",
"environment/public/inputs/public/pairs.json",
"environment/public/inputs/public/scales.json",
"environment/public/public_refs.json",
"environment/public/science.py",
"environment/public/validate.py",
"environment/runtime/wheels/mpmath... | tasks/math-na-2512-24405v3 | ||
math-na-2512-24634v1 | # Positive finite-interval solitary waves
Implement `/app/solve.py`. It must compute the positive decreasing branch of a mixed-boundary nonlinear profile problem, not the zero solution or an infinite-interval approximation.
## Mathematical target
For each case solve
\[
Q''(x)=bQ(x)-\gamma Q(x)^p,\qquad Q'(0)=0,\qu... | [] | {
"task": {
"id": "finite_solitary_branch",
"title": "Positive finite-interval solitary profiles"
},
"verifier": {
"timeout_sec": 1000
},
"execution": {
"schema": "scientific_execution/v1",
"instruction_sha256": "b91b0d7ddd6cad6ef7d933898dd2396dd375ff8e17af7bd90cac0b8d742faba5",
"execu... | {} | [
"README.md",
"environment/Dockerfile",
"environment/RUNTIME.md",
"environment/public/inputs/cases.json",
"environment/public/inputs/public.json",
"environment/public/reference_table.json",
"environment/public/validate.py",
"environment/runtime/wheels/mpmath-1.3.0-py3-none-any.whl",
"environment/runt... | tasks/math-na-2512-24634v1 | ||
Gaussian operator posteriors | # Gaussian posteriors with redundant quadratic features
Implement `/app/solve.py`, a Python command-line program computing row-wise Gaussian operator posteriors. All data are dimensionless. Observations Z are supplied state derivatives, not derivatives you must estimate. This task concerns the conditional regression p... | [] | {
"task": {
"id": "gaussian-quadratic-posteriors",
"name": "Gaussian operator posteriors"
},
"verifier": {
"timeout_sec": 900
},
"execution": {
"json_norm": true,
"schema": "scientific_execution/v1",
"instruction_sha256": "fd80dc45b75f4dc6c11d6ed5b2963ff40aea50f3a482c568e67228efff20994... | {} | [
"README.md",
"environment/Dockerfile",
"environment/RUNTIME.md",
"environment/public/_framework_norm_comparator.py",
"environment/public/_framework_npz_admission.py",
"environment/public/_framework_numeric_comparator.py",
"environment/public/_framework_numeric_predicates.py",
"environment/public/_fram... | tasks/math-na-2601-00038v2 | ||
physics-comp-ph-2512-22670v2 | # Complete spring–adhesion equilibria
Implement `/app/solve.py` to enumerate **all** positive equilibria of a dimensionless spring–adhesion energy, including unstable stationary points and both locally stable branches when present. Selecting only a minimum or only the global minimum is not sufficient.
## Mathematical... | [] | {
"task": {
"id": "arxiv_2512_22670v2_idea0",
"title": "Complete spring–adhesion equilibria"
},
"verifier": {
"timeout_sec": 1200
},
"execution": {
"schema": "scientific_execution/v1",
"instruction_sha256": "5970c135d797373d626187b08296aa0dbe7fb25073563b77e390e96104aa9591",
"execution_... | {} | [
"README.md",
"environment/Dockerfile",
"environment/RUNTIME.md",
"environment/public/feedback.py",
"environment/public/inputs/cases.json",
"environment/public/inputs/public.json",
"environment/public/public_table.json",
"environment/public/solve.py",
"environment/public/validate.py",
"environment/... | tasks/physics-comp-ph-2512-22670v2 | ||
Quadratic box stress and isotropic fit | # Quadratic-field box stress and isotropic fit
Implement `/app/solve.py` for a CPU-only scientific calculation. For each case, compute the exact box-filter covariance of a quadratic incompressible velocity field, then its best isotropic leading-gradient surrogate. No simulation data are needed.
