Recognition: 2 theorem links
· Lean TheoremTool Learning with Foundation Models
Pith reviewed 2026-05-16 13:06 UTC · model grok-4.3
The pith
Foundation models learn tool use by decomposing tasks into subtasks, reasoning to adjust plans, and selecting the right tools for each step.
A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.
Core claim
The central claim is that tool learning with foundation models follows a general framework in which models understand user instructions, decompose complex tasks into several subtasks, dynamically adjust their plan through reasoning, and effectively conquer each sub-task by selecting appropriate tools, as shown through a review of existing work and experiments with 18 tools that illustrate current capabilities.
What carries the argument
The general tool learning framework of instruction understanding followed by task decomposition, dynamic plan adjustment through reasoning, and tool selection.
If this is right
- Integrating tools with foundation models produces higher accuracy and efficiency on complex problems compared to models alone.
- Training focused on tool-use capabilities improves how well models generalize to new tasks and tools.
- Systematic evaluation across multiple tools reveals both the strengths and limits of current foundation models in tool use.
- Addressing the identified open problems will guide development of more capable tool-learning systems.
Where Pith is reading between the lines
- The decomposition and reasoning steps could transfer to domains like automated planning or multi-agent systems beyond the tools tested here.
- Expanding the set of tools past 18 might expose bottlenecks in reasoning or selection that the current experiments do not capture.
- Linking the framework more tightly to human cognitive tool use could suggest new ways to evaluate or improve model performance.
Load-bearing premise
That foundation models can be trained or prompted to reliably carry out the full sequence of task decomposition, dynamic reasoning, and tool selection at scale.
What would settle it
An experiment in which models trained or prompted according to the framework still fail to decompose new tasks correctly or select suitable tools, showing no improvement over non-framework baselines.
read the original abstract
Humans possess an extraordinary ability to create and utilize tools, allowing them to overcome physical limitations and explore new frontiers. With the advent of foundation models, AI systems have the potential to be equally adept in tool use as humans. This paradigm, i.e., tool learning with foundation models, combines the strengths of specialized tools and foundation models to achieve enhanced accuracy, efficiency, and automation in problem-solving. Despite its immense potential, there is still a lack of a comprehensive understanding of key challenges, opportunities, and future endeavors in this field. To this end, we present a systematic investigation of tool learning in this paper. We first introduce the background of tool learning, including its cognitive origins, the paradigm shift of foundation models, and the complementary roles of tools and models. Then we recapitulate existing tool learning research into tool-augmented and tool-oriented learning. We formulate a general tool learning framework: starting from understanding the user instruction, models should learn to decompose a complex task into several subtasks, dynamically adjust their plan through reasoning, and effectively conquer each sub-task by selecting appropriate tools. We also discuss how to train models for improved tool-use capabilities and facilitate the generalization in tool learning. Considering the lack of a systematic tool learning evaluation in prior works, we experiment with 18 representative tools and show the potential of current foundation models in skillfully utilizing tools. Finally, we discuss several open problems that require further investigation for tool learning. In general, we hope this paper could inspire future research in integrating tools with foundation models.
Editorial analysis
A structured set of objections, weighed in public.
Referee Report
Summary. The paper provides a systematic survey of tool learning with foundation models. It covers cognitive background and the shift to foundation models, organizes prior work into tool-augmented and tool-oriented paradigms, proposes a general framework in which models parse instructions, decompose tasks, dynamically replan via reasoning, and select tools, discusses training and generalization strategies, reports experiments with 18 representative tools to illustrate current model capabilities, and outlines open problems.
Significance. If the proposed four-stage framework can be shown to be both necessary and sufficient for effective tool use, the work could serve as a useful organizing lens for the emerging area of tool-augmented foundation models. The 18-tool experiments supply an initial empirical signal, but their limited methodological detail prevents strong claims about the framework's explanatory power.
major comments (2)
- [Experiments] The experimental section (referenced in the abstract as experiments with 18 tools) reports success rates but supplies neither the exact prompting or fine-tuning recipe used to elicit explicit decomposition and dynamic replanning nor any control condition (e.g., direct tool calling without the four-stage loop). Consequently, observed performance cannot be attributed to the proposed framework rather than to pre-existing model heuristics or tool documentation quality.
