Whatever is precise enough to benchmark is also precise enough to optimize for.

WorldMark establishes a standardized benchmark for evaluating interactive video generation models with unified controls, identical scenarios, and comprehensive evaluation metrics across multiple model architectures.

Our fourth metric, an index constructed from WeirdML V2 results, showed no sign of acceleration. A single global linear trend fit the data best.

Our fourth metric, an index constructed from WeirdML V2 results, showed no sign of acceleration. A single global linear trend fit the data best.

WeirdML V2 places models in an unusually resource-constrained environment: models get only five attempts to submit working code, with no access to external tools. This setup has not been the focus of recent RL training.

Our fourth metric, an index constructed from WeirdML V2 results, showed no sign of acceleration. A single global linear trend fit the data best.

We select the median-difficulty question from the set with maximum model coverage and standardize it to 0.

A senior engineer to own and evolve the game engine and real-time play infrastructure behind the ARC-AGI series.

Tracks the evolution of LLM security capabilities across benchmarks (CyberGym, Cybench, etc.), calculates capability doubling times, detects emergence patterns, and monitors cost-efficiency trends.

Performance was compared against 57 historical scores from human experts in the AI-bio field.

We present a comprehensive adoption snapshot of the leading open language models and who is building them, focusing on the ~1.5K mainline open models

Simplify benchmarks to webVoyager-only with Pi SDK runner

Add benchmark framework and release submission overview - Add benchmark runner with onlineMind2Web benchmark support - Add agent client abstraction for codex/claude backends - Add CLI entry point for running benchmarks (pnpm benchmark)

Add GCP WebVoyager benchmark runner and worktree tooling - Create benchmarks/infra/setup.sh — an idempotent script that provisions: - GCS bucket: gs://libretto-benchmarks - Artifact Registry repo: libretto-benchmarks (Docker) - Cloud Run Job: webvoyager-bench (4 CPU, 8Gi, 2h timeout)

It is not common for real software to be developed the way MirrorCode tasks are structured — against a precise, programmatically checkable specification.

the 𝜏-benchmark [ 104] explicitly incorporates the pass^𝑘 metric toevaluate the consistency of an agent

It is critical to be systematic when benchmarking code.

Benchmarking is the practice of comparing business processes and performance metrics to industry bests and best practices from other companies. Dimensions typically measured are quality, time and cost.

Benchmarking Python code refers to comparing the performance of one program to variations of the program.

a benchmark tells you how slow your code is ("it took 20 seconds to do X Y Z") and a profiler tells you why it's slow ("35% of that time was spent doing compression").

before(:all) do @fiber = Fiber.new do Benchmark.ips do |benchmark| @benchmark = benchmark Fiber.yield benchmark.compare! end end @fiber.resume end