# Self-Guided Function Calling in Large Language Models via Stepwise Experience Recall

arXiv: https://arxiv.org/abs/2508.15214 · HTML: https://mlsanity.com/paper/2508.15214/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Placed in Tool learning and function calling, one of 16 directions in LLM agents and tool use.
- Builds on 25 of its 44 references that are in the map.
- First posted on arXiv 21 Aug 2025.

## Placed in

- LLM agents and tool use / [Tool learning and function calling](https://mlsanity.com/agents/tool-learning-and-function-calling/)

## Builds on (most cited first, 25 of 25)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [Qwen2.5 Technical Report](https://mlsanity.com/paper/2412.15115/) (2024)
- [Constitutional AI: Harmlessness from AI Feedback](https://mlsanity.com/paper/2212.08073/) (2022)
- [Self-Instruct: Aligning Language Models with Self-Generated Instructions](https://mlsanity.com/paper/2212.10560/) (2022)
- [ToolLLM: Facilitating Large Language Models to Master 16000+ Real-world APIs](https://mlsanity.com/paper/2307.16789/) (2023)
- [Rethinking the Role of Demonstrations: What Makes In-Context Learning Work?](https://mlsanity.com/paper/2202.12837/) (2022)
- [Calibrate Before Use: Improving Few-Shot Performance of Language Models](https://mlsanity.com/paper/2102.09690/) (2021)
- [ExpeL: LLM Agents Are Experiential Learners](https://mlsanity.com/paper/2308.10144/) (2023)
- [A Survey on In-context Learning](https://mlsanity.com/paper/2301.00234/) (2022)
- [C-Pack: Packed Resources For General Chinese Embeddings](https://mlsanity.com/paper/2309.07597/) (2023)
- [From Generation to Judgment: Opportunities and Challenges of LLM-as-a-judge](https://mlsanity.com/paper/2411.16594/) (2024)
- [ToolQA: A Dataset for LLM Question Answering with External Tools](https://mlsanity.com/paper/2306.13304/) (2023)
- [MetaTool Benchmark for Large Language Models: Deciding Whether to Use Tools and Which to Use](https://mlsanity.com/paper/2310.03128/) (2023)
- [Toward Self-Improvement of LLMs via Imagination, Searching, and Criticizing](https://mlsanity.com/paper/2404.12253/) (2024)
- [What Are Tools Anyway? A Survey from the Language Model Perspective](https://mlsanity.com/paper/2403.15452/) (2024)
- [Large Language Models Can Solve Real-World Planning Rigorously with Formal Verification Tools](https://mlsanity.com/paper/2404.11891/) (2024)
- [Synthetic Data Generation & Multi-Step RL for Reasoning & Tool Use](https://mlsanity.com/paper/2504.04736/) (2025)
- [LLM4EDA: Emerging Progress in Large Language Models for Electronic Design Automation](https://mlsanity.com/paper/2401.12224/) (2023)
- [Amulet: ReAlignment During Test Time for Personalized Preference Adaptation of LLMs](https://mlsanity.com/paper/2502.19148/) (2025)
- [Equipping Language Models with Tool Use Capability for Tabular Data Analysis in Finance](https://mlsanity.com/paper/2401.15328/) (2024)
- [Strategy-Augmented Planning for Large Language Models via Opponent Exploitation](https://mlsanity.com/paper/2505.08459/) (2025)

## Built on by (most cited first, 7 of 7)

- [ExpSeek: Self-Triggered Experience Seeking for Web Agents](https://mlsanity.com/paper/2601.08605/) (2026)
- [Dynamic Tool Dependency Retrieval for Lightweight Function Calling](https://mlsanity.com/paper/2512.17052/) (2025)
- [HyMem: Hierarchical Context Management for Long-Horizon Agents via Information Isolation](https://mlsanity.com/paper/2608.15703/) (2026)
- [FitText: Evolving Agent Tool Ecologies via Memetic Retrieval](https://mlsanity.com/paper/2605.02411/) (2026)
- [Trace2Tower: Transition-Aware EigenTrace Induction of Multi-Level Skills for LLM Agents](https://mlsanity.com/paper/2609.05261/) (2026)
- [ConvMem: Convolutional Memory for Long-Context Reasoning](https://mlsanity.com/paper/2609.10441/) (2026)
- [Success Leaves Detours: Learning Executable Walkthroughs for Long-Horizon Agents](https://mlsanity.com/paper/2609.22120/) (2026)

## Abstract (arXiv)

Function calling enables large language models (LLMs) to interact with external systems by leveraging tools and APIs. When faced with multi-step tool usage, LLMs still struggle with tool selection, parameter generation, and tool-chain planning. Existing methods typically rely on manually designing task-specific demonstrations, or retrieving from a curated library. These approaches demand substantial expert effort and prompt engineering becomes increasingly complex and inefficient as tool diversity and task difficulty scale. To address these challenges, we propose a self-guided method, Stepwise Experience Recall (SEER), which performs fine-grained, stepwise retrieval from a continually updated experience pool. Instead of relying on static or manually curated library, SEER incrementally augments the experience pool with past successful trajectories, enabling continuous expansion of the pool and improved model performance over time. Evaluated on the ToolQA benchmark, SEER achieves an average improvement of 6.1% on easy and 4.7% on hard questions. We further test SEER on τ-bench, which includes two real-world domains. Powered by Qwen2.5-7B and Qwen2.5-72B models, SEER demonstrates substantial accuracy gains of 7.44% and 23.38%, respectively.