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<title>ToolTree: Efficient LLM Agent Tool Planning via Dual-Feedback Monte Carlo Tree Search and Bidirectional Pruning</title>
<meta name="description" content="ToolTree plans LLM agent tool use via dual-feedback Monte Carlo tree search with bidirectional pruning — state-of-the-art across 4 benchmarks with an average gain of ~10% over existing methods.">
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 <a href="#overview">Overview</a>
 <a href="#efficiency">Efficiency</a>
 <a href="#method">Method</a>
 <a href="#case-study">Case Study</a>
 <a href="#results">Results</a>
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 <h1><span class="hero-name">ToolTree</span>: Efficient LLM Agent Tool Planning via Dual-Feedback Monte Carlo Tree Search and Bidirectional Pruning</h1>
 <p class="authors">
 Shuo Yang, Caren Han, Yihao Ding, Shuhe Wang, Eduard Hovy
 </p>
 <p class="affiliation">The University of Melbourne &#183; The University of Western Australia</p>
 <p class="venue"><span class="venue-chip">ICLR 2026</span></p>
 <nav class="button-row" aria-label="Paper links">
 <a class="btn" href="https://openreview.net/forum?id=Ef5O9gNNLE" target="_blank" rel="noopener"><svg class="btn-ico" viewBox="0 0 24 24" aria-hidden="true" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"><path d="M14 2H6a2 2 0 0 0-2 2v16a2 2 0 0 0 2 2h12a2 2 0 0 0 2-2V8z"/><polyline points="14 2 14 8 20 8"/></svg>Paper</a>
 <a class="btn" href="https://arxiv.org/abs/2603.12740" target="_blank" rel="noopener"><svg class="btn-ico" viewBox="0 0 24 24" aria-hidden="true" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"><path d="M14 2H6a2 2 0 0 0-2 2v16a2 2 0 0 0 2 2h12a2 2 0 0 0 2-2V8z"/><polyline points="14 2 14 8 20 8"/></svg>arXiv</a>
 <a class="btn" href="https://github.com/SYang2000/ICLR_2026_ToolTree" target="_blank" rel="noopener"><svg class="btn-ico" viewBox="0 0 24 24" aria-hidden="true" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"><polyline points="16 18 22 12 16 6"/><polyline points="8 6 2 12 8 18"/></svg>Code</a>
 <a class="btn" href="demo.html"><svg class="btn-ico" viewBox="0 0 24 24" aria-hidden="true" fill="currentColor" stroke="none"><polygon points="7 4 20 12 7 20 7 4"/></svg>Demo</a>
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<main>
<!-- ================= Metric callouts ================= -->
<section class="metrics">
 <div class="container">
 <div class="metric-grid reveal stagger">
 <div class="metric-card">
 <div class="metric-value">~10%</div>
 <div class="metric-label">Average gain over existing methods</div>
 <div class="metric-sub">Across both closed-set and open-set tool planning scenarios</div>
 </div>
 <div class="metric-card">
 <div class="metric-value">SOTA</div>
 <div class="metric-label">State-of-the-art across all 4 benchmarks</div>
 <div class="metric-sub">GTA &#183; m&amp;m &#183; ToolBench &#183; RestBench</div>
 </div>
 <div class="metric-card">
 <div class="metric-value">#1</div>
 <div class="metric-label">Highest efficiency (performance gain per second)</div>
 <div class="metric-sub">Compared with all baselines across step limits</div>
 </div>
 </div>
 </div>
</section>
<!-- ================= Overview ================= -->
<section id="overview">
 <div class="container">
 <h2>Overview</h2>
 <div class="abstract-card reveal">
 <p class="abstract-text">
 <strong>ToolTree</strong> is a novel Monte Carlo tree search-inspired planning paradigm for
 LLM agent tool planning. It explores possible tool usage trajectories using a
 <strong>dual-stage LLM evaluation</strong> and <strong>bidirectional pruning</strong> mechanism
 that enables the agent to make informed, adaptive decisions over extended tool-use sequences
 while pruning less promising branches before and after the tool execution.
