Abstract
Knowledge workers in enterprise environments can face challenges when resolving complex inquiries, as they may need to manually parse and synthesize information from multiple historical precedents. Existing retrieval systems may identify relevant cases but can leave the cognitive burden of synthesis to the user. Systems are described that can automate the synthesis of insights. For example, a system may use a semantic similarity model to retrieve a set of procedurally analogous historical cases from a data repository. A generative synthesis module could then compile data from these retrieved cases into a structured prompt for a large language model. The model may then perform a meta-analysis to generate a consolidated, actionable guide that highlights common resolution patterns and potential pitfalls. This process can reduce the cognitive load on a user by presenting a synthesized summary within their workflow.
Publication Date
2026-01-07
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Agrawal, Pooja; Kayande, Tanmay; Dambhare, Akash; Raithatha, Deep; and P, Mohith, "Generative Synthesis of Historical Precedents for Automated Procedural Guidance", Technical Disclosure Commons, (January 08, 2026)
https://www.tdcommons.org/dpubs_series/9143