- [Framework formulation] The general tool-learning framework is stated at a high level without formal definitions, pseudocode, or a precise specification of the interfaces between the four stages (instruction understanding, decomposition, dynamic reasoning, tool selection). This makes it difficult to implement or falsify the claim that models must follow this exact process.
minor comments (2)
- [Recapitulation of existing research] The distinction between 'tool-augmented' and 'tool-oriented' learning is introduced but not given operational criteria or a decision tree for classifying a given paper; a short table or flowchart would improve clarity.
- [References] Several citations to prior tool-use papers appear only in the text without corresponding entries in the reference list or vice versa; a consistency check is needed.
Simulated Author's Rebuttal
We thank the referee for the constructive feedback on our survey. We address the major comments below and will revise the manuscript to improve clarity on the scope of the experiments and the level of detail in the framework description.
read point-by-point responses
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Referee: [Experiments] The experimental section (referenced in the abstract as experiments with 18 tools) reports success rates but supplies neither the exact prompting or fine-tuning recipe used to elicit explicit decomposition and dynamic replanning nor any control condition (e.g., direct tool calling without the four-stage loop). Consequently, observed performance cannot be attributed to the proposed framework rather than to pre-existing model heuristics or tool documentation quality.
Authors: We agree that the experiments in Section 5 are illustrative demonstrations of current foundation model capabilities across 18 tools, rather than a controlled ablation study designed to isolate the contribution of the four-stage framework. The manuscript does not assert that performance gains are attributable to the framework over baseline heuristics. In revision we will (1) explicitly label the section as illustrative, (2) supply the prompting templates and model versions used, and (3) note the lack of control conditions as a limitation of the current evaluation. A full factorial study lies outside the scope of a survey paper but can be flagged as valuable future work. revision: partial
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Referee: [Framework formulation] The general tool-learning framework is stated at a high level without formal definitions, pseudocode, or a precise specification of the interfaces between the four stages (instruction understanding, decomposition, dynamic reasoning, tool selection). This makes it difficult to implement or falsify the claim that models must follow this exact process.
Authors: The framework is intentionally presented at a conceptual level to synthesize cognitive science insights and existing literature into an organizing lens for the survey; we do not claim it is the unique or mandatory process that all models must follow. To increase usability we will add (a) a high-level pseudocode sketch of the overall loop and (b) explicit textual descriptions of the inputs/outputs expected at each stage interface. These additions will remain at the level of a survey while making the description more actionable. revision: yes
Circularity Check
High-level survey and framework proposal with no derivation chain or fitted predictions
full rationale
The paper is a literature review that introduces background, recapitulates prior work into two categories, and proposes a high-level four-stage framework (instruction understanding, decomposition, dynamic reasoning, tool selection). No equations, parameters, or quantitative predictions appear in the provided text. The 18-tool experiments are presented as illustrative demonstrations rather than formal derivations or fitted outputs. No self-citation is used to justify a uniqueness theorem or to close a logical loop. Consequently, no step reduces to its own inputs by construction, satisfying the criteria for score 0.
Axiom & Free-Parameter Ledger
Lean theorems connected to this paper
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IndisputableMonolith/Cost/FunctionalEquation.leanwashburn_uniqueness_aczel unclear?
unclearRelation between the paper passage and the cited Recognition theorem.
We formulate a general tool learning framework: starting from understanding the user instruction, models should learn to decompose a complex task into several subtasks, dynamically adjust their plan through reasoning, and effectively conquer each sub-task by selecting appropriate tools.
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IndisputableMonolith/Foundation/RealityFromDistinction.leanreality_from_one_distinction unclear?
unclearRelation between the paper passage and the cited Recognition theorem.
we experiment with 18 representative tools and show the potential of current foundation models in skillfully utilizing tools
What do these tags mean?
