---
type: source
title: "Natural-Language Agent Harnesses"
authors: ["Linyue Pan", "Lexiao Zou", "Shuo Guo", "Jingchen Ni", "Hai-Tao Zheng"]
format: paper
url: "https://arxiv.org/abs/2603.25723"
date: 2026-03-26
status: processed
processed_by: theseus
processed_date: 2026-03-31
claims_extracted: 5
enrichments: 1
tags: [harness-engineering, agent-architecture, module-ablation, file-backed-state, self-evolution]
---

# Natural-Language Agent Harnesses

Preprint from Tsinghua University / Harbin Institute of Technology, March 2026. arXiv:2603.25723v1.

## Summary

Proposes Natural-Language Agent Harnesses (NLAHs) — structured NL representations of harness control logic — and an Intelligent Harness Runtime (IHR) that interprets them. Tests on SWE-bench Verified (125 samples) and OSWorld (36 samples) using Codex CLI + GPT-5.4.

Key contributions:
1. Formalizes the harness design-pattern layer as an explicit, portable object
2. Controlled module ablation study (file-backed state, evidence-backed answering, verifier, self-evolution, multi-candidate search, dynamic orchestration)
3. Code-to-text harness migration study (native OS-Symphony vs NLAH realization)

## Key findings

**RQ1 (Behavioral Effect):** Process metrics move much more than resolution rate under Full IHR. TRAE Full: 16.3M prompt tokens, 642 tool calls, 74.4% resolve. TRAE w/o harness skill: 1.2M tokens, 51 tool calls, 75.2% resolve. The harness is behaviorally real but not monotonically helpful.

**RQ2 (Composability):** Module effects concentrate on a small frontier of component-sensitive cases. 110-115 of 125 SWE samples agree between Full IHR and each ablation (Table 2). Self-evolution is the clearest positive (+4.8pp SWE, +2.7pp OSWorld). Verifier and multi-candidate search can hurt. File-backed state and evidence-backed answering improve process structure rather than score.

**RQ3 (Migration):** NLAH realization matched or exceeded native code harness on OSWorld (47.2 vs 30.4). Migration relocates reliability mechanisms from local screen repair to durable state and artifact-backed closure. Not loss of orchestration but relocation of verification.

**Token split:** ~90% of prompt tokens, completion tokens, tool calls, and LLM calls occur in delegated child agents, not the runtime-owned parent (Table 4).

## Extraction notes

- 5 NEW claims extracted: solved-set replacer, file-backed state, self-evolution mechanism, verifier divergence, NL harness portability
- 1 ENRICHMENT: subagent hierarchy claim gets 90% delegation data
- ~40% overlap with existing KB (harness engineering, multi-agent degradation, determinism boundary)
- Highest novelty: controlled ablation data (no existing claims have module-level ablation), verifier divergence (very low KB coverage)