HTAA: Enhancing LLM Planning via Hybrid Toolset Agentization & Adaptation

Chengrui Huang; Gang Zeng; JunShuo Zhang; Menghua Jiang; Shen Gao; Shuo Shang; Xikun Wang; Ximeng Wang; Zhaobing Han; Zhiyuan Ma

arxiv: 2604.10917 · v1 · submitted 2026-04-13 · 💻 cs.CL

HTAA: Enhancing LLM Planning via Hybrid Toolset Agentization & Adaptation

Chengrui Huang , JunShuo Zhang , Zhiyuan Ma , Xikun Wang , Ximeng Wang , Menghua Jiang , Gang Zeng , Zhaobing Han

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Shen Gao Shuo Shang

This is my paper

Pith reviewed 2026-05-10 16:29 UTC · model grok-4.3

classification 💻 cs.CL

keywords LLM tool usehierarchical planningagent toolsasymmetric adaptationtask success ratetrajectory lengthcontext overheadproduction deployment

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The pith

HTAA groups frequently co-used tools into agent tools and adapts the planner asymmetrically to improve success and shorten trajectories in LLM tool planning.

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

The paper presents HTAA as a way to make large language models handle large numbers of tools without the errors and inefficiency of calling each one individually. It does this by bundling tools that are often used together into single specialized agent tools, which shrinks the choices the main planner has to make. A separate training step called asymmetric adaptation then aligns the planner to these new agents through backward and forward adjustments on example trajectories. This setup is tested on a real internal dataset for ride-hailing data verification plus standard benchmarks. The approach targets practical problems where flat tool lists cause long, error-prone sequences and high context costs.

Core claim

HTAA is a hierarchical framework consisting of toolset agentization, which encapsulates frequently co-used tools into specialized agent tools to reduce the planner's action space and mitigate redundancy, combined with Asymmetric Planner Adaptation that aligns the high-level planner to these agent tools via backward reconstruction and forward refinement on trajectories, resulting in higher task success rates, shorter tool calling sequences, and lower context overhead on InfoVerify and other benchmarks.

What carries the argument

Toolset agentization, which turns groups of frequently co-used tools into single specialized agent tools, together with Asymmetric Planner Adaptation that uses trajectory-based backward reconstruction and forward refinement to coordinate the planner with the new agents.

If this is right

HTAA produces higher task success rates than strong baselines on InfoVerify and common benchmarks.
The method generates shorter tool-calling trajectories.
Context overhead during planning is reduced substantially.
In a production deployment for POI validation, manual validation effort and operational cost decrease.

Where Pith is reading between the lines

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

The same bundling idea might apply to other tool-heavy domains such as code generation or scientific experiment planning where certain tool sequences repeat.
If adaptation succeeds reliably, it suggests planners can learn to treat agent tools as atomic units rather than requiring full re-planning at every step.
Scalability claims would be strengthened by tests on tool libraries several times larger than those in the reported experiments.

Load-bearing premise

That bundling co-used tools into agents will not remove needed flexibility or create coordination failures that the adaptation step cannot fix.

What would settle it

Running HTAA on a new long-horizon task set where the success rate or trajectory length becomes worse than a flat tool-calling baseline after adaptation is applied.

Figures

Figures reproduced from arXiv: 2604.10917 by Chengrui Huang, Gang Zeng, JunShuo Zhang, Menghua Jiang, Shen Gao, Shuo Shang, Xikun Wang, Ximeng Wang, Zhaobing Han, Zhiyuan Ma.

**Figure 2.** Figure 2: Scaling experiments of HTAA with varying planner sizes and tool agent sizes. Claude DeepSeek GPT Qwen3 ToolACE xLAM 70 80 90 100 110 Percentage (%) -8.8% -10.1% -17.5% -22.0% +2.3% 21.07 +6.0% 19.21 14.17 12.73 9.71 8.01 8.05 6.28 10.36 10.60 16.39 17.36 Relative Change Rate of Average Trajectory Length Vanilla w HTA [PITH_FULL_IMAGE:figures/full_fig_p009_2.png] view at source ↗

read the original abstract

Enabling large language models to scale and reliably use hundreds of tools is critical for real-world applications, yet challenging due to the inefficiency and error accumulation inherent in flat tool-calling architectures. To address this, we propose Hybrid Toolset Agentization & Adaptation (HTAA), a hierarchical framework for scalable tool-use planning. We propose a novel toolset agentization paradigm, which encapsulates frequently co-used tools into specialized agent tools, thereby reducing the planner's action space and mitigating redundancy. To ensure effective coordination, we design Asymmetric Planner Adaptation, a trajectory-based training paradigm that aligns the high-level planner with agent tools via backward reconstruction and forward refinement. To validate the performance of HTAA, we conduct experiments on a real-world internal dataset, InfoVerify, based on the POI validation workflow of China's largest online large-scale ride-hailing platform, featuring long-horizon executable tool trajectories. Experiments on InfoVerify and widely-used benchmarks show that HTAA consistently achieves higher task success rates, requires short tool calling trajectories, and significantly reduces context overhead compared to strong baselines. Furthermore, in a production deployment, HTAA substantially reduces manual validation effort and operational cost, demonstrating its practical efficacy.