 </p>
 </div>
<figure class="paper-figure reveal">
 <img src="static/paper/comparison.png" alt="Concept comparison on a street photo asking how many wheels are in total: greedy-based planning commits to a single tool chain and answers incorrectly; search-based planning explores more tool branches but still returns a wrong count; ToolTree prunes branches before (pre-pruning) and after (post-pruning) execution and answers correctly.">
 <figcaption>Comparison of ToolTree with greedy search and search-based tool planning. ToolTree chooses
 the optimal tool trajectory and answers correctly with bidirectional pruning.</figcaption>
 </figure>
 </div>
</section>
<!-- ================= Efficiency (Figure 3) ================= -->
<section class="fig3-band section-alt" id="efficiency" aria-label="Progressive efficiency analysis">
 <div class="container">
 <h2>Efficiency</h2>
 <figure class="paper-figure reveal">
 <img src="static/paper/efficiency.png" alt="Three line charts across step limits comparing ReAct, Best-first, ToT, LATS, and ToolTree: performance versus step limit, running time versus step limit, and efficiency (performance gain per second) versus step limit — ToolTree sits highest on the performance and efficiency panels at every step limit.">
 <figcaption><span class="fig-tag">Figure 3:</span> Progressive efficiency analysis across step limits.
 ToolTree achieves the highest efficiency (performance gain per second) compared with all baselines.</figcaption>
 </figure>
 </div>
</section>
<!-- ================= Method ================= -->
<section id="method">
 <div class="container">
 <h2>Method</h2>
 <figure class="paper-figure reveal">
 <img src="static/paper/architecture.png" alt="ToolTree pipeline: an input query enters a repeated loop over the tool tree — selection, pre-evaluation of a candidate tool against a threshold before execution, expansion, execution, post-evaluation of the observed output, and backward-propagation — after which the answer predictor produces the final answer.">
 <figcaption>Architecture overview of ToolTree. An input query is processed sequentially via iterative
 dual evaluation-guided Monte Carlo Tree Search, including selection, pre-evaluation, expansion,
 execution, post-evaluation and backward-propagation.</figcaption>
 </figure>
<figure class="paper-figure reveal">
 <img src="static/paper/tooltree_search.svg" alt="Schematic tree of tool calls for a flight-search query: from the user query, candidate tool calls branch out with value scores, the low-scoring branches are drawn dashed as pruned, and one bold path leads to a final answer bar.">
 <figcaption>Illustrative schematic of the search over tool-call trajectories: each node is a candidate
 tool call scored by the value estimate, low-value branches are pruned, and the highest-reward
 trajectory is selected as the final plan.</figcaption>
 </figure>
<div class="mech-grid mech-grid-4 reveal stagger">
 <div class="mech-card">
 <span class="mech-num">1</span>
 <h3>Pre-Evaluation</h3>
 <p>
 A fast predictive signal that estimates the utility of a tool <em>before</em> execution,
 filtering schema- or slot-incompatible calls before expansion.
 </p>
 </div>
 <div class="mech-card">
 <span class="mech-num">2</span>
 <h3>Post-Evaluation</h3>
 <p>
 Assesses the actual contribution of a tool <em>after</em> execution based on observed
 outcomes, pruning unproductive branches using real feedback.
 </p>
 </div>
 <div class="mech-card">
 <span class="mech-num">3</span>
 <h3>Bidirectional Pruning</h3>
 <p>
 Combines pre- and post-evaluation to eliminate unpromising branches, concentrating
 computational budget on promising tool chains.
 </p>
 </div>
 <div class="mech-card">
 <span class="mech-num">4</span>
 <h3>Answer Predictor</h3>
 <p>
 Incorporates the tool trajectories with the highest reward found by the MCTS to produce
 the final prediction.
 </p>
 </div>
 </div>
 </div>
</section>
<!-- ================= Case study ================= -->
<section id="case-study" class="section-alt">
 <div class="container">
 <h2>Case Study</h2>
 <span class="band-label">Qualitative case studies from the paper</span>
 <div class="case-figure reveal">
 <img src="static/paper/case_study.png" alt="Iterative inferencing on a road-sign query asking for the distance in miles: early iterations read the kilometre figure off the sign and return it unchanged for a low post-evaluation reward; later iterations add retrieval and unit-conversion tools, the reward rises, and the final iteration answers in miles.">
 </div>
 <p class="case-text reveal">
 The query asks how many <em>miles</em> the journey is, while the road sign in the image gives the
 distance in kilometres. In early iterations the agent simply reads the kilometre figure off the
 sign and returns it unchanged, earning a low post-evaluation reward. Across later iterations the
 search brings in retrieval and unit-conversion tools, the post-evaluation reward rises, and the
 final trajectory converts the distance and answers in miles.
