What Happens Inside Agent Memory? Circuit Analysis from Emergence to Diagnosis
On the map: LLM agents and tool use → Agent memory systems
Abstract (arXiv)
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. We trace feature circuits across the Qwen-3 family (0.6B--14B), two memory frameworks (mem0, A-MEM), and a matched Gemma-3 replication, reporting two mechanistic findings plus one deliverable. First, under mem0, control is detectable before content. Routing circuitry is causally active at 0.6B, while content circuitry produces no detectable signal until 4B. Gemma-3 reproduces the same order at 1B and 4B. Small models therefore 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, on which memory framing recruits a memory-specific functional direction. The hub and the early routing circuit carry over to A-MEM, indicating that these computations belong to the base model rather than to a particular interface. Building on this circuit structure, we develop an unsupervised stage-level diagnostic that localizes silent failures to the responsible operation at 76.2% accuracy. It outperforms a strong LLM trace judge by 3.1 points as well as a supervised hidden-state probe by 8.8 points. Together, these results establish circuit-level signatures as a practical handle for monitoring agent memory and guiding its structural design.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Placed in Agent memory systems, one of 16 directions in LLM agents and tool use. Builds on 45 of its 61 references that are in the map. First posted on arXiv 5 May 2026.
Builds on
45 of 61 references.
Closest ancestors · 15
Same direction, newest first
- 2026Memory for Autonomous LLM Agents:Mechanisms, Evaluation, and Emerging Frontiers8 Mar
- 2026AMA-Bench: Evaluating Long-Horizon Memory for Agentic Applications26 Feb
- 2026MemoryArena: Benchmarking Agent Memory in Interdependent Multi-Session Agentic Tasks18 Feb
- 2026ActMem: Bridging the Gap Between Memory Retrieval and Reasoning in LLM Agents4 Feb
- 2026AtomMem : Learnable Dynamic Agentic Memory with Atomic Memory Operation13 Jan
- 2026MAGMA: A Multi-Graph based Agentic Memory Architecture for AI Agents6 Jan
- 2026Agentic Memory: Learning Unified Long-Term and Short-Term Memory Management for Large Language Model Agents5 Jan
- 2025Memory in the Age of AI Agents15 Dec
- 2025Memory-R1: Enhancing Large Language Model Agents to Manage and Utilize Memories via Reinforcement Learning27 Aug
- 2025Evaluating Memory in LLM Agents via Incremental Multi-Turn Interactions7 Jul
- 2025How Memory Management Impacts LLM Agents: An Empirical Study of Experience-Following Behavior21 May
- 2025Mem0: Building Production-Ready AI Agents with Scalable Long-Term Memory28 Apr
3 more
Other references · 30
- 2025Qwen3 Technical Reportcited by 8,195
- 2023Generative Agents: Interactive Simulacra of Human Behaviorcited by 3,500
- 2022Locating and Editing Factual Associations in GPTcited by 2,609
- 2023MemGPT: Towards LLMs as Operating Systemscited by 1,295
- 2022Interpretability in the Wild: a Circuit for Indirect Object Identification in GPT-2 smallcited by 1,130
- 2022Mass-Editing Memory in a Transformercited by 976
- 2022In-context Learning and Induction Headscited by 930
- 2023Progress measures for grokking via mechanistic interpretabilitycited by 822
- 2023Towards Automated Circuit Discovery for Mechanistic Interpretabilitycited by 700
- 2024LongMemEval: Benchmarking Chat Assistants on Long-Term Interactive Memorycited by 656
- 2023The Internal State of an LLM Knows When It's Lyingcited by 644
- 2025Why Do Multi-Agent LLM Systems Fail?cited by 559
18 more
- 2023Dissecting Recall of Factual Associations in Auto-Regressive Language Modelscited by 512
- 2024Sparse Feature Circuits: Discovering and Editing Interpretable Causal Graphs in Language Modelscited by 412
- 2021Explaining Neural Scaling Lawscited by 353
- 2024Improving Alignment and Robustness with Circuit Breakerscited by 302
- 2024Semantic Entropy Probes: Robust and Cheap Hallucination Detection in LLMscited by 246
- 2024Retrieval Head Mechanistically Explains Long-Context Factualitycited by 219
- 2024Transcoders Find Interpretable LLM Feature Circuitscited by 186
- 2025Which Agent Causes Task Failures and When? On Automated Failure Attribution of LLM Multi-Agent Systemscited by 170
- 2023Circuit Component Reuse Across Tasks in Transformer Language Modelscited by 105
- 2025AgenTracer: Who Is Inducing Failure in the LLM Agentic Systems?cited by 105
- 2024ReDeEP: Detecting Hallucination in Retrieval-Augmented Generation via Mechanistic Interpretabilitycited by 100
- 2024LLM Circuit Analyses Are Consistent Across Training and Scalecited by 60
- 2026General Agent Evaluationcited by 13
- 2024Circuit Compositions: Exploring Modular Structures in Transformer-Based Language Modelscited by 9
- 2025Toward Faithful Retrieval-Augmented Generation with Sparse Autoencoderscited by 6
- 2025Scaling sparse feature circuit finding for in-context learningcited by 5
- 2026Latent Planning Emerges with Scalecited by 5
- 2026ContextFocus: Activation Steering for Contextual Faithfulness in Large Language Modelscited by 3
Built on by
Citing papers per year: 2026: 2.
2 papers.
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- 2026MemAgent: Learning to Manage Heterogeneous Memory Providers for LLM Agents
- 2026Agent Memory: Characterization and System Implications of Stateful Long-Horizon Workloads
- 2026Stop When Memory Suffices: Evidence-Conditioned Progressive Execution for LLM Agents
- 2026Remembering More, Risking More: Longitudinal Safety Risks in Memory-Equipped LLM Agents
- 2026Memory as a Controlled Process: Learned Adaptive Memory Management for LLM Agents
- 2026Agent Memory Distillation: Empowering Small LLM Agents with Hierarchical Teacher Memory