Language-model agents increasingly act through tools, APIs, databases, and other stateful environments, where task success depends on external evidence rather than final text alone. Existing reward paradigms face a trade-off: preference and rubric-based feedback are flexible but often weakly grounded, while verifier-based rewards are grounded but typically tied to narrow task-specific checkers. We argue that agentic reinforcement learning needs reward interfaces: mechanisms that explicitly connect task requirements to environment evidence, provenance, and reward aggregation. We introduce **Agentic Rubrics**, an executable reward-interface view with four components: criteria, evidence acquisition, provenance, and aggregation. This view separates what should be evaluated from how evidence is obtained, audited, and converted into reward. We also specify a minimal environment contract for auditable reward construction and give an illustrative instantiation in stateful tool-use environments. In this case study, Agentic Rubrics augment final environment feedback with process-aware rewards derived from tool traces and state records, yielding modest improvements in task performance and process reliability while revealing domain-dependent limitations. These results support the position that grounded agent learning should treat reward construction as an interface problem, not only as scalar reward modeling.

Word sense disambiguation (WSD), identifying the most suitable meaning of ambiguous words in the given contexts according to a predefined sense inventory, is one of the most classical and challenging tasks in natural language processing. Benefiting from the powerful ability of deep neural networks, WSD has achieved a great advancement in recent years. Reformulating WSD as a text span extraction task is an effective approach, which accepts a sentence context of an ambiguous word together with all definitions of its candidate senses simultaneously, and requires to extract the text span corresponding with the right sense. However, the approach merely depends on a short definition to learn sense representation, which neglects abundant semantic knowledge from related senses and leads to data-inefficient learning and suboptimal WSD performance. To address the limitations, we propose a novel WSD method with Knowledge-Enhanced and Local Self-Attention-based Extractive Sense Comprehension (KELESC). Specifically, a knowledge-enhanced method is proposed to enrich semantic representation by incorporating additional examples and definitions of the related senses in WordNet. Then, in order to avoid the huge computing complexity induced by the additional information, a local self-attention mechanism is utilized to constrain attention to be local, which allows longer input texts without large-scale computing burdens. Extensive experimental results demonstrate that KELESC achieves better performance than baseline models on public benchmark datasets.