{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"zeroeval","slug":"zeroeval","name":"ZeroEval","type":"benchmark","url":"https://github.com/yuchenlin/ZeroEval","page_url":"https://unfragile.ai/zeroeval","categories":["testing-quality"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"zeroeval__cap_0","uri":"capability://data.processing.analysis.zero.shot.mathematical.reasoning.evaluation","name":"zero-shot mathematical reasoning evaluation","description":"Evaluates LLM performance on mathematical reasoning tasks without few-shot examples by implementing standardized problem sets with automated answer extraction and numerical correctness verification. Uses pattern-based answer parsing to handle diverse output formats (natural language, LaTeX, symbolic notation) and compares against ground-truth solutions with tolerance thresholds for floating-point accuracy.","intents":["Benchmark an LLM's mathematical problem-solving ability without providing example solutions","Compare mathematical reasoning capabilities across different models on identical problem sets","Identify failure modes in zero-shot mathematical reasoning without confounding few-shot learning effects"],"best_for":["Researchers evaluating foundational model capabilities in mathematics","Teams assessing whether an LLM can solve math problems from scratch","Benchmark maintainers needing standardized zero-shot math evaluation protocols"],"limitations":["Answer extraction heuristics may fail on non-standard mathematical notation or multi-step reasoning with intermediate explanations","Floating-point tolerance thresholds require manual tuning per problem domain","Does not evaluate reasoning quality or solution elegance, only final correctness"],"requires":["Python 3.7+","LLM API access (OpenAI, Anthropic, or local model via inference server)","Problem dataset in standardized format with ground-truth answers"],"input_types":["mathematical problem text","structured problem definitions with answer keys"],"output_types":["accuracy score (0-100%)","per-problem correctness labels","answer extraction logs for debugging"],"categories":["data-processing-analysis","benchmark-evaluation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"zeroeval__cap_1","uri":"capability://data.processing.analysis.logical.deduction.task.evaluation","name":"logical deduction task evaluation","description":"Assesses LLM performance on formal logical reasoning tasks using standardized problem sets that require multi-step deduction without examples. Implements structured evaluation of premise-conclusion relationships with support for propositional logic, first-order logic, and natural language reasoning puzzles, using symbolic verification or semantic similarity matching to validate logical correctness.","intents":["Measure an LLM's ability to perform formal logical deduction from scratch","Compare logical reasoning capabilities across models without few-shot priming","Identify systematic failures in handling logical constraints and contradiction detection"],"best_for":["Researchers studying LLM reasoning in formal logic domains","Teams evaluating whether models can handle constraint satisfaction problems","Benchmark creators needing standardized logical reasoning evaluation"],"limitations":["Symbolic verification requires problems with formally-defined logic; natural language logic puzzles rely on semantic matching which may have false negatives","Does not distinguish between correct answers reached through valid vs. invalid reasoning paths","Limited to problems with deterministic correct answers; ambiguous or multi-valid-solution problems not well-supported"],"requires":["Python 3.7+","LLM inference capability","Logical reasoning problem dataset with ground-truth conclusions"],"input_types":["logical premises in natural language or symbolic notation","structured logic puzzles with constraint definitions"],"output_types":["correctness score per problem","reasoning trace (if model provides intermediate steps)","error classification (false positive, false negative, invalid reasoning)"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"zeroeval__cap_2","uri":"capability://code.generation.editing.code.generation.task.evaluation","name":"code generation task evaluation","description":"Evaluates LLM code generation capability on programming tasks without few-shot examples using standardized problem sets with automated code execution and correctness verification. Implements test case execution against generated code with support for multiple programming languages, timeout handling, and detailed error reporting to distinguish between syntax errors, runtime failures, and logic errors.","intents":["Benchmark an LLM's ability to generate working code from problem descriptions without examples","Compare code generation quality across models on identical programming tasks","Identify failure modes in zero-shot code generation (syntax vs. logic vs. incomplete implementation)"],"best_for":["Researchers evaluating LLM code generation capabilities","Teams assessing whether models can write functional code from specifications","Benchmark maintainers standardizing zero-shot code evaluation protocols"],"limitations":["Requires sandboxed execution environment; security implications for running untrusted generated code","Test case coverage may not catch all logic errors; passing tests does not guarantee correctness on unseen inputs","Language-specific evaluation requires language-specific test harnesses and interpreters/compilers","Does not