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arxiv: 2606.10875 · v1 · pith:3CQINEZ5new · submitted 2026-06-09 · 💻 cs.CL

Pushing the Limits of LLM Tool Calling via Experiential Knowledge Integration and Activation

Yupu Hao , Zhuoran Jin , Huanxuan Liao , Kang Liu , Jun Zhao This is my paper

Pith reviewed 2026-06-27 13:22 UTC · model grok-4.3

classification 💻 cs.CL
keywords LLM tool callingexperiential knowledgeknowledge activationtool usereinforcement learningKATE frameworkBFCL-V3AppWorld
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The pith

The KATE framework improves LLM multi-step tool execution by integrating instance-level experiential knowledge, reasoning-width expansion, and knowledge-aware training.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

Large language models often fail at complex tool-use tasks due to insufficient experiential knowledge about tools and poor activation of what knowledge they possess. The paper examines three stages: acquiring different forms of knowledge, activating it during inference, and internalizing it during training. It finds that simple instance-level examples deliver reliable gains while abstract knowledge adds little, that sampling multiple reasoning paths in parallel activates knowledge better than single deeper chains, and that reinforcement learning on augmented data internalizes knowledge more effectively than supervised fine-tuning. These elements are combined into the KATE framework, which produces consistent gains over strong baselines on BFCL-V3 and AppWorld across model sizes. A sympathetic reader would care because improved tool calling could make LLMs more dependable autonomous agents.

Core claim

We present a systematic study on how knowledge influences tool-use performance, covering acquisition, activation, and internalization. Simple instance-level knowledge provides strong and reliable gains, while abstract intent-level knowledge offers limited benefits. Expanding reasoning width via parallel sampling with aggregation activates latent knowledge more effectively than deepening single paths. Post-training with knowledge-augmented data further improves performance, with reinforcement learning outperforming supervised fine-tuning. The resulting KATE framework integrates these elements and demonstrates consistent and substantial improvements over strong baselines across model scales on

What carries the argument

The Knowledge-Augmented Tool Execution (KATE) framework that integrates experiential knowledge acquisition, reasoning-width-expanded inference through parallel sampling and aggregation, and knowledge-aware training with reinforcement learning.

If this is right

  • Simple instance-level experiential knowledge yields strong and reliable gains in tool-calling performance.
  • Expanding reasoning width by parallel sampling with aggregation activates latent experiential knowledge more effectively than expanding the depth of a single reasoning chain.
  • Knowledge-augmented post-training with reinforcement learning outperforms supervised fine-tuning for improving tool-use capabilities.
  • The integrated KATE framework produces consistent and substantial improvements over strong baselines across model scales on BFCL-V3 and AppWorld.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Curating high-quality sets of instance-level tool examples could become a practical, scalable route to stronger agent performance without larger models.
  • The preference for reasoning width over depth implies that diversity among generated paths matters more than sequential length for surfacing stored knowledge during tool use.
  • The same knowledge-acquisition and activation pattern may extend to other multi-step agent tasks such as web navigation or code generation.
  • Combining KATE-style methods with retrieval mechanisms could test whether external knowledge sources amplify the observed internalization effects.

Load-bearing premise

That performance differences are attributable to the specific knowledge forms and activation methods rather than to differences in total inference compute, data selection, or unstated implementation details.

What would settle it

A controlled comparison in which a baseline matches KATE's total inference compute and training data volume but omits the instance-level knowledge, width expansion, and knowledge-augmented RL components, yet shows equivalent gains on BFCL-V3, would falsify the attribution.

Figures

Figures reproduced from arXiv: 2606.10875 by Huanxuan Liao, Jun Zhao, Kang Liu, Yupu Hao, Zhuoran Jin.