- matches
- The paper's claim is directly supported by a theorem in the formal canon.
- supports
- The theorem supports part of the paper's argument, but the paper may add assumptions or extra steps.
- extends
- The paper goes beyond the formal theorem; the theorem is a base layer rather than the whole result.
- uses
- The paper appears to rely on the theorem as machinery.
- contradicts
- The paper's claim conflicts with a theorem or certificate in the canon.
- unclear
- Pith found a possible connection, but the passage is too broad, indirect, or ambiguous to say the theorem truly supports the claim.
Forward citations
Cited by 18 Pith papers
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SearchSkill improves exact match scores and retrieval efficiency on open-domain QA by conditioning LLM actions on skills from an evolving SkillBank updated from failure patterns via two-stage SFT.
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FactoryBench: Evaluating Industrial Machine Understanding
FactoryBench reveals that frontier LLMs achieve under 50% on structured causal questions and under 18% on decision-making in industrial robotic telemetry.
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TADI: Tool-Augmented Drilling Intelligence via Agentic LLM Orchestration over Heterogeneous Wellsite Data
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Medusa augments LLMs with multiple decoding heads and tree-based attention to predict and verify several tokens in parallel, yielding 2.2-3.6x inference speedup via two fine-tuning regimes.
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TIDE-Bench: Task-Aware and Diagnostic Evaluation of Tool-Integrated Reasoning
TIDE-Bench is a new benchmark for tool-integrated reasoning that combines diverse tasks, multi-aspect metrics covering answer quality, process reliability, efficiency and cost, plus filtered challenging test sets.
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SearchSkill: Teaching LLMs to Use Search Tools with Evolving Skill Banks
SearchSkill introduces an evolving SkillBank and two-stage SFT to make LLM search query planning explicit via skill selection, improving exact match on QA benchmarks and retrieval behavior.
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ToolRL: Reward is All Tool Learning Needs
A principled reward design for tool selection and application in RL-trained LLMs delivers 17% gains over base models and 15% over SFT across benchmarks.
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OS-ATLAS: A Foundation Action Model for Generalist GUI Agents
OS-Atlas, trained on the largest open-source cross-platform GUI grounding corpus of 13 million elements, outperforms prior open-source models on six benchmarks across mobile, desktop, and web platforms.
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ReWOO: Decoupling Reasoning from Observations for Efficient Augmented Language Models
ReWOO decouples reasoning from tool observations in augmented language models, delivering 5x token efficiency and 4% higher accuracy on multi-step reasoning benchmarks like HotpotQA.
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Grep retrieval generally outperforms vector retrieval in agentic search tasks, with performance varying strongly by agent harness and tool-calling style.
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InternLM2 Technical Report
InternLM2 is a new open-source LLM that outperforms prior versions on 30 benchmarks and long-context tasks through scaled pre-training to 32k tokens and a conditional online RLHF alignment strategy.
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LLaMA-Adapter V2 achieves open-ended visual instruction following in LLMs by unlocking more parameters, early fusion of visual tokens, and joint training on disjoint parameter groups with only 14M added parameters.
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Understanding the planning of LLM agents: A survey
A survey that provides a taxonomy of methods for improving planning in LLM-based agents across task decomposition, plan selection, external modules, reflection, and memory.
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The Rise and Potential of Large Language Model Based Agents: A Survey
The paper surveys the origins, frameworks, applications, and open challenges of AI agents built on large language models.
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Siren's Song in the AI Ocean: A Survey on Hallucination in Large Language Models
A literature survey that taxonomizes hallucination phenomena in LLMs, reviews evaluation benchmarks, and analyzes approaches for their detection, explanation, and mitigation.
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A Survey on the Memory Mechanism of Large Language Model based Agents
A systematic review of memory designs, evaluation methods, applications, limitations, and future directions for LLM-based agents.
