MATH vs cua

Provides a curated dataset of 12,500 authentic competition mathematics problems sourced from AMC, AIME, and similar olympiad-style competitions, enabling systematic evaluation of LLM mathematical reasoning across 7 subject domains. Each problem includes ground-truth step-by-step solutions that serve as reference implementations for answer verification and reasoning chain validation. The dataset uses a 5-level difficulty stratification to enable fine-grained performance analysis across problem complexity ranges, allowing researchers to identify capability thresholds and reasoning degradation patterns.

Unique: Sourced directly from authentic competition mathematics (AMC, AIME) rather than synthetic or textbook problems, ensuring problems test genuine mathematical reasoning under time pressure and novelty constraints. Includes detailed step-by-step solutions for each problem, enabling not just answer verification but reasoning chain analysis and intermediate step correctness evaluation.

vs alternatives: More rigorous than general math benchmarks (SVAMP, MathQA) because competition problems are designed to be unsolvable by pattern-matching alone; more comprehensive than single-competition datasets because it spans 7 mathematical domains and 5 difficulty levels, enabling fine-grained capability profiling

Organizes the 12,500 problems across 7 discrete mathematical subjects (Prealgebra, Algebra, Number Theory, Counting and Probability, Geometry, Intermediate Algebra, Precalculus), enabling targeted performance analysis by mathematical domain. This stratification allows researchers to identify which mathematical reasoning capabilities their models have acquired and which remain deficient, rather than collapsing performance into a single aggregate score. The subject taxonomy maps to standard high school and early undergraduate mathematics curricula, making results interpretable to educators and curriculum designers.

Unique: Explicitly organizes problems by 7 mathematical subject domains rather than treating mathematics as a monolithic capability, enabling fine-grained capability profiling. This mirrors how mathematical education is structured (separate courses for Algebra, Geometry, etc.), making results actionable for curriculum-aligned training and evaluation.

vs alternatives: More granular than aggregate math benchmarks (GSM8K, MATH500) which report single accuracy scores; enables identification of domain-specific weaknesses that aggregate metrics would mask, critical for targeted model improvement and application-specific evaluation

Stratifies all 12,500 problems across 5 difficulty levels (1-5), enabling researchers to construct difficulty-aware evaluation curves and identify at what problem complexity threshold model performance degrades. This enables analysis of whether mathematical reasoning scales smoothly with problem difficulty or exhibits sharp capability cliffs. The difficulty stratification allows researchers to evaluate whether models have acquired robust reasoning or are brittle to increased complexity, and to identify the 'frontier' difficulty level where models transition from reliable to unreliable performance.

Unique: Provides explicit 5-level difficulty stratification across all 12,500 problems, enabling construction of difficulty-aware evaluation curves rather than single aggregate scores. This enables researchers to identify capability cliffs and scaling behavior, critical for understanding whether models have acquired robust reasoning or brittle pattern-matching.

vs alternatives: More nuanced than pass/fail benchmarks (MATH500) because it enables difficulty-stratified analysis; more interpretable than raw problem sets because difficulty annotations guide researchers to focus evaluation on capability frontiers rather than averaging across trivial and impossible problems

Provides detailed step-by-step solutions for all 12,500 problems, enabling not just binary answer correctness evaluation but intermediate reasoning chain validation. These reference solutions serve as ground truth for analyzing whether models generate correct reasoning steps in correct order, enabling fine-grained evaluation of reasoning quality beyond final answer accuracy. The solutions can be used to train models via supervised fine-tuning on step-by-step reasoning, or to validate intermediate steps in chain-of-thought outputs, enabling detection of 'right answer, wrong reasoning' failure modes.

Unique: Includes detailed step-by-step solutions for all 12,500 problems rather than just final answers, enabling intermediate reasoning validation and supervised fine-tuning on reasoning chains. This enables training approaches like outcome supervision and process supervision that have shown significant improvements in mathematical reasoning capability.

vs alternatives: Richer than answer-only benchmarks (SVAMP, MathQA) because it enables reasoning chain validation; more actionable than problem-only datasets because solutions provide training signal for supervised fine-tuning and intermediate step verification

Provides published baseline scores from multiple model generations (GPT-3 at 6.9%, o3 at 90%+, DeepSeek R1, etc.), enabling researchers to position their models within the landscape of known capabilities and track improvement over time. The dataset's stability and fixed problem set enable longitudinal comparison — researchers can evaluate their models against the same 12,500 problems and directly compare results to published baselines, identifying whether improvements come from better reasoning or from model scale/compute. This enables tracking of progress in mathematical reasoning as a research community.

Unique: Provides published baseline scores from multiple model generations (GPT-3, o3, DeepSeek R1) on the same fixed problem set, enabling direct longitudinal comparison and tracking of progress in mathematical reasoning capability. The fixed problem set ensures that improvements over time reflect genuine capability gains rather than dataset changes.

vs alternatives: More useful for tracking progress than one-off benchmarks because the fixed problem set enables direct comparison across time and models; more interpretable than relative rankings because absolute scores on the same problems enable understanding of capability gaps and improvement trajectories

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.