---
title: "Tree-of-Evidence — paper by Nnamdi et al. (2026)"
description: "The authors propose Tree-of-Evidence (ToE), which recasts explanation as a discrete search for the smallest set of input… 38 references."
authors: ["Micky C. Nnamdi", "Benoit Louis Marteau", "Yishan Zhong", "J. Ben Tamo", "May Dongmei Wang"]
year: 2026
venue: "Findings of the Association for Computational Linguistics: ACL 2026"
source: "https://aclanthology.org/2026.findings-acl.1460/"
doi: "10.18653/v1/2026.findings-acl.1460"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/nnamdi-2026-tree-evidence-efficient-system-2/"
---
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# Tree-of-Evidence: Efficient “System 2” Search for Faithful Multimodal Grounding

**Authors:** [Micky C. Nnamdi](https://md.research.elamur.ai/free/en/authors/micky-c-nnamdi/), Benoit Louis Marteau, Yishan Zhong, [J. Ben Tamo](https://md.research.elamur.ai/free/en/authors/j-ben-tamo/) and [May Dongmei Wang](https://md.research.elamur.ai/free/en/authors/may-dongmei-wang/) · **Year:** 2026 · **Venue:** [Findings of the Association for Computational Linguistics: ACL 2026](https://md.research.elamur.ai/free/en/venues/findings/) ([Findings of the Association for Computational Linguistics 2026](https://md.research.elamur.ai/free/en/venues/findings/2026/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2026.findings-acl.1460/) · [PDF](https://aclanthology.org/2026.findings-acl.1460.pdf) · [DOI 10.18653/v1/2026.findings-acl.1460](https://doi.org/10.18653/v1/2026.findings-acl.1460) · [OpenAlex](https://openalex.org/W7166806271)

**Citations:** 0 citations (OpenAlex, 2026-10-14)

## Summary
Large multimodal models used in high-stakes settings such as intensive care are hard to audit. Attention maps and post-hoc attributions often fail to reflect how these models actually combine modalities. The authors propose Tree-of-Evidence (ToE), which recasts explanation as a discrete search for the smallest set of input pieces that still reproduces the model's prediction and confidence.

ToE has two parts:
- **Evidence Bottlenecks:** lightweight MLP selectors trained with straight-through top-k masking. They score coarse units, namely hourly windows of ICU vitals/labs and chunks of radiology reports.
- **Beam search:** run at inference over these units. It balances agreement with the original decision, stability of the predicted probability, and sparsity.

CXR and ECG information is not searched; it enters as fixed patient context. Across six tasks on three datasets in clinical and industrial domains, ToE keeps nearly all predictive performance using only a handful of units. It also gives more faithful explanations than standard attribution methods and much larger LLMs. This makes it a practical tool for auditing multimodal predictors and flagging unreliable outputs.

## Key points
- Evaluation covers four MIMIC-IV clinical tasks, cross-center testing on eICU (208 hospitals), and non-clinical fault detection on LEMMA-RCA. Notes are encoded with BioClinicalBERT.
- With as few as five evidence units, ToE retains over 98% of full-model AUROC.
- Under sparse budgets, ToE's explanations match the model's decisions more often and deviate less from its outputs than those of LIME, SHAP, gradient saliency, Top-k ranking, and concept-bottleneck models. Unlike concept-bottleneck models, it needs no concept annotations.
- The ~109M-parameter system outperforms LLMs of up to 70B parameters. It adds roughly 13 ms of overhead, and beam search comes within 0.001 AUROC of exhaustive search at small k on MIMIC-IV.
- Search exhaustion is 4–26× more frequent for false positives than for true positives, so it can support selective abstention.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

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## Cite

Cite as: Micky C. Nnamdi, Benoit Louis Marteau, Yishan Zhong, J. Ben Tamo and May Dongmei Wang. 2026. Tree-of-Evidence: Efficient “System 2” Search for Faithful Multimodal Grounding. Findings of the Association for Computational Linguistics: ACL 2026.

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{nnamdi2026tree,
  title = {{Tree-of-Evidence: Efficient “System 2” Search for Faithful Multimodal Grounding}},
  author = {Micky C. Nnamdi, Benoit Louis Marteau, Yishan Zhong, J. Ben Tamo and May Dongmei Wang},
  booktitle = {Findings of the Association for Computational Linguistics: ACL 2026},
  year = {2026},
  doi = {10.18653/v1/2026.findings-acl.1460},
  url = {https://doi.org/10.18653/v1/2026.findings-acl.1460}
}
```

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- [↑ back to papers of 2026](https://md.research.elamur.ai/free/en/papers/2026/page/46/)

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37. Shunyu Yao, Dian Yu, Jeffrey Zhao, Izhak Shafran, Tom Griffiths, Yuan Cao, and Karthik Narasimhan. 2023. Tree of thoughts: Deliberate problem solving with large language models. Advances in neural information processing systems, 36:11809–11822. [find](https://scholar.google.com/scholar?q=Shunyu+Yao%2C+Dian+Yu%2C+Jeffrey+Zhao%2C+Izhak+Shafran%2C+Tom+Griffiths%2C+Yuan+Cao%2C+and+Karthik+Narasimhan%2E+2023%2E+Tree+of+thoughts%3A+Deliberate+problem+solving+with+large+language+models%2E+Advances+in+neural+inf)
38. Evidence Size by Mortality Outcome Table A1: ToE Comprehensiveness (↑) across evidence budgets and four MIMIC-IV tasks (mean ± std, 5 seeds). E4: Post-Obs Mort. 1 3 5 8 12 16 20 24 0.0413 ± 0.0050 0.0885 ± 0.0104 0.1112 ± 0.0143 0.1293 ± 0.0177 0.1417 ± 0.0199 0.1489 ± 0.0216 0.1549 ± 0.0233 0.1612… [find](https://scholar.google.com/scholar?q=Evidence+Size+by+Mortality+Outcome+Table+A1%3A+ToE+Comprehensiveness+%28%E2%86%91%29+across+evidence+budgets+and+four+MIMIC%2DIV+tasks+%28mean+%C2%B1+std%2C+5+seeds%29%2E+E4%3A+Post%2DObs+Mort%2E+1+3+5+8+12+16+20+24+0%2E0413+%C2%B1+0%2E0050+0%2E0)

*Updated 1 November 2026*