## Mathematical defini... | [] | {
"task": {
"id": "quadratic_box_stress",
"name": "Quadratic box stress and isotropic fit"
},
"verifier": {
"timeout_sec": 300
},
"execution": {
"schema": "scientific_execution/v1",
"instruction_sha256": "4ffb6c68b7f4d848b7f7a0c4c919022da7be89975f5fc51798753cd7c51a603f",
"execution_dis... | {} | [
"README.md",
"environment/Dockerfile",
"environment/RUNTIME.md",
"environment/public/feedback_core.py",
"environment/public/inputs/public_cases.json",
"environment/public/public_reference.json",
"environment/public/validate.py",
"environment/runtime/wheels/mpmath-1.3.0-py3-none-any.whl",
"environmen... | tasks/physics-comp-ph-2512-22717v1 | ||
Sharp weighted-energy Euler threshold | # Sharp energy-stable forward Euler steps
Implement `/app/solver.py` for dimensionless balanced graph convection–diffusion systems. Run it as `python /app/solver.py --input INPUT --output OUTPUT`. With no arguments use `/app/inputs/cases.json` and `/app/outputs/results.json`. Create missing output parent directories.
... | [] | {
"task": {
"id": "balanced_graph_energy",
"name": "Sharp weighted-energy Euler threshold"
},
"verifier": {
"timeout_sec": 900
},
"execution": {
"schema": "scientific_execution/v1",
"instruction_sha256": "4f5da93efb149a86ddc39b3dd8b8aa14adc084abe98883af4e8eaa048abedcdb",
"execution_dis... | {} | [
"README.md",
"environment/Dockerfile",
"environment/RUNTIME.md",
"environment/public/feedback.py",
"environment/public/inputs/cases.json",
"environment/public/inputs/public.json",
"environment/public/public_table.json",
"environment/public/validate.py",
"environment/runtime/wheels/mpmath-1.3.0-py3-n... | tasks/physics-comp-ph-2512-22994v1 | ||
Mixed periodic PDE residual and gradient evaluation | # Mixed periodic PDE residuals and coefficient gradients
Implement `/app/solve.py`. This is a CPU numerical evaluation task, not PDE training or time integration. All quantities are dimensionless.
## Interface and data
Run `python /app/solve.py --input INPUT.json --output OUTPUT.json`. Create missing output parent di... | [] | {
"task": {
"id": "mixed-periodic-pde",
"name": "Mixed periodic PDE residual and gradient evaluation"
},
"verifier": {
"timeout_sec": 900
},
"execution": {
"schema": "scientific_execution/v1",
"instruction_sha256": "e208842462bbf6a7970b0f2d93f5c92fa26c9ad2e4db5102dcc0e69d3d388ca2",
"ex... | {} | [
"README.md",
"environment/Dockerfile",
"environment/RUNTIME.md",
"environment/public/feedback.py",
"environment/public/inputs/public/cubic.json",
"environment/public/inputs/public/mixed.json",
"environment/public/public_table.json",
"environment/public/solve.py",
"environment/public/validate.py",
... | tasks/physics-comp-ph-2512-23056v1 | ||
physics-comp-ph-2512-23057v1 | # Tangent-linear ideal relativistic MHD residual
Implement `/app/outputs/rmhd_tangent.py`. It must accept two positional arguments, INPUT_NPZ and OUTPUT_NPZ, and with no arguments use `/app/inputs/jets.npz` and `/app/outputs/tangent.npz`. Create missing output parent directories. The supplied script is a failing stub.... | [] | {
"task_id": "rmhd_tangent",
"name": "Tangent-linear ideal RMHD residual",
"version": "0.1.0",
"verifier": {
"timeout_sec": 600
},
"execution": {
"schema": "scientific_execution/v1",
"instruction_sha256": "b49d7232e821bafee9b80653d8a405c581b1543e4ed0870b41d81049693c6465",
"execution_disclosu... | {} | [
"README.md",
"environment/Dockerfile",
"environment/RUNTIME.md",
"environment/public/inputs/check_output.py",
"environment/public/inputs/check_tangent.py",
"environment/public/inputs/constants.json",
"environment/public/inputs/feedback_core.py",
"environment/public/inputs/public.npz",
"environment/p... | tasks/physics-comp-ph-2512-23057v1 | ||
Stable exponential divided-difference ratios | # Stable exponential divided-difference ratios
Construct `/app/outputs/solve.py` and a short numerical validation report at `/app/outputs/validation.md`. Implement a numerical evaluator, not a lookup table. The report is advisory and is not used as an accuracy certificate.
