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

TL;DR

This paper introduces a linear-time runtime verifier for LLM conversations that maintains an explicit dependency graph to ensure logical consistency and detect unsupported continuations, improving accuracy and reliability.

Contribution

It presents a novel, efficient verifier that uses formal logic and dependency tracking to verify LLM outputs in real-time, addressing a key gap in conversation reliability.

Findings

Verifier achieves 89.7% accuracy on LongMemEval-KU, outperforming baselines.

Verifier maintains linear per-turn cost, enabling scalable real-time verification.

Constructed multi-agent and grounding scenarios demonstrate verifier effectiveness in diverse settings.

Abstract

In long conversations, an LLM can produce a next utterance that sounds plausible but rests on premises the conversation has already abandoned. Context-manipulation attacks against deployed agents now actively exploit this gap. We close it with a runtime verifier that maintains an explicit dependency graph: an LLM classifies each turn into one of 8 update operations drawn from four formalisms (dynamic epistemic logic, abductive reasoning, awareness logic, argumentation), and a symbolic engine records which claims depend on which evidence. Checking whether a continuation is supported reduces to a graph walk; retraction propagates through the same graph to flag exactly the conclusions that lose support, with linear per-turn cost and a formal conflict-free guarantee. On LongMemEval-KU oracle (n=78), the verifier reaches 89.7% accuracy vs. 88.5% for the LLM-only baseline (+1.3pp) and 87.2% for a transcript-RAG baseline matched on retrieval budget (+2.6pp); wins among disagreements are correct abstentions where the baseline confabulates. On LoCoMo's 60 official QA items the verifier is competitive with retrieval-augmented baselines. Beyond external benchmarks, we construct two multi-agent scenarios and a 50-item grounding test: on the 15-item stale-premise subset, the verifier reaches 100% accuracy vs. 93.3% (+6.7pp). These instantiate a soundness-faithfulness decomposition: the structural check is sound by construction, and per-deployment LLM extraction faithfulness is the empirical question we measure across four LLM families. The retraction check plateaus at microseconds while history-replay grows linearly with conversation length.