{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"livecodebench","slug":"livecodebench","name":"LiveCodeBench","type":"benchmark","url":"https://livecodebench.github.io","page_url":"https://unfragile.ai/livecodebench","categories":["testing-quality"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"livecodebench__cap_0","uri":"capability://safety.moderation.temporal.contamination.detection.via.problem.release.dating","name":"temporal-contamination-detection-via-problem-release-dating","description":"Annotates each benchmark problem with its release date from source platforms (LeetCode, AtCoder, Codeforces), enabling detection of data contamination by comparing model performance across temporal cohorts. When a model's performance drops sharply at its training cutoff date, it indicates earlier problems were likely in training data. This design allows researchers to identify which models have been exposed to benchmark problems during pretraining without requiring explicit data audits.","intents":["detect whether a model was trained on benchmark problems by analyzing performance cliffs at release dates","separate contaminated from clean evaluation results to maintain benchmark integrity","identify which problem cohorts are safe to use for evaluating models with known training dates","track contamination trends across model releases and training methodologies"],"best_for":["benchmark maintainers validating model integrity","researchers comparing models across different training periods","organizations auditing LLM training data exposure"],"limitations":["only detects contamination for problems released after model training cutoff; problems from May 2023 onwards may still be in training data for models trained after that period","requires accurate model training date metadata; undisclosed or approximate training dates reduce detection reliability","performance variance from other factors (model scale, fine-tuning, inference parameters) can obscure contamination signals"],"requires":["problem metadata with precise release dates from source platforms","model training cutoff date or release date for comparison","sufficient problem coverage across multiple temporal cohorts for statistical signal"],"input_types":["model evaluation results with per-problem scores","problem metadata including source platform and release date","model metadata including training/release date"],"output_types":["contamination detection report with performance cliff analysis","temporal performance curves showing degradation at cutoff","contamination confidence scores or flags"],"categories":["safety-moderation","benchmark-integrity"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"livecodebench__cap_1","uri":"capability://data.processing.analysis.continuous.problem.ingestion.from.competitive.platforms","name":"continuous-problem-ingestion-from-competitive-platforms","description":"Automatically or semi-automatically ingests new coding problems from active competitive programming platforms (LeetCode, AtCoder, Codeforces) with release date metadata, maintaining a rolling window of 300+ problems spanning May 2023 to February 2024 and beyond. Problems are curated for quality and difficulty distribution, then integrated into the benchmark evaluation pipeline with standardized input/output formats and test case extraction.","intents":["maintain an ever-fresh benchmark that stays ahead of model training cutoffs","avoid static benchmark saturation where models memorize solutions","capture evolving problem difficulty and diversity as platforms release new content","enable longitudinal tracking of model capability improvements over time"],"best_for":["benchmark maintainers needing to stay ahead of model training data","researchers studying model generalization on unseen problem distributions","organizations running continuous model evaluation pipelines"],"limitations":["problem ingestion pipeline and curation criteria are not documented; unclear how problems are selected, validated, or filtered for quality","exact distribution across difficulty levels, problem categories, and language paradigms is unknown","continuous updates may introduce inconsistency if curation standards drift over time","dependency on external platform availability and API stability"],"requires":["access to competitive programming platform APIs or web scraping infrastructure","problem parsing and standardization pipeline to extract problem statements, test cases, and expected outputs","metadata extraction for release dates and problem difficulty ratings","quality control process to filter low-quality or duplicate problems"],"input_types":["problem statements from LeetCode, AtCoder, Codeforces","test case specifications and expected outputs","problem metadata (difficulty, category, release date)"],"output_types":["standardized problem format with statement, test cases, and metadata","curated problem pool with quality and diversity guarantees","versioned benchmark snapshots with problem release dates"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"livecodebench__cap_10","uri":"capability://automation.workflow.open.source.benchmark.infrastructure.and.reproducibility","name":"open-source-benchmark-infrastructure-and-reproducibility","description":"Provides open-source code repository and data access for the benchmark, enabling researchers to reproduce evaluation results, extend the benchmark with new problems or scenarios, and run local evaluations without relying on a centralized service. Code repository includes evaluation scripts, problem parsing logic, and leaderboard infrastructure. Data access includes problem statements, test cases, and evaluation results, enabling offline analysis and custom evaluation pipelines.","intents":["enable researchers to reproduce