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PDE Generalization Literature Index

IAIP Research
pde-generalization

PDE Generalization Literature Index

This document serves as an organized index and guide to academic literature relevant to the Prompt Decomposition Engine (PDE) project. Its purpose is to assist other LLMs in quickly understanding the available research, its relevance to PDE's architecture and implementation, and its contribution to the overall literature survey.

The PDE aims to decompose complex, multi-intent prompts into actionable workflows, addressing limitations in current agentic systems. This literature base supports the development of PDE's five progressive layers: Intent Extraction & Classification, Dependency Graph Construction, Medicine Wheel Direction Assignment, Workflow Template Generation, and Execution Plan with Checkpoints, as well as its academic grounding.

Processed Literature References:

Here is an overview of the academic papers that have been processed and converted into Markdown, along with their key relevance to the PDE project:

  1. Decomposed Prompting: A Modular Approach For Solving Complex Tasks

    • Original Source: https://arxiv.org/pdf/2210.02406.pdf
    • Relevance to PDE: Directly relevant to the core concept of PDE, especially Layer 1 (Intent Extraction) and Layer 4 (Workflow Template Generation). This paper explores decomposing complex tasks into simpler sub-tasks via prompting, utilizing a shared library of LLMs. It highlights advantages like modularity, debuggability, and extensibility, which are foundational principles for PDE's layered architecture and prompt chaining approach. It also discusses hierarchical and recursive decomposition, which aligns with PDE's dependency graph construction.

  2. Navigating Complexity: Orchestrated Problem Solving with Multi-Agent LLMs

    • Original Source: http://arxiv.org/pdf/2402.16713.pdf
    • Relevance to PDE: Highly relevant to PDE's overall architecture and Layer 4 (Workflow Template Generation). This paper proposes leveraging decomposition and an orchestrating LLM to tackle complex and vague problems in multi-agent systems. It details how an orchestrating LLM can decompose problems, ask follow-up questions, and assign sub-problems to specialized agents, directly mirroring PDE's goal of transforming raw user input into executable agent workflows within a multi-agent context.

  3. LLM Multi-Agent Systems: Challenges and Open Problems

    • Original Source: https://arxiv.org/pdf/2402.03578.pdf
    • Relevance to PDE: Provides critical context for PDE's operational environment, specifically informing Layer 2 (Dependency Graph Construction), Layer 5 (Execution Plan with Checkpoints), and the "Multi-Agent Systems Challenges" section of the literature survey. It discusses challenges in multi-agent systems such as task allocation, robust reasoning, managing complex context, and memory management. Understanding these challenges is essential for designing a resilient and effective PDE.

  4. MARCO: Multi-Agent Real-time Chat Orchestration

    • Original Source: https://arxiv.org/pdf/2410.21784.pdf
    • Relevance to PDE: Very relevant to Layer 5 (Execution Plan with Checkpoints) and the "Multi-Agent Systems Challenges" section, particularly concerning guardrails and error recovery. This paper introduces a framework for automating tasks in multi-agent LLM systems, emphasizing robust guardrails to steer LLM behavior, validate outputs, and recover from errors. These concepts are directly applicable to building reliable execution plans and checkpointing mechanisms within PDE.

  5. TOWARDS HIERARCHICAL MULTI-AGENT WORKFLOWS FOR ZERO-SHOT PROMPT OPTIMIZATION

    • Original Source: https://arxiv.org/pdf/2405.20252.pdf
    • Relevance to PDE: Directly relevant to Prompt Engineering for Decomposition and Workflow Orchestration, impacting Layer 1 (Intent Extraction) and Layer 4 (Workflow Template Generation). This paper explores giving LLMs the freedom to design optimal prompts hierarchically (CEO → Manager → Worker workflow). This zero-shot, task-agnostic, and query-specific approach to prompt optimization offers insights into how PDE can dynamically generate and refine prompts for its various decomposition layers.

  6. Agent-Oriented Planning in Multi-Agent Systems

    • Original Source: https://arxiv.org/pdf/2410.02189v2.pdf
    • Relevance to PDE: Highly relevant to Layer 2 (Dependency Graph Construction) and Layer 4 (Workflow Template Generation), as well as general Multi-Agent Systems Challenges. This paper identifies design principles for agent-oriented planning (solvability, completeness, non-redundancy) and proposes AOP, a framework for fast task decomposition and allocation with reward model-based evaluation and feedback loops. This directly informs how PDE can structure its sub-task assignments and ensure effective multi-agent collaboration.

  7. Open Intent Extraction from Natural Language Interactions (Extended Abstract)

    • Original Source: https://www.ijcai.org/proceedings/2021/0663.pdf
    • Relevance to PDE: Directly relevant to Layer 1 (Intent Extraction & Classification) and the "Intent Recognition & NLU" section of the literature survey. This paper introduces OPINE, a domain-agnostic approach for open intent discovery, framing it as a sequence tagging task. Understanding how to extract explicit and implicit user intents from diverse natural language inputs is fundamental to PDE's initial layer of decomposing prompts.

  8. Agents Under Siege: Breaking Pragmatic Multi-Agent LLM Systems with Optimized Prompt Attacks

    • Original Source: https://arxiv.org/pdf/2504.00218v2.pdf
    • Relevance to PDE: Provides critical context for the "Communication Vulnerabilities" within "Multi-Agent Systems Challenges" (Section 3.5) of PDE's literature survey. This paper discusses adversarial attacks and prompt propagation in multi-agent LLM systems, highlighting vulnerabilities in inter-agent communication. While not directly about constructive prompt decomposition, understanding these attack vectors can inform the design of robust and secure PDE implementations, particularly regarding prompt sanitization and communication protocols.

  9. Which Agent Causes Task Failures and When? On Automated Failure Attribution of LLM Multi-Agent Systems

    • Original Source: https://arxiv.org/pdf/2505.00212v3.pdf
    • Relevance to PDE: Highly relevant and foundational for Layer 5 (Execution Plan with Checkpoints) and the "Failure Attribution" section (Section 3.5) of PDE's literature survey. This paper directly addresses automated failure attribution in multi-agent LLM systems, including identifying agents and steps responsible for task failures. This knowledge is crucial for developing PDE's capabilities for progress tracking, precise failure diagnosis, and effective recovery mechanisms.