Portable Agent Memory: A Protocol for Cryptographically-Verified Memory Transfer Across Heterogeneous AI Agents

• URL: http://arxiv.org/abs/2605.11032v1
• Date: Sun, 10 May 2026 23:24:06 GMT
• Title: Portable Agent Memory: A Protocol for Cryptographically-Verified Memory Transfer Across Heterogeneous AI Agents
• Abstract summary: We present Portable Agent Memory, an open protocol and reference implementation for transferring persistent memory state across AI agents.<n>We provide a Python SDK with 54 passing tests, and demonstrate cross-model memory transfer between GPT-4, Claude, Gemini, Llama architectures.<n>The protocol is open-source under Apache 2.0.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: We present Portable Agent Memory, an open protocol and reference implementation for transferring persistent memory state across heterogeneous AI agents. Modern AI agents accumulate rich context -- episodic events,semantic knowledge, procedural skills, working state, and identity preferences -- but this context remains locked within vendor-specific runtimes. Portable Agent Memory addresses this through: (1) a five-component structured memory model with content-addressable entries linked by a Merkle-DAG provenance graph providing tamper-evidence; (2) capability-based access control enabling selective, scoped disclosure of memory segments; (3) an injection-resistant rehydration protocol that adapts recalled content to heterogeneous target models while mitigating indirect prompt injection; and (4) a JSON-first serialization format with optional CBOR compaction for efficient transport. We provide a Python SDK with 54 passing tests, agent skills for multiple platforms, and demonstrate cross-model memory transfer between GPT-4, Claude, Gemini, and Llama architectures. The protocol is open-source under Apache 2.0.

