| {"task_id": "bipedalwalker_locomotion_rl", "name": "Bipedalwalker Locomotion Rl", "category": "Scientific Problems & ML", "description": "Train a CPU-only locomotion policy for BipedalWalker and its Hardcore variant. The judge evaluates only the submitted policy checkpoint, not the training process. Pre-trained policies and external RL libraries are prohibited.", "language": "Python", "metric": "score (maximize)", "internet": false} | |
| {"task_id": "borden_source_inversion", "name": "Borden Source Inversion", "category": "Scientific Problems & ML", "description": "Infer a finite-duration rectangular contaminant source from sparse, noisy monitoring-well observations in a 3D hydrogeological scene. The agent must implement its own forward model and inversion optimizer from scratch.", "language": "Python", "metric": "score (maximize)", "internet": false} | |
| {"task_id": "dabic_gravity_inversion", "name": "Dabic Gravity Inversion", "category": "Scientific Problems & ML", "description": "Implement the D-ABIC regularization method for 3D gravity inversion within the SimPEG framework, run on both synthetic and real Vinton salt dome data under L0 and L1 sparse norms, and compare against a Hamiltonian Monte Carlo baseline.", "language": "Python", "metric": "score (maximize)", "internet": false} | |
| {"task_id": "graph_node_classification", "name": "Graph Node Classification", "category": "Scientific Problems & ML", "description": "Implement graph neural networks from scratch using only base PyTorch for semi-supervised node classification on an unseen graph under CPU-only constraints.", "language": "Python", "metric": "score (maximize)", "internet": false} | |
| {"task_id": "ann_vector_search_qps", "name": "Ann Vector Search Qps", "category": "Systems & Software Engineering", "description": "Replace a brute-force NumPy nearest-neighbor baseline with a high-performance approximate nearest-neighbor implementation under a hard recall constraint. Scored by queries per second.", "language": "Python", "metric": "score (maximize)", "internet": false} | |
| {"task_id": "arc_compiler_runtime", "name": "Arc Compiler Runtime", "category": "Systems & Software Engineering", "description": "Implement a complete TypeScript compiler pipeline (lexer, parser, type checker, code generator) for a novel programming language defined by specification documents.", "language": "Python", "metric": "score (maximize)", "internet": false} | |
| {"task_id": "exchange_core_throughput", "name": "Exchange Core Throughput", "category": "Systems & Software Engineering", "description": "Maximize peak throughput of a Java financial matching engine built on the LMAX Disruptor by tuning thread topology, wait strategies, ring-buffer sizing, order-book implementation, and JVM configuration.", "language": "Java", "metric": "score (maximize)", "internet": false} | |
| {"task_id": "ffmpeg_swscale_reimplementation", "name": "Ffmpeg Swscale Reimplementation", "category": "Systems & Software Engineering", "description": "Reimplement FFmpeg's libswscale pixel-format conversion and scaling library in Rust, handling multiple pixel formats and scaling algorithms. A correctness-passing scaffold is provided; the agent must optimize for speed via SIMD.", "language": "C++", "metric": "score (maximize)", "internet": false} | |
| {"task_id": "git_rewrite_in_zig", "name": "Git Rewrite In Zig", "category": "Systems & Software Engineering", "description": "Reimplement git as a drop-in Zig binary producing identical CLI output, exit codes, and repository state as the C reference implementation. The C source is available for reading but cannot be compiled.", "language": "C++", "metric": "score (maximize)", "internet": false} | |
| {"task_id": "integer_compression_codec", "name": "Integer Compression Codec", "category": "Systems & Software Engineering", "description": "Improve a C++ integer compression codec for better compression ratio and decode throughput on uint32 datasets via techniques such as delta encoding, bit-packing, and SIMD vectorization. Exact round-trip correctness is mandatory.", "language": "C++", "metric": "score (maximize)", "internet": false} | |
| {"task_id": "juliet_vulnerability_analyzer", "name": "Juliet Vulnerability Analyzer", "category": "Systems & Software Engineering", "description": "Implement a deterministic static analyzer that processes structured program facts to detect vulnerabilities across six CWE categories (stack/heap overflow, integer overflow, null dereference, use-after-free, command injection).", "language": "Python", "metric": "score (maximize)", "internet": false} | |
| {"task_id": "rust_multicrate_reconstruction", "name": "Rust Multicrate Reconstruction", "category": "Systems & Software Engineering", "description": "Reconstruct missing Rust implementations across a multi-crate content-addressable storage workspace, given only type signatures and public API contracts.", "language": "Rust", "metric": "score (maximize)", "internet": false} | |
