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arxiv: 2606.16591 · v2 · pith:2JHF4JY6new · submitted 2026-06-15 · 💻 cs.CL

SING: Synthetic Intention Graph for Scalable Active Tool Discovery in LLM Agents

Qiao Xiao , Haochen Shi , Yisen Gao , Wenbin Hu , Huihao Jing , Tianshi Zheng , Baixuan Xu , Ziheng Zhang
show 4 more authors
Weiqi Wang Haoran Li Jiaxin Bai Yangqiu Song
This is my paper

Pith reviewed 2026-06-27 03:54 UTC · model grok-4.3

classification 💻 cs.CL
keywords LLM agentstool discoveryintention graphactive retrievaltool selectionagent ecosystems
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The pith

SING constructs an intention-tool graph to allow LLM agents to discover relevant tools dynamically from large corpora without full schema exposure.

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

The paper introduces SING as a framework for active tool discovery in LLM agents. It builds a graph that connects user intentions with tool capabilities and collaboration patterns. This structure supports dynamic retrieval as tasks evolve over multiple turns, avoiding the need to expose all tool schemas at once. Evaluations on three benchmarks with 7,471 tools show gains in recall and success rates alongside major reductions in context usage.

Core claim

SING is an intention-aware active tool discovery framework that builds an intention-tool graph linking user intentions, tool capabilities, and tool collaboration patterns, and dynamically retrieves tools according to evolving task states. Using a unified corpus of 7,471 tools, we evaluate SING on three real-world tool-use benchmarks. SING improves Global Recall@5 by up to 59.8% and downstream success rate by up to 28.9% over baselines, while reducing full-corpus tool-schema exposure by 99.8%.

What carries the argument

The intention-tool graph that links user intentions, tool capabilities, and tool collaboration patterns for dynamic retrieval based on evolving task states.

If this is right

  • LLM agents can operate in larger tool ecosystems without context overload from exhaustive schema injection.
  • Tool selection aligns better with true task intentions, especially in long-horizon tasks involving decomposition and subgoals.
  • Agent harnesses can manage context more efficiently by retrieving only relevant tools on demand.
  • Scalability improves as the number of available tools grows to thousands.

Where Pith is reading between the lines

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

  • Graph-based intention modeling could extend to other areas like API composition or multi-agent coordination.
  • Updating the graph with new tools and observed collaborations might allow continuous adaptation without retraining.
  • Testing the approach in open-ended environments with user-provided tools would reveal its robustness beyond curated corpora.

Load-bearing premise

The unified corpus and constructed graph accurately capture real-world user intentions, tool capabilities, and collaboration patterns that generalize to unseen tasks and tool sets.

What would settle it

Running the system on a new benchmark containing tools and tasks not represented in the original 7,471 tool corpus and measuring whether the reported gains in recall and success rate hold.

Figures

Figures reproduced from arXiv: 2606.16591 by Baixuan Xu, Haochen Shi, Haoran Li, Huihao Jing, Jiaxin Bai, Qiao Xiao, Tianshi Zheng, Weiqi Wang, Wenbin Hu, Yangqiu Song, Yisen Gao, Ziheng Zhang.

Figure 1
Figure 1. Figure 1: Domain distribution of the MCP server corpus, [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Overview of SING. user goal supported by the tool. We denote the nor￾malized intention set of tool t as I(t). To reduce redundancy, semantically similar intentions are merged across tools and servers, producing shared intention nodes for future steps. Appendix C.1 and [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Schema-token exposure under ALL-TOOLS￾IN-CONTEXT and SING retrieval as the MCP tool pool size increases [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Analysis of SING discovery behavior. Left: failure analysis on MCP-Atlas under Restricted and Global [PITH_FULL_IMAGE:figures/full_fig_p008_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Overview of our MCP server collection and [PITH_FULL_IMAGE:figures/full_fig_p012_5.png] view at source ↗
read the original abstract

Large language model (LLM) agents increasingly rely on agent harnesses that manage context, tools, and multi-turn execution, making tools a central interface for acting in realistic digital environments. As harness-connected tool ecosystems expand to hundreds or thousands of APIs, services, and task-specific skills, exhaustive tool schema injection becomes costly and imposes a closed-world assumption that limits agents to a predefined static inventory. Retrieval-augmented tool selection offers a natural alternative, but existing one-shot retrieval methods often fail to align isolated tool descriptions with the agent's true task intention, especially in long-horizon tasks where required capabilities emerge through decomposition, observations, and newly induced subgoals. We propose SING, an intention-aware active tool discovery framework that builds an intention-tool graph linking user intentions, tool capabilities, and tool collaboration patterns, and dynamically retrieves tools according to evolving task states. Using a unified corpus of 7,471 tools, we evaluate SING on three real-world tool-use benchmarks. SING improves Global Recall@5 by up to 59.8% and downstream success rate by up to 28.9% over baselines, while reducing full-corpus tool-schema exposure by 99.8%, demonstrating that intention-aware graph structure enables more accurate and context-efficient tool discovery in large-scale agentic ecosystems.

