Knowledge-Based Zero-Replay Debugging of Multi-Agent LLM Traces

2026-06-16 · Source: cs.SE updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics · Depth: Expert, medium

Summary

A new method, BranchPoint-Latent, addresses the high cost of debugging multi-agent large language model (LLM) systems by introducing zero-replay counterfactual-effect prediction. Instead of expensive counterfactual replay, which scales linearly with candidate events, this approach compiles LLM execution traces into a structured "event knowledge graph." This graph captures routing, memory, tool-use, uncertainty, and latent evidence. BranchPoint-Latent, a lightweight gradient-boosted predictor, leverages 13 knowledge-graph features to predict which events an oracle would mark as high-effect, without performing any actual replay. Calibrated against a deterministic replay oracle across 37 trace families, the predictor significantly improves per-trace localization, raising Branch Recall@5 from 0.73 to 0.93 on held-out families at zero oracle-replay cost. It also boosts NDCG@5 from 0.83 to 0.92, offering an auditable and cost-efficient decision-support system for AI reliability debugging.

Key takeaway

For MLOps Engineers or AI Scientists managing multi-agent LLM systems, if you are struggling with the prohibitive cost of counterfactual replay for debugging, consider implementing a zero-replay prediction system. This approach allows you to prioritize causally decisive events using an event knowledge graph and a trained predictor, significantly reducing debugging time and computational expense. You can achieve high localization accuracy (Branch Recall@5 of 0.93) without incurring per-event replay costs, making your debugging process more efficient and auditable.

Key insights

Zero-replay prediction using event knowledge graphs significantly reduces the cost of debugging multi-agent LLM traces.

Principles

• Debugging can be framed as knowledge-based decision support.
• Event knowledge graphs structure unstructured LLM traces.
• Predictors can replace expensive replay oracles after offline training.

Method

Compile raw multi-agent LLM traces into an event knowledge graph. Extract 13 cheap CPU features. Use a gradient-boosted learning-to-rank predictor to score events and generate a budget-bounded replay agenda.

In practice

• Prioritize replay budget using predicted event effects.
• Use event knowledge graphs for auditable debugging decisions.
• Apply learning-to-rank predictors to reduce debugging costs.

Topics

• Multi-agent LLM systems
• Debugging
• Counterfactual replay
• Event knowledge graph
• Zero-replay prediction
• AI reliability

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, MLOps Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.SE updates on arXiv.org.