## Mathematical target
All quantities are di... | [] | {
"task": {
"id": "exponential-ratio",
"name": "Stable exponential divided-difference ratios"
},
"verifier": {
"timeout_sec": 3600
},
"execution": {
"schema": "scientific_execution/v1",
"instruction_sha256": "6d13f3d86a7433faaa080fbbfb0dbb12fffec76a146ede5c1b948ba26e57eaaf",
"execution... | {} | [
"README.md",
"environment/Dockerfile",
"environment/RUNTIME.md",
"environment/public/check.py",
"environment/public/make_public.py",
"environment/public/table.json",
"environment/runtime/wheels/mpmath-1.3.0-py3-none-any.whl",
"environment/runtime/wheels/numpy-2.2.6-cp311-cp311-manylinux_2_17_x86_64.ma... | tasks/physics-comp-ph-2512-23061v1 | ||
Rigid solvent-distance evaluator | # Rigid solvent-distance evaluator
Implement `/app/outputs/evaluate.py` (Python CLI). This is a synthetic analytic mechanics evaluation, not a chemical-accuracy model. No periodicity, cutoffs, relaxation, or chain-rule forces with respect to the original coordinates are used.
## Files and interface
Run `python /app/... | [] | {
"task": {
"id": "rigid_solvent_distance",
"name": "Rigid solvent-distance evaluator",
"version": "1.0"
},
"verifier": {
"timeout_sec": 2400
},
"execution": {
"schema": "scientific_execution/v1",
"instruction_sha256": "13f9abdb8fefe1c9e6aff859903fbc1a976f08b817367b64621e5bd1b2ddc55c",... | {} | [
"README.md",
"environment/Dockerfile",
"environment/RUNTIME.md",
"environment/public/inputs/cases.npz",
"environment/public/inputs/check_content.py",
"environment/public/inputs/content.py",
"environment/public/inputs/example.json",
"environment/public/inputs/table.json",
"environment/public/inputs/v... | tasks/physics-comp-ph-2512-23117v2 | ||
Transient polymer stress propagation | # Transient polymer stress in prescribed homogeneous flow
Implement `/app/solve.py`, invoked as `python /app/solve.py --input INPUT.json --output OUTPUT.json`. Create the output's parent directories when absent. No no-argument mode is required. Inputs and outputs are dimensionless. This is a constitutive-equation benc... | [] | {
"task": {
"id": "homogeneous_stress_propagation",
"name": "Transient polymer stress propagation"
},
"verifier": {
"timeout_sec": 1800
},
"execution": {
"schema": "scientific_execution/v1",
"instruction_sha256": "788dac1a7106c300581cf827dd866c0cfbca809a4665e1cd5d40326d2ae8a7ae",
"exec... | {} | [
"README.md",
"environment/Dockerfile",
"environment/RUNTIME.md",
"environment/public/feedback_table.json",
"environment/public/inputs/public/relaxation.json",
"environment/public/inputs/public/shear.json",
"environment/public/inputs/public/switched.json",
"environment/public/metrics.py",
"environmen... | tasks/physics-comp-ph-2512-23315v1 | ||
physics-comp-ph-2512-23358v2 | # Moving half-plane geometric moments
Implement `/app/solution.py`, a CPU-only executable Python program computing conservative space-time geometry. Run `python /app/solution.py --inputs INPUT --output OUTPUT`. With no arguments use `/app/inputs/cases.json` and `/app/outputs/moments.json`. Create missing output parent... | [] | {
"task": {
"id": "moving_halfplane_moments",
"title": "Conservative space-time geometric moments"
},
"verifier": {
"timeout_sec": 1800
},
"execution": {
"schema": "scientific_execution/v1",
"instruction_sha256": "f670bef6048c8371162c8135ef791cd56bb55531e06df1bc914b561a52a4111a",
"exec... | {} | [
"README.md",
"environment/Dockerfile",
"environment/RUNTIME.md",
"environment/public/geometry_checks.py",
