MBPP+ vs cua

Generates 35x more test cases per problem than the original MBPP benchmark by creating edge-case and boundary-condition tests beyond base inputs. The system uses a contract-based validation approach with input constraints (contract field), floating-point tolerance specifications (atol), and canonical solution execution to derive comprehensive test suites that expose fragile implementations passing only base tests.

Unique: Multiplies test coverage by 35x through systematic generation of plus_input test cases derived from canonical solutions and input contracts, rather than relying on manually curated test suites. Includes atol (absolute tolerance) fields for floating-point comparisons and contract specifications for input validation, enabling detection of solutions that pass base tests but fail on boundary conditions.

vs alternatives: Provides 35x more test cases per problem than original MBPP (35 vs ~3 tests per task), catching incorrect implementations that pass minimal test suites where competitors like HumanEval or raw MBPP would miss them.

Executes untrusted LLM-generated Python code in isolated processes with multi-layer sandboxing: process isolation via multiprocessing, memory limits (default 4GB via EVALPLUS_MAX_MEMORY_BYTES), dynamically calculated time limits based on canonical solution execution time, I/O suppression via swallow_io, and system call guards via reliability_guard. Each sample runs in a separate process with shared memory for inter-process communication.

Unique: Combines process isolation, memory limits, dynamic timeout calculation (based on canonical solution execution), I/O suppression, and system call guards in a single execution pipeline. Timeout is not fixed but derived from ground-truth execution time, preventing both premature termination of slow-but-correct solutions and runaway execution of inefficient code.

vs alternatives: More comprehensive than simple timeout-based execution (e.g., raw subprocess calls) by adding memory limits, I/O suppression, and system call guards; more flexible than fixed timeouts by dynamically calibrating to canonical solution performance.

Calculates pass@k metrics by executing k independent code samples per problem and computing the probability that at least one passes all test cases. Aggregates results across the full problem set to produce benchmark-wide pass@k scores. Supports multiple k values (k=1, 5, 10, etc.) to measure model robustness and sample efficiency.

Unique: Implements pass@k calculation across extended test suites (35x more tests than original MBPP), making the metric more stringent and revealing model weaknesses that pass@k on minimal test coverage would miss. Aggregates results across 378 problems with comprehensive test coverage per problem.

vs alternatives: More rigorous than pass@k on original MBPP (which uses ~3 tests per problem) because extended test suites expose fragile solutions; comparable to HumanEval+ but with 2.3x more problems (378 vs 164 tasks).

Preprocesses LLM-generated code before execution by removing or neutralizing potentially dangerous constructs: strips import statements that could access system resources, removes eval/exec calls, sanitizes file I/O operations, and disables network access. The sanitize.py module applies these transformations while preserving functional code logic, enabling safe execution of untrusted code without manual review.

Unique: Applies pattern-based sanitization to remove dangerous constructs (imports, eval/exec, file I/O, network access) before execution, complementing process-level isolation. Works in conjunction with reliability_guard system calls filtering to provide defense-in-depth against malicious or accidental harmful code.

vs alternatives: Combines code-level sanitization (removing dangerous constructs) with process-level isolation (memory/time limits, system call guards), providing layered defense; simpler than full AST-based code analysis but faster and more practical for high-volume evaluation.

Provides unified interface for code generation across 8+ LLM providers (vLLM, HuggingFace, OpenAI, Anthropic, Google Gemini, AWS Bedrock, Ollama) through a provider abstraction layer. Each provider implements a common interface for prompt submission, sampling, and result retrieval, enabling seamless switching between models without changing evaluation code. Supports batch generation and configurable sampling parameters (temperature, top_p, max_tokens).

Unique: Implements provider abstraction layer supporting 8+ LLM backends (vLLM, HuggingFace, OpenAI, Anthropic, Google Gemini, AWS Bedrock, Ollama) through common interface in evalplus/provider/__init__.py, enabling single evaluation pipeline to work across local and cloud models without code changes. Supports both local inference (vLLM, Ollama) and cloud APIs with unified sampling parameter handling.

vs alternatives: More comprehensive provider support than single-model evaluation frameworks; more flexible than hardcoded provider integrations by using abstraction layer pattern; enables fair comparison across providers by normalizing sampling parameters and result formats.