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.

Desk Editor's Note private letter to a colleague

HTAA bundles co-used tools into agent tools and adapts the planner asymmetrically to cut action space, but the gains rest on unshown ablations and the risk of losing coverage on mixed sequences remains open.

read the letter

The paper's core move is to group tools that often appear together into higher-level agent tools, shrinking what the LLM planner has to choose from, then train it with backward reconstruction followed by forward refinement so it can still handle coordination. That combination is presented as new, and the production angle on InfoVerify data from a real ride-hailing workflow gives it a practical flavor that most tool-use papers lack. They report shorter trajectories, higher success rates, and lower context costs against baselines, plus a deployment claim that manual effort dropped. Those are the kind of outcomes worth noticing if they hold up. The adaptation step is the part that tries to fix the obvious downside of encapsulation, and the abstract frames it as trajectory-based rather than just prompt engineering. That distinction matters for whether this scales beyond the training distribution. The soft spot is the missing detail on how often optimal paths cross the learned agent-tool boundaries and whether the adaptation actually recovers them. Without an ablation that turns off cross-boundary recovery or breaks out success by trajectory type, it's hard to separate the hierarchy's benefit from dataset quirks or better overall training. The stress-test concern lands here: if InfoVerify workflows mix tools across groups more than the co-use clustering assumes, the reduced action space could trade away expressiveness that the adaptation cannot fully restore. This is aimed at people building agents for large tool libraries in industry settings where long-horizon reliability matters. It deserves a serious referee because the problem is concrete and the method is spelled out enough to test, even though the current evidence section needs more breakdowns and statistical checks before the claims can be taken as settled.

Referee Report

2 major / 2 minor

Summary. The paper proposes HTAA, a hierarchical framework for LLM tool-use planning that first encapsulates frequently co-used tools into specialized 'agent tools' to shrink the planner's action space and reduce redundancy, then applies Asymmetric Planner Adaptation (trajectory-based training via backward reconstruction and forward refinement) to maintain coordination across the resulting hierarchy. Experiments on the internal InfoVerify dataset (long-horizon POI validation workflows from a ride-hailing platform) plus standard benchmarks are claimed to show higher task success rates, shorter tool-calling trajectories, lower context overhead versus strong baselines, and reduced manual effort in a production deployment.

Significance. If the reported gains are robustly attributable to the hybrid agentization rather than dataset artifacts or unablated components, the approach could provide a practical route to scaling reliable tool orchestration for LLMs in complex, long-horizon applications. The grounding in a real-world internal workflow and production deployment is a positive feature, though the absence of public data or detailed ablations limits immediate generalizability.

major comments (2)

[§4 (Experiments and Results)] §4 (Experiments and Results): The central claim that HTAA achieves higher success rates and shorter trajectories via the hierarchy requires evidence that Asymmetric Planner Adaptation reliably recovers cross-group tool sequences. The manuscript supplies no breakdown of how often optimal InfoVerify trajectories mix tools across the learned agent-tool boundaries, nor an ablation that disables forward refinement (or the full adaptation) to isolate whether the reduced action space trades away expressiveness for non-co-occurring combinations.
[§3.2 (Asymmetric Planner Adaptation)] §3.2 (Asymmetric Planner Adaptation): The description of backward reconstruction and forward refinement lacks any formal analysis, coverage guarantees, or empirical test showing that arbitrary cross-boundary sequences can be reconstructed without loss. This is load-bearing for the claim that encapsulation into agent tools does not introduce unrecoverable coordination failures.

minor comments (2)

[Abstract] Abstract: The phrase 'widely-used benchmarks' is used without naming the specific benchmarks or providing even high-level metrics; this should be expanded for immediate clarity even in the abstract.
[§3.1 (Toolset Agentization)] §3.1 (Toolset Agentization): The criterion for determining 'frequently co-used' tools (e.g., frequency threshold, clustering method) is not stated explicitly; a short paragraph or pseudocode would improve reproducibility.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the detailed and constructive comments. We address each major comment below and indicate where revisions will be made to strengthen the manuscript.

read point-by-point responses

Referee: [§4 (Experiments and Results)] §4 (Experiments and Results): The central claim that HTAA achieves higher success rates and shorter trajectories via the hierarchy requires evidence that Asymmetric Planner Adaptation reliably recovers cross-group tool sequences. The manuscript supplies no breakdown of how often optimal InfoVerify trajectories mix tools across the learned agent-tool boundaries, nor an ablation that disables forward refinement (or the full adaptation) to isolate whether the reduced action space trades away expressiveness for non-co-occurring combinations.