"environment/public/inputs/cases.json",
"environment/public/inputs/public_cases.json",
"environment/public/public_reference.json",
"environment/public/validate.py",
"environment/runtime/wheels/m... | tasks/physics-comp-ph-2512-23358v2 | ||
physics-comp-ph-2512-23396v2 | # Polynomial energy-balance evaluator
Implement `/app/solver.py`. Given one snapshot of polynomial electromagnetic candidate fields in a rectangular perfectly conducting cavity, compute two energy-balance losses and their coefficient gradients. This is evaluation, not training or solving Maxwell's equations. Zero loss... | [] | {
"task": {
"id": "polynomial_cavity_energy",
"title": "Polynomial cavity energy-balance evaluator"
},
"verifier": {
"timeout_sec": 900
},
"execution": {
"schema": "scientific_execution/v1",
"instruction_sha256": "01717d6ac8bbcc8eeeba2a53b360747ca94cf7a810b578fc3b3b007af7cfa014",
"exec... | {} | [
"README.md",
"environment/Dockerfile",
"environment/RUNTIME.md",
"environment/public/inputs/public/cancellation.json",
"environment/public/inputs/public/conserved.json",
"environment/public/inputs/public/uniform_rate.json",
"environment/public/public_check.py",
"environment/public/references/cancellat... | tasks/physics-comp-ph-2512-23396v2 | ||
stat-co-2512-17868v2 | # Weighted two-factor slice analysis
Implement `/app/solution/analyze.py` using Python. This is a deterministic analysis task, not a Monte Carlo estimation task. All quantities are dimensionless.
## Mathematical target
There are n disjoint open cells (2i,2i+1), i=0,...,n-1. The reference density on cell i is w_i, th... | [] | {
"task": {
"id": "arxiv_2512_17868v2_idea0",
"title": "Weighted two-factor slice analysis"
},
"verifier": {
"timeout_sec": 900
},
"execution": {
"schema": "scientific_execution/v1",
"instruction_sha256": "637c4bf4d88f7346a99a4635c1446bf0e7e967ff2cf16c87f4ac630b68a9e74f",
"execution_di... | {} | [
"README.md",
"environment/Dockerfile",
"environment/RUNTIME.md",
"environment/public/inputs/cases.json",
"environment/public/solution/analyze.py",
"environment/public/validation/check.py",
"environment/public/validation/reference.json",
"environment/public/validation/rules.py",
"environment/runtime/... | tasks/stat-co-2512-17868v2 | ||
Brownian admixture inference | # Brownian inference on admixture networks
Implement `/app/solution.py`, a CPU inference program for the following fixed-root linear Gaussian model. It must support `python /app/solution.py --input INPUT --output OUTPUT` and, without arguments, read `/app/inputs/cases.json` and write `/app/outputs/results.json`. Creat... | [] | {
"task": {
"id": "arxiv_2512_18139v1_idea0",
"name": "Brownian admixture inference"
},
"verifier": {
"timeout_sec": 900
},
"execution": {
"schema": "scientific_execution/v1",
"instruction_sha256": "36a965cd5e3f36a130d9b44fca2f9d56e5a9b34975ec0dce1b3e345db8cf910c",
"execution_disclosur... | {} | [
"README.md",
"environment/Dockerfile",
"environment/RUNTIME.md",
"environment/public/feedback_core.py",
"environment/public/inputs/public.json",
"environment/public/public_table.json",
"environment/public/table_constants.py",
"environment/public/validate.py",
"environment/runtime/wheels/mpmath-1.3.0... | tasks/stat-co-2512-18139v1 | ||
Certified lifted wireframe | # Certified lifted wireframe
Implement `/app/solve.py`. It reads one JSON case and writes a triangulation certificate and a lifted, occupied-bin wireframe. No external data are needed; coordinates are dimensionless.
