Eyla: Toward an Identity-Anchored LLM Architecture with Integrated Biological Priors -- Vision, Implementation Attempt, and Lessons from AI-Assisted Development

• URL: http://arxiv.org/abs/2604.00009v1
• Date: Mon, 09 Mar 2026 17:22:48 GMT
• Title: Eyla: Toward an Identity-Anchored LLM Architecture with Integrated Biological Priors -- Vision, Implementation Attempt, and Lessons from AI-Assisted Development
• Abstract summary: Eyla is a proposed identity-anchored LLM architecture that integrates biologically-inspired subsystems.<n>We present the design rationale, implementation attempt, and failure analysis of Eyla.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present the design rationale, implementation attempt, and failure analysis of Eyla, a proposed identity-anchored LLM architecture that integrates biologically-inspired subsystems -- including HiPPO-initialized state-space models, zero-initialized adapters, episodic memory retrieval, and calibrated uncertainty training -- into a unified agent operating system running on consumer hardware. Unlike existing approaches that optimize models for generic helpfulness, Eyla targets identity consistency: the ability to maintain a coherent self-model under adversarial pressure, admit uncertainty, and resist manipulation. We propose the Identity Consistency Score (ICS), a novel benchmark for evaluating this property across LLMs. We then present an honest account of attempting to implement this architecture using AI coding assistants (Claude Code, Cursor) as a non-programmer, documenting a $1,000+ failure that produced a 1.27B parameter model with 86 brain subsystems contributing less than 2% to output. Our analysis identifies five systematic failure modes of AI-assisted development for novel architectures and offers concrete recommendations. To our knowledge, this is the first paper to combine an architectural vision with a documented first-person failure analysis of AI-assisted LLM development, providing lessons for both the AI systems and AI-assisted software engineering communities.

Related papers

• Beyond Models: Reflections on Engineering AI-enabled Systems in a Project-Based Course [6.295638112781735]
  This paper reflects on the design and implementation of a project-based master's-level course titled AI Algorithms: Theory and Engineering, at the University of Bremen.<n>Students developed a movie recommendation system while making architectural design decisions to address challenges related to scalability, deployment, and evolving requirements.<n>Our results indicate persistent difficulties in early architectural decisions, heterogeneous ML integration, evolving requirements, and data management, largely due to uneven ML and software engineering expertise.
  arXiv  Detail & Related papers  (2026-06-15T15:21:54Z)
• Engineering AI Agents for Clinical Workflows: A Case Study in Architecture,MLOps, and Governance [0.21748200848556345]
  We show how a Human-in-the-Loop governance model is technically integrated not merely as a safety check, but as a critical, event-driven data source for continuous improvement.<n>We present the platform as a reference architecture, offering practical lessons for engineers building maintainable, scalable, and accountable AI-enabled systems in high-stakes domains.
  arXiv  Detail & Related papers  (2026-01-31T14:33:57Z)
• EmboCoach-Bench: Benchmarking AI Agents on Developing Embodied Robots [68.29056647487519]
  Embodied AI is fueled by high-fidelity simulation and large-scale data collection.<n>However, this scaling capability remains bottlenecked by a reliance on labor-intensive manual oversight.<n>We introduce textscEmboCoach-Bench, a benchmark evaluating the capacity of LLM agents to autonomously engineer embodied policies.
  arXiv  Detail & Related papers  (2026-01-29T11:33:49Z)
• AI-NativeBench: An Open-Source White-Box Agentic Benchmark Suite for AI-Native Systems [52.65695508605237]
  We introduce AI-NativeBench, the first application-centric and white-box AI-Native benchmark suite grounded in Model Context Protocol (MCP) and Agent-to-Agent (A2A) standards.<n>By treating agentic spans as first-class citizens within distributed traces, our methodology enables granular analysis of engineering characteristics beyond simple capabilities.<n>This work provides the first systematic evidence to guide the transition from measuring model capability to engineering reliable AI-Native systems.
  arXiv  Detail & Related papers  (2026-01-14T11:32:07Z)
• SelfAI: Building a Self-Training AI System with LLM Agents [79.10991818561907]
  SelfAI is a general multi-agent platform that combines a User Agent for translating high-level research objectives into standardized experimental configurations.<n>An Experiment Manager orchestrates parallel, fault-tolerant training across heterogeneous hardware while maintaining a structured knowledge base for continuous feedback.<n>Across regression, computer vision, scientific computing, medical imaging, and drug discovery benchmarks, SelfAI consistently achieves strong performance and reduces redundant trials.
  arXiv  Detail & Related papers  (2025-11-29T09:18:39Z)
• A Survey of Vibe Coding with Large Language Models [93.88284590533242]
  "Vibe Coding" is a development methodology where developers validate AI-generated implementations through outcome observation.<n>Despite its transformative potential, the effectiveness of this emergent paradigm remains under-explored.<n>This survey provides the first comprehensive and systematic review of Vibe Coding with large language models.
  arXiv  Detail & Related papers  (2025-10-14T11:26:56Z)
• Fundamentals of Building Autonomous LLM Agents [64.39018305018904]
  This paper reviews the architecture and implementation methods of agents powered by large language models (LLMs)<n>The research aims to explore patterns to develop "agentic" LLMs that can automate complex tasks and bridge the performance gap with human capabilities.
  arXiv  Detail & Related papers  (2025-10-10T10:32:39Z)
• A Comprehensive Survey on Benchmarks and Solutions in Software Engineering of LLM-Empowered Agentic System [56.40989626804489]
  This survey provides the first holistic analysis of Large Language Models-powered software engineering.<n>We review over 150 recent papers and propose a taxonomy along two key dimensions: (1) Solutions, categorized into prompt-based, fine-tuning-based, and agent-based paradigms, and (2) Benchmarks, including tasks such as code generation, translation, and repair.
  arXiv  Detail & Related papers  (2025-10-10T06:56:50Z)
• Towards the Next Generation of Software: Insights from Grey Literature on AI-Native Applications [13.876049229274114]
  AI-native applications are a new paradigm in software engineering that fundamentally redefines how software is designed, developed, and evolved.<n>Despite their growing prominence, AI-native applications still lack a unified engineering definition and architectural blueprint.<n>This study seeks to establish a comprehensive understanding of AI-native applications by identifying their defining characteristics, key quality attributes, and typical technology stacks.
  arXiv  Detail & Related papers  (2025-09-16T15:01:23Z)
• MeLA: A Metacognitive LLM-Driven Architecture for Automatic Heuristic Design [16.216869444746898]
  MeLA is a Metacognitive LLM-Driven Architecture that presents a new paradigm for Automatic Heuristic Design (AHD)<n>MeLA evolves the instructional prompts used to guide a Large Language Model (LLM) in generating theses.<n>This process of "prompt evolution" is driven by a novel metacognitive framework.
  arXiv  Detail & Related papers  (2025-07-28T05:56:40Z)
• Generalising from Self-Produced Data: Model Training Beyond Human Constraints [0.0]
  This paper introduces a novel framework in which AI models autonomously generate and validate new knowledge.<n>Central to this approach is an unbounded, ungamable numeric reward that guides learning without requiring human benchmarks.
  arXiv  Detail & Related papers  (2025-04-07T03:48:02Z)

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.