Pith Number

pith:V5OFYKXZ

pith:2026:V5OFYKXZJMGD6WZHVSHQO6AI3D

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X-SYNTH: Beyond Retrieval -- Enterprise Context Synthesis from Observed Human Attention

George Nychis, Guruprasad Raghavan, Rohan Narayana Murthy

Enterprise context synthesis succeeds by deriving relevance from human attention traces instead of retrieving stored system state.

arxiv:2605.15505 v1 · 2026-05-15 · cs.AI · cs.IR · cs.LG

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Claims

C1strongest claim

Enterprise context synthesis is not a retrieval problem. It is a relevance problem, and human attention is its most reliable ground truth. On a sales lead identification task, a frontier model unaided achieves 9.5% True Lead Rate (TLR) with 90.5% False Lead Rate (FLR). Augmented with X-SYNTH, TLR rises to 61.9% (6.5x) while FLR falls to 18.8%.

C2weakest assumption

Behavioral traces preceding positive outcomes are distinguishable from those that did not, without external labeling, allowing implicit reward signals in the data to identify causally relevant activity signatures.

C3one line summary

X-SYNTH synthesizes enterprise context from human behavioral attention traces modeled as Digital Twin Signatures using seven per-individual attention filters, raising true lead rate from 9.5% to 61.9% on a sales identification task.

References

76 extracted · 76 resolved · 2 Pith anchors

[1] Eugene Agichtein, Eric Brill, and Susan Dumais. 2006. Improving Web Search Ranking by Incorporating User Behavior Information. InACM SIGIR Conference on Research and Development in Information Retriev 2006

[2] Anonymous. 2025. ENTROPHY: Multi-Modal User Interaction Data from Live Enterprise Business Workflows. InAdvances in Neural Information Processing Systems (NeurIPS). Workfabric AI / Soroco 2025

[3] Akari Asai, Zeqiu Wu, Yizhong Wang, Avirup Sil, and Hannaneh Hajishirzi. 2024. Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection. InInternational Conference on Learning Re 2024

[4] Jakob E. Bardram. 2005. Activity-Based Computing: Support for Mobility and Collaboration in Ubiquitous Computing.Personal and Ubiquitous Computing9, 5 (2005), 312–322 2005

[5] Paul N. Bennett, Ryen W. White, Wei Chu, Susan T. Dumais, Peter Bailey, Fedor Borisyuk, and Xiaoyuan Cui. 2012. Modeling the Impact of Short- and Long-Term Behavior on Search Personalization. InACM SI 2012

Receipt and verification

First computed	2026-05-20T00:01:02.118129Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

af5c5c2af94b0c3f5b27ac8f077808d8f88b0be5a47c76194ed8302a04a767c5

Aliases

arxiv: 2605.15505 · arxiv_version: 2605.15505v1 · doi: 10.48550/arxiv.2605.15505 · pith_short_12: V5OFYKXZJMGD · pith_short_16: V5OFYKXZJMGD6WZH · pith_short_8: V5OFYKXZ

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/V5OFYKXZJMGD6WZHVSHQO6AI3D \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: af5c5c2af94b0c3f5b27ac8f077808d8f88b0be5a47c76194ed8302a04a767c5

Canonical record JSON

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      "cs.IR",
      "cs.LG"
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    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "cs.AI",
    "submitted_at": "2026-05-15T00:54:02Z",
    "title_canon_sha256": "edc251aaf8313bdf4387953902b9dd1c6d9de3121a6eb726d8e27d509ba5a63c"
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