Targeted and Traceable Investigation of Multi-Agent LLM Dialogue via Semantic Bundling of Knowledge Graphs
3.40T1 sourcearXiv cs.HC
Source record
Published by arXiv cs.HC (T1 source). The original is at https://arxiv.org/abs/2609.35786.
Pipeline notes
The summary and note below are generated by the signal pipeline — they are Beyond Desk’s reading, not quotations from the source.
SummaryResearch paper presenting AgentK, a visual analytics system that converts multi-agent LLM dialogue into a knowledge graph and uses semantic bundling to help analysts trace behaviors, attribute actions to specific actors, and summarize long exchanges. Applied to the VAST Challenge 2026 MC1 dataset.
Why it mattersConcrete method and tool for debugging multi-agent LLM systems by turning conversation logs into navigable knowledge graphs, addressing attribution and summarization as agent stacks scale.
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