Bonsai: Interpretable Tree-Adaptive Grounded Reasoning
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866912309770715136 |
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| author | Sanders, Kate Van Durme, Benjamin |
| author_facet | Sanders, Kate Van Durme, Benjamin |
| contents | To develop general-purpose collaborative agents, humans need reliable AI systems that can (1) adapt to new domains and (2) transparently reason with uncertainty to allow for verification and correction. Black-box models demonstrate powerful data processing abilities but do not satisfy these criteria due to their opaqueness, domain specificity, and lack of uncertainty awareness. We introduce Bonsai, a compositional and probabilistic reasoning system that generates adaptable inference trees by retrieving relevant grounding evidence and using it to compute likelihoods of sub-claims derived from broader natural language inferences. Bonsai's reasoning power is tunable at test-time via evidence scaling and it demonstrates reliable handling of varied domains including transcripts, photographs, videos, audio, and databases. Question-answering and human alignment experiments demonstrate that Bonsai matches the performance of domain-specific black-box methods while generating interpretable, grounded, and uncertainty-aware reasoning traces. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2504_03640 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Bonsai: Interpretable Tree-Adaptive Grounded Reasoning Sanders, Kate Van Durme, Benjamin Computation and Language Artificial Intelligence Computer Vision and Pattern Recognition 68T50, 68T37 I.2.7 To develop general-purpose collaborative agents, humans need reliable AI systems that can (1) adapt to new domains and (2) transparently reason with uncertainty to allow for verification and correction. Black-box models demonstrate powerful data processing abilities but do not satisfy these criteria due to their opaqueness, domain specificity, and lack of uncertainty awareness. We introduce Bonsai, a compositional and probabilistic reasoning system that generates adaptable inference trees by retrieving relevant grounding evidence and using it to compute likelihoods of sub-claims derived from broader natural language inferences. Bonsai's reasoning power is tunable at test-time via evidence scaling and it demonstrates reliable handling of varied domains including transcripts, photographs, videos, audio, and databases. Question-answering and human alignment experiments demonstrate that Bonsai matches the performance of domain-specific black-box methods while generating interpretable, grounded, and uncertainty-aware reasoning traces. |
| title | Bonsai: Interpretable Tree-Adaptive Grounded Reasoning |
| topic | Computation and Language Artificial Intelligence Computer Vision and Pattern Recognition 68T50, 68T37 I.2.7 |
| url | https://arxiv.org/abs/2504.03640 |