Verification Allocation in Stylized LLM Agent Pipelines under Hidden Tool Degradation: A Discrete-Time Mean-Field Analysis

We study a stylized verification-allocation model for role-structured pipelines with binary inherited corruption and hidden tool degradation. Standard contraction arguments give serial and multi-role fixed points and a finite-population deviation bound under independence. When verification actions affect neither future errors nor observations, the belief-state choice is myopic. We characterize a two-action threshold under an explicit monotonicity condition and a restricted four-action interval and tail policy when generation rates are equal and only one check degrades. Synthetic experiments check these model-specific results and find counterexamples outside the restriction. This is conditional model analysis, not a general mean-field-game result or empirical evidence about deployed LLM systems.

Authors

Publication Details

Journal
arXiv (Cornell University)
Published
2026-11-02
DOI
https://doi.org/10.48550/arxiv.2611.03192
Primary Topic
Game Theory and Applications
Type
preprint
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
preprint

Verification Allocation in Stylized LLM Agent Pipelines under Hidden Tool Degradation: A Discrete-Time Mean-Field Analysis

Chandramani Singh, Shereen Anand
arXiv (Cornell University)
Game Theory and Applications
preprint

Verification Allocation in Stylized LLM Agent Pipelines under Hidden Tool Degradation: A Discrete-Time Mean-Field Analysis

Chandramani Singh, Shereen Anand
preprint en

Abstract

We study a stylized verification-allocation model for role-structured pipelines with binary inherited corruption and hidden tool degradation. Standard contraction arguments give serial and multi-role fixed points and a finite-population deviation bound under independence. When verification actions affect neither future errors nor observations, the belief-state choice is myopic. We characterize a two-action threshold under an explicit monotonicity condition and a restricted four-action interval and tail policy when generation rates are equal and only one check degrades. Synthetic experiments check these model-specific results and find counterexamples outside the restriction. This is conditional model analysis, not a general mean-field-game result or empirical evidence about deployed LLM systems.

arXiv (Cornell University)
Game Theory and Applications
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Laika access coming soon