What Happens Inside Agent Memory? Circuit Analysis from Emergence to Diagnosis

TL;DR

This paper investigates the internal circuit mechanisms of agent memory in large language models, revealing how control and content signals emerge and how silent failures can be diagnosed through circuit signatures.

Contribution

It uncovers the circuit-level dynamics of memory in LLMs and introduces an unsupervised diagnostic method for localizing memory failures.

Findings

Control signals are detectable before content signals in small models.

Shared hubs are recruited from existing model structures, not created anew.

An unsupervised diagnostic achieves 76.2% accuracy in localizing failures.

Abstract

Agent memory failures are silent: an LLM-based agent can produce a fluent response even when it fails to extract, retain, or retrieve the information needed across sessions. The write-manage-read loop describes the external pipeline of these systems but leaves open which internal computations implement each stage. Tracing feature circuits across the Qwen-3 family (0.6B--14B) and two memory frameworks (mem0 and A-MEM), we report two mechanistic findings and one deliverable. First, control is detectable before content: routing circuitry is causally active at 0.6B, while content circuitry produces no detectable signal until 4B, exposing a deployment regime where small models route memory decisions before they can reliably extract or ground the underlying facts. Second, the shared hub is recruited, not created: Write and Read converge on a late-layer hub that already exists in the base model as a context-grounding substrate, and memory framing recruits a memory-specific functional direction on this substrate rather than building one of its own. Both findings transfer across mem0 and A-MEM, indicating that the underlying computations are properties of the base model rather than of any particular interface. Building on this circuit structure, we develop an unsupervised stage-level diagnostic that localizes silent failures to the responsible operation up to 76.2% accuracy, outperforming the strongest supervised baseline by 13 points. Together, these results point to circuit-level signatures as a practical handle for monitoring and structurally-guided design of agent memory.