Figure 1
Figure 1. Figure 1: The augmentation results of different experiential knowledge. “All” indicates incorporating all the [PITH_FULL_IMAGE:figures/full_fig_p004_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: The Prompt-Hint results on BFCL-V3 dataset. [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: The average results of parallel sampling with [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: The inference and training framework of the method [PITH_FULL_IMAGE:figures/full_fig_p006_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: The Error type analysis of different methods [PITH_FULL_IMAGE:figures/full_fig_p008_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: The reward scores of training process of [PITH_FULL_IMAGE:figures/full_fig_p012_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: The results of parallel sampling with different sampling size. [PITH_FULL_IMAGE:figures/full_fig_p013_7.png] view at source ↗
read the original abstract

Large language models (LLMs) rely on tool use to act as autonomous agents, yet often fail in multi-step execution due to insufficient tool-related knowledge and ineffective knowledge activation. Therefore, we present a systematic study on how knowledge influences tool-use performance, covering the stages of knowledge acquisition, activation, and internalization. In the knowledge acquisition stage, we acquire and evaluate various forms of experiential knowledge, and our analysis shows that simple instance-level knowledge can already provide strong and reliable gains, while abstract intent-level knowledge offers limited benefits. At inference time, to activate knowledge, we find that prompting LLM to expand the depth of reasoning yields diminishing returns, whereas expanding the width of reasoning by parallel sampling with aggregation more effectively activates latent experiential knowledge. At training time, for knowledge internalization, post-training with knowledge-augmented data further improves performance, with reinforcement learning outperforming supervised fine-tuning. Based on these insights, we propose the Knowledge-Augmented Tool Execution (KATE), a knowledge-augmented tool execution framework that integrates experiential knowledge with reasoning-width-expanded inference and knowledge-aware training. Experiments on BFCL-V3 and AppWorld demonstrate consistent and substantial improvements over strong baselines across model scales. Our Code is available at https://github.com/hypasd-art/KATE.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 0 minor

Summary. The paper presents a systematic study of how experiential knowledge affects LLM tool-use performance across acquisition, activation, and internalization stages. It evaluates different knowledge forms, finds that width-expanded parallel sampling at inference activates knowledge more effectively than depth expansion, and shows that knowledge-augmented post-training (especially RL) improves results. These insights are combined into the KATE framework, which is reported to yield consistent and substantial gains over strong baselines on BFCL-V3 and AppWorld across model scales. Code is released publicly.

Significance. If the reported gains are shown to be attributable to the specific knowledge forms, width-expanded sampling, and knowledge-aware training rather than unmatched compute or data selection, the work would offer concrete, actionable guidance on knowledge integration for tool-calling agents. The public code release supports reproducibility and is a positive contribution.

major comments (2)
  1. [Abstract] Abstract: The abstract asserts 'consistent and substantial improvements' and 'strong and reliable gains' over baselines but supplies no quantitative results, error bars, baseline details, or ablation controls. This absence makes it impossible to evaluate the magnitude or reliability of the claimed improvements.
  2. [Abstract] Abstract / experimental claims: The central attribution of gains to KATE's knowledge forms, width-expanded inference, and knowledge-aware training requires explicit evidence that total inference compute (number of samples, tokens, or FLOPs) and data selection criteria are matched between the proposed method and the 'strong baselines.' Without such controls, observed differences cannot be confidently ascribed to the proposed components rather than compute or data confounds.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive comments. We address each major point below and will revise the manuscript accordingly to improve clarity and rigor.

read point-by-point responses

  1. Referee: [Abstract] Abstract: The abstract asserts 'consistent and substantial improvements' and 'strong and reliable gains' over baselines but supplies no quantitative results, error bars, baseline details, or ablation controls. This absence makes it impossible to evaluate the magnitude or reliability of the claimed improvements.

    Authors: We agree that the abstract would be strengthened by including key quantitative results. In the revision we will update the abstract to report specific improvement percentages (e.g., relative gains on BFCL-V3 and AppWorld), name the primary baselines, and reference the error bars and ablation results presented in the main body. revision: yes

  2. Referee: [Abstract] Abstract / experimental claims: The central attribution of gains to KATE's knowledge forms, width-expanded inference, and knowledge-aware training requires explicit evidence that total inference compute (number of samples, tokens, or FLOPs) and data selection criteria are matched between the proposed method and the 'strong baselines.' Without such controls, observed differences cannot be confidently ascribed to the proposed components rather than compute or data confounds.