Authors: We appreciate this observation. The original experiments report aggregate success rates, trajectory lengths, and component ablations, but do not include a dedicated breakdown of cross-boundary mixes on InfoVerify or an ablation that isolates forward refinement. In the revised manuscript we will add: (i) a table or figure quantifying the fraction of optimal InfoVerify trajectories that require tools from different learned agent groups, and (ii) an ablation that disables forward refinement (while retaining backward reconstruction) to measure its specific contribution to recovering cross-group sequences. These additions will directly test whether the reduced action space compromises expressiveness. revision: yes
Referee: [§3.2 (Asymmetric Planner Adaptation)] §3.2 (Asymmetric Planner Adaptation): The description of backward reconstruction and forward refinement lacks any formal analysis, coverage guarantees, or empirical test showing that arbitrary cross-boundary sequences can be reconstructed without loss. This is load-bearing for the claim that encapsulation into agent tools does not introduce unrecoverable coordination failures.

Authors: We agree that the current description in §3.2 is primarily algorithmic. Backward reconstruction is intended to decompose any valid trajectory into high-level planner actions and low-level agent-tool calls, thereby preserving cross-boundary sequences by construction; forward refinement then improves the planner’s policy on the resulting hierarchy. While we do not supply formal coverage guarantees (the method is data-driven rather than provably complete), we will augment the revision with additional empirical evidence: success rates on held-out trajectories that explicitly cross agent boundaries, plus qualitative examples of reconstructed cross-boundary sequences. These results will be placed in §3.2 or an appendix to substantiate that coordination failures are not introduced. revision: partial

Circularity Check

0 steps flagged

No circularity: empirical framework with independent experimental validation

full rationale

The paper describes a hierarchical tool-use planning method (HTAA) consisting of toolset agentization by co-use frequency and Asymmetric Planner Adaptation via backward/forward trajectory alignment. No equations, derivations, or parameter-fitting steps are present in the provided text. The central claims rest on experimental outcomes (success rates, trajectory lengths, context overhead) measured on InfoVerify and standard benchmarks, with no evidence that these metrics are computed from or forced by the same fitted groupings used in training. The method's design choices are presented as engineering decisions rather than self-referential definitions or self-citation chains. This is a standard non-circular empirical contribution.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 1 invented entities

Abstract-only review; full technical details on parameters and assumptions are unavailable. The ledger captures the high-level premises stated in the abstract.

axioms (1)

domain assumption Frequently co-used tools can be encapsulated into specialized agent tools without substantial loss of capability or flexibility.
This premise enables the reduction in planner action space described in the toolset agentization step.

invented entities (1)

specialized agent tools no independent evidence
purpose: To bundle groups of frequently co-used tools so the high-level planner operates over a smaller action space.
New construct introduced by the HTAA paradigm.

pith-pipeline@v0.9.0 · 5537 in / 1372 out tokens · 89747 ms · 2026-05-10T16:29:10.439382+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

2 extracted references · 2 canonical work pages

[1]

Limitations

doi: 10.18653/v1/2025.findings-emnlp.882. URL https://aclanthology.org/2025. findings-emnlp.882/. Nathan Lambert, Jacob Morrison, Valentina Pyatkin, Shengyi Huang, Hamish Ivison, Faeze Brahman, Lester James V . Miranda, Alisa Liu, Nouha Dziri, Shane Lyu, Yuling Gu, Saumya Malik, Victoria Graf, Jena D. Hwang, Jiangjiang Yang, Ronan Le Bras, Oyvind Tafjord,...

work page doi:10.18653/v1/2025.findings-emnlp.882 2025
[2]

Guidelines: • The answer [N/A] means that the paper does not involve crowdsourcing nor research with human subjects

Institutional review board (IRB) approvals or equivalent for research with human subjects Question: Does the paper describe potential risks incurred by study participants, whether such risks were disclosed to the subjects, and whether Institutional Review Board (IRB) approvals (or an equivalent approval/review based on the requirements of your country or ...

work page

[1] [1]

Limitations

doi: 10.18653/v1/2025.findings-emnlp.882. URL https://aclanthology.org/2025. findings-emnlp.882/. Nathan Lambert, Jacob Morrison, Valentina Pyatkin, Shengyi Huang, Hamish Ivison, Faeze Brahman, Lester James V . Miranda, Alisa Liu, Nouha Dziri, Shane Lyu, Yuling Gu, Saumya Malik, Victoria Graf, Jena D. Hwang, Jiangjiang Yang, Ronan Le Bras, Oyvind Tafjord,...

work page doi:10.18653/v1/2025.findings-emnlp.882 2025

[2] [2]

Guidelines: • The answer [N/A] means that the paper does not involve crowdsourcing nor research with human subjects

Institutional review board (IRB) approvals or equivalent for research with human subjects Question: Does the paper describe potential risks incurred by study participants, whether such risks were disclosed to the subjects, and whether Institutional Review Board (IRB) approvals (or an equivalent approval/review based on the requirements of your country or ...

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