这个框架解决了高可信场景下声明验证的忠实性和不确定性表达问题，做事实核查、医疗或金融 AI 的团队可以直接参考其可解释的推理机制。
该论文提出推理时论证（ITA），一种可训练的神经符号框架，用于三元声明验证（真/假/不确定）。ITA 使用形式论证语义指导 LLM 生成论证并分配基础分数，同时计算三元预测。训练时，论证生成和评分根据预测质量优化；推理时，最终预测忠实于决定判决的论证和分数，而非事后推理痕迹。在两项三元声明验证数据集上，ITA 优于论证基线，并与非论证直接预测基线竞争，同时提供可检查的论证结构。
Neurosymbolic Learning for Inference-Time Argumentation
Claim verification is an important problem in high-stakes settings, including health and finance. When information underpinning claims is incomplete or conflicting, uncertain answers may be more appropriate than binary true or false classifications. In all cases, faithful explanations of the considerations determining the final verdict are crucial. We introduce inference-time argumentation (ITA), a trainable neurosymbolic framework for ternary claim verification in which a formal argumentation semantics giving the strength of claims is used both (i) to guide LLM training as models learn to generate arguments and assign them base scores (representing intrinsic strengths) and (ii) to compute ternary (true/false/uncertain) predictions from generated, scored arguments. As a result, at training time, argument generation and scoring can be optimised according to the quality of the induced argumentative predictions. Moreover, at inference time, the final prediction is faithful, by construction, to the arguments and scores determining the verdict, rather than being justified by a potentially unfaithful post-hoc reasoning trace as in conventional reasoning models. We finally show that, on two datasets for ternary claim verification, ITA improves upon argumentative baselines and can perform competitively against non-argumentative direct-prediction baselines, while providing verdicts that are computed deterministically from explicit, inspectable argumentative structures.