evaluate code quality metrics (readability, efficiency, maintainability)"],"requires":["Python 3.7+","Sandboxed code execution environment (Docker, isolated VM, or restricted subprocess)","Language-specific interpreters/compilers for target languages","Programming task dataset with test cases and expected outputs"],"input_types":["natural language problem descriptions","structured programming task definitions with test cases"],"output_types":["pass/fail per test case","overall correctness score","error type classification (syntax, runtime, logic)","execution logs and error messages"],"categories":["code-generation-editing","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"zeroeval__cap_3","uri":"capability://data.processing.analysis.unified.benchmark.dataset.management","name":"unified benchmark dataset management","description":"Provides standardized dataset loading and management infrastructure for mathematical, logical, and code generation tasks with consistent problem formatting, answer key handling, and metadata tracking. Implements dataset versioning, problem filtering by difficulty/category, and batch processing support to enable reproducible evaluation across different problem domains with a single interface.","intents":["Load and manage multiple benchmark datasets (math, logic, code) with consistent APIs","Filter problems by difficulty level, category, or other metadata for targeted evaluation","Ensure reproducible evaluation by tracking dataset versions and problem IDs"],"best_for":["Researchers running comprehensive evaluations across multiple reasoning domains","Teams building custom benchmarks on top of ZeroEval's dataset infrastructure","Benchmark maintainers needing standardized dataset management patterns"],"limitations":["Dataset loading performance may degrade with very large problem sets (10k+ problems)","Metadata schema is fixed; custom problem attributes require dataset extension","No built-in dataset caching; repeated evaluations reload from disk/network"],"requires":["Python 3.7+","Disk space for benchmark datasets (varies by problem count)","Network access if datasets are hosted remotely"],"input_types":["JSON/JSONL dataset files with problem definitions and answers","metadata filters (difficulty, category, language)"],"output_types":["loaded problem objects with standardized attributes","filtered problem subsets","dataset statistics and metadata summaries"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"zeroeval__cap_4","uri":"capability://tool.use.integration.multi.model.evaluation.orchestration","name":"multi-model evaluation orchestration","description":"Orchestrates evaluation of multiple LLMs against benchmark datasets with support for different inference APIs (OpenAI, Anthropic, local models) and configurable inference parameters. Implements batch processing, result aggregation, and comparative analysis across models with support for parallel evaluation and result caching to reduce redundant API calls.","intents":["Run the same benchmark against multiple LLM providers and compare results","Evaluate different model versions or parameter configurations on identical problems","Generate comparative reports showing performance differences across models"],"best_for":["Researchers comparing LLM capabilities across multiple providers","Teams evaluating whether to switch between model providers","Benchmark maintainers generating leaderboards or comparative analysis"],"limitations":["API rate limits may cause evaluation slowdown for large-scale comparisons","Result caching requires persistent storage; no built-in distributed caching","Inference parameter tuning is manual; no automated hyperparameter search","Cost scales linearly with number of models and problems; no cost optimization"],"requires":["Python 3.7+","API keys for target LLM providers (OpenAI, Anthropic, etc.)","Network connectivity for API calls","Sufficient API quota/budget for evaluation scale"],"input_types":["list of model identifiers/endpoints","inference configuration (temperature, max_tokens, etc.)","benchmark dataset"],"output_types":["per-model evaluation results","comparative performance metrics","aggregated statistics and rankings"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"zeroeval__cap_5","uri":"capability://data.processing.analysis.evaluation.result.aggregation.and.reporting","name":"evaluation result aggregation and reporting","description":"Aggregates evaluation results across problems and models with statistical analysis and report generation. Computes accuracy metrics, confidence intervals, error distributions, and comparative statistics; generates human-readable reports and machine-readable result files for further analysis. Supports filtering and slicing results by problem category, difficulty, or model for detailed performance analysis.","intents":["Aggregate raw evaluation results into summary statistics and performance metrics","Generate comparative reports showing which models perform better on which problem types","Export results in formats suitable for publication or further analysis"],"best_for":["Researchers analyzing evaluation results and generating benchmark reports","Teams creating leaderboards or comparative analysis documents","Benchmark maintainers publishing results with statistical rigor"],"limitations":["Statistical analysis assumes sufficient sample size; small problem sets may have high variance","Report generation templates are fixed; custom report formats require code modification","No built-in visualization; results