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 / 1 minor

Summary. The paper proposes SING, an intention-aware active tool discovery framework that constructs a Synthetic Intention Graph linking user intentions, tool capabilities, and collaboration patterns from a unified corpus of 7,471 tools. It dynamically retrieves tools for LLM agents based on evolving task states and evaluates the approach on three real-world tool-use benchmarks, claiming improvements of up to 59.8% in Global Recall@5 and 28.9% in downstream success rate over baselines, alongside a 99.8% reduction in full-corpus tool-schema exposure.

Significance. If the reported gains hold under proper controls for generalization and baseline fidelity, the work would offer a practical path toward scalable tool use in large agentic systems by replacing exhaustive schema injection with structured, intention-driven retrieval; the emphasis on graph-based decomposition of long-horizon tasks addresses a recognized bottleneck in current retrieval-augmented agents.

major comments (2)
  1. [Abstract / Evaluation] Abstract and Evaluation section: the headline gains (Global Recall@5 +59.8%, success rate +28.9%) are obtained on a fixed corpus of 7,471 tools whose intention-tool graph is constructed from the same data; the manuscript must demonstrate that the synthetic intentions and collaboration patterns transfer to held-out tasks or novel tool inventories, as this is the load-bearing assumption for the generalization claim.
  2. [Evaluation] Evaluation section: no information is supplied on statistical significance, error bars, variance across runs, or the precise implementation details of the baselines; without these, it is impossible to assess whether the reported deltas are robust or could be artifacts of post-hoc benchmark selection or corpus construction.
minor comments (1)
  1. [Abstract] Abstract: the description of graph construction and dynamic update mechanism is too high-level to allow replication or to judge whether the 99.8% exposure reduction is achieved without sacrificing recall on complex multi-turn tasks.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the thoughtful feedback. Below we respond point-by-point to the major comments, offering clarifications on the evaluation design and committing to additions that strengthen the claims.

read point-by-point responses

  1. Referee: [Abstract / Evaluation] Abstract and Evaluation section: the headline gains (Global Recall@5 +59.8%, success rate +28.9%) are obtained on a fixed corpus of 7,471 tools whose intention-tool graph is constructed from the same data; the manuscript must demonstrate that the synthetic intentions and collaboration patterns transfer to held-out tasks or novel tool inventories, as this is the load-bearing assumption for the generalization claim.

    Authors: The intention-tool graph is built solely from the static corpus of 7,471 tool descriptions and synthetically generated intentions; the three benchmarks supply task instances and evolving state sequences that were never used in graph construction. This separation already tests generalization to unseen task decompositions. Nevertheless, to directly address transfer to novel tool inventories we will add an explicit held-out tool partition experiment in the revised manuscript. revision: yes

  2. Referee: [Evaluation] Evaluation section: no information is supplied on statistical significance, error bars, variance across runs, or the precise implementation details of the baselines; without these, it is impossible to assess whether the reported deltas are robust or could be artifacts of post-hoc benchmark selection or corpus construction.

    Authors: We agree these details are required. The revised manuscript will report means and standard deviations over multiple random seeds, include statistical significance tests for the key deltas, and supply complete baseline implementations, hyperparameters, and prompt templates. revision: yes

Circularity Check

0 steps flagged

No significant circularity; claims rest on empirical evaluation of constructed graph

full rationale

The paper constructs an intention-tool graph from a unified corpus of 7,471 tools and reports measured improvements (Global Recall@5, success rate, exposure reduction) on three benchmarks. No equations, parameter-fitting steps, or self-citation chains are present that reduce any claimed result to its inputs by definition or construction. The central claims are externally falsifiable via the reported benchmark metrics and do not rely on self-definitional loops, fitted inputs renamed as predictions, or load-bearing self-citations.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 1 invented entities

The central claim depends on the existence and utility of the synthetic intention graph built from the 7,471-tool corpus; no explicit free parameters, axioms, or invented entities beyond the graph itself are named in the abstract.

invented entities (1)
  • Synthetic Intention Graph (SING) no independent evidence
    purpose: Links user intentions, tool capabilities, and collaboration patterns for dynamic retrieval
    Introduced as the core structure enabling the reported improvements; no independent evidence outside the paper's evaluation is provided in the abstract.

