# Constructing Executable Analytical Knowledge Representations for Meta-Analysis Synthesis Using an Agentic Harness

arXiv: https://arxiv.org/abs/2608.01711 · HTML: https://mlsanity.com/paper/2608.01711/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Web, search, and research agents, one of 16 directions in LLM agents and tool use.
- Builds on 13 of its 54 references that are in the map.
- First posted on arXiv 3 Aug 2026.

## Placed in

- LLM agents and tool use / [Web, search, and research agents](https://mlsanity.com/agents/web-search-and-research-agents/)

## Builds on (most cited first, 13 of 13)

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- [Transforming Evidence Synthesis: A Systematic Review of the Evolution of Automated Meta-Analysis in the Age of AI](https://mlsanity.com/paper/2504.20113/) (2025)
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- [Empowering Meta-Analysis: Leveraging Large Language Models for Scientific Synthesis](https://mlsanity.com/paper/2411.10878/) (2024)
- [Manalyzer: End-to-end Automated Meta-analysis with Multi-agent System](https://mlsanity.com/paper/2505.20310/) (2025)
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- [Formal Definition and Implementation of Reproducibility Tenets for Computational Workflows](https://mlsanity.com/paper/2406.01146/) (2024)
- [Introducing SWIRL: An Intermediate Representation Language for Scientific Workflows](https://mlsanity.com/paper/2407.01838/) (2024)

## Abstract (arXiv)

Meta-analysis synthesis highlights a fundamental challenge in knowledge-based scientific analysis: structured evidence does not by itself represent the analytical knowledge required for executable computation. Decisions about evidence assignment, analytical contrasts, outcome and time-point alignment, effect-size formulation, and methodological admissibility must be explicit before statistical execution. Existing automated approaches often embed these decisions in model outputs, generated code, or workflow traces rather than representing them as independently verifiable knowledge. We introduce the Executable Analytical Knowledge Representation (EAKR), a machine-actionable representation of the knowledge required to transform structured evidence into executable meta-analysis. An EAKR represents evidence, relations, numerical inputs, constraints, provenance, and unresolved issues. We operationalise EAKR in MetaSynDec, an agentic harness in which large language models propose structured updates and deterministic services govern schema- and contract-based validation and execution. Across 58 synthesis units, MetaSynDec constructed all EAKRs, with 57 proceeding to statistical execution. Of 56 units with sufficient information to define a reference analysis object, 38 (67.9%) achieved complete object fidelity and 42 (75.0%) exact evidence-set agreement, with a mean Jaccard similarity of 0.909. Generated and published confidence intervals overlapped in 54 of 55 units (98.2%). MetaSynDec outperformed direct LLM generation in reference synthesis-structure agreement (57/58 versus 23/58; p<0.001) and among 23 jointly completed units, exact reference-formulation agreement (23/23 versus 1/23; p<0.001). These findings provide feasibility evidence that EAKR supports formal validation, traceability, statistical execution, and improved methodological agreement relative to direct LLM generation.