## Invocation
`python /app/solve.py --input INPUT --output OUTPUT`
With no arguments use `/app/input... | [] | {
"task": {
"id": "arxiv_2512_18166v1_idea0",
"name": "Certified lifted wireframe",
"version": "1.0"
},
"verifier": {
"timeout_sec": 1800
},
"execution": {
"schema": "scientific_execution/v1",
"instruction_sha256": "0db806152153240cb6ce2b83ebc8575cc6b5ef407ac60039ed582c4a9b038951",
... | {} | [
"README.md",
"environment/Dockerfile",
"environment/RUNTIME.md",
"environment/public/cert.py",
"environment/public/feedback.py",
"environment/public/inputs/case.json",
"environment/public/inputs/center.json",
"environment/public/inputs/irregular.json",
"environment/public/inputs/public_cases.json",
... | tasks/stat-co-2512-18166v1 | ||
Reciprocal Metropolis drift | # Reciprocal Lyapunov drift for a Metropolis kernel
Implement a deterministic drift evaluator for a one-dimensional random-walk Metropolis chain. All variables are dimensionless. Finite evaluations here do not establish a global drift inequality or a mixing rate.
## Mathematical target
A case has four independent bi... | [] | {
"task": {
"id": "reciprocal_metropolis_drift",
"name": "Reciprocal Metropolis drift"
},
"verifier": {
"timeout_sec": 14400
},
"execution": {
"schema": "scientific_execution/v1",
"instruction_sha256": "47d668a2639694262301ca11705180a507040ad6b5632e3c18e7fcdf733cda9d",
"execution_discl... | {} | [
"README.md",
"environment/Dockerfile",
"environment/RUNTIME.md",
"environment/public/inputs/cases.json",
"environment/public/inputs/public_cases.json",
"environment/public/public_table.json",
"environment/public/solution.py",
"environment/public/validate.py",
"environment/runtime/wheels/mpmath-1.3.0... | tasks/stat-co-2512-18255v1 | ||
Exact wildcard-aware discard scores | # Exact wildcard-aware discard scores
Implement `/app/solve.py`, a CPU program that evaluates every possible discard from a 14-card hand. This benchmark scores optimal disjoint meld arrangements, not the legality of a declaration.
## Interface
Use the Python standard library or other installed libraries. The require... | [] | {
"task": {
"id": "rummy_exact_discard_scores",
"name": "Exact wildcard-aware discard scores",
"version": "1.0"
},
"verifier": {
"timeout_sec": 1800
},
"execution": {
"schema": "scientific_execution/v1",
"instruction_sha256": "d14d0a4d06c200bc04ffaa07ccfdb660eed5113c1b5cb18bcc12827b616... | {} | [
"README.md",
"environment/Dockerfile",
"environment/RUNTIME.md",
"environment/public/feedback.py",
"environment/public/inputs/hands.json",
"environment/public/inputs/public.json",
"environment/public/public_answers.json",
"environment/public/validate.py",
"environment/runtime/wheels/mpmath-1.3.0-py3... | tasks/stat-co-2512-18858v1 | ||
stat-co-2512-18884v2 | # Finite-band spatial fourth cumulants
Implement `/app/cumulants.py`, a deterministic numerical evaluator of the unconditional fourth cumulant of a zero-mean finite-band random field. Gaussian one-point marginals do not imply joint Gaussian dependence.
## Mathematical target
For sites S_i in Euclidean space define h_... | [] | {
"task": {
"id": "arxiv_2512_18884v2_idea0",
"title": "Finite-band spatial fourth cumulants"
},
"verifier": {
"timeout_sec": 900
},
"execution": {
"schema": "scientific_execution/v1",
"instruction_sha256": "da22f8e28f8a270550f088657462d64015e4b8aff0b4b82a7d7dd2fa8e373109",
"execution_... | {} | [
"README.md",
"environment/Dockerfile",
"environment/RUNTIME.md",
"environment/public/inputs/cases.json",
"environment/public/inputs/public_cases.json",
"environment/public/validation/assess.py",
"environment/public/validation/check.py",
"environment/public/validation/feedback_core.py",
"environment/... | tasks/stat-co-2512-18884v2 | ||
Projected pairwise comparison schedules | # Projected pairwise comparison schedules
Implement `/app/solve.py`. This task computes an allocation of pairwise comparisons in a prescribed reduced comparison space, including singular prior covariances. No observations or prior means are used.