benchmark results and verify claims","allow organizations to run local evaluations without external dependencies","facilitate community contributions of new problems or evaluation scenarios","support custom evaluation pipelines and analysis workflows"],"best_for":["researchers requiring reproducible, auditable benchmarks","organizations running private evaluations with proprietary models","benchmark contributors adding new problems or scenarios","teams building custom evaluation pipelines"],"limitations":["code repository structure and documentation are not detailed in provided content","data access format and licensing terms are not specified","reproducibility may be limited by undocumented dependencies (e.g., specific Python versions, library versions)","local evaluation requires setting up code execution environment with sandboxing, which may be complex","no clear contribution guidelines or review process for community submissions"],"requires":["GitHub or similar repository hosting","evaluation script implementation in a supported language (Python, etc.)","problem data in a standardized format (JSON, YAML, etc.)","documentation for setup, usage, and contribution","license specification (MIT, Apache 2.0, etc.)"],"input_types":["benchmark code repository","problem data and test cases","evaluation scripts and configuration"],"output_types":["evaluation results in standardized format","leaderboard data for analysis","reproducible benchmark snapshots"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"livecodebench__cap_11","uri":"capability://data.processing.analysis.problem.difficulty.and.category.stratification","name":"problem-difficulty-and-category-stratification","description":"Organizes benchmark problems by difficulty levels and categories (implied from competitive programming problem taxonomies), enabling evaluation of model performance across problem subsets. Allows analysis of whether models perform consistently across difficulty levels or show degradation on harder problems. Enables targeted evaluation of specific problem categories (e.g., dynamic programming, graph algorithms, string manipulation) to identify capability gaps.","intents":["analyze model performance across difficulty levels to identify capability ceilings","evaluate whether models generalize across problem categories or specialize","identify problem categories where models struggle","compare models on difficulty-stratified subsets for fair comparison"],"best_for":["researchers studying model capability scaling with problem difficulty","practitioners identifying which problem types models handle well","benchmark designers ensuring balanced difficulty distribution"],"limitations":["difficulty levels and categories are not documented in provided content; unclear what taxonomy is used","no analysis of performance degradation across difficulty levels in provided content","category definitions may not align with real-world problem taxonomies","no metrics for inter-category transfer or generalization"],"requires":["problem metadata with difficulty ratings and category tags","difficulty rating system (e.g., 1-5 stars, easy/medium/hard)","category taxonomy aligned with competitive programming problem types","evaluation results stratified by difficulty and category"],"input_types":["problem metadata with difficulty and category","evaluation results per problem"],"output_types":["performance curves by difficulty level","per-category performance analysis","difficulty-stratified leaderboard rankings"],"categories":["data-processing-analysis","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"livecodebench__cap_12","uri":"capability://automation.workflow.continuous.leaderboard.updates.with.new.problem.results","name":"continuous leaderboard updates with new problem results","description":"Automatically updates the public leaderboard as new problems are added to the benchmark and models are re-evaluated against the expanded problem set. This ensures the leaderboard reflects the current benchmark state and prevents models from achieving artificially high scores on a fixed problem set. The continuous update mechanism is enabled by the automated problem ingestion pipeline and evaluation infrastructure.","intents":["Maintain a living leaderboard that reflects current model capabilities","Prevent benchmark gaming through continuous problem addition","Track model capability evolution as new problems are added"],"best_for":["Benchmark maintainers seeking to prevent gaming and stagnation","Model developers tracking their performance over time","Researchers studying model capability trends"],"limitations":["Leaderboard update frequency is not documented; unclear if updated daily, weekly, or on-demand","Unclear whether all models are re-evaluated on new problems or only new submissions are evaluated","No versioning of leaderboard snapshots; difficult to track historical performance","Submission process and evaluation SLA are not documented; unclear how long evaluations take","No notification mechanism for leaderboard changes; users must manually check for updates"],"requires":["Automated evaluation infrastructure","Problem ingestion pipeline","Leaderboard database with versioning","Model submission mechanism"],"input_types":["New problem from competitive programming platform","Model submission (API credentials or model weights)"],"output_types":["Updated leaderboard with new results","Performance change notification (optional)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"livecodebench__cap_2","uri":"capability://code.generation.editing.multi.scenario.code.capability.evaluation","name":"multi-scenario-code-capability-evaluation","description":"Evaluates models across four distinct code-related scenarios: (1) free-form code generation from