From Tool Calling to Symbolic Thinking: LLMs in a Persistent Lisp Metaprogramming Loop
On the map: LLM reasoning → Reasoning for agents, planning and code
also in 1 more
Abstract (arXiv)
We propose a novel architecture for integrating large language models (LLMs) with a persistent, interactive Lisp environment. This setup enables LLMs to define, invoke, and evolve their own tools through programmatic interaction with a live REPL. By embedding Lisp expressions within generation and intercepting them via a middleware layer, the system allows for stateful external memory, reflective programming, and dynamic tool creation. We present a design framework and architectural principles to guide future implementations of interactive AI systems that integrate symbolic programming with neural language generation.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Placed in Reasoning for agents, planning and code, one of 17 directions in LLM reasoning. Builds on 10 of its 25 references that are in the map. First posted on arXiv 8 Jun 2025.
Builds on
10 of 25 references.
References · 10
- 2017Attention Is All You Needcited by 59,932
- 2020Language Models are Few-Shot Learnerscited by 32,199
- 2022Training language models to follow instructions with human feedbackcited by 15,712
- 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,302
- 2020Scaling Laws for Neural Language Modelscited by 6,411
- 2017Deep reinforcement learning from human preferencescited by 3,846
- 2022Constitutional AI: Harmlessness from AI Feedbackcited by 2,950
- 2020Learning to summarize from human feedbackcited by 2,562
- 2021Multitask Prompted Training Enables Zero-Shot Task Generalizationcited by 1,462
- 2024Converging Paradigms: The Synergy of Symbolic and Connectionist AI in LLM-Empowered Autonomous Agentscited by 9
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Citing papers per year: 2026: 2.
2 papers.
Similar, not cited
Close in wording, no citation either way
- 2025Pel, A Programming Language for Orchestrating AI Agents
- 2024If LLM Is the Wizard, Then Code Is the Wand: A Survey on How Code Empowers Large Language Models to Serve as Intelligent Agents
- 2024Tool Learning in the Wild: Empowering Language Models as Automatic Tool Agents
- 2024An LLM-Tool Compiler for Fused Parallel Function Calling
- 2024Creating an LLM-based AI-agent: A high-level methodology towards enhancing LLMs with APIs
- 2023CRAFT: Customizing LLMs by Creating and Retrieving from Specialized Toolsets
- 2024Designing Algorithms Empowered by Language Models: An Analytical Framework, Case Studies, and Insights
- 2024LLM With Tools: A Survey