## Input and mathematical target
Read UTF-8 JSON with exactly one key ... | [] | {
"task": {
"id": "projected_pairwise_schedules",
"name": "Projected pairwise comparison schedules",
"version": "1.0"
},
"verifier": {
"timeout_sec": 900
},
"execution": {
"schema": "scientific_execution/v1",
"instruction_sha256": "c3a832ff8736d662316bb2ceb973a7290c22d434e3f4dcaa122ccb... | {} | [
"README.md",
"environment/Dockerfile",
"environment/RUNTIME.md",
"environment/public/feedback.py",
"environment/public/inputs/cases.json",
"environment/public/inputs/public.json",
"environment/public/metrics.py",
"environment/public/public_reference.json",
"environment/public/solve.py",
"environme... | tasks/stat-co-2512-19398v2 | ||
stat-co-2512-19589v1 | # Full-path shadow-rate conditional quantiles
Implement `/app/outputs/shadow_kernel.py`. It is a Python CLI accepting `--input INPUT.npz --output OUTPUT.npz`. With no arguments it reads `/app/inputs/cases.npz` and writes `/app/outputs/results.npz`. Create a missing output parent directory. Use ordinary CPU computation... | [] | {
"task": {
"id": "arxiv_2512_19589v1_idea0",
"title": "Full-path shadow-rate conditional quantiles"
},
"verifier": {
"timeout_sec": 1800
},
"execution": {
"schema": "scientific_execution/v1",
"instruction_sha256": "da5bf87df1a4cf63c1e0e61c397c902c038ec7e7be1f3a34e6bdff022d360ec8",
"ex... | {} | [
"README.md",
"environment/Dockerfile",
"environment/RUNTIME.md",
"environment/public/inputs/cases.npz",
"environment/public/inputs/check_pair.py",
"environment/public/inputs/check_shadow.py",
"environment/public/inputs/constants.json",
"environment/public/inputs/public_check.py",
"environment/public... | tasks/stat-co-2512-19589v1 | ||
Certified attainable finite-experiment power gains | # Certified attainable power gains in finite experiments
Implement `/app/solve.py` for finite composite hypothesis tests. A single randomized test must improve the worst alternative power relative to a supplied feasible comparator. Return both that test and a certificate of near-optimality. Different valid certificate... | [] | {
"task": {
"id": "finite_power_certificates",
"name": "Certified attainable finite-experiment power gains",
"version": "1.0"
},
"verifier": {
"timeout_sec": 600
},
"execution": {
"schema": "scientific_execution/v1",
"instruction_sha256": "68517a70b10c212038817c411d7ce6e7b4094124faf613... | {} | [
"README.md",
"environment/Dockerfile",
"environment/RUNTIME.md",
"environment/public/certificate.py",
"environment/public/inputs/experiments.json",
"environment/public/inputs/public.json",
"environment/public/solve.py",
"environment/public/validate.py",
"environment/runtime/wheels/mpmath-1.3.0-py3-n... | tasks/stat-co-2512-19843v1 | ||
stat-co-2512-20051v1 | # Certified weighted quantile–ridge optimization
Implement `/app/solve.py`. It must solve the nonsmoothed, dimensionless optimization problems supplied in an NPZ archive and return both coefficients and certificates. NumPy and SciPy are installed; any algorithm using the available environment is permitted.