problem descriptions, (2) self-repair of broken code, (3) test output prediction without execution, and (4) code execution with result validation. Each scenario tests different aspects of code understanding and generation, with separate scoring and leaderboard rankings. Models are ranked differently across scenarios, revealing capability gaps (e.g., Claude-3-Opus excels at test output prediction but not code generation).","intents":["measure multiple dimensions of code capability beyond simple generation accuracy","identify which models excel at reasoning-heavy tasks like test output prediction vs. pure code synthesis","detect overfitting to code generation benchmarks by testing repair and prediction capabilities","provide nuanced capability profiles rather than single-number rankings"],"best_for":["researchers studying different facets of code understanding and generation","teams selecting models for specific code tasks (generation vs. debugging vs. analysis)","benchmark designers wanting to avoid single-metric saturation"],"limitations":["self-repair mechanism is not documented; unclear whether it tests error-driven debugging or simple code fixing","test output prediction scenario may not reflect real-world debugging workflows where developers see error messages and stack traces","no metrics for code quality beyond correctness (readability, efficiency, maintainability)","code execution scenario requires sandboxing and timeout handling, which are not documented","scenario-dependent rankings make it unclear which model is 'best' overall, potentially confusing for practitioners"],"requires":["problem statements with clear specifications for code generation scenario","broken code samples with known fixes for self-repair scenario","test cases with expected outputs for test output prediction scenario","code execution environment with sandboxing and resource limits","separate scoring logic and leaderboard aggregation for each scenario"],"input_types":["natural language problem descriptions","broken or incomplete code snippets","test case specifications","generated code for execution"],"output_types":["generated code solutions","repaired code","predicted test outputs","execution results with pass/fail status","per-scenario accuracy scores","scenario-specific leaderboard rankings"],"categories":["code-generation-editing","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"livecodebench__cap_3","uri":"capability://code.generation.editing.pass.at.k.scoring.with.multiple.generation.attempts","name":"pass-at-k-scoring-with-multiple-generation-attempts","description":"Evaluates code generation by allowing models multiple attempts to produce a correct solution (pass@k metric), where k typically ranges from 1 to 10. A problem is marked as 'passed' if any of the k generated solutions produces correct output on all test cases. This metric accounts for the stochastic nature of LLM generation and rewards models that can explore solution space diversity, rather than penalizing single-attempt failures.","intents":["measure code generation capability accounting for sampling variance in LLM outputs","compare models fairly when they have different generation diversity or temperature settings","evaluate whether models can find correct solutions through multiple attempts","identify models that explore solution space effectively vs. those that get stuck in local optima"],"best_for":["researchers comparing code generation models with different sampling strategies","practitioners evaluating whether multiple generation attempts improve success rates","benchmark designers wanting fair comparison across models with different generation characteristics"],"limitations":["exact pass@k formula and k values used in LiveCodeBench are not documented in provided content","pass@k rewards diversity but may not reflect real-world usage where users typically see only top-1 or top-3 generations","computational cost scales linearly with k; evaluating pass@10 requires 10x more API calls or inference time than pass@1","no partial credit for partially correct solutions or solutions that pass some test cases","timeout and resource limit handling during multiple attempts is not documented"],"requires":["code generation model with configurable sampling (temperature, top-p, etc.)","test case suite with deterministic expected outputs","code execution environment for validating each generated solution","aggregation logic to compute pass@k across multiple attempts"],"input_types":["problem statement","k (number of generation attempts)","sampling parameters (temperature, top-p, etc.)"],"output_types":["k generated code solutions","pass@k score (0 or 1, indicating whether any solution passed)","per-attempt execution results","pass@1, pass@3, pass@5, pass@10 curves"],"categories":["code-generation-editing","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"livecodebench__cap_4","uri":"capability://code.generation.editing.code.execution.validation.with.test.case.matching","name":"code-execution-validation-with-test-case-matching","description":"Executes generated code against a suite of test cases extracted from competitive programming problems, comparing actual output to expected output with exact string matching or semantic equivalence checking. Execution occurs in a controlled environment (sandboxing details unknown) with timeout and resource limits to prevent infinite loops or resource exhaustion. Problems are marked as 'passed' only if generated code produces correct output on all test cases.","intents":["validate that generated code is functionally correct, not just syntactically valid","detect off-by-one errors, logic bugs, and edge case failures through comprehensive test coverage","measure code generation quality in a way that correlates with