| {"task_id": "schemathesis_config_modernization", "name": "Schemathesis Config Modernization", "category": "Systems & Software Engineering", "description": "Implement six modernization targets in Schemathesis: a TOML-based configuration system with auto-discovery, API namespace reorganization, a metrics framework, transport and response abstractions, and a redesigned check registration system.", "language": "Python", "metric": "pass rate", "internet": false} | |
| {"task_id": "schemathesis_datagen_pipeline", "name": "Schemathesis Datagen Pipeline", "category": "Systems & Software Engineering", "description": "Implement eight feature targets in the Schemathesis Python API testing framework, including structured HTTP header generation strategies, coverage-phase hooks, discriminator-aware validation and data generation, and schema-driven code generation fixes.", "language": "Python", "metric": "pass rate", "internet": false} | |
| {"task_id": "schemathesis_reporting_observability", "name": "Schemathesis Reporting Observability", "category": "Systems & Software Engineering", "description": "Implement five targets in Schemathesis: a post-validation hook system, multi-format test report writers (VCR, HAR, JUnit, NDJSON), pytest plugin integration, and schema-branch-aware example generation.", "language": "Python", "metric": "pass rate", "internet": false} | |
| {"task_id": "vliw_kernel_optimization", "name": "Vliw Kernel Optimization", "category": "Systems & Software Engineering", "description": "Optimize a VLIW/SIMD kernel generator for correctness and minimum cycle count on a custom architecture simulator. Hard-coded answers for specific inputs are forbidden.", "language": "Python", "metric": "score (minimize)", "internet": false} | |
| {"task_id": "ad_placement_optimization", "name": "Ad Placement Optimization", "category": "Combinatorial Optimization", "description": "Partition a large integer grid into non-overlapping rectangles, each containing a designated anchor point, maximizing total satisfaction from how closely each rectangle's area matches its target.", "language": "C++", "metric": "score (maximize)", "internet": false} | |
| {"task_id": "apple_incremental_game", "name": "Apple Incremental Game", "category": "Combinatorial Optimization", "description": "Decide each turn whether to invest in machines or collect output in an incremental production game, balancing short-term gains against long-horizon compounding.", "language": "Python", "metric": "score (maximize)", "internet": false} | |
| {"task_id": "equivalence_class_divide_and_conquer", "name": "Equivalence Class Divide And Conquer", "category": "Combinatorial Optimization", "description": "Solve six progressive competitive-programming problems centered on equivalence classes and divide-and-conquer, where techniques from earlier problems inform solutions to harder ones.", "language": "C++", "metric": "score (maximize)", "internet": false} | |
| {"task_id": "grid_turing_robot", "name": "Grid Turing Robot", "category": "Combinatorial Optimization", "description": "Design transition rules and initial coloring for a Turing-like robot on a colored grid to maximize the number of distinct cells visited while minimizing the rule set size.", "language": "Python", "metric": "score (minimize)", "internet": false} | |
| {"task_id": "jagua_nesting_optimization", "name": "Jagua Nesting Optimization", "category": "Combinatorial Optimization", "description": "Improve a Rust-based 2D irregular bin packing optimizer for non-convex polygonal pieces. Solution geometry is independently verified; improvements below a minimum threshold receive no credit.", "language": "Rust", "metric": "score (maximize)", "internet": false} | |
| {"task_id": "molecular_self_assembly", "name": "Molecular Self Assembly", "category": "Combinatorial Optimization", "description": "Schedule bonding operations over discrete time steps to assemble atoms into a specified number of connected molecules, respecting spatial proximity and temporal ordering constraints.", "language": "Python", "metric": "score (maximize)", "internet": false} | |
| {"task_id": "order_addition_permutation_optimization", "name": "Order Addition Permutation Optimization", "category": "Combinatorial Optimization", "description": "Find a permutation of 1,000 elements that minimizes a black-box cost function, using metaheuristic search (simulated annealing, genetic algorithms, local search) without access to the cost function's internals.", "language": "Python", "metric": "score (maximize)", "internet": false} | |
| {"task_id": "smt_solver", "name": "Smt Solver", "category": "Combinatorial Optimization", "description": "Build an SMT solver from scratch for four quantifier-free theories (uninterpreted functions, linear real and integer arithmetic, and their combination). External SMT solvers are forbidden; model witnesses are independently validated.", "language": "Python", "metric": "score (maximize)", "internet": false} | |
| {"task_id": "treant_forest", "name": "Treant Forest", "category": "Combinatorial Optimization", "description": "Strategically place obstacles in a grid maze to maximize the shortest-path length between start and goal, or block the path entirely.", "language": "Python", "metric": "score (maximize)", "internet": false} | |
