Pith Number

pith:KFHNVTWH

pith:2026:KFHNVTWH2N35ZKOCFK4MUYMN4X

not attested not anchored not stored refs resolved

A Hormone-inspired Emotion Layer for Transformer language models (HELT)

Eslam Reda, Sara El-Metwally

A hormone-emotion block added to T5 computes six continuous hormone values from specialized attention heads to modulate hidden states for more appropriate emotional responses.

arxiv:2605.13858 v1 · 2026-04-13 · cs.NE · cs.CL · cs.LG

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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

Experimental results on our curated emotion-labeled dataset demonstrate that HormoneT5 achieves 85%+ per-hormone accuracy within a 0.15 tolerance threshold, with hormone differentiation ranges exceeding 0.85 across all six hormones between contrasting emotional tones.

C2weakest assumption

That the six hormone-like values computed by the per-hormone attention heads meaningfully simulate human emotional processing and that modulating encoder hidden states with the resulting embedding produces measurably more appropriate responses.

C3one line summary

HormoneT5 augments T5 with a hormone-inspired block that predicts six continuous emotion values and uses them to modulate responses, reporting over 85% per-hormone accuracy and human preference for emotional quality.

References

82 extracted · 82 resolved · 8 Pith anchors

[1] New approach in quantification of emotional intensity from the speech signal: emotional temperature.Expert Systems with Applications, 42(24):9554– 9564, 2015 2015

[2] A wide evaluation of chatgpt on affective computing tasks.IEEE Transactions on Affective Computing, 15(4):2204–2212, 2024 2024

[3] The molecular basis of love.International Journal of Molecular Sciences, 26(4):1533, 2025 2025

[4] Physiology of emotion 2019

[5] Isabel Barradas, Zartasha Naeem Khan, and Angelika Peer. Emotion recognition from peripheral physiological signals: A systematic review of trends, challenges and opportunities.ACM Transactions on Inte 2026

Formal links

2 machine-checked theorem links

Receipt and verification

First computed	2026-05-17T23:39:19.521616Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

514edacec7d377dca9c22ab8ca618de5dd06cc54261f923d5a9d68a67c739741

Aliases

arxiv: 2605.13858 · arxiv_version: 2605.13858v1 · doi: 10.48550/arxiv.2605.13858 · pith_short_12: KFHNVTWH2N35 · pith_short_16: KFHNVTWH2N35ZKOC · pith_short_8: KFHNVTWH

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/KFHNVTWH2N35ZKOCFK4MUYMN4X \
  | 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: 514edacec7d377dca9c22ab8ca618de5dd06cc54261f923d5a9d68a67c739741

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "6637700b66d15b9b8056a5d42934f9be84a6f157707476cc131098a825c386ea",
    "cross_cats_sorted": [
      "cs.CL",
      "cs.LG"
    ],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "cs.NE",
    "submitted_at": "2026-04-13T11:53:51Z",
    "title_canon_sha256": "288cda10bd2bf4278d931eca1eca3972f8dddffde9eea9ab2baecd03e8b68cb2"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2605.13858",
    "kind": "arxiv",
    "version": 1
  }
}