OOBench
LLM Systems Evaluation & Agentic Benchmarking Suite
Problem Statement
Evaluating non-deterministic LLM agent pipelines, structured output schemas, and multi-model tool calls requires reproducible evaluation harnesses rather than ad-hoc manual testing. OOBench provides a standardized benchmarking suite designed to measure accuracy, schema adherence, latency, and cost efficiency across edge-case tool invocations and model releases.
Approach
The benchmark scores resolver output against prediction markets that have already closed, so every question has a known ground truth rather than a human judgement call. The pipeline is split into a dataset builder, a resolver, an evaluation loop, resilience and regression checks, and a report step. Every resolver call must return a fixed schema — outcome, confidence, reasoning, sources, and any conflicts detected between sources — which is validated before it is scored.
Scope and evaluation design are defined; the implementation is not public yet. This page will be replaced with measured results — not projections — once the first full run is done.