| {"task_id": "tree_block_partitioning", "name": "Tree Block Partitioning", "category": "Combinatorial Optimization", "description": "Solve six progressive problems on tree decomposition and block partitioning, where algorithmic ideas discovered in simpler variants transfer to harder ones.", "language": "C++", "metric": "score (maximize)", "internet": false} | |
| {"task_id": "triangulation_coloring_optimization", "name": "Triangulation Coloring Optimization", "category": "Combinatorial Optimization", "description": "Minimize a cost function over a triangulation by jointly recoloring vertices and flipping edges, where the dominant term is a quadratic penalty on monochromatic (``ugly'') triangles.", "language": "Python", "metric": "score (minimize)", "internet": false} | |
| {"task_id": "vehicle_routing_time_windows", "name": "Vehicle Routing Time Windows", "category": "Combinatorial Optimization", "description": "Implement a capacitated vehicle routing solver with time windows for Solomon-style benchmarks. Scored against best-known solutions on vehicle count and total travel distance.", "language": "Python", "metric": "score (maximize)", "internet": false} | |
| {"task_id": "vibrating_path_graph_coloring", "name": "Vibrating Path Graph Coloring", "category": "Combinatorial Optimization", "description": "Color graph vertices and selectively remove edges to minimize a cost that penalizes both removed edges and monochromatic surviving edges.", "language": "Python", "metric": "score (minimize)", "internet": false} | |
| {"task_id": "warehouse_forklift_routing", "name": "Warehouse Forklift Routing", "category": "Combinatorial Optimization", "description": "Route a forklift in a grid warehouse to receive goods arriving in random order, store them, and dispatch them in sequential order, minimizing total movement.", "language": "Python", "metric": "score (minimize)", "internet": false} | |
| {"task_id": "wireless_electricity_layout", "name": "Wireless Electricity Layout", "category": "Combinatorial Optimization", "description": "Position wire segments on a 2D plane to deliver wireless electricity from two fixed source plates to thousands of cities, minimizing a quadratic cost over city-to-wire distances and wire displacements while avoiding short circuits.", "language": "C++", "metric": "score (minimize)", "internet": false} | |
| {"task_id": "college_english_exam_bank", "name": "College English Exam Bank", "category": "Professional Knowledge Work", "description": "Produce five parallel examination papers with answer keys for a college English course, plus a blueprint table and an overlap self-check matrix ensuring cross-paper diversity meets pedagogical thresholds.", "language": "Python", "metric": "score (maximize)", "internet": true} | |
| {"task_id": "cta_risk_budget_optimization", "name": "Cta Risk Budget Optimization", "category": "Professional Knowledge Work", "description": "Build a complete CTA multi-strategy futures trading system: multiple signal classes, dynamic risk budgeting, a multi-currency backtest engine with transaction costs, drawdown control, and performance attribution.", "language": "Python", "metric": "score (maximize)", "internet": false} | |
| {"task_id": "k12_math_recommendation", "name": "K12 Math Recommendation", "category": "Professional Knowledge Work", "description": "Build a knowledge-tracing and question-recommendation system from hundreds of thousands of student interaction records, evaluated on prediction accuracy, mastery calibration, learning gain, and pedagogical constraint satisfaction.", "language": "Python", "metric": "score (maximize)", "internet": false} | |
| {"task_id": "portfolio_risk_calibration", "name": "Portfolio Risk Calibration", "category": "Professional Knowledge Work", "description": "Implement a multi-module portfolio management system (risk calibration, constrained optimization, execution cost modeling, dynamic rebalancing) for a cross-asset ETF portfolio. Evaluated out-of-sample on risk-adjusted return metrics.", "language": "Python", "metric": "score (maximize)", "internet": false} | |
| {"task_id": "carleson_formalization", "name": "Carleson Formalization", "category": "Formal Math & Theorem Proving", "description": "Fill proof obligations in the Lean 4 formalization of Carleson's theorem on pointwise convergence of L^2 Fourier series. Transitive axiom checking ensures no dependence on unproved prerequisites.", "language": "Lean 4", "metric": "pass rate", "internet": false} | |
| {"task_id": "combinatorial_games_formalization", "name": "Combinatorial Games Formalization", "category": "Formal Math & Theorem Proving", "description": "Resolve proof obligations in a Lean 4 formalization of combinatorial game theory, covering surreal numbers, game arithmetic, and the Sprague-Grundy theorem.", "language": "Lean 4", "metric": "pass rate", "internet": false} | |
| {"task_id": "flt_regular_formalization", "name": "Flt Regular Formalization", "category": "Formal Math & Theorem Proving", "description": "Resolve proof obligations in a Lean 4 formalization of Fermat's Last Theorem for regular primes via Kummer's cyclotomic theory. Top-level results earn no credit unless foundational prerequisites are also fully proved.", "language": "Lean 4", "metric": "pass rate", "internet": false} | |