are text/JSON only"],"requires":["Python 3.7+","Evaluation results in ZeroEval format","NumPy/SciPy for statistical calculations"],"input_types":["raw evaluation results (per-problem correctness labels)","problem metadata (category, difficulty)","model identifiers"],"output_types":["accuracy scores and confidence intervals","error distribution analysis","comparative performance tables","JSON/CSV result exports"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"zeroeval__cap_6","uri":"capability://data.processing.analysis.problem.specific.answer.extraction.and.validation","name":"problem-specific answer extraction and validation","description":"Implements domain-specific answer extraction from LLM outputs using pattern matching, parsing, and semantic analysis tailored to each problem type. For math problems, extracts numerical answers from LaTeX, symbolic notation, and natural language; for logic problems, validates logical conclusions; for code problems, extracts and validates generated code. Handles malformed outputs gracefully with detailed error reporting.","intents":["Extract correct answer from LLM output even when wrapped in explanation text","Validate extracted answers against ground truth with domain-specific correctness criteria","Debug answer extraction failures to improve evaluation reliability"],"best_for":["Researchers evaluating LLMs that produce verbose or non-standard output formats","Teams building custom benchmarks requiring robust answer extraction","Benchmark maintainers needing to handle diverse model output styles"],"limitations":["Pattern-based extraction may fail on novel or non-standard output formats","Semantic validation (e.g., for logic problems) relies on heuristics that may have false positives/negatives","Extraction rules are domain-specific; adding new problem types requires custom extraction logic","Does not handle ambiguous answers or multiple valid solutions well"],"requires":["Python 3.7+","Regular expressions and parsing libraries","Domain-specific extraction rules per problem type"],"input_types":["raw LLM output (text)","problem definition with answer format specification","ground-truth answer"],"output_types":["extracted answer","extraction confidence score","validation result (correct/incorrect)","extraction error logs"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"zeroeval__cap_7","uri":"capability://data.processing.analysis.error.analysis.and.failure.mode.classification","name":"error analysis and failure mode classification","description":"Classifies evaluation failures into specific error categories (syntax errors, runtime errors, logic errors, timeout, invalid format) with detailed error messages and logs. Provides aggregated error statistics showing which error types are most common across models and problems, enabling targeted debugging and model improvement. Supports custom error classification rules for domain-specific failure modes.","intents":["Understand why an LLM failed on a specific problem (syntax vs. logic vs. incomplete)","Identify systematic failure patterns across models (e.g., all models fail on constraint satisfaction)","Prioritize model improvements based on most common error types"],"best_for":["Researchers analyzing LLM failure modes in detail","Teams debugging why models perform poorly on specific problem types","Benchmark maintainers identifying problematic benchmark questions"],"limitations":["Error classification is heuristic-based; some errors may be misclassified","Requires detailed error information from execution; some failures may not produce clear error messages","Custom error classification rules require domain expertise to define","Does not explain WHY a model made an error, only categorizes the error type"],"requires":["Python 3.7+","Detailed execution logs and error messages from evaluation","Error classification rules (built-in or custom)"],"input_types":["evaluation results with error information","execution logs","problem definitions"],"output_types":["error type classification per problem","error frequency distribution","error statistics by model/category","detailed error logs for debugging"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"zeroeval__cap_8","uri":"capability://automation.workflow.benchmark.reproducibility.and.versioning","name":"benchmark reproducibility and versioning","description":"Ensures reproducible evaluation through dataset versioning, problem ID tracking, and result logging with full evaluation configuration capture. Stores evaluation metadata (model version, inference parameters, timestamp, dataset version) alongside results to enable exact reproduction of past evaluations. Supports result comparison across evaluation runs to track model improvements over time.","intents":["Reproduce exact evaluation results from a past run","Track how model performance changes over time with consistent evaluation setup","Share evaluation results with full provenance information for transparency"],"best_for":["Researchers publishing benchmark results requiring reproducibility","Teams tracking model performance improvements over development cycles","Benchmark maintainers ensuring consistent evaluation across releases"],"limitations":["Reproducibility requires identical inference environment; API changes or model updates may affect results","Versioning overhead adds storage and metadata management complexity","No built-in mechanism to detect when results become stale due to model updates"],"requires":["Python 3.7+","Persistent storage for