real-world correctness","enable automated evaluation without manual code review"],"best_for":["researchers evaluating code generation models on correctness metrics","practitioners needing automated validation of generated code","benchmark maintainers requiring objective, reproducible evaluation"],"limitations":["sandboxing mechanism and security model are not documented; unclear how malicious or resource-intensive code is contained","timeout and resource limits are not specified; unclear what happens when code exceeds limits","test case coverage is inherited from competitive programming problems; may not cover all edge cases or real-world scenarios","exact string matching may be too strict for problems with multiple valid output formats (e.g., floating-point precision, whitespace variations)","no metrics for code efficiency; a correct but slow solution passes the same as an optimized solution","execution environment (Python, Java, C++, etc.) and language support are not documented"],"requires":["code execution environment with support for multiple programming languages","sandboxing infrastructure (containers, VMs, or language-level isolation) to prevent code escape","timeout and resource limit enforcement (CPU time, memory, file I/O)","test case suite with expected outputs for each problem","output comparison logic with configurable matching rules (exact, semantic, etc.)"],"input_types":["generated code in supported programming languages","test case inputs","expected outputs","timeout and resource limits"],"output_types":["execution status (success, timeout, error, wrong output)","actual output from code execution","comparison result (pass/fail)","error messages or stack traces if execution fails"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"livecodebench__cap_5","uri":"capability://code.generation.editing.test.output.prediction.without.code.execution","name":"test-output-prediction-without-code-execution","description":"Evaluates models' ability to predict the output of code without executing it, testing code understanding and reasoning about program behavior. Models are given code and test inputs, then asked to predict the output. Predictions are compared against expected outputs with accuracy scoring. This scenario tests whether models understand code semantics deeply enough to trace execution mentally, without relying on actual runtime behavior.","intents":["measure code understanding and reasoning capability independent of code generation","identify models that can analyze and reason about code behavior","test whether models can debug code by predicting incorrect outputs","evaluate reasoning-heavy capabilities where Claude-3-Opus and Mistral-Large excel"],"best_for":["researchers studying code understanding and reasoning capabilities","teams needing models for code review and analysis tasks","benchmark designers wanting to test reasoning beyond generation"],"limitations":["mechanism for generating code-to-predict is not documented; unclear whether code is generated by the model itself or provided as input","accuracy metric is not detailed; unclear whether partial credit is given for partially correct outputs","this scenario may not reflect real-world debugging workflows where developers see error messages and stack traces","models may use heuristics or pattern matching rather than true semantic understanding","no distinction between correct predictions due to reasoning vs. lucky guesses"],"requires":["code samples (generated or provided) with test inputs","expected outputs for comparison","model inference capability to generate or predict outputs","accuracy scoring logic"],"input_types":["code snippet","test inputs","optional: problem context or specification"],"output_types":["predicted output as text","accuracy score (0 or 1, or partial credit)","per-model accuracy on test output prediction scenario"],"categories":["code-generation-editing","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"livecodebench__cap_6","uri":"capability://code.generation.editing.self.repair.capability.evaluation","name":"self-repair-capability-evaluation","description":"Evaluates models' ability to identify and fix broken code, testing debugging and repair capabilities. Models are given code with known bugs or errors, then asked to produce corrected versions. Corrected code is validated against test cases to determine if repairs were successful. This scenario tests whether models can reason about code correctness and apply fixes, beyond just generating code from scratch.","intents":["measure code debugging and repair capability independent of generation from scratch","identify models that can fix common programming errors","test whether models understand error messages and can apply targeted fixes","evaluate practical capabilities for code review and refactoring tasks"],"best_for":["researchers studying code debugging and repair capabilities","teams needing models for code review and refactoring","benchmark designers wanting to test repair beyond generation"],"limitations":["self-repair mechanism is not documented; unclear whether models see error messages, stack traces, or just broken code","broken code generation process is not specified; unclear how bugs are introduced or selected","success metric is not detailed; unclear whether partial repairs or improvements count as success","may not reflect real-world debugging workflows where developers have access to error messages and execution context","no distinction between models that understand the bug vs. those that rewrite code entirely"],"requires":["broken code samples with known bugs or errors","mechanism for generating broken code (mutation, intentional errors, etc.)","optional: error messages or execution traces to guide repair","test case suite for validating