Pith Number

pith:YOAO5UFJ

pith:2026:YOAO5UFJ3BIWQL3NSP76Z5LPI3

not attested not anchored not stored refs resolved

PULSE: Agentic Investigation with Passive Sensing for Proactive Intervention in Cancer Survivorship

Ariful Islam, Indrajeet Ghosh, Katharine E. Daniel, Laura E. Barnes, Philip Chow, Subigya Nepal, Xinyu Chen, Zhiyuan Wang

Agentic LLM investigation of passive smartphone sensing data raises prediction accuracy for when cancer survivors need mental health support.

arxiv:2605.17679 v1 · 2026-05-17 · cs.HC · cs.AI

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Record completeness

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2 Internet Archive

3 Author claim open · sign in to claim

4 Citations open

5 Replications open

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The bundle contains the canonical record plus signed events. A mirror can host it anywhere and recompute the same current state with the deterministic merge algorithm.

Claims

C1strongest claim

Agentic investigation may be a cornerstone for unlocking the clinical value of passive sensing, advancing the feasibility of proactive just-in-time mental health support.

C2weakest assumption

That LLM agents equipped with the eight purpose-built tools can produce reliable, unbiased inferences from raw sensing streams without hallucination or systematic misinterpretation of behavioral signals, an assumption required for the reported accuracy gains to translate to real clinical value.

C3one line summary

PULSE demonstrates that agentic LLM-based investigation of passive smartphone sensing data achieves balanced accuracies of 0.743 (with diary) and 0.713 (sensing-only) for predicting emotion regulation desire and intervention availability in 50 cancer survivors.

References

86 extracted · 86 resolved · 6 Pith anchors

[1] Bewick, and Mowafa Househ 2023 · doi:10.1038/s41746-023-00979-5

[2] A user’s guide to the encyclopedia of dna elements (encode).PLoS Biology, 9(4):e1001046 2022 · doi:10.1371/journal

[3] Adler, Yuewen Yang, Thalia Viranda, Xuhai Xu, David C 2024 · doi:10.1145/3699755

[4] Retrieval augmented generation for large language models in healthcare: A systematic review.PLOS Digital Health, 4(6):e0000877 2025 · doi:10.1371/journal.pdig.0000877

[5] Tuo An et al. 2025. IoT-LLM: Enhancing Real-World IoT Task Reasoning with Large Language Models.Patterns6, 1 (2025), 101131 2025

Formal links

2 machine-checked theorem links

Receipt and verification

First computed	2026-05-20T00:04:52.384578Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

c380eed0a9d851682f6d93ffecf56f46c3c113d78ae5d32c07493bba25973d49

Aliases

arxiv: 2605.17679 · arxiv_version: 2605.17679v1 · doi: 10.48550/arxiv.2605.17679 · pith_short_12: YOAO5UFJ3BIW · pith_short_16: YOAO5UFJ3BIWQL3N · pith_short_8: YOAO5UFJ

Agent API

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/YOAO5UFJ3BIWQL3NSP76Z5LPI3 \
  | 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: c380eed0a9d851682f6d93ffecf56f46c3c113d78ae5d32c07493bba25973d49

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "df327fe827d1a262092ba3f93d84fd4966fc38ce5c3cda4a3464ef5616173940",
    "cross_cats_sorted": [
      "cs.AI"
    ],
    "license": "http://creativecommons.org/licenses/by-sa/4.0/",
    "primary_cat": "cs.HC",
    "submitted_at": "2026-05-17T22:39:21Z",
    "title_canon_sha256": "6644e796bed6dff0492ad2644c1ff18d8357c7ddf250704048133b3275f10882"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2605.17679",
    "kind": "arxiv",
    "version": 1
  }
}