Springdrift: An Auditable Persistent Runtime for LLM Agents with Case-Based Memory, Normative Safety, and Ambient Self-Perception

• URL: http://arxiv.org/abs/2604.04660v1
• Date: Mon, 06 Apr 2026 13:14:37 GMT
• Title: Springdrift: An Auditable Persistent Runtime for LLM Agents with Case-Based Memory, Normative Safety, and Ambient Self-Perception
• Abstract summary: We present Springdrift, a persistent runtime for long-lived LLM agents.<n>We introduce the term Artificial Retainer for this category.<n>This is a technical report on a systems design and deployment case study, not a benchmark-driven evaluation.
• Score: 0.20305676256390928
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present Springdrift, a persistent runtime for long-lived LLM agents. The system integrates an auditable execution substrate (append-only memory, supervised processes, git-backed recovery), a case-based reasoning memory layer with hybrid retrieval (evaluated against a dense cosine baseline), a deterministic normative calculus for safety gating with auditable axiom trails, and continuous ambient self-perception via a structured self-state representation (the sensorium) injected each cycle without tool calls. These properties support behaviours difficult to achieve in session-bounded systems: cross-session task continuity, cross-channel context maintenance, end-to-end forensic reconstruction of decisions, and self-diagnostic behaviour. We report on a single-instance deployment over 23 days (19 operating days), during which the agent diagnosed its own infrastructure bugs, classified failure modes, identified an architectural vulnerability, and maintained context across email and web channels -- without explicit instruction. We introduce the term Artificial Retainer for this category: a non-human system with persistent memory, defined authority, domain-specific autonomy, and forensic accountability in an ongoing relationship with a specific principal -- distinguished from software assistants and autonomous agents, drawing on professional retainer relationships and the bounded autonomy of trained working animals. This is a technical report on a systems design and deployment case study, not a benchmark-driven evaluation. Evidence is from a single instance with a single operator, presented as illustration of what these architectural properties can support in practice. Implemented in approximately Gleam on Erlang/OTP. Code, artefacts, and redacted operational logs will be available at https://github.com/seamus-brady/springdrift upon publication.

