Pooling Experience Spends Independence Twice: Shared Memory in LLM Agent Teams Correlates Errors and Opens a Common-Mode Channel, Why Little of the First Was Left to Spend, and the Order, Gate and Readout That May Decide Whether Pooling Pays

Abstract

Teams of language-model agents increasingly read and write a common memory: a message pool, a blackboard, a bank of distilled experience, a knowledge base served to several frameworks at once. The case for pooling is efficiency, since no agent rediscovers what another already found. The case for teams is usually error correction, which rests on agents erring independently. The two cases appear to conflict, because conditioning on the same records is what removes independence. This paper examines the conflict against the published evidence and finds it both weaker and stronger than it looks. It is weaker because, as earlier analyses have documented, there was less independence to spend than the jury arithmetic assumes: models from different providers make the same mistakes on the same items, and much of the reported multi-agent gain comes from voting or from task decomposition rather than from uncorrelated error. It is stronger because a shared memory spends independence through a concentrated channel as well as a diffuse one: a single admitted record reaches every agent that retrieves it, so one error or one poisoned write can become a common-mode failure, and published attacks exploit exactly this. Whether model diversity protects against that channel, as it partly protects against coupling in debate, has not been measured. The trade-off has been written down before, in collective estimation, informational cascades, organizational learning, social learning and multi-agent reinforcement learning; that work agrees that the sign of pooling depends on whether agents commit before reading, whether admission is checked by something outside the team's shared error, whether the readout uses disagreement, and how isolated subgroups are. How those conditions carry over to a store of records retrieved across tasks is untested. No located study measures per-agent accuracy and between-agent error correlation, with and without a shared memory, on the same LLM team. The paper consolidates the evidence, gives a decision procedure and states the tests that would settle the rest. It ran no experiments.

The literature search, drafting and citation verification for this paper were carried out with AI assistance under the author's direction. Every arXiv citation was machine-verified against its live arXiv Atom API record, and every other citation against its Crossref or DataCite record, during drafting; title and author list were checked against the record returned. The full texts (arXiv HTML renderings) of the load-bearing sources were read for the passages and numbers attributed to them, namely Kim, Y. et al. (2025, Towards a Science of Scaling Agent Systems), Kohli (2026, Nine Judges, Two Effective Votes) and Xiong, Z. et al. (2025, How Memory Management Impacts LLM Agents). The remaining sources were read at abstract level, and the claims attached to them are limited to what their abstracts state; where a classic paper carries no abstract in its record, the claim attached to it is limited to its title or to a verified secondary source that describes it, and the text says which. No experiment was run and no number in this paper was measured by its author. Table 1 and Figure 1 re-present published values, each named with its source; the Condorcet reference bars in Figure 1(a) are the source's panel accuracy plus the source's reported Condorcet gap. The two-channel account, the four conditions, Table 2, Algorithm 1 and the proposed tests in Section 11 are conceptual synthesis by the author, not empirical results, and are presented as such.

About the author

Pranay Mahendrakar is an ai specialist and large language model engineer based in Bengaluru, India. He builds production artificial intelligence systems and publishes open-access research on how those systems fail. See all 68 papers by Pranay Mahendrakar, or his ORCID record.