An agent is a model plus a harness -- the runtime that couples an LLM to the world through a loop, tools, context management, safety controls, orchestration, and extension surfaces. Harness engineering, named as a discipline in early 2026, is the design and evolution of that runtime. This paper gives the young discipline its most comprehensive empirical foundation to date: a source-code anatomy of eleven production coding harnesses (Claude Code, Codex CLI, Gemini CLI, Mistral Vibe, OpenHands, Aider, Mini-SWE-Agent, Hermes, Pi, OpenCode, OpenClaw), plus Omnigent, the first meta-harness, analyzed as a contrast point. We define what a harness is, map its seven canonical subsystems with the minimal and maximal implementation of each, and dissect all eleven systems along those subsystems. The audit yields 13 cross-cutting observations and a catalog of 29 recurring design patterns. Two absences survive a threefold corpus expansion: across roughly four million lines of Python, TypeScript, and Rust, no agent runtime imports a general-purpose agentic framework, and none retrieves code with vector embeddings; the field runs on hand-rolled async loops and deterministic retrieval. SKILL.md skills lead MCP in adoption (9/11 vs. 8/11), and ACP ships in six systems with a new third role: harness hosting. Because the original eight systems were re-pinned rather than replaced, the study also contains a controlled longitudinal sample -- the same harnesses source-diffed across one quarter -- showing convergence becoming imitation and behavioral policy migrating from prompt prose to configuration. These threads converge on the paper's thesis: in the first half of 2026 the coding harness completed a turn from tool to platform. The paper closes with 18 design recommendations and a 90-line minimum-viable-harness scaffold.
Paul Barbaste, Tristan Darrigol, Germain Vu et al.· 0 citations
Robust EEG motor imagery decoding remains limited by strong inter-individual variability, making it difficult to identify pipelines that generalize across users. We present a large-scale, standardized within-session benchmark of decoding pipelines across three public datasets: Cho2017 (52 subjects), PhysionetMI (109 subjects), and Zhou2016 (4 subjects). Using a common MOABB LeftRightImagery setting, two frequency bands (8-15 Hz and 8-30 Hz), and a broad combination of feature extraction, preprocessing, and classification steps, we analyzed 216,714 raw evaluation rows, which after structured aggregation yielded 44,928, 109,000, and 4,192 subject-level observations respectively. Covariance tangent-space projection (cov-tgsp) and Common Spatial Patterns (CSP) consistently defined the strongest methodological families, though their relative ordering was dataset-dependent. On Cho2017, the best family-level mean accuracy came from cov-tgsp in 8-30 Hz (0.712 +/- 0.140), whereas Zhou2016 favored CSP (0.832 +/- 0.121 in 8-15 Hz). These aggregate rankings concealed substantial subject-level heterogeneity: 42 distinct winning pipelines across 52 Cho2017 subjects, and 93 across 109 PhysionetMI subjects. We then used the benchmark as an empirical performance landscape for building compact portfolios of pipelines of size K. Several construction procedures were compared, including a ranking-based Top-K Mean heuristic and search-based strategies. Results were broadly consistent, with Top-K Mean giving the best trade-off. A single best global pipeline already retained 94.2% of the oracle in Cho2017 and 81.8% in PhysionetMI; at K = 12, oracle retention rose to 96.5% and 90.0%. The landscape is therefore subject-dependent, and this heterogeneity can be exploited through compact portfolios that make personalization more feasible.
Xavier Vasques, Paul Barbaste, Olivier Oullier· 0 citations