Related papers

• DuMateBench: Evaluating Autonomous Agents in Complex Real-World Workflows [69.12339622053588]
  We introduce DuMateBench, a real-session benchmark reconstructed from anonymized and privacy-screened user sessions.<n>The resulting benchmark comprises 200 tasks spanning 8 broad scenarios and 17 fine-grained capability categories.<n>We execute these tasks in isolated Docker containers injected with three forms of real-world environmental complexity: Insufficient, Unstable, and Noisy.
  arXiv  Detail & Related papers  (2026-08-27T02:36:23Z)
• Formal Verification of Agentic Systems over Operational Data [59.98246281888422]
  Agentic systems driven by large language models (LLMs) are increasingly deployed in real-world where they act on persistent operational data.<n>Existing approaches do not provide such system-level guarantees.<n>We study here the verification of agentic systems comprising a single LLM and a tool orchestration harness over relational operational data.
  arXiv  Detail & Related papers  (2026-08-04T13:01:30Z)
• ScrambleToolBench: Agents Search Exhaustively Even When Their Own Map Points to the Next Step [74.83535434786145]
  We introduce ScrambleToolBench, an interactive terminal benchmark designed to isolate behavioral reasoning.<n>The benchmark requires agents to uncover hidden tool behaviors entirely through trial-and-error interaction.<n>Our evaluation of state-of-the-art language models reveals that successful initial discovery does not translate into robust adaptation.
  arXiv  Detail & Related papers  (2026-08-03T15:07:46Z)
• From Profiling to Synthesis: Benchmarking Implicit Behavioral Alignment in Personalized LLM Agents [56.187590188698955]
  Large Language Models have enabled increasingly capable autonomous agents, yet personalization remains critical for making such agents practically useful.<n>Recent benchmarks have begun evaluating personalization in agents, but they largely rely on static preference snapshots, fixed interaction logs, or question answering over predefined user profiles.<n>We introduce IBA-Bench, a benchmark for implicit behavioral alignment constructed from longitudinal interaction histories.<n>We propose IBA-Agent, an agent framework that reconciles conflicting priorities through broad retrieval and trajectory-level alignment.
  arXiv  Detail & Related papers  (2026-08-03T12:52:04Z)
• 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)
• Memory-Orchestrated Semantic System (MOSS): An Auditable Agentic Memory Architecture [0.0]
  We present the Memory-Orchestrated Semantic System (MOSS), an agentic memory architecture in which the agent drives retrieval over a structured relational database.<n>MOSS is model-agnostic, storage-agnostic, and API-agnostic; it runs on any relational engine, connects to any LLM provider, and deploys on any infrastructure, local or cloud.<n>We report on a longitudinal deployment unique in the agentic-memory literature: a year of continuous production over an individual scholar's working corpus--a conversational corpus reaching back to October 2024.
  arXiv  Detail & Related papers  (2026-07-05T16:35:19Z)
• Observability for Delegated Execution in Agentic AI Systems [0.6445605125467572]
  We present an agent-aware observability substrate consisting of a lightweight gateway and a common information model.<n>This enables reliable cross-tool delegation-scoped reconstruction and direct forensic queries without time-window correlation.
  arXiv  Detail & Related papers  (2026-06-08T16:10:05Z)
• An Organization-Scoped LLM Agent Runtime Architecture for Regulated Cybersecurity Operations [0.3013679260442808]
  Regulated cybersecurity lack a runtime substrate that enforces organization-level scope.<n>Recent large language model (LLM) agent systems report strong results on isolated cybersecurity tasks.<n>This paper proposes an organization-scoped agent runtime architecture for financial cybersecurity.
  arXiv  Detail & Related papers  (2026-05-28T21:51:38Z)
• Monitoring Data-aware Temporal Properties (Extended Version) [56.386411908764494]
  We consider anticipatory monitoring of linear-time properties enriched with an arbitrary SMT theory over finite traces.<n>Anticipatory monitoring in this setting is highly challenging, as the monitoring state depends on both the trace prefix seen so far and all its possible finite continuations.<n>We present and formally prove the correctness of a novel foundational framework for monitoring properties in an expressive fragment offMT.
  arXiv  Detail & Related papers  (2026-05-14T10:23:11Z)
• Towards Security-Auditable LLM Agents: A Unified Graph Representation [22.355591892994642]
  We propose Agent-BOM, a unified structural representation for agent security auditing.<n>We show that Agent-BOM can reconstruct stealthy attack chains, including cross-session memory poisoning and tool misuse.<n>Agent-BOM provides a unified and auditable foundation for root-cause analysis and security adjudication in complex agentic ecosystems.
  arXiv  Detail & Related papers  (2026-05-07T18:14:29Z)
• Dynamic analysis enhances issue resolution [53.50448142467294]
  DAIRA (Dynamic Analysis-enhanced Issue Resolution Agent) is an automated repair framework that embeds dynamic analysis into the agent's reasoning cycle.<n> Driven by a Test Tracing-Driven methodology, DAIRA utilizes lightweight monitors to extract critical runtime data.<n>When powered by Gemini 3 Flash Preview, DAIRA establishes a new state-of-the-art (SOTA) performance, achieving a 79.4% resolution rate on the SWE-bench Verified dataset.
  arXiv  Detail & Related papers  (2026-03-23T14:48:54Z)
• Reasoning Provenance for Autonomous AI Agents: Structured Behavioral Analytics Beyond State Checkpoints and Execution Traces [0.0]
  Agent Execution Record (AER) is a structured reasoning primitive that captures intent, observation, and inference as first-class queryable fields on every step.<n>We show how AERs enable population-level behavioral analytics: reasoning pattern mining, confidence calibration, cross-agent comparison, counterfactual regression testing via mock replay.
  arXiv  Detail & Related papers  (2026-03-23T08:27:54Z)
• A Trace-Based Assurance Framework for Agentic AI Orchestration: Contracts, Testing, and Governance [0.22940141855172028]
  This paper presents an assurance framework for Agentic AI systems using Large Language Models (LLMs)<n>Executions are instrumented as Message-Action Traces (MAT) with explicit step and trace contracts.<n>The framework includes stress testing, formulated as a budgeted counterexample search over bounded perturbations.
  arXiv  Detail & Related papers  (2026-03-18T10:23:48Z)
• ReqToCode: Embedding Requirements Traceability as a Structural Property of the Codebase [0.0]
  This paper introduces ReqToCode, an approach that prevents trace degradation by embedding traceable system elements directly into the system.<n>We describe the approach, its architectural principles, the Traceable lifecycle, and illustrate it with a generic example spanning requirement definition, artifact generation, code integration, and build-time validation.
  arXiv  Detail & Related papers  (2026-03-14T16:00:09Z)
• Automated Self-Testing as a Quality Gate: Evidence-Driven Release Management for LLM Applications [51.56484100374058]
  We present an automated self-testing framework that introduces quality gates with evidence-based release decisions.<n>We evaluate the framework through a longitudinal case study of an internally deployed multi-agent conversational AI system.
  arXiv  Detail & Related papers  (2026-03-13T20:44:15Z)
• Understanding by Reconstruction: Reversing the Software Development Process for LLM Pretraining [66.89012795621349]
  Large Language Models (LLMs) often struggle with the deep, long-horizon reasoning required for complex software engineering.<n>We propose a novel paradigm: understanding via reconstruction.<n>We introduce a framework that synthesizes latent agentic trajectories using a multi-agent simulation.
  arXiv  Detail & Related papers  (2026-03-11T09:23:20Z)
• AutoAgent: Evolving Cognition and Elastic Memory Orchestration for Adaptive Agents [26.380991138110925]
  AutoAgent is a self-evolving multi-agent framework built on evolving cognition, on-the-fly contextual decision-making, and elastic memory orchestration.<n>Each agent maintains structured prompt-level cognition over tools, self-capabilities, peer expertise, and task knowledge.<n>AutoAgent consistently improves task success, tool-use efficiency, and collaborative robustness over static and memory-augmented baselines.
  arXiv  Detail & Related papers  (2026-03-10T14:23:49Z)
• The Why Behind the Action: Unveiling Internal Drivers via Agentic Attribution [63.61358761489141]
  Large Language Model (LLM)-based agents are widely used in real-world applications such as customer service, web navigation, and software engineering.<n>We propose a novel framework for textbfgeneral agentic attribution, designed to identify the internal factors driving agent actions regardless of the task outcome.<n>We validate our framework across a diverse suite of agentic scenarios, including standard tool use and subtle reliability risks like memory-induced bias.
  arXiv  Detail & Related papers  (2026-01-21T15:22:21Z)
• A Benchmark for Procedural Memory Retrieval in Language Agents [0.023227405857540805]
  Current AI agents excel in familiar settings, but fail sharply when faced with novel tasks with unseenProc.<n>We present the first benchmark that isolates procedural memory retrieval from task execution.<n>Our results expose a clear generalization cliff: embedding-based methods perform strongly on familiar contexts, yet degrade considerably on novel ones.
  arXiv  Detail & Related papers  (2025-11-21T08:08:53Z)

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.