Pith Number

pith:3YFV42NP

pith:2026:3YFV42NP7N4Y7BXHMNLLI5UTEQ

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PersonalAI 2.0: Enhancing knowledge graph traversal/retrieval with planning mechanism for Personalized LLM Agents

Alexander Kharitonov, Alina Bogdanova, Artyom Sosedka, Ekaterina Lisitsyna, Evgeny Burnaev, Ilia Perepechkin, Matvey Iskornev, Mikhail Belkin, Mikhail Menschikov, Ruslan Kostoev, Victoria Dochkina

PAI-2 improves LLM factual accuracy through adaptive graph traversal and planning on knowledge graphs.

arxiv:2605.13481 v1 · 2026-05-13 · cs.CL

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Claims

C1strongest claim

PAI-2 achieves 4% average gain by LLM-as-a-Judge across four benchmarks, reflecting its effectiveness in reducing hallucination rates and increasing precision. Use of graph traversal algorithms gain superior results compared to standard flatten retriever on average 6%, while enabled search plan enhancement mechanism gain 18% boost compared to disabled one by LLM-as-a-Judge across six datasets.

C2weakest assumption

That the reported benchmark gains from the planning and traversal mechanisms will generalize beyond the six evaluated datasets and the specific LLMs tested, and that LLM-as-a-Judge reliably measures factual correctness without introducing its own biases.

C3one line summary

PAI-2 improves factual correctness in LLM answers by 4% on average across benchmarks using adaptive graph traversal and planning, with 6% gains from traversal algorithms and 18% from enabled planning.

References

40 extracted · 40 resolved · 3 Pith anchors

[1] Qwen3 technical report, 2025 2025

[2] DeepSeek-AI, Aixin Liu, Bei Feng, Bing Xue, Bingxuan Wang, Bochao Wu, Chengda Lu, Chenggang Zhao, Chengqi Deng, Chenyu Zhang, Chong Ruan, Damai Dai, Daya Guo, Dejian Y ang, Deli Chen, Dongjie Ji, Erha 2025

[3] Glm-4.5: Agentic, reasoning, and coding (arc) foundation models, 2025 2025

[4] Wikontic: Constructing Wikidata-aligned, ontology-aware knowledge graphs with large language models 2026

[5] Autoschemakg: Autonomous knowledge graph construction through dynamic schema induction from web-scale corpora, 2025 2025

Receipt and verification

First computed	2026-05-18T02:44:41.365725Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

de0b5e69affb798f86e76356b47693242d8d2179a4264b86779cfc3d165a3ed0

Aliases

arxiv: 2605.13481 · arxiv_version: 2605.13481v1 · doi: 10.48550/arxiv.2605.13481 · pith_short_12: 3YFV42NP7N4Y · pith_short_16: 3YFV42NP7N4Y7BXH · pith_short_8: 3YFV42NP

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/3YFV42NP7N4Y7BXHMNLLI5UTEQ \
  | 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: de0b5e69affb798f86e76356b47693242d8d2179a4264b86779cfc3d165a3ed0

Canonical record JSON

{
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    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "cs.CL",
    "submitted_at": "2026-05-13T13:06:30Z",
    "title_canon_sha256": "8a7031f0b151a167714ccc193e4b0b63686ff2f904da2d0c8d87cd9e69f02b32"
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