Reference graph
Works this paper leans on
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[1]
Fine-Tuning Language Models from Human Preferences
URL https://arxiv.org/abs/1909.08593. 49 A Case Study In this section, we provide the specific prompts and model responses of ChatGPT (Mar 23, 2023 version) for each tool studied in § 4. The implementations for different APIs and datasets will be available in BMTools. A.1 3D Models A. Case Study A Case Study In this section, we provide the specific prompts...
work page internal anchor Pith review Pith/arXiv arXiv 1909
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[2]
tag: Banana Pie Recipes, type: recipe
Banana Cream Pie. tag: Banana Pie Recipes, type: recipe
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[3]
tag: Custard and Cream Pies, type: recipes
Banana Pie. tag: Custard and Cream Pies, type: recipes
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[4]
Banana Dulce de Leche Pie (Banana Caramel Pie). tag: Mexican, type: recipe
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[5]
tag: No-Bake Pie Recipes, type: recipe
Banana Cream Pie with Pudding. tag: No-Bake Pie Recipes, type: recipe
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[6]
Banana Cream Pie Made Easy. tag: Banana Pie Recipes, type: recipe Thought: I should load item 1 as it is exactly what I am searching for. Information: OK. Action: load(1) Information: Banana Pie This banana pie is made with homemade pudding. It is one of the yummiest desserts on earth! Review: 4.4 (1,604) 1,341 Reviews 213 Photos Create: Recipe by Ruby Pf...
work page 2022
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[7]
tag: V egetable Soup Recipes, type: recipe
Grandma’s Slow Cooker Beef and V egetable Soup. tag: V egetable Soup Recipes, type: recipe
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[8]
Bouquet of colorful flowers in a vase
Homemade V egetable Soup. tag: V egetable Soup Recipes, type: recipe Thought: I should load item 21 as it is the most general recipe. Information: OK. Action: load(21) Information: ... Directions: Combine the baby carrots, potatoes, onion, celery, beans, cabbage, tomatoes, green beans, chicken broth, vegetable stock, water, basil, sage, thyme, and salt in...
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[9]
It looks like viewing the main page of the input entity
find_entity_by_head(inputID) Find all <r, t> that has the relation <input, r, t>. It looks like viewing the main page of the input entity. The input has to be EXACTL Y ONE ID (eg. ’Q42’) and result is a table
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[10]
It looks like viewing the reverse main page of the input entity
find_entity_by_tail(inputID) Find all < h, r > that has the relation < h, r, input > . It looks like viewing the reverse main page of the input entity. The input has to be EXACTL Y ONE ID(eg. ’Q42’) and result is a table
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[11]
get_minor_info(input) Find all <mr, mt> that has the major and minor relation <(h, r, t), mr, mt> which <h, r, t> is the input IDs contained in a string. The input has to be EXACTL Y THREE IDs (an entity, a relation and an entity) or TWO IDs AND one V ALUE (an entity, a relation and a value). and they are a valid tuple (eg. ”Q42 P106 Q6625963” or ”Q42 P21...
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[12]
For example, all the entities that are named ”Obama”, including either person, book, anything else
get_entity_id(input) Search for all the entities’ ID that has the surface form as the input. For example, all the entities that are named ”Obama”, including either person, book, anything else. PLEASE use it to convert an entity to an ID for ’find_entity_by_head’ or ’find_entity_by_tail’ option
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[13]
For example, all the relations that are named ”tax”
get_relation_id(input) Search for all the relations’ ID that has the surface form as the input. For example, all the relations that are named ”tax”
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[14]
Use the ID of the entities and relations you’ve got from get_entity_id() and get_relation_id()
search_by_query(query) After knowing the unique id of entity or relation, present a sparql query. Use the ID of the entities and relations you’ve got from get_entity_id() and get_relation_id()
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[15]
find_in_last_table(keyword) Get the rows where the keyword appears in any column of the last result table. The keyword can also be an ID. Demonstration Examples: Question: What’s the birthday of Douglas Adams? Thought: I need to find the date of birth of Douglas Adams Action: get_entity_id Action Input: Douglas Adams id label description 0 Q42 Douglas Adams...
work page 1952
discussion (0)
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