Generative retrieval (GR) retrieves documents by directly generating document identifiers (DocIDs) from queries using a sequence-to-sequence model. Recent reasoning-enhanced GR methods improve retrieval effectiveness by generating intermediate reasoning before DocID decoding. However, they typically apply reasoning uniformly to all queries, even though many queries can already be handled effectively through direct DocID decoding. This introduces unnecessary token generation and retrieval latency when reasoning provides little additional benefit. In this work, we propose Generative Retrieval with Adaptive Reasoning (GEAR), a retrieval framework that dynamically allocates reasoning computation based on query-specific needs. Instead of treating reasoning as a fixed pre-retrieval procedure, GEAR learns when to reason, what retrieval-oriented context to generate, and when to transition from free-form generation to constrained DocID decoding. This adaptive behavior is implemented through a learnable retrieval transition policy optimized jointly for retrieval quality and reasoning cost. Experiments on NQ, MS MARCO, and TREC DL 2022 show that GEAR maintains retrieval effectiveness comparable to strong reasoning-enhanced GR methods while substantially reducing reasoning overhead, triggering non-trivial reasoning for only 37.1% of queries and reducing both generated tokens and retrieval latency.

Large language models (LLMs) have demonstrated powerful capabilities in both text understanding and generation. Companies have begun to offer Embedding as a Service (EaaS) based on these LLMs, which can benefit various natural language processing (NLP) tasks for customers. However, previous studies have shown that EaaS is vulnerable to model extraction attacks, which can cause significant losses for the owners of LLMs, as training these models is extremely expensive. To protect the copyright of LLMs for EaaS, we propose an Embedding Watermark method called {pasted macro ‘METHOD’} that implants backdoors on embeddings. Our method selects a group of moderate-frequency words from a general text corpus to form a trigger set, then selects a target embedding as the watermark, and inserts it into the embeddings of texts containing trigger words as the backdoor. The weight of insertion is proportional to the number of trigger words included in the text. This allows the watermark backdoor to be effectively transferred to EaaS-stealer’s model for copyright verification while minimizing the adverse impact on the original embeddings’ utility. Our extensive experiments on various datasets show that our method can effectively protect the copyright of EaaS models without compromising service quality. Our code is available at https://github.com/yjw1029/EmbMarker.