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pith:2026:AOCFJRKNWFOQWHTC3BMWC774CO
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Grounded Continuation: A Linear-Time Runtime Verifier for LLM Conversations

Qisong He, Xiaowei Huang, Yi Dong

A runtime verifier builds a dependency graph from eight formal update operations to keep LLM continuations grounded on active premises.

arxiv:2605.14175 v1 · 2026-05-13 · cs.AI

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2 Internet Archive
3 Author claim open · sign in to claim
4 Citations open
5 Replications open
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Claims

C1strongest claim

On LongMemEval-KU the verifier reaches 89.7% accuracy with linear per-turn cost and a formal conflict-free guarantee; on the 15-item stale-premise subset it reaches 100% accuracy.

C2weakest assumption

The LLM can be prompted to classify each conversation turn into one of the eight update operations drawn from dynamic epistemic logic, abductive reasoning, awareness logic, and argumentation with sufficient faithfulness for the dependency graph to remain accurate.

C3one line summary

A hybrid LLM-symbolic verifier maintains a dependency graph over conversation turns classified into eight formal update operations, enabling linear-time groundedness checks and precise retraction propagation with a conflict-free guarantee.

References

47 extracted · 47 resolved · 3 Pith anchors

[1] International Conference on Learning Representations (ICLR) , year =
[2] Wang, Peiran and Liu, Yang and Lu, Yunfei and Cai, Yifeng and Chen, Hongbo and Yang, Qingyou and Zhang, Jie and Hong, Jue and Wu, Ye , journal =
[3] Logic and the Foundations of Game and Decision Theory (
[4] Moss and Slawomir Solecki , title =
[5] Halpern and Yoram Moses and Moshe Y

Formal links

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Receipt and verification
First computed 2026-05-17T23:39:11.308891Z
Builder pith-number-builder-2026-05-17-v1
Signature Pith Ed25519 (pith-v1-2026-05) · public key
Schema pith-number/v1.0

Canonical hash

038454c54db15d0b1e62d859617ffc13883d789f8be2b9417efe96e2136eaba5

Aliases

arxiv: 2605.14175 · arxiv_version: 2605.14175v1 · doi: 10.48550/arxiv.2605.14175 · pith_short_12: AOCFJRKNWFOQ · pith_short_16: AOCFJRKNWFOQWHTC · pith_short_8: AOCFJRKN
Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/AOCFJRKNWFOQWHTC3BMWC774CO \
  | 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: 038454c54db15d0b1e62d859617ffc13883d789f8be2b9417efe96e2136eaba5
Canonical record JSON
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    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "cs.AI",
    "submitted_at": "2026-05-13T22:54:16Z",
    "title_canon_sha256": "6a10eb7e5fd14f6f704a3df7d0ff85ed96631150f1d75c250dd168f5c26a2220"
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