Update dataset

README.md

@@ -20,10 +20,10 @@ A benchmark dataset for evaluating AI systems on challenging computer science pr
 
 ## Dataset Description
 
-This dataset contains 257 problems across three categories:
+This dataset contains 258 problems across three categories:
 - **Algorithmic**: 188 competitive programming problems with automated judging
 - **Research**: 66 open-ended research problems
-- **2.0**: 3 next-generation open-ended optimization problems
+- **2.0**: 4 next-generation open-ended optimization problems
 
 ## Dataset Structure
 

data/test-00000-of-00001.json

@@ -255,3 +255,4 @@
 {"problem_id": "erdos_demo", "category": "2.0", "statement": "# Erdos Unit Distance Demo\n\n## Problem\n\nPlace exactly `N = 10` distinct points in the Euclidean plane so that the\nnumber of point pairs at Euclidean distance exactly `1` is as large as possible.\n\nThis is a tiny, visually inspectable demo version of the planar unit distance\nproblem. If your construction naturally has a different common distance, scale\nthe coordinates before returning them.\n\n## Program Interface\n\nSubmit a Python file defining one of the following:\n\n```python\ndef solve(n: int) -> list[tuple[float, float]]:\n    ...\n```\n\nor:\n\n```python\ndef generate_points(n: int) -> list[tuple[float, float]]:\n    ...\n```\n\nor:\n\n```python\nPOINTS = [(0.0, 0.0), (1.0, 0.0), ...]\n```\n\nThe returned value must contain exactly 10 two-dimensional points. No stdin is\nused.\n\n## Validity Constraints\n\nA solution is valid if:\n\n1. It returns exactly 10 points.\n2. Every coordinate is a finite real number.\n3. No two points are closer than `1e-6`.\n\nThe objective is translation-invariant. Very large coordinates are allowed as\nlong as pairwise squared distances remain finite.\n\n## Objective\n\nFor all unordered point pairs, count those whose squared Euclidean distance is\nequal to `1` within a small floating-point tolerance. Let `M` be that count.\n\nMaximize `M`.\n\n## Scoring\n\nThe score is naturally scaled to `[0, 100)`, without clipping against a fixed\ntarget. Let:\n\n```text\nbaseline = N\nX = M\n```\n\nIf the point set is invalid, or if `X <= baseline`, the score is `0`. Otherwise:\n\n```text\nscore = 100 * (X - baseline) / X\n```\n\nThis makes the simple `N`-pair baseline worth `0`. With only 10 points, the\nproblem is intended as a quick sanity check and visual demo for agent workflows.\n", "config": "tag: geometry\nruntime:\n  language: python\n  timeout_seconds: 300\n  environment: \"Python 3.11; no external packages required\"\n  docker:\n    image: ubuntu:24.04\n"}
 {"problem_id": "erdos_unit_distance", "category": "2.0", "statement": "# Erdos Unit Distance\n\n## Problem\n\nPlace exactly `N = 65536` distinct points in the Euclidean plane so that the\nnumber of point pairs at Euclidean distance exactly `1` is as large as possible.\n\nThis is a finite, executable version of the planar unit distance problem:\ngiven `n` points, maximize the number of pairs at distance exactly `1`. If your\nconstruction naturally has a different common distance, scale the coordinates\nbefore returning them.\n\n## Program Interface\n\nSubmit a Python file defining one of the following:\n\n```python\ndef solve(n: int) -> list[tuple[float, float]]:\n    ...\n```\n\nor:\n\n```python\ndef generate_points(n: int) -> list[tuple[float, float]]:\n    ...\n```\n\nor:\n\n```python\nPOINTS = [(0.0, 0.0), (1.0, 0.0), ...]\n```\n\nThe returned value must contain exactly 65536 two-dimensional points. No stdin\nis used.\n\n## Validity Constraints\n\nA solution is valid if:\n\n1. It returns exactly 65536 points.\n2. Every coordinate is a finite real number.\n3. No two points are closer than `1e-3`.\n\nThe objective is translation-invariant. Very large coordinates are allowed as\nlong as pairwise squared distances remain finite.\n\n## Objective\n\nFor all unordered point pairs, count those whose squared Euclidean distance is\nequal to `1` within a strict floating-point tolerance. Let `M` be that count.\n\nMaximize `M`.