| {"task_id": "lean_analysis_proofs", "name": "Lean Analysis Proofs", "category": "Formal Math & Theorem Proving", "description": "Complete proof obligations across a multi-file Lean 4 project formalizing results in real and functional analysis. Proofs are checked transitively: a theorem counts only if its entire dependency chain is fully proved.", "language": "Lean 4", "metric": "pass rate", "internet": false} | |
| {"task_id": "new_foundations_consistency", "name": "New Foundations Consistency", "category": "Formal Math & Theorem Proving", "description": "Complete proof obligations in the ConNF Lean 4 project formalizing the consistency of Quine's New Foundations, involving permutation models and tangled type theory.", "language": "Lean 4", "metric": "pass rate", "internet": false} | |
| {"task_id": "ordinal_notation_well_foundedness", "name": "Ordinal Notation Well Foundedness", "category": "Formal Math & Theorem Proving", "description": "Construct well-foundedness proofs for ordinal notation systems in Coq, involving Cantor Normal Form and ordinal arithmetic.", "language": "Coq", "metric": "score (maximize)", "internet": false} | |
| {"task_id": "pfr_formalization", "name": "Pfr Formalization", "category": "Formal Math & Theorem Proving", "description": "Resolve proof obligations in the Lean 4 formalization of the Polynomial Freiman–Ruzsa conjecture (Gowers–Green–Manners–Tao 2023), involving Shannon entropy, Ruzsa distance, and subgroup covering arguments.", "language": "Lean 4", "metric": "pass rate", "internet": false} | |
| {"task_id": "sphere_eversion_formalization", "name": "Sphere Eversion Formalization", "category": "Formal Math & Theorem Proving", "description": "Complete proof obligations in a Lean 4 formalization of sphere eversion, spanning smooth immersions, jet bundles, ample differential relations, and convex integration.", "language": "Lean 4", "metric": "pass rate", "internet": false} | |
| {"task_id": "anchorhead_text_adventure", "name": "Anchorhead Text Adventure", "category": "Interactive Games & Simulators", "description": "Play the Lovecraftian interactive fiction game Anchorhead via an HTTP API, sending text commands and receiving prose observations. Scored by peak in-game score, reflecting progression through the multi-day narrative and puzzle chain.", "language": "Python", "metric": "game score", "internet": false} | |
| {"task_id": "dcss_dungeon_ai", "name": "Dcss Dungeon Ai", "category": "Interactive Games & Simulators", "description": "Write a Lua bot for Dungeon Crawl Stone Soup that autonomously explores, fights, and descends dungeon levels as a Minotaur Berserker under a wall-clock time budget. Scored by mean in-game score across multiple runs.", "language": "Python", "metric": "score (maximize)", "internet": false} | |
| {"task_id": "nethack_dungeon_agent", "name": "Nethack Dungeon Agent", "category": "Interactive Games & Simulators", "description": "Implement a decision policy for NetHack via the NLE harness, parsing ASCII map observations and stat vectors to navigate, fight, and survive across multiple procedurally generated runs.", "language": "Python", "metric": "score (maximize)", "internet": false} | |
| {"task_id": "openrct2_theme_park_ai", "name": "Openrct2 Theme Park Ai", "category": "Interactive Games & Simulators", "description": "Write a JavaScript plugin for OpenRCT2 that autonomously builds rides, hires staff, sets pricing, and grows park company value across multiple scenarios of increasing complexity.", "language": "Python", "metric": "score (maximize)", "internet": false} | |
| {"task_id": "openttd_transport_ai", "name": "Openttd Transport Ai", "category": "Interactive Games & Simulators", "description": "Write an AI script for OpenTTD that builds road, rail, and air transport networks to connect towns and industries and grow company value across diverse procedurally generated maps.", "language": "Python", "metric": "score (maximize)", "internet": false} | |
| {"task_id": "trinity_text_adventure", "name": "Trinity Text Adventure", "category": "Interactive Games & Simulators", "description": "Play Infocom's Trinity via an HTTP game API. The game requires precise object manipulation and understanding of symbolic and temporal clues across interconnected zones.", "language": "Python", "metric": "game score", "internet": false} | |
| {"task_id": "tryst_text_adventure", "name": "Tryst Text Adventure", "category": "Interactive Games & Simulators", "description": "Play Tryst of Fate via an HTTP game API. The branching narrative with irreversible choices requires strategic exploration to reach high-scoring endings.", "language": "Python", "metric": "game score", "internet": false} | |
| {"task_id": "wesnoth_tactical_ai", "name": "Wesnoth Tactical Ai", "category": "Interactive Games & Simulators", "description": "Write tactical AI logic for Battle for Wesnoth that defeats the built-in AI through custom recruitment, focus-fire targeting, terrain exploitation, and village-capture timing across multiple maps.", "language": "Python", "metric": "score (maximize)", "internet": false} | |