pith-pipeline@v0.9.1-grok · 5797 in / 1245 out tokens · 29253 ms · 2026-06-27T03:54:12.760300+00:00 · methodology

discussion (0)

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Reference graph

Works this paper leans on

17 extracted references · 1 linked inside Pith

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    Ziyang Luo, Zhiqi Shen, Wenzhuo Yang, Zirui Zhao, Prathyusha Jwalapuram, Amrita Saha, Doyen Sahoo, Silvio Savarese, Caiming Xiong, and Junnan Li

    Mcpagentbench: A real-world task bench- mark for evaluating llm agent mcp tool use.Preprint, arXiv:2512.24565. Ziyang Luo, Zhiqi Shen, Wenzhuo Yang, Zirui Zhao, Prathyusha Jwalapuram, Amrita Saha, Doyen Sahoo, Silvio Savarese, Caiming Xiong, and Junnan Li. 2025. Mcp-universe: Benchmarking large language mod- els with real-world model context protocol serv...

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    Mcp-bench: Benchmarking tool-using llm agents with complex real-world tasks via mcp servers. Preprint, arXiv:2508.20453. Tianbao Xie, Danyang Zhang, Jixuan Chen, Xiaochuan Li, Siheng Zhao, Ruisheng Cao, Toh J Hua, Zhoujun Cheng, Dongchan Shin, Fangyu Lei, and 1 others

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    Osworld: Benchmarking multimodal agents for open-ended tasks in real computer environments. Advances in Neural Information Processing Systems, 37:52040–52094. Zhangchen Xu, Adriana Meza Soria, Shawn Tan, Anurag Roy, Ashish Sunil Agrawal, Radha Pooven- dran, and Rameswar Panda. 2025. Toucan: Synthe- sizing 1.5m tool-agentic data from real-world mcp environ...

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    Yuanhang Zheng, Peng Li, Wei Liu, Yang Liu, Jian Luan, and Bin Wang

    Clawbench: Can ai agents complete everyday online tasks?Preprint, arXiv:2604.08523. Yuanhang Zheng, Peng Li, Wei Liu, Yang Liu, Jian Luan, and Bin Wang. 2024. Toolrerank: Adap- tive and hierarchy-aware reranking for tool re- trieval. InProceedings of the 2024 joint international conference on computational linguistics, language resources and evaluation (L...

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  5. [5]

    Each instruction should be a natural user request (as if a user is asking an AI assistant)

  6. [6]

    Instructions should be DIVERSE -- vary the specific entities, parameters, and complexity

  7. [7]

    Arguments must match the tool's parameter schema -- use realistic values

  8. [8]

    xxx" or

    Do NOT use placeholder values like "xxx" or "example" -- use realistic but fictional data

  9. [9]

    source_instruction

    Output ONLY a JSON array, each element has " source_instruction" (string) and "arguments" (object) Example output format: [ {{"source_instruction": "FindallflightsfromNew York to London on December 15th", "arguments": {{"origin": "JFK", "destination": "LHR", "date": "2024-12-15"}}}}, {{"source_instruction": "Searchforthe cheapest flights to Tokyonextweek"...

    2024

  10. [10]

    Evaluate whether any of the available tools can NATURALLY extend the user's query

  11. [11]

    The extension must be a logical follow-up or complement -- NOT forced or artificial

  12. [12]

    can_extend

    The extended query should read as a SINGLE coherent user request that naturally requires both old and new tools Key criteria for a MEANINGFUL extension: - The new tool provides genuine additional value (e.g., transforming results, enriching data, performing a next step) - A real user would plausibly combine these tools in one request - It is NOT just rest...

  13. [13]

    fetch historical stock prices

    Each intention should describe a CONCRETE tool action, e.g. "fetch historical stock prices", "geocode address to coordinates", "parse HTML table into structured data"

  14. [14]

    Include 1-2 intentions for IMPLICIT prerequisite or follow- up actions -- tools the user would ALSO need in the same workflow but didn't explicitly mention

  15. [15]

    A researcher wants to

    Do NOT describe user scenarios or personas -- no "A researcher wants to..." or "planning a trip..."

  16. [16]

    Do NOT rephrase the tool description -- focus on WHAT DATA the tool operates on and HOW

  17. [17]

    ServerA__geocode

    Keep each intention 4-8 words, starting with an action verb Output ONLY a JSON object mapping tool_key to intention array, no explanation. Example: {{"ServerA__geocode": ["resolve city names to GPS coordinates", "convert postal codes to lat/lng", " prepare locationinput forroute planning", "validate addressformatbefore geocoding"]}} Tools to analyze: {too...

    1965