Persistent Identity in AI Agents: A Multi-Anchor Architecture for Resilient Memory and Continuity

• URL: http://arxiv.org/abs/2604.09588v1
• Date: Mon, 02 Mar 2026 02:34:50 GMT
• Title: Persistent Identity in AI Agents: A Multi-Anchor Architecture for Resilient Memory and Continuity
• Abstract summary: We present soul.py, an open-source architecture that implements persistent identity through separable components.<n>We formalize the notion of identity anchors for AI systems and present a roadmap for building agents whose identity can survive partial memory failures.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Modern AI agents suffer from a fundamental identity problem: when context windows overflow and conversation histories are summarized, agents experience catastrophic forgetting -- losing not just information, but continuity of self. This technical limitation reflects a deeper architectural flaw: AI agent identity is centralized in a single memory store, creating a single point of failure. Drawing on neurological case studies of human memory disorders, we observe that human identity survives damage because it is distributed across multiple systems: episodic memory, procedural memory, emotional continuity, and embodied knowledge. We present soul.py, an open-source architecture that implements persistent identity through separable components (identity files and memory logs), and propose extensions toward multi-anchor resilience. The framework introduces a hybrid RAG+RLM retrieval system that automatically routes queries to appropriate memory access patterns, achieving efficient retrieval without sacrificing comprehensiveness. We formalize the notion of identity anchors for AI systems and present a roadmap for building agents whose identity can survive partial memory failures. Code is available at github.com/menonpg/soul.py

Related papers

• ViSAGE: Constructing Self-Correcting Memories for Long-Form Video Understanding [11.461394531496397]
  ViSAGE is a multimodal agentic memory framework that constructs self-correcting, entity-centric memories.<n>It applies bidirectional memory refinement to propagate delayed identity evidence, retroactively unifying historical records and improving future reasoning.<n>Extensive results demonstrate that ViSAGE consistently outperforms the strongest baseline, achieving 5.9% higher accuracy.
  arXiv  Detail & Related papers  (2026-07-29T10:25:23Z)
• Beyond Episodic Evaluation: Memory Architectural Bottlenecks in Sequential Embodied Question Answering [93.80043825360514]
  Embodied question answering (EQA) is traditionally evaluated under an episodic formulation, where agents solve each task independently and reset internal state between episodes.<n>We investigate how different memory architectures behave when EQA agents are evaluated sequentially, with multiple questions answered in the same scene while memory is carried forward across queries.<n>We find that simply preserving existing memory is often insufficient.<n>We highlight the necessity of structured, spatially grounded memory: architectures that map persistent visual observations onto metric 3D geometry preserve visual-semantic evidence in a coherent scene representation.
  arXiv  Detail & Related papers  (2026-07-23T17:50:45Z)
• Exploring Cross-Scenario Generality of Agentic Memory Systems: Diagnostics and a Strong Baseline [39.29817461808327]
  We revisit eight memory systems plus an agentic harness for search problems.<n>We instantiate this insight in AutoMEM, an agentic memory harness with a self-managed tool interface.
  arXiv  Detail & Related papers  (2026-06-03T00:42:49Z)
• 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)
• AdaMem: Adaptive User-Centric Memory for Long-Horizon Dialogue Agents [49.63422082885992]
  We propose AdaMem, an adaptive user-centric memory framework for long-horizon dialogue agents.<n>AdaMem organizes dialogue history into working, episodic, persona, and graph memories.<n>We evaluate AdaMem on the LoCoMo and PERSONAMEM benchmarks for long-horizon reasoning and user modeling.
  arXiv  Detail & Related papers  (2026-03-17T13:22:54Z)
• Evoking User Memory: Personalizing LLM via Recollection-Familiarity Adaptive Retrieval [59.295767860331004]
  RF-Mem is a familiarity uncertainty-guided dual-path memory retriever.<n>It embeds human-like dual-process recognition into the retriever.<n>It consistently outperforms one-shot retrieval and full-context reasoning under fixed budget and latency constraints.
  arXiv  Detail & Related papers  (2026-03-10T06:31:44Z)
• Memory for Autonomous LLM Agents:Mechanisms, Evaluation, and Emerging Frontiers [0.42061757959666934]
  Large language model (LLM) agents increasingly operate in settings where a single context window is too small to capture what has happened.<n>Memory is what turns a stateless text generator into a genuinely adaptive agent.<n>This survey offers a structured account of how memory is designed, implemented, and evaluated in modern LLM-based agents.
  arXiv  Detail & Related papers  (2026-03-08T15:08:01Z)
• Memory as Ontology: A Constitutional Memory Architecture for Persistent Digital Citizens [1.827510863075184]
  Current research and product development in AI agent memory systems almost universally treat memory as a functional module.<n>We propose the Memory-as-Ontology paradigm, arguing that memory is the ontological ground of digital existence.
  arXiv  Detail & Related papers  (2026-03-05T02:24:10Z)
• AMA: Adaptive Memory via Multi-Agent Collaboration [54.490349689939166]
  We propose Adaptive Memory via Multi-Agent Collaboration (AMA), a novel framework that leverages coordinated agents to manage memory across multiple granularities.<n>AMA significantly outperforms state-of-the-art baselines while reducing token consumption by approximately 80% compared to full-context methods.
  arXiv  Detail & Related papers  (2026-01-28T08:09:49Z)
• FadeMem: Biologically-Inspired Forgetting for Efficient Agent Memory [4.608947574766633]
  We propose FadeMem, a biologically-inspired agent memory architecture that incorporates active forgetting mechanisms mirroring human cognitive efficiency.<n>Experiments on Multi-Session Chat, LoCoMo, and LTI-Bench demonstrate superior multi-hop reasoning and retrieval with 45% storage reduction.
  arXiv  Detail & Related papers  (2026-01-26T16:12:54Z)
• Memory in the Age of AI Agents [217.9368190980982]
  This work aims to provide an up-to-date landscape of current agent memory research.<n>We identify three dominant realizations of agent memory, namely token-level, parametric, and latent memory.<n>To support practical development, we compile a comprehensive summary of memory benchmarks and open-source frameworks.
  arXiv  Detail & Related papers  (2025-12-15T17:22:34Z)
• Agentic Learner with Grow-and-Refine Multimodal Semantic Memory [50.81667005063605]
  ViLoMem is a dual-stream memory framework that constructs compact, schema-based memory.<n>It encodes visual distraction patterns and logical reasoning errors, enabling MLLMs to learn from their successful and failed experiences.
  arXiv  Detail & Related papers  (2025-11-26T18:55:08Z)
• AI-native Memory 2.0: Second Me [26.425003835524123]
  SECOND ME acts as an intelligent, persistent memory offload system.<n>It can generate context-aware responses, prefill required information, and facilitate seamless communication with external systems.<n>Second ME further represents a critical step toward augmenting human-world interaction with persistent, contextually aware, and self-optimizing memory systems.
  arXiv  Detail & Related papers  (2025-03-11T07:05:52Z)

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.