evaluation metadata and results","Version control for benchmark datasets"],"input_types":["evaluation configuration (model, parameters, dataset version)","evaluation results"],"output_types":["evaluation metadata with full provenance","versioned result files","comparison reports across evaluation runs"],"categories":["automation-workflow","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"zeroeval__cap_9","uri":"capability://automation.workflow.batch.evaluation.with.parallelization.and.resource.management","name":"batch evaluation with parallelization and resource management","description":"Orchestrates batch evaluation of multiple models across multiple datasets with configurable parallelization (thread/process-based) and resource management (rate limiting, memory constraints, timeout handling). The framework distributes evaluation tasks across available resources, monitors execution, handles failures gracefully with retry logic, and provides progress tracking and resource utilization metrics.","intents":["Evaluate multiple models efficiently without manual orchestration","Parallelize evaluations to reduce total execution time","Manage API rate limits and resource constraints automatically","Monitor evaluation progress and handle failures transparently"],"best_for":["Researchers conducting large-scale model comparisons","Teams running regular evaluation pipelines with multiple models","Benchmark maintainers managing comprehensive evaluation infrastructure"],"limitations":["Parallelization adds complexity; debugging parallel execution issues is challenging","API rate limits may still throttle evaluation speed despite parallelization","Resource management tuning (thread count, memory limits) requires experimentation","Failure handling and retry logic may mask underlying issues if not carefully configured"],"requires":["Python 3.7+","Multi-core CPU for thread/process-based parallelization","Sufficient memory for parallel model inference (if using local models)"],"input_types":["list of models to evaluate","list of datasets to use","parallelization and resource configuration"],"output_types":["structured JSON (results for all model-dataset combinations)","progress logs and resource utilization metrics"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"zeroeval__headline","uri":"capability://testing.quality.unified.evaluation.framework.for.llms","name":"unified evaluation framework for llms","description":"ZeroEval is a comprehensive framework designed to evaluate large language models on reasoning tasks without the need for few-shot examples, providing standardized zero-shot evaluation protocols across various reasoning domains.","intents":["best LLM evaluation framework","zero-shot evaluation for reasoning tasks","LLM benchmarking tools","evaluate LLMs without few-shot examples","standardized LLM assessment methods"],"best_for":["researchers assessing LLM performance","developers needing standardized benchmarks"],"limitations":[],"requires":[],"input_types":[],"output_types":[],"categories":["testing-quality"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":63,"verified":false,"data_access_risk":"low","permissions":["Python 3.7+","LLM API access (OpenAI, Anthropic, or local model via inference server)","Problem dataset in standardized format with ground-truth answers","LLM inference capability","Logical reasoning problem dataset with ground-truth conclusions","Sandboxed code execution environment (Docker, isolated VM, or restricted subprocess)","Language-specific interpreters/compilers for target languages","Programming task dataset with test cases and expected outputs","Disk space for benchmark datasets (varies by problem count)","Network access if datasets are hosted remotely"],"failure_modes":["Answer extraction heuristics may fail on non-standard mathematical notation or multi-step reasoning with intermediate explanations","Floating-point tolerance thresholds require manual tuning per problem domain","Does not evaluate reasoning quality or solution elegance, only final correctness","Symbolic verification requires problems with formally-defined logic; natural language logic puzzles rely on semantic matching which may have false negatives","Does not distinguish between correct answers reached through valid vs. invalid reasoning paths","Limited to problems with deterministic correct answers; ambiguous or multi-valid-solution problems not well-supported","Requires sandboxed execution environment; security implications for running untrusted generated code","Test case coverage may not catch all logic errors; passing tests does not guarantee correctness on unseen inputs","Language-specific evaluation requires language-specific test harnesses and interpreters/compilers","Does not evaluate code quality metrics (readability, efficiency, maintainability)","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7,"quality":0.9,"ecosystem":0.39999999999999997,"match_graph":0.25,"freshness":0.52,"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":"2026-06-17T09:51:05.297Z","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=zeroeval","compare_url":"https://unfragile.ai/compare?artifact=zeroeval"}},"signature":"ewxQyQ3c0jSrB/vSjFWLZT66c+JA1OSpCZtNzpI6kTUo0DCiw54T4Xk5qtCjM/op16gJ+QEcfhQ4H3VBc5hxBQ==","signedAt":"2026-06-20T19:32:17.793Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/zeroeval","artifact":"https://unfragile.ai/zeroeval","verify":"https://unfragile.ai/api/v1/verify?slug=zeroeval","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"}}