Bonsai通过可解释的推理树实现跨领域自适应问答，支持人类验证与修正。
Bonsai: Interpretable Tree-Adaptive Grounded Reasoning
- 用检索证据构建可调的推理树，支持动态调整推理强度
- 在多种模态数据上表现稳定，性能媲美专用黑箱模型
- 适合需要透明推理与可控不确定性的高可信场景
为构建通用协作型智能体，人类需要可靠的人工智能系统，能够（1）适应新领域，（2）透明地处理不确定性以支持验证与修正。黑箱模型虽具强大数据处理能力，但因不可解释、领域局限和缺乏不确定性感知而不满足要求。我们提出Bonsai，一种组合式概率推理系统，通过检索相关支撑证据，生成可适应的推理树，并利用这些证据计算从自然语言推断出的子命题的似然性。Bonsai在测试时可通过证据缩放调节推理能力，在对话、照片、视频、音频及数据库等多种模态上均表现出可靠性能。问答与人类对齐实验表明，Bonsai性能可媲美特定领域黑箱方法，同时生成可解释、有依据且具备不确定性意识的推理过程。
原文摘要 · Abstract (English)
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.
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