 </p>
 <figure class="paper-figure reveal">
 <div class="figure-stack">
 <img src="static/paper/case_study_medical.png" alt="Case study (a): a radiology image question about lung cancer risk — greedy search relies on generic image captioning and heuristic search on shallow heuristics, both missing the finding, while the MCTS-planned trajectory orchestrates domain-specific imaging tools and detects the risk.">
 <img src="static/paper/case_study_reasoning.png" alt="Case study (b): a multi-hop knowledge reasoning question about the building seen behind an amusement area — greedy search and heuristic search answer from broad scene cues and the most prominent text, while the MCTS-planned trajectory chains recognition and search tools to identify the building correctly.">
 </div>
 <figcaption>Two qualitative case studies showcasing ToolTree&#8217;s iterative tool orchestration on
 (a) a radiology image question and (b) a multi-hop knowledge reasoning task.</figcaption>
 </figure>
 </div>
</section>
<!-- ================= Results ================= -->
<section id="results">
 <div class="container">
 <h2>Results</h2>
 <p>
 ToolTree achieves state-of-the-art performance across 4 benchmarks spanning both closed-set and
 open-set tool planning scenarios, with an average gain of ~10% over existing methods.
 </p>
<div class="table-wrap reveal">
 <table class="results">
 <thead>
 <tr><th>Benchmark</th><th>Setting</th><th>Tasks &amp; Tools</th><th>Official source</th></tr>
 </thead>
 <tbody>
 <tr><td>GTA</td><td>Closed-set</td><td>229 real-world tasks, 14 executable tools</td><td><a href="https://github.com/open-compass/GTA" target="_blank" rel="noopener">open-compass/GTA</a> &#183; <a href="https://huggingface.co/datasets/Jize1/GTA" target="_blank" rel="noopener">HF dataset</a></td></tr>
 <tr><td>m&amp;m</td><td>Closed-set</td><td>882 human-verified multi-step multimodal tasks, 33 tools</td><td><a href="https://github.com/RAIVNLab/mnms" target="_blank" rel="noopener">RAIVNLab/mnms</a> &#183; <a href="https://huggingface.co/datasets/zixianma/mnms" target="_blank" rel="noopener">HF dataset</a></td></tr>
 <tr><td>ToolBench</td><td>Open-set</td><td>16,464 real-world REST APIs (RapidAPI)</td><td><a href="https://github.com/OpenBMB/ToolBench" target="_blank" rel="noopener">OpenBMB/ToolBench</a></td></tr>
 <tr><td>RestBench</td><td>Open-set</td><td>TMDB &amp; Spotify REST scenarios</td><td><a href="https://github.com/Yifan-Song793/RestGPT" target="_blank" rel="noopener">Yifan-Song793/RestGPT</a></td></tr>
 </tbody>
 </table>
 </div>
<p class="table-note reveal">
 This repository ships no benchmark data; each benchmark is downloaded from its official source.
 </p>
</div>
</section>
<!-- ================= Ablation ================= -->
<section id="ablation" class="section-alt">
 <div class="container">
 <h2>Pruning Ablation</h2>
<div class="ablation-block reveal">
 <figure class="paper-figure figure-medium">
 <img src="static/paper/ablation_pruning.png" alt="Two box plots comparing ToolTree against variants without pre-pruning, without post-pruning, and without both: the number of rollouts and the number of expanded nodes are lowest for full ToolTree and grow as pruning stages are disabled.">
 </figure>
 <p class="takeaway">Disabling pre-pruning, post-pruning, or both consistently increases the number of
 rollouts and expanded nodes, confirming that bidirectional pruning concentrates the computational
 budget on promising tool chains.</p>
 </div>
 </div>
</section>
<!-- ================= BibTeX ================= -->
<section id="bibtex">
 <div class="container">
 <h2>BibTeX</h2>
 <div class="bibtex-box reveal">
 <button class="copy-btn" id="copy-btn" type="button">Copy</button>
 <pre id="bibtex-text">@inproceedings{yang2026tooltree,
title={ToolTree: Efficient {LLM} Tool Planning via Dual-Feedback Monte Carlo Tree Search and Bidirectional Pruning},
author={Shuo Yang and Caren Han and Yihao Ding and Shuhe Wang and Eduard Hovy},
booktitle={The Fourteenth International Conference on Learning Representations},
year={2026},
url={https://openreview.net/forum?id=Ef5O9gNNLE}
}</pre>
 </div>
 </div>
</section>
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 &#169; 2026 the authors &#183; The University of Melbourne &#183; The University of Western Australia &#183; Code released under the MIT License.
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