Pith Number

pith:CQGIDWML

pith:2026:CQGIDWMLLCYQAN5KWACLG5YFZL

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DRL-STAF: A Deep Reinforcement Learning Framework for State-Aware Forecasting of Complex Multivariate Hidden Markov Processes

Chen Zhang, Jingru Huang, Manrui Jiang, Yong Chen

DRL-STAF jointly forecasts observations and estimates discrete hidden states in complex multivariate hidden Markov processes by combining deep neural networks with reinforcement learning.

arxiv:2605.14632 v1 · 2026-05-14 · cs.LG · stat.AP

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Claims

C1strongest claim

DRL-STAF outperforms HMM variants, standalone deep learning models, and existing DL-HMM hybrids in most cases, while also providing reliable hidden-state estimates.

C2weakest assumption

Reinforcement learning can effectively estimate discrete hidden states and learn flexible transition dynamics from data without relying on predefined structures, assuming the RL formulation captures the underlying process accurately.

C3one line summary

DRL-STAF uses deep RL to predict observations and estimate discrete hidden states for multivariate hidden Markov processes, outperforming HMMs, deep learning models, and hybrids in experiments.

References

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[1] ACM SIGIR , pages =

[2] NeurIPS , pages =

[4] ICLR , year =

[5] ICLR , year =

[6] IEEE Transactions on Audio, Speech, and Language Processing , author = 2012

Receipt and verification

First computed	2026-05-17T23:39:03.960812Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

140c81d98b58b10037aab004b37705cae363a84c806c6f68c141ae957fbbb686

Aliases

arxiv: 2605.14632 · arxiv_version: 2605.14632v1 · doi: 10.48550/arxiv.2605.14632 · pith_short_12: CQGIDWMLLCYQ · pith_short_16: CQGIDWMLLCYQAN5K · pith_short_8: CQGIDWML

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/CQGIDWMLLCYQAN5KWACLG5YFZL \
  | 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: 140c81d98b58b10037aab004b37705cae363a84c806c6f68c141ae957fbbb686

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "2c20719c1fca4d24a33a9e717ed041ab2b67b702c0a848b4b1c4b0a93d0f5351",
    "cross_cats_sorted": [
      "stat.AP"
    ],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "cs.LG",
    "submitted_at": "2026-05-14T09:44:11Z",
    "title_canon_sha256": "063a9cba2f0771048e5352c4994b9d415a5e094ac5b56ca8108d0fb26480ca65"
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  "source": {
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    "kind": "arxiv",
    "version": 1
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