X-SYNTH: Beyond Retrieval -- Enterprise Context Synthesis from Observed Human Attention
The paper presents X-SYNTH, a framework for enterprise context synthesis based on observed human attention. It addresses the limitations of traditional retrieval methods in AI tasks by utilizing behavioral patterns to improve context relevance. The results show a significant increase in True Lead Rate while reducing False Lead Rate in sales lead identification tasks.
- ▪X-SYNTH synthesizes enterprise context by analyzing human attention and behavioral patterns.
- ▪Traditional retrieval methods struggle with complex tasks, leading to low True Lead Rates and high False Lead Rates.
- ▪The framework achieved a True Lead Rate of 61.9% and a False Lead Rate of 18.8% in sales lead identification.
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Computer Science > Artificial Intelligence arXiv:2605.15505 (cs) [Submitted on 15 May 2026] Title:X-SYNTH: Beyond Retrieval -- Enterprise Context Synthesis from Observed Human Attention Authors:Guruprasad Raghavan, George Nychis, Rohan Narayana Murthy View a PDF of the paper titled X-SYNTH: Beyond Retrieval -- Enterprise Context Synthesis from Observed Human Attention, by Guruprasad Raghavan and 2 other authors View PDF HTML (experimental) Abstract:In enterprise operations, the context required for an AI agent task is scattered across systems of record, static information stores, and communication channels. What is stored is system state, a lossy representation of the work that actually happened [2, 52].
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