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.
arXiv cs.AI files mainly under ai research. We currently carry 1,128 of its stories.
Story provenance
Source · retrieval · rights · ranking — open for full record
inspect →
Story provenance
Attribution is not the same as permission. This drawer separates discovery metadata, excerpts, WeSearch-generated summaries, reuse status, and whether the publisher receives the visit. Nothing here claims a legal grant the publisher has not made.
Record
| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.15505 |
| Publication time | Mon, 18 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-18T04:04:54.418Z |
| Last seen | 2026-05-18T04:04:54.418Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | 7lrmj-2UeYe- |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
Rights status (four layers)
WeSearch handling by dimension
| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
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].
…
Excerpt limited to ~120 words for fair-use compliance. The full article is at arXiv cs.AI.
Discussion
0 commentsMore from arXiv cs.AI
