Pith Number

pith:2FYQSPJC

pith:2026:2FYQSPJCZXFBIDPZMAD22XNK5S

not attested not anchored not stored refs resolved

Text Knows What, Tables Know When: Clinical Timeline Reconstruction via Retrieval-Augmented Multimodal Alignment

Jeremy C. Weiss, Juyong Kim, Sayantan Kumar, Shahriar Noroozizadeh

Retrieving structured EHR rows to calibrate text-derived clinical timelines improves absolute timestamp accuracy without losing event coverage.

arxiv:2605.15168 v1 · 2026-05-14 · cs.CL · cs.AI · cs.LG · stat.ML

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\usepackage{pith}
\pithnumber{2FYQSPJCZXFBIDPZMAD22XNK5S}

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Record completeness

1 Bitcoin timestamp

2 Internet Archive

3 Author claim open · sign in to claim

4 Citations open

5 Replications open

✓ Portable graph bundle live · download bundle · merged state

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

our multimodal pipeline consistently improves absolute timestamp accuracy (AULTC) and improves temporal concordance across nearly all evaluated models over unimodal text-only reconstruction, without compromising event match rates

C2weakest assumption

Retrieved structured EHR rows provide unbiased and accurate external temporal evidence that correctly calibrates non-central events placed relative to text-derived anchors without introducing selection or alignment errors.

C3one line summary

A graph-based retrieval-augmented alignment method improves absolute timestamp accuracy and temporal concordance of text-derived clinical timelines by incorporating structured EHR evidence.

References

42 extracted · 42 resolved · 0 Pith anchors

[1] First line must be the header: ”event”

[2] Each subsequent line contains one central event

[4] Prompt to compute time difference between pairs of central events Pairwise temporal relations among central events Task: Compute time distances between pairs of central events

[5] Output must be in BSV (Bar-Separated Values) format 21 Clinical Timeline Reconstruction via Retrieval-Augmented Multimodal Alignment

[6] First line must be the header: ”event1|event2|e 2 −e 1 |confidence”

Formal links

2 machine-checked theorem links

Receipt and verification

First computed	2026-05-17T21:40:25.312470Z
Last reissued	2026-05-17T21:57:18.645454Z
Builder	pith-number-builder-2026-05-17-v1
Signature	unsigned_v0
Schema	pith-number/v1.0

Canonical hash

d171093d22cdca140df96007ad5daaec9e9213fdd7ea82bdef0f01174d13fb95

Aliases

arxiv: 2605.15168 · arxiv_version: 2605.15168v1 · pith_short_12: 2FYQSPJCZXFB · pith_short_16: 2FYQSPJCZXFBIDPZ · pith_short_8: 2FYQSPJC

Agent API

Resolver JSON Graph JSON Events JSON Schema Signing key

Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/2FYQSPJCZXFBIDPZMAD22XNK5S \
  | 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: d171093d22cdca140df96007ad5daaec9e9213fdd7ea82bdef0f01174d13fb95

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "ff66b6188b8cdca9d9762fbdefa4327662fbd995ec96d87c73766478b8a57bd9",
    "cross_cats_sorted": [
      "cs.AI",
      "cs.LG",
      "stat.ML"
    ],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "cs.CL",
    "submitted_at": "2026-05-14T17:55:27Z",
    "title_canon_sha256": "c3ac18acfd54a3185e780065543a4c3ccaded980d92a1f459c4cf20c8508a12b"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2605.15168",
    "kind": "arxiv",
    "version": 1
  }
}