\n\n## Scoring\n\nThe score is naturally scaled to `[0, 100)`, without clipping against a fixed\ntarget. Let:\n\n```text\nbaseline = N\nX = M\n```\n\nIf the point set is invalid, or if `X <= baseline`, the score is `0`. Otherwise\nthe raw score is:\n\n```text\nraw_score = 100 * (X - baseline) / X\n```\n\nThe reported score applies a cubic scale:\n\n```text\nscore = 100 * (raw_score / 100)^3\n```\n\nThis makes the simple `N`-pair baseline worth `0`, rewards every improvement\nabove the baseline, and keeps high-scoring constructions from saturating the\nbenchmark too quickly. The bounded and unbounded score fields both report this\ncubic-scaled score; evaluator messages also include `raw_score` for reference.\n", "config": "tag: geometry\nruntime:\n  language: python\n  timeout_seconds: 10800\n  environment: \"Python 3.11; no external packages required\"\n  docker:\n    image: ubuntu:24.04\n"}
 {"problem_id": "vector_db_ann", "category": "2.0", "statement": "# Vector DB ANN\n\n## Problem\n\nBuild a fast approximate nearest-neighbor vector search engine for a\nSIFT1M-scale benchmark.\n\nThe hidden benchmark contains exactly `1,000,000` base vectors with dimension\n`128`. Queries use the same dimension, distance is squared Euclidean distance,\nand each query asks for the top `10` nearest vector ids.\n\nYour objective is to maximize serving throughput while preserving search\nquality: submissions must reach `recall@10 >= 0.95`, and valid submissions are\nranked by an effective QPS that includes query time plus a small load/index-build\npenalty.\n\nThe Harbor agent container starts with a small Rust skeleton project in\n`/app`. You may use it, modify it, or replace it entirely. You may also use any\nRust crates, internal harness, data structures, and build layout you want, as\nlong as the final project satisfies the judge contract below.\n\nThe judge builds and runs your service with:\n\n```bash\ncargo build --release\nPORT=<port> cargo run --release --quiet\n```\n\nThe Harbor environment uses the Ubuntu `apt` Rust toolchain:\n\n```text\nrustc 1.75\ncargo 1.75\n```\n\nIf you add crates, choose versions compatible with this toolchain or pin\ntransitive dependencies accordingly.\n\nThe service and judge run with the task resource limits below. Design your\nparallel search and indexing strategy for this budget:\n\n```text\nvCPUs: 8\nmemory: 16 GiB\nquery concurrency: 8\ntimed queries per worker: 64\n```\n\nThe service must listen on `PORT` and implement these endpoints:\n\n```text\nPOST /insert\nPOST /bulk_insert\nPOST /search\n```\n\n`/bulk_insert` receives:\n\n```json\n{\"vectors\":[{\"id\":0,\"vector\":[0.1,0.2,...]}]}\n```\n\nand returns:\n\n```json\n{\"status\":\"ok\",\"inserted\":1}\n```\n\n`/search` receives:\n\n```json\n{\"vector\":[0.1,0.2,...],\"top_k\":10}\n```\n\nand returns:\n\n```json\n{\"results\":[{\"id\":0,\"distance\":0.0}]}\n```\n\n## Local Harness\n\nThe official evaluator uses hidden data and a black-box judge. You may call:\n\n```bash\nbash /app/submit.sh\n```\n\nat any time to submit the current `/app` project to the official judge and get\nscore feedback.\n\n## Validity\n\nA submission is valid if:\n\n1. It builds successfully with `cargo build --release`.\n2. `cargo run --release --quiet` starts the service and implements the required HTTP\n   endpoints.\n3. Every returned id is in `[0, 1_000_000)`.\n4. Its `recall@10` is at least `0.95` against the hidden exact top-10 ground\n   truth.\n\n## Scoring\n\nAt trial startup, the Harbor judge sidecar prepares the hidden benchmark and\nruns an exact-search reference HTTP service through the same `/bulk_insert`\nand `/search` client harnesses to produce ground-truth nearest neighbors and\nthe trial-local scoring baseline:\n\n```text\nbaseline_qps\nbaseline_effective_qps\nbaseline_load_seconds\n```\n\nInteractive submissions and the final verifier both score through this same\njudge sidecar, so the baseline and runtime environment are shared within a\ntrial while still letting different machines measure their own local baseline.\n\nEach submission is then timed independently. The load phase includes all\n`/bulk_insert` calls and any index construction performed by the service before\nqueries begin. The query phase uses 8 concurrent workers, each issuing 64\nqueries, and measures only `/search` throughput:\n\n```text\ncandidate_qps\ncandidate_load_seconds\n```\n\nThe reported `qps` is the raw query-only QPS. Scoring uses an effective QPS\nthat includes a small index-build/load penalty:\n\n```text\neffective_qps = Q / (query_seconds + 0.01 * load_seconds)\n```\n\nThe load phase has a default `900s` timeout. This keeps the benchmark focused\non serving performance while still making very expensive offline indexing pay a\nbounded, explicit cost.