RecaLLM: Addressing the Lost-in-Thought Phenomenon with Explicit In-Context Retrieval

ArXi:2604.09494v1 Announce Type: cross We propose RecaLLM, a set of reasoning language models post-trained to make effective use of long-context information. In-context retrieval, which identifies relevant evidence from context, and reasoning are deeply intertwined: retrieval s reasoning, while reasoning often determines what must be retrieved. However, their interaction remains largely underexplored.