Pith Number

pith:I3L53N3O

pith:2026:I3L53N3OMULTQN7UW3MSSQLWHR

not attested not anchored not stored refs resolved

Multimodal Hidden Markov Models for Persistent Emotional State Tracking

Anamika Ragu, Aneesh Jonelagadda

Sticky HDP-HMMs recover more interpretable persistent emotional regimes from multimodal valence-arousal trajectories than Gaussian HMM baselines.

arxiv:2605.12838 v1 · 2026-05-13 · cs.AI

Open paper page JSON Open Graph Bundle Merged state Verified badge What is a Pith Number?

Add to your LaTeX paper

\usepackage{pith}
\pithnumber{I3L53N3OMULTQN7UW3MSSQLWHR}

Prints a linked badge after your title and injects PDF metadata. Compiles on arXiv. Learn more · Embed verified badge

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

the sticky HDP-HMM produces more interpretable regime sequences than the baseline Gaussian HMM at a fraction of the computational cost of LLM-based dialogue state tracking methods. In addition, Question-Answer experiments in a clinical dataset suggest that meaningful emotional phases can reliably be recovered from multimodal valence-arousal trajectories and used to improve the quality of LLM responses in unstable affective regimes via context augmentation.

C2weakest assumption

That valence-arousal representations extracted from simultaneous video, audio, and text inputs faithfully capture the underlying persistent emotional regimes, and that LLM-as-a-Judge plus geometric/temporal metrics provide a reliable proxy for interpretability and clinical usefulness.

C3one line summary

Sticky factorial HDP-HMMs applied to multimodal valence-arousal trajectories identify interpretable persistent emotional regimes in conversations, outperforming Gaussian HMM baselines in consistency metrics and enabling context-augmented LLM responses.

References

21 extracted · 21 resolved · 0 Pith anchors

[1] IEEE access , volume= 2019

[2] arXiv preprint arXiv:2206.07359 , year=

[3] WIREs Data Mining and Knowledge Discovery , volume = · doi:10.1002/widm.1563

[4] and Bergeman, Cindy S 2006

[5] arXiv preprint arXiv:2503.08857 , year=

Formal links

2 machine-checked theorem links

Receipt and verification

First computed	2026-05-18T03:09:12.002835Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

46d7ddb76e65173837f4b6d92941763c58b9b682324925ff4c13a8824a795778

Aliases

arxiv: 2605.12838 · arxiv_version: 2605.12838v1 · doi: 10.48550/arxiv.2605.12838 · pith_short_12: I3L53N3OMULT · pith_short_16: I3L53N3OMULTQN7U · pith_short_8: I3L53N3O

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/I3L53N3OMULTQN7UW3MSSQLWHR \
  | 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: 46d7ddb76e65173837f4b6d92941763c58b9b682324925ff4c13a8824a795778

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "a85b9755fa54dc588f8291d9f9c1064c2a2a1130194b740e05d92bc27675ec8f",
    "cross_cats_sorted": [],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "cs.AI",
    "submitted_at": "2026-05-13T00:16:05Z",
    "title_canon_sha256": "f49870feddfdbd2fdd9115e778f352c9479f70fbffb64642dbe8629348f14570"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2605.12838",
    "kind": "arxiv",
    "version": 1
  }
}