repaired code","success scoring logic"],"input_types":["broken code snippet","optional: error message or execution trace","optional: problem context or specification"],"output_types":["repaired code","repair success indicator (pass/fail)","per-model repair success rate"],"categories":["code-generation-editing","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"livecodebench__cap_7","uri":"capability://data.processing.analysis.multi.model.leaderboard.with.scenario.rankings","name":"multi-model-leaderboard-with-scenario-rankings","description":"Maintains a public leaderboard ranking 29+ LLMs across four evaluation scenarios (code generation, self-repair, test output prediction, code execution), with separate rankings per scenario and optional aggregate rankings. Leaderboard includes both closed-access API models (GPT-4-Turbo, Claude-3-Opus, Mistral-Large) and open-access models (fine-tuned variants of 30+B parameter models). Rankings are updated as new problems are added and models are re-evaluated, enabling longitudinal tracking of capability improvements.","intents":["provide transparent, scenario-specific rankings of code generation models","enable practitioners to select models for specific code tasks based on leaderboard performance","track model capability improvements over time as new problems are added","identify overfitting to other benchmarks (e.g., HumanEval) by comparing cross-benchmark performance"],"best_for":["practitioners selecting models for code generation tasks","researchers comparing model capabilities across scenarios","organizations tracking model performance over time","benchmark maintainers validating benchmark quality"],"limitations":["submission process and criteria for adding new models are not documented","leaderboard aggregation logic (how scenario rankings are combined into overall ranking) is not specified","no confidence intervals or statistical significance testing; rankings appear qualitative","model metadata (training date, fine-tuning details, inference parameters) may be incomplete or outdated","leaderboard may incentivize overfitting to LiveCodeBench problems as they become known","scenario-dependent rankings make it unclear which model is 'best' overall, potentially confusing for practitioners"],"requires":["evaluation infrastructure to run models on all benchmark problems","per-scenario scoring and ranking logic","leaderboard database and web interface","model metadata management (name, organization, training date, etc.)","update pipeline to re-evaluate models as new problems are added"],"input_types":["model evaluation results across all scenarios","model metadata (name, organization, training date, parameters)","problem metadata (release date, difficulty, category)"],"output_types":["per-scenario leaderboard rankings","aggregate leaderboard ranking (if applicable)","per-model performance curves over time","scenario-specific performance comparisons"],"categories":["data-processing-analysis","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"livecodebench__cap_8","uri":"capability://safety.moderation.contamination.evidence.analysis.and.reporting","name":"contamination-evidence-analysis-and-reporting","description":"Analyzes and reports evidence of data contamination by comparing model performance across temporal cohorts of problems. When a model shows a 'stark drop in performance' at its training cutoff date (e.g., DeepSeek models perform well on problems from May 2023 to September 2023, then drop sharply on problems released after September 2023), this indicates the earlier problems were likely in training data. Reports include performance curves, statistical summaries, and contamination confidence assessments, enabling researchers to identify and flag contaminated models.","intents":["detect data contamination in models by analyzing performance cliffs at training dates","provide evidence-based contamination reports for model auditing","identify which problem cohorts are safe for evaluating specific models","track contamination trends across model releases and training methodologies"],"best_for":["benchmark maintainers validating model integrity","researchers auditing LLM training data exposure","organizations evaluating model trustworthiness","model developers identifying training data leakage"],"limitations":["contamination detection relies on performance cliffs; gradual performance degradation may not be detected","requires accurate model training dates; undisclosed or approximate dates reduce detection reliability","performance variance from other factors (model scale, fine-tuning, inference parameters) can obscure contamination signals","only detects contamination for problems released after model training cutoff; earlier problems may still be in training data","no formal statistical significance testing; reports appear qualitative ('stark drop', 'relatively stable')"],"requires":["model evaluation results with per-problem scores","problem metadata with precise release dates","model metadata with training/release dates","analysis pipeline to compute performance curves and detect cliffs","reporting infrastructure to visualize and communicate findings"],"input_types":["per-problem evaluation results for each model","problem release dates","model training/release dates"],"output_types":["contamination detection report with performance curves","temporal performance analysis (performance by problem release date)","contamination confidence scores or flags","list of safe problem cohorts for each model"],"categories":["safety-moderation","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"livecodebench__cap_9","uri":"capability://safety.moderation.overfitting.detection.across.benchmarks","name":"overfitting-detection-across-benchmarks","description":"Identifies