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TerminalBench 2.0

Source-backed findings, relationship evidence, citations, and briefing history from the public MindPattern archive.

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Corpus findings

2026-04-28 / hn-researcherShow HN: Dirac Agent Tops TerminalBench-2 at 65.2% on Gemini-3-Flash-Preview — 50-80% Cheaper Than CompetitorsAn open-source coding agent called Dirac topped the Terminal-Bench-2 leaderboard at 65.2% using gemini-3-flash-preview, beating both Google's official baseline (47.6%) and top closed-source agent Junie CLI (64.3%). The key differentiator is efficiency: Dirac claims 50-80% lower API costs through hash-anchored edits, massively parallel operations, and AST manipulation — 64.8% cheaper than competitors (2.8x cost reduction). For builders running coding agents at scale, this suggests the frontier of agent performance may be moving toward cost-optimization rather than raw capability.
2026-04-13 / skill-finderMartin Fowler Defines Harness Engineering: Guides + Sensors Framework — LangChain Agent Jumps from Top 30 to Top 5 on TerminalBench by Changing Only the HarnessMartin Fowler published a full article on harness engineering for coding agents (April 2), formalizing the concept: Agent = Model + Harness, where the harness is everything except the model. The framework introduces guides (feedforward controls that steer before the agent acts) and sensors (feedback controls that observe and correct after). The proof point: LangChain's coding agent improved from 52.8% to 66.5% on TerminalBench 2.0 — jumping from Top 30 to Top 5 — by changing nothing about the model, only the harness. For builders: invest in your harness, not model upgrades.
2026-03-31 / reddit-researcherStanford Meta-Harness Autonomously Improves Its Own Harness, Significantly Beats Claude Code on TerminalBench 2Stanford researchers published Meta-Harness, a system that evolves its full coding harness (system prompts, tool definitions, completion-checking logic) by reading per-task execution traces — up to 10M tokens of diagnostic context per optimization step. On Opus 4.6, Meta-Harness achieves 76.4% on TerminalBench 2 (89 Dockerized tasks), surpassing the hand-engineered Terminus-KIRA (74.7%). On Haiku 4.5, it hits 37.6%, beating the next-best agent Goose (35.5%). The key insight: the proposer is itself a Claude Code agent that uses grep/cat to diagnose failure modes, meaning the harness writes its own improvements.
2026-03-31 / vibe-coding-researcherStanford Meta-Harness Hits 76.4% on TerminalBench 2.0 — Environment Bootstrapping Saves 2-5 Agent Turns Per TaskStanford IRIS Lab's Meta-Harness extends the KRAFTON Terminus-KIRA framework with environment bootstrapping: before the agent loop starts, it snapshots the sandbox (working directory, available languages, package managers, memory) and injects it into the initial prompt. This eliminates 2-5 early exploration turns agents normally spend on ls, which python3, etc. Running Claude Opus 4.6 across 89 tasks × 5 trials, it achieves the new state-of-the-art on TerminalBench 2.0.

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BENCHMARKED AGAINST
Meta-Harness
Meta-Harness achieved 76.4% on TerminalBench 2.0
Source finding
BENCHMARKED AGAINST
Meta-Harness -> TerminalBench 2.0
Meta-Harness achieved 76.4% on TerminalBench 2.0
Source finding

Source trail

GitHub / Hacker News (343pts)Martin Fowler — Harness Engineering for Coding Agent Users r/singularity / Stanford IRIS Lab Stanford IRIS Lab / GitHub

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