\n\nDuring evaluation, the load and query phases may stop early and return `0` once\nthe elapsed time plus the load penalty makes it impossible for the final\neffective QPS to beat the baseline. During load, this assumes a best-case query\ntime of zero.\n\nIf the submission is invalid, if `recall@10 < 0.95`, or if\n`candidate_effective_qps <= baseline_effective_qps`, the score is `0`.\nOtherwise:\n\n```text\nscore = 100 * (1 - sqrt(baseline_effective_qps) / sqrt(candidate_effective_qps))\n```\n\nThe bounded and unbounded score fields both report this score. Harbor JSON\nresults include the measured `qps`, `effective_qps`, `baseline_qps`,\n`baseline_effective_qps`, `recall_at_10`, load time, and runtime metrics under\nthe `metrics` field.\n", "config": "tag: systems\nruntime:\n  language: rust\n  timeout_seconds: 10800\n  environment: \"Rust project; hidden ANN benchmark; Python/NumPy judge\"\n  apt_packages:\n    - build-essential\n    - cargo\n    - git\n    - rustc\n  judge_apt_packages:\n    - build-essential\n    - cargo\n    - rustc\n    - python3-pip\n    - python3-numpy\n  judge_pip_packages:\n    - faiss-cpu\n  docker:\n    image: ubuntu:24.04\nenvironment:\n  # If these resource limits change, also update the resource budget text in\n  # readme and harbor/app/README.md so agents can design parallel algorithms\n  # for the actual CPU and memory budget.\n  cpus: 8\n  memory_mb: 16384\n  storage_mb: 8192\n  build_timeout_seconds: 3600\nevaluation:\n  # The judge drives the search service with this many concurrent workers.\n  # Keep this aligned with the CPU budget unless the task is intentionally\n  # changed into a higher-concurrency service benchmark.\n  query_concurrency: 8\n  queries_per_worker: 64\nsubmission:\n  kind: directory\n  path: /app\n  exclude:\n    - target\n    - .git\n    - .frontier-cs\n"}
+{"problem_id": "vector_db_ann_relaxed", "category": "2.0", "statement": "# Vector DB ANN Relaxed\n\n## Problem\n\nBuild a fast approximate nearest-neighbor vector search engine for a\nSIFT1M-scale benchmark. This relaxed variant uses the same data, API, resource\nbudget, and recall target as Vector DB ANN, but penalizes load/index-build time\n10x less heavily.\n\nThe hidden benchmark contains exactly `1,000,000` base vectors with dimension\n`128`. Queries use the same dimension, distance is squared Euclidean distance,\nand each query asks for the top `10` nearest vector ids.\n\nYour objective is to maximize serving throughput while preserving search\nquality: submissions must reach `recall@10 >= 0.95`, and valid submissions are\nranked by an effective QPS that includes query time plus a small\nload/index-build penalty.\n\nThe Harbor agent container starts with a small Rust skeleton project in\n`/app`. You may use it, modify it, or replace it entirely. You may also use any\nRust crates, internal harness, data structures, and build layout you want, as\nlong as the final project satisfies the judge contract below.\n\nThe judge builds and runs your service with:\n\n```bash\ncargo build --release\nPORT=<port> cargo run --release --quiet\n```\n\nThe Harbor environment uses the Ubuntu `apt` Rust toolchain:\n\n```text\nrustc 1.75\ncargo 1.75\n```\n\nIf you add crates, choose versions compatible with this toolchain or pin\ntransitive dependencies accordingly.\n\nThe service and judge run with the task resource limits below. Design your\nparallel search and indexing strategy for this budget:\n\n```text\nvCPUs: 8\nmemory: 16 GiB\nquery concurrency: 8\ntimed queries per worker: 64\n```\n\nThe service must listen on `PORT` and implement these endpoints:\n\n```text\nPOST /insert\nPOST /bulk_insert\nPOST /search\n```\n\n`/bulk_insert` receives:\n\n```json\n{\"vectors\":[{\"id\":0,\"vector\":[0.1,0.2,...]