models that overfit to other benchmarks (e.g., HumanEval) by comparing their performance on LiveCodeBench against their HumanEval scores. Models are clustered into two groups: those that generalize well to LiveCodeBench-Easy (green cluster) and those that overfit to HumanEval (red cluster). Example: 'DS-Ins-1.3B model outperforms Gemini-Pro and Claude-Ins-1 on HumanEval but performs considerably worse on LCB-Easy', indicating overfitting to HumanEval's specific problem distribution.","intents":["identify models that overfit to HumanEval or other static benchmarks","select models that generalize well to diverse code generation tasks","detect fine-tuning strategies that improve benchmark performance at the cost of generalization","guide model selection toward truly capable models rather than benchmark-optimized ones"],"best_for":["researchers studying generalization and overfitting in code generation models","practitioners selecting models that generalize well beyond HumanEval","organizations auditing fine-tuning practices for overfitting"],"limitations":["overfitting detection is based on visual clustering (green vs. red clusters) rather than formal statistical tests","no quantitative overfitting metrics or thresholds; clustering appears qualitative","only compares against HumanEval; overfitting to other benchmarks (MBPP, CodeXGLUE, etc.) is not detected","fine-tuned open-access models dominate the overfitting cluster, but it's unclear whether this reflects true overfitting or just different model classes","no analysis of why certain models overfit (e.g., training data, fine-tuning methodology, model architecture)"],"requires":["HumanEval evaluation results for all models","LiveCodeBench evaluation results for all models","clustering or visualization logic to identify overfitting patterns","cross-benchmark performance comparison"],"input_types":["HumanEval scores for models","LiveCodeBench scores for models","model metadata (fine-tuning status, training methodology)"],"output_types":["overfitting detection report with clustering visualization","list of models with high HumanEval but low LiveCodeBench performance","generalization assessment for each model"],"categories":["safety-moderation","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"livecodebench__headline","uri":"capability://testing.quality.code.generation.benchmarking.tool","name":"code generation benchmarking tool","description":"LiveCodeBench is a continuously updated benchmarking tool for evaluating code generation capabilities of language models using new problems from competitive programming, ensuring no data contamination.","intents":["best code generation benchmark","benchmark tool for evaluating AI code generation","code generation evaluation for competitive programming","how to test code generation models","top tools for benchmarking AI coding capabilities"],"best_for":["evaluating AI models' code generation capabilities","ensuring data integrity in benchmarks"],"limitations":[],"requires":[],"input_types":[],"output_types":[],"categories":["testing-quality"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":62,"verified":false,"data_access_risk":"high","permissions":["problem metadata with precise release dates from source platforms","model training cutoff date or release date for comparison","sufficient problem coverage across multiple temporal cohorts for statistical signal","access to competitive programming platform APIs or web scraping infrastructure","problem parsing and standardization pipeline to extract problem statements, test cases, and expected outputs","metadata extraction for release dates and problem difficulty ratings","quality control process to filter low-quality or duplicate problems","GitHub or similar repository hosting","evaluation script implementation in a supported language (Python, etc.)","problem data in a standardized format (JSON, YAML, etc.)"],"failure_modes":["only detects contamination for problems released after model training cutoff; problems from May 2023 onwards may still be in training data for models trained after that period","requires accurate model training date metadata; undisclosed or approximate training dates reduce detection reliability","performance variance from other factors (model scale, fine-tuning, inference parameters) can obscure contamination signals","problem ingestion pipeline and curation criteria are not documented; unclear how problems are selected, validated, or filtered for quality","exact distribution across difficulty levels, problem categories, and language paradigms is unknown","continuous updates may introduce inconsistency if curation standards drift over time","dependency on external platform availability and API stability","code repository structure and documentation are not detailed in provided content","data access format and licensing terms are not specified","reproducibility may be limited by undocumented dependencies (e.g., specific Python versions, library versions)","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7,"quality":0.9,"ecosystem":0.3,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.25,"quality":0.35,"ecosystem":0.15,"match_graph":0.2,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:23.327Z","last_scraped_at":null,"last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=livecodebench","compare_url":"https://unfragile.ai/compare?artifact=livecodebench"}},"signature":"cf/7JK9iRxMSvtn/hYZavry/h7Z9iT3i6JcdwFKWwIY3UG9GkoRXW4ulhi1ztL+nql3WCyvLE9Eup97iyHbDDw==","signedAt":"2026-06-19T20:41:30.498Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/livecodebench","artifact":"https://unfragile.ai/livecodebench","verify":"https://unfragile.ai/api/v1/verify?slug=livecodebench","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}