Retrieval-Agents

TL;DR for operators A retrieval agent does not merely retrieve documents. It repeatedly decides what to do next: search again, rewrite a query, record evidence, combine what it has found, verify a claim, or stop. The Fellowship of the Query: Learning Retrieval Actions1 shows that these decisions can be taught to small language models as a supervised classification problem. Across the evaluated models, LoRA fine-tuning sharply improves prediction of the next retrieval action. For Granite 4.1 3B, macro-F1 rises from 0.1736 zero-shot to 0.6536 after fine-tuning. ...

TL;DR for operators A visual support assistant shown an unfamiliar machine, product, bird, or venue cannot answer by retrieval alone. It must first determine what the image depicts, then locate the missing fact, while deciding whether another search is worth the delay. A wrong first match can redirect every later step toward the wrong entity. ...