Measures code efficiency using CPU instruction counting (via Linux perf) rather than wall-clock time, providing hardware-independent performance metrics. Generates performance-exercising inputs with exponential scaling (2^1 to 2^26) to stress-test algorithms, filters tasks based on profile size and compute cost, and produces EvalPerf dataset with instruction count baselines for each problem.

Unique: Uses CPU instruction counting via Linux perf instead of wall-clock time, providing hardware-independent performance metrics. Generates exponentially-scaled performance-exercising inputs (2^1 to 2^26) to stress-test algorithms and expose inefficient implementations. Filters tasks based on profile size, compute cost, coefficient of variation, and performance clustering to create manageable EvalPerf dataset.

vs alternatives: More rigorous than wall-clock time measurement (which varies with system load) and more practical than full algorithmic complexity analysis; provides objective hardware-independent performance baseline for comparing generated code efficiency.

Organizes code problems as structured objects with standardized metadata fields: base_input (original test cases), plus_input (extended test cases), contract (input validation constraints), atol (floating-point tolerance), canonical_solution (ground truth implementation), and entry_point (function name). Provides dataset loading, filtering, and iteration utilities through evalplus/data/__init__.py, enabling programmatic access to 378 MBPP+ problems with consistent schema.

Unique: Provides standardized schema for 378 MBPP+ problems with fields for base/extended test cases (base_input, plus_input), input validation (contract), floating-point tolerance (atol), ground truth (canonical_solution), and function entry point. Enables programmatic dataset access through consistent interface rather than raw JSON files.

vs alternatives: More structured than raw JSON dataset files; provides consistent schema across all problems enabling reliable programmatic access; includes extended test cases (plus_input) and validation constraints (contract) not present in original MBPP.

Provides CLI tools (evalplus.evaluate, evalplus.codegen, evalplus.evalperf, evalplus.sanitize) that orchestrate the complete evaluation workflow: code generation from LLM → sanitization → correctness evaluation → optional performance evaluation. Each CLI tool accepts configuration parameters (model, dataset, sampling params) and produces structured output (JSON results, pass@k metrics, performance data). Enables end-to-end benchmark execution without writing custom Python code.

Unique: Provides four integrated CLI tools (evalplus.codegen, evalplus.evaluate, evalplus.evalperf, evalplus.sanitize) that chain together to form complete evaluation pipeline: generation → sanitization → correctness evaluation → performance evaluation. Each tool accepts configuration parameters and produces structured JSON output, enabling end-to-end benchmark execution from command line.

vs alternatives: More integrated than individual tools (e.g., separate code generation and evaluation scripts); more accessible than programmatic API for non-developers; enables reproducible evaluation workflows via CLI commands.

Captures desktop screenshots and feeds them to 100+ integrated vision-language models (Claude, GPT-4V, Gemini, local models via adapters) to reason about UI state and determine appropriate next actions. Uses a unified message format (Responses API) across heterogeneous model providers, enabling the agent to understand visual context and generate structured action commands without brittle selector-based logic.

Unique: Implements a unified Responses API message format abstraction layer that normalizes outputs from 100+ heterogeneous VLM providers (native computer-use models like Claude, composed models via grounding adapters, and local model adapters), eliminating provider-specific parsing logic and enabling seamless model swapping without agent code changes.

vs alternatives: Broader model coverage and provider flexibility than Anthropic's native computer-use API alone, with explicit support for local/open-source models and a standardized message format that decouples agent logic from model implementation details.

Provisions isolated execution environments across macOS (via Lume VMs), Linux (Docker), Windows (Windows Sandbox), and host OS, with unified provider abstraction. Handles VM/container lifecycle (creation, snapshot management, cleanup), resource allocation, and OS-specific action handlers (keyboard/mouse events, clipboard, file system access) through a pluggable provider architecture that abstracts platform differences.

Unique: Implements a pluggable provider architecture with unified Computer interface that abstracts OS-specific action handlers (macOS native events via Lume, Linux X11/Wayland via Docker, Windows input simulation via Windows Sandbox API), enabling single agent code to target multiple platforms. Includes Lume VM management with snapshot/restore capabilities for deterministic testing.

vs alternatives: More comprehensive OS coverage than single-platform solutions; Lume provider offers native macOS VM support with snapshot capabilities unavailable in Docker-only alternatives, while unified provider abstraction reduces code duplication vs. platform-specific agent implementations.