    Authors: We acknowledge the importance of demonstrating matched compute and data selection. Our experimental protocol already equates total inference budget across methods by fixing the number of samples and tokens for parallel sampling versus deeper-reasoning baselines; data selection follows identical criteria for all conditions. We will add an explicit paragraph and a small comparison table in the experimental setup section of the revised manuscript to document these controls. revision: yes

Circularity Check

0 steps flagged

Empirical study with no derivation chain or self-referential reductions

full rationale

The paper presents an empirical investigation of knowledge forms, inference strategies, and training methods for LLM tool use, culminating in the KATE framework and benchmark results on BFCL-V3 and AppWorld. No equations, derivations, or mathematical claims appear in the provided abstract or description. Performance improvements are reported as experimental outcomes rather than quantities derived from fitted parameters or prior self-citations. The work includes public code, making results externally verifiable without reduction to internal definitions. No load-bearing steps match any of the enumerated circularity patterns.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

No free parameters, invented entities, or non-standard axioms are mentioned in the abstract; the work relies on standard empirical ML assumptions about benchmark validity and the representativeness of the chosen evaluation tasks.

axioms (1)
  • domain assumption BFCL-V3 and AppWorld benchmarks are valid proxies for real-world multi-step tool-use performance.
    The abstract reports improvements on these two benchmarks as evidence for the framework's effectiveness.

pith-pipeline@v0.9.1-grok · 5761 in / 1193 out tokens · 27465 ms · 2026-06-27T13:22:01.842904+00:00 · methodology

discussion (0)

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Reference graph

Works this paper leans on

36 extracted references · 4 canonical work pages

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    **Iterative Refinement**: With each step, you should refine your approach based on what has been known so far, gradually moving towards a complete solution. --- ### Output Format: ```python # The code for the current step in the task. Add your code here ``` Prompt 2: Aggregation Prompt used in KA TE on dataset AppWorld Before invoking any tools, clearly i...

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    Identify the user 's current intent based on the conversation history and the latest user message

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    Break down this intent into clear, actionable subtasks or goals

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    Your reasoning should focus on *clarity* (what the user wants), *structure* (how to achieve it), and *efficiency* (which tool or reasoning step should come next)

    Determine which tools (if any) are needed for each subtask, and specify their expected inputs and outputs. Your reasoning should focus on *clarity* (what the user wants), *structure* (how to achieve it), and *efficiency* (which tool or reasoning step should come next). You do not have to fully adhere to the above suggestions. But you need to analyze the r...

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    Determine whether the previous tool calls were correct, sufficient, or complete. If a tool call failed or produced suboptimal results due to insufficient or missing parameters or functions, reflect on what information was lacking, how it could be inferred or obtained

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    If issues exist (e.g., wrong parameters, missing calls, failed execution), explain briefly why they occurred

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    You do not have to fully adhere to the above suggestions

    Analyze future multi-step tool calls during the analysis process, rather than just focusing on the next step. You do not have to fully adhere to the above suggestions. But you need to analyze the relevant points in the conversation history about the correctness and necessary of previous tool call in the thinking process. Prompt 4: Reflection prompt in Dept...

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    Analyze the user 's question to detect any implicit state dependencies (e.g., user login status, file existence, context variables)

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    Determine what specific states must be confirmed before continuing

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    name": "api_docs

    If verification is required, decide which tools should be invoked to confirm those states. If no state verification is needed, proceed with reasoning toward tool selection or response generation. You do not have to fully adhere to the above suggestions. But you need to analyze the relevant points in the conversation history about the state requirements in...

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    spotify_password) in the example above were only for demonstration

    The email addresses, access tokens and variables (e.g. spotify_password) in the example above were only for demonstration. Obtain the correct information by calling relevant APIs yourself

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    Any thoughts should be put as code comments

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    Make sure everything is working correctly before making any irreversible change

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    But modules and functions that have a risk of affecting the underlying OS, file system or process are disabled

    The provided Python environment has access to its standard library. But modules and functions that have a risk of affecting the underlying OS, file system or process are disabled. You will get an error if do call them

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    So do not write code making calls to os-level modules and functions

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    All calls to APIs and parsing its outputs must be as per this documentation

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    **Generate a Small Portion of Code**: Produce only the part of the code needed to address the first step of the task. Do not try to complete the entire solution in one go

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    **Iterate Based on Feedback**: If previous steps have errors or issues, do not just fix them all at once. Focus on understanding the specific issue and generating the next part of the solution, keeping previous code intact as much as possible

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    **Iterative Refinement**: With each step, you should refine your approach based on what has been known so far, gradually moving towards a complete solution. And you need to call apis.api_docs.show_app_descriptions(), apis.api_docs.show_api_descriptions(app_name='<app>') and apis.api_docs.show_api_doc(app_name='<app>', api_name= '<app_name>') before utiliz...

    2024