Pith Number

pith:5LMGCWWV

pith:2026:5LMGCWWVWGXDKZJDW7MBXFPRCY

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An Agentic AI Framework with Large Language Models and Chain-of-Thought for UAV-Assisted Logistics Scheduling with Mobile Edge Computing

Dusit Niyato, Hanwen Zhang, Malcolm Yoke Hean Low, Wei Zhang, Xin Lou

An agentic AI framework with large language models and chain-of-thought reasoning produces consistent mathematical formulations for hybrid UAV logistics and mobile edge computing scheduling, solved via hierarchical proximal policy优化.

arxiv:2605.13221 v1 · 2026-05-13 · cs.AI · cs.LG

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3 Author claim open · sign in to claim

4 Citations 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

The proposed framework yields more consistent formulations, while the hierarchical PPO achieves full product collection in 99.6% of the last 500 episodes and maintains a 100% deadline satisfaction rate, with more stable performance than the advantage actor-critic approach.

C2weakest assumption

The agentic AI component reliably produces correct and complete mathematical formulations from user input without introducing errors or omissions that would invalidate the subsequent optimization.

C3one line summary

An agentic AI framework with LLMs generates formulations for coupled UAV product collection and MEC task scheduling, solved by hierarchical PPO that reaches 99.6% collection success and 100% deadline compliance in simulations.

References

57 extracted · 57 resolved · 1 Pith anchors

[1] Masc: Large language model-based multi-agent scheduling chain for flexible job shop scheduling problem, 2025

[2] Large language model-empowered dynamic scheduling for intelligent hybrid flow shop using multi-agent deep reinforcement learning, 2026

[3] InFindings of the Association for Compu- tational Linguistics: ACL 2025, pages 17398–17429 2025

[4] H. Abgaryan, A. Harutyunyan, and T. Cazenave, “Llms can schedule,” arXiv:2408.06993, 2024 2024

[5] Preprint (2024) 2024

Receipt and verification

First computed	2026-05-18T03:08:48.372446Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

ead8615ad5b1ae356523b7d81b95f11633cc18bba799fa70bd11506d18d73ed9

Aliases

arxiv: 2605.13221 · arxiv_version: 2605.13221v1 · doi: 10.48550/arxiv.2605.13221 · pith_short_12: 5LMGCWWVWGXD · pith_short_16: 5LMGCWWVWGXDKZJD · pith_short_8: 5LMGCWWV

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/5LMGCWWVWGXDKZJDW7MBXFPRCY \
  | 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: ead8615ad5b1ae356523b7d81b95f11633cc18bba799fa70bd11506d18d73ed9

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "deb7a74815457d6ddc02c31e843e376649ba306a03080c75c9bdd430d5341b3d",
    "cross_cats_sorted": [
      "cs.LG"
    ],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "cs.AI",
    "submitted_at": "2026-05-13T09:13:19Z",
    "title_canon_sha256": "33b9774a9a1475110bb20748f9a3bebb805c5225d737e65582d19814a800d2b6"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2605.13221",
    "kind": "arxiv",
    "version": 1
  }
}