## Scienti... | [] | {
"task_id": "arxiv_2512_20051v1_idea0",
"name": "Certified weighted quantile ridge",
"version": "0.1.0",
"verifier": {
"timeout_sec": 1800
},
"execution": {
"schema": "scientific_execution/v1",
"instruction_sha256": "675a3874cdbf1c7dc41d1daec90f3593067bd2c2a9da525839eda4c39684e99c",
"execut... | {} | [
"README.md",
"environment/Dockerfile",
"environment/RUNTIME.md",
"environment/public/certificate.py",
"environment/public/evaluate.py",
"environment/public/inputs/boundary.npz",
"environment/public/inputs/boundary_constants.json",
"environment/public/inputs/cases.npz",
"environment/public/inputs/con... | tasks/stat-co-2512-20051v1 | ||
stat-co-2512-20385v1 | # Exact empirical-bootstrap L-moment covariance
Implement `/app/solve.py` as a Python command-line program. Compute the population covariance of the first three unbiased sample L-moments under the empirical bootstrap. No random simulation, matrix inversion or regularization is requested. Singular covariance matrices a... | [] | {
"task_id": "empirical_bootstrap_lmoments",
"version": "1.0",
"verifier": {
"timeout_sec": 900
},
"execution": {
"schema": "scientific_execution/v1",
"instruction_sha256": "08170146698b686d28f88e42ec6e6ac76f3bb8391a2c7a711ba329b8c79a1d19",
"execution_disclosure": true,
"interface_checks_s... | {} | [
"README.md",
"environment/Dockerfile",
"environment/RUNTIME.md",
"environment/public/feedback.py",
"environment/public/inputs/cases.json",
"environment/public/inputs/public.json",
"environment/public/public_targets.json",
"environment/public/scoring.py",
"environment/public/validate.py",
"environm... | tasks/stat-co-2512-20385v1 | ||
Decoded Haar dephasing channel | # Decoded Haar dephasing channel
Implement `/app/solve.py`, a CPU float64 file-to-file program computing real mixed density matrices. No training, denoising score, eigenvector convention, or pure-state decomposition is requested.
## Files and interface
Run `python /app/solve.py --inputs INPUT --results OUTPUT`. With... | [] | {
"task": {
"id": "haar_dephasing_density",
"name": "Decoded Haar dephasing channel",
"version": "1.0"
},
"verifier": {
"timeout_sec": 1800
},
"execution": {
"schema": "scientific_execution/v1",
"instruction_sha256": "113841765469278308d64b3aeb17af45bd983a3a1876268e9409d5b8248df4c0",
... | {} | [
"README.md",
"environment/Dockerfile",
"environment/RUNTIME.md",
"environment/public/channel_checks.py",
"environment/public/inputs/constants.json",
"environment/public/inputs/public.npz",
"environment/public/public_reference.json",
"environment/public/public_tests.py",
"environment/public/validate.... | tasks/stat-co-2512-21478v1 | ||
Privacy-corrected directed-degree moment classification | # Privacy-corrected directed-degree moments
Implement `/app/solve.py`. It must classify and solve small rational synthetic moment problems; the degree vectors are mathematical inputs, not asserted observations from a random privacy mechanism.
## Files and numbers
Run `python /app/solve.py --inputs INPUT --results OU... | [] | {
"task": {
"id": "privacy_degree_moments",
"name": "Privacy-corrected directed-degree moment classification"
},
"verifier": {
"timeout_sec": 1800
},
"execution": {
"schema": "scientific_execution/v1",
"instruction_sha256": "a9fce9dc149b844718b11a4ef39f26e24e0bcc39594478bd9e1002fe90d0262b"... | {} | [
"README.md",
"environment/Dockerfile",
"environment/RUNTIME.md",
"environment/public/inputs/cases.json",
"environment/public/inputs/public.json",
"environment/public/public_reference.json",
"environment/public/scientific.py",
"environment/public/solve.py",
"environment/public/validate.py",
"enviro... | tasks/stat-co-2512-21700v1 | ||
stat-co-2601-00015v1 | # Weak polynomial drift projection
Implement a rank-aware polynomial L2 projector for dimensionless probability-law snapshots. Standard numerical libraries and any mathematically equivalent algorithm are permitted.
## Scientific target
For each case let ξ have d independent standard normal components (variance **one*... | [] | {
"task_id": "weak_polynomial_projector",
"version": "0.1.0",
"verifier": {
"timeout_sec": 4800
},
"execution": {
"schema": "scientific_execution/v1",
"instruction_sha256": "ede72085511fd575187e4dd1817d3fb726d13945f41cbe5a9f35689e2e589efb",
"execution_disclosure": true,
"interface_checks_s... | {} | [
"README.md",
"environment/Dockerfile",
"environment/RUNTIME.md",
"environment/public/check_public.py",
"environment/public/inputs/cases.json",
"environment/public/inputs/public_cases.json",
"environment/public/projector.py",
"environment/public/public_reference.json",
"environment/public/solution.py... | tasks/stat-co-2601-00015v1 |
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