Related papers

• InjecMEM: Memory Injection Attack on LLM Agent Memory Systems [31.104106193922956]
  InjecMEM is a novel memory injection attack paradigm.<n>We craft the injection with a retriever-agnostic anchor and an adversarial command.<n>We evaluate it across multiple memory systems and backbone models.
  arXiv  Detail & Related papers  (2026-08-24T16:37:50Z)
• PhyAgentOS: A Self-Evolving Operating System for Embodied Agents with Decoupled Cognitive Planning and Physical Execution [49.776611937968]
  We present PhyAgentOS, a foundation delivering scheduling, verification, memory, benchmarking, and safety as system-level services.<n>Its Session-Centered treats a session, not an action, as the minimum unit of scheduling, compatibility, supervised execution, evidence collection, and acceptance.<n>SessionVerifier distinguishes execution termination from semantic task completion via evidence-grounded verdicts of success, failure, or replan.<n> Benchmarking reuses the deployment session and verification path, so results trace to real execution.
  arXiv  Detail & Related papers  (2026-07-18T04:46:53Z)
• When Claws Remember but Do Not Tell: Stealthy Memory Injection in Persistent Personal Agents [55.87577638514179]
  We study the threat as stealth memory injection, in which a remote black-box delivers a single email payload that must induce the agent to poisoned memory.<n>We propose MemGhost, a one-shot payload generation framework.<n>Across 56 held-out test cases, MemGhost achieves 87.5% end-to-end success on OpenClaw with GPT-5.4 and 71.4% on Claude Code SDK with Sonnet 4.6.
  arXiv  Detail & Related papers  (2026-07-06T15:08:58Z)
• PLACEMEM: Toward a Compute-Aware Memory Plane for Lifelong Agents [0.0]
  We present PLACEMEM as a systems position on lifelong-agent memory, instantiated by an executable control-plane prototype.<n>The central claim is that agent memory should be represented as versioned capsules that unify semantics, provenance, validity, and reusable runtime state under one correction-aware.<n>The result is both an executable artifact that demonstrates correction-aware control-plane behavior today and a concrete roadmap for replay-aware serving integration in future lifelong-agent systems.
  arXiv  Detail & Related papers  (2026-07-05T02:55:10Z)
• A Technical Taxonomy of LLM Agent Communication Protocols [60.76747983053368]
  This study develops a technical taxonomy to classify and analyze large language models (LLMs) agent communication protocols.<n>The framework guides protocol selection and highlights open research gaps such as privacy and policy enforcement.
  arXiv  Detail & Related papers  (2026-06-17T14:45:20Z)
• MemVenom: Triggered Poisoning of Multimodal Memories in Web Agents [74.64265314956441]
  We propose a unified black-box attack framework that poisons graph-structured external memory with coordinated text-image evidence.<n>MemVenom achieves strong end-to-end attack success with minimal impact on benign performance, reaching up to 99.15% on GPT-5-family web agents.
  arXiv  Detail & Related papers  (2026-06-09T11:53:25Z)
• LongMemEval-V2: Evaluating Long-Term Agent Memory Toward Experienced Colleagues [80.29362825271768]
  LongMemEval-V2 is a benchmark for evaluating whether memory systems can help agents acquire the experience needed to become knowledgeable colleagues in customized environments.<n>LME-V2 contains 451 manually curated questions covering five core memory abilities for web agents.<n>We propose a suite of two memory methods: AgentRunbook-R, an efficient RAG-based memory with knowledge pools for raw state observations, events, and strategy notes, and AgentRunbook-C, which stores trajectories as files and invokes a coding agent to gather evidence in an augmented sandbox.
  arXiv  Detail & Related papers  (2026-05-12T17:59:34Z)
• Bridging the Cognitive Gap: A Unified Memory Paradigm for 6G Agentic AI-RAN [62.429669462281645]
  6G must transcend traditional automation to embrace agentic AI capable of perception, reasoning, and evolution.<n>This article envisions a shift from interface-bound to memory-centric architectures.<n>By replacing message passing with zero-copy observability, we empower AI agents to bridge the gap between real-time responsiveness and long-horizon context.
  arXiv  Detail & Related papers  (2026-05-11T06:04:10Z)
• When Agents Handle Secrets: A Survey of Confidential Computing for Agentic AI [6.1511441479993225]
  Agentic AI systems that plan, invoke tools, maintain persistent memory, and delegate tasks to peer agents introduce a threat surface that differs materially from standalone model inference.<n> Confidential computing offers a hardware-rooted alternative: Trusted Execution Environments (TEEs) isolate agent code and data from privileged system software.<n>This survey synthesizes the design space in four parts: (i) a unified taxonomy of six TEE platforms covering deployment roles and performance tradeoffs; (ii) an agent-centric threat model spanning perception, planning, memory, action, and coordination layers mapped to nine security goals; (iii) a comparative survey of CC
  arXiv  Detail & Related papers  (2026-05-04T23:09:16Z)
• Memory-Augmented LLM-based Multi-Agent System for Automated Feature Generation on Tabular Data [52.80567461420316]
  We propose a Memory-Augmented LLM-based Multi-Agent System (textbfMALMAS) for automated feature generation.<n>MALMAS decomposes the generation process into agents with distinct responsibilities, and a Router Agent activates an appropriate subset of agents per iteration.<n>We further integrate a memory module comprising procedural memory, feedback memory, and conceptual memory, enabling iterative refinement that adaptively guides subsequent feature generation.
  arXiv  Detail & Related papers  (2026-04-22T07:09:30Z)
• MemEvoBench: Benchmarking Memory MisEvolution in LLM Agents [78.95081012334116]
  Large Language Models (LLMs) with persistent memory enhances interaction continuity and personalization but introduces new safety risks.<n> contaminated or biased memory accumulation can trigger abnormal agent behaviors.<n>MemeEvoBench is the first benchmark evaluating memory safety in LLM agents against adversarial memory injection, noisy tool outputs, and biased feedback.
  arXiv  Detail & Related papers  (2026-04-17T07:29:52Z)
• Autogenesis: A Self-Evolving Agent Protocol [60.15939127351914]
  We introduce Autogenesis Protocol (AGP), a self evolution protocol that decouples what evolves from how evolution occurs.<n>We present Autogenesis System (AGS), a self-evolving multi-agent system that dynamically instantiates, retrieves, and refines protocol-registered resources during execution.
  arXiv  Detail & Related papers  (2026-04-16T14:04:06Z)
• MPAC: A Multi-Principal Agent Coordination Protocol for Interoperable Multi-Agent Collaboration [2.643905034529239]
  MPAC is an application-layer protocol that fills this gap with explicit coordination semantics across five layers.<n> specification defines 21 message types, three state machines with normative transition tables, Lamport-clock causal watermarking, two execution models, three security profiles, and optimistic control on shared state.<n>A controlled three-agent code review benchmark shows a 95 percent reduction in coordination overhead and a 4.8 times wall-clock speedup.
  arXiv  Detail & Related papers  (2026-04-10T01:12:05Z)
• AgentSys: Secure and Dynamic LLM Agents Through Explicit Hierarchical Memory Management [47.49917373646469]
  Existing defenses treat bloated memory as given and focus on remaining resilient.<n>We present AgentSys, a framework that defends against indirect prompt injection through explicit memory management.
  arXiv  Detail & Related papers  (2026-02-07T06:28:51Z)
• PlugMem: A Task-Agnostic Plugin Memory Module for LLM Agents [74.4491017652226]
  We propose PlugMem, a task-agnostic memory module that can be attached to arbitrary LLM agents without task-specific redesign.<n>Motivated by the fact that decision-relevant information is concentrated as abstract knowledge rather than raw experience, we draw on cognitive science to structure episodic memories into a compact, knowledge-centric memory graph.<n>We evaluate PlugMem unchanged across three benchmarks (long-horizon conversational question answering, multi-hop knowledge retrieval, and web agent tasks)
  arXiv  Detail & Related papers  (2026-02-06T18:55:38Z)
• PBFT-Backed Semantic Voting for Multi-Agent Memory Pruning [0.0]
  This paper introduces the Co-Forgetting Protocol to enable synchronized memory pruning in multi-agent systems (MAS)<n>The protocol integrates three key components: (1) context-aware voting, where agents utilize a lightweight DistilBERT model to assess the relevance of memory items based on their content and the current operational context; (2) multi-scale temporal decay functions, which assign diminishing importance to memories based on their age and access frequency across different time horizons; and (3) a Practical Byzantine Fault Tolerance (PBFT)-based consensus mechanism, ensuring that decisions to retain or discard memory items are agreed upon by a qualified and fault-tolerant majority of agents
  arXiv  Detail & Related papers  (2025-06-19T08:28:29Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.