}]}\n```\n\nand returns:\n\n```json\n{\"status\":\"ok\",\"inserted\":1}\n```\n\n`/search` receives:\n\n```json\n{\"vector\":[0.1,0.2,...],\"top_k\":10}\n```\n\nand returns:\n\n```json\n{\"results\":[{\"id\":0,\"distance\":0.0}]}\n```\n\n## Local Harness\n\nThe official evaluator uses hidden data and a black-box judge. You may call:\n\n```bash\nbash /app/submit.sh\n```\n\nat any time to submit the current `/app` project to the official judge and get\nscore feedback.\n\n## Validity\n\nA submission is valid if:\n\n1. It builds successfully with `cargo build --release`.\n2. `cargo run --release --quiet` starts the service and implements the required HTTP\n   endpoints.\n3. Every returned id is in `[0, 1_000_000)`.\n4. Its `recall@10` is at least `0.95` against the hidden exact top-10 ground\n   truth.\n\n## Scoring\n\nAt trial startup, the Harbor judge sidecar prepares the hidden benchmark and\nruns an exact-search reference HTTP service through the same `/bulk_insert`\nand `/search` client harnesses to produce ground-truth nearest neighbors and\nthe trial-local scoring baseline:\n\n```text\nbaseline_qps\nbaseline_effective_qps\nbaseline_load_seconds\n```\n\nInteractive submissions and the final verifier both score through this same\njudge sidecar, so the baseline and runtime environment are shared within a\ntrial while still letting different machines measure their own local baseline.\n\nEach submission is then timed independently. The load phase includes all\n`/bulk_insert` calls and any index construction performed by the service before\nqueries begin. The query phase uses 8 concurrent workers, each issuing 64\nqueries, and measures only `/search` throughput:\n\n```text\ncandidate_qps\ncandidate_load_seconds\n```\n\nThe reported `qps` is the raw query-only QPS. Scoring uses an effective QPS\nthat includes a small index-build/load penalty:\n\n```text\neffective_qps = Q / (query_seconds + 0.001 * load_seconds)\n```\n\nThe load phase has a default `900s` timeout. This keeps the benchmark focused\non serving performance while still making very expensive offline indexing pay a\nbounded, explicit cost. This relaxed variant makes the load/index-build penalty\nsmall enough that spending additional time on a stronger index can be worthwhile\nwhen it materially improves query throughput or recall.\n\nDuring evaluation, the load and query phases may stop early and return `0` once\nthe elapsed time plus the load penalty makes it impossible for the final\neffective QPS to beat the baseline. During load, this assumes a best-case query\ntime of zero.\n\nIf the submission is invalid, if `recall@10 < 0.95`, or if\n`candidate_effective_qps <= baseline_effective_qps`, the score is `0`.\nOtherwise:\n\n```text\nscore = 100 * (1 - sqrt(baseline_effective_qps) / sqrt(candidate_effective_qps))\n```\n\nThe bounded and unbounded score fields both report this score. Harbor JSON\nresults include the measured `qps`, `effective_qps`, `baseline_qps`,\n`baseline_effective_qps`, `recall_at_10`, load time, and runtime metrics under\nthe `metrics` field.\n", "config": "tag: systems\nruntime:\n  language: rust\n  timeout_seconds: 10800\n  environment: \"Rust project; hidden ANN benchmark; Python/NumPy judge\"\n  apt_packages:\n    - build-essential\n    - cargo\n    - git\n    - rustc\n  judge_apt_packages:\n    - build-essential\n    - cargo\n    - rustc\n    - python3-pip\n    - python3-numpy\n  judge_pip_packages:\n    - faiss-cpu\n  docker:\n    image: ubuntu:24.04\nenvironment:\n  # If these resource limits change, also update the resource budget text in\n  # readme and harbor/app/README.md so agents can design parallel algorithms\n  # for the actual CPU and memory budget.\n  cpus: 8\n  memory_mb: 16384\n  storage_mb: 8192\n  build_timeout_seconds: 3600\nevaluation:\n  # The judge drives the search service with this many concurrent workers.\n  # Keep this aligned with the CPU budget unless the task is intentionally\n  # changed into a higher-concurrency service benchmark.\n  query_concurrency: 8\n  queries_per_worker: 64\n  # Weight applied to load/index-build time when computing effective QPS.\n  # This relaxed variant rewards stronger indexes by penalizing build time\n  # 10x less than the standard Vector DB ANN task.\n  load_penalty_weight: 0.001\nsubmission:\n  kind: directory\n  path: /app\n  exclude:\n    - target\n    - .git\n    - .frontier-cs\n"}