Picorer: Agentic Memory, One Clue at a Time

Agentic memory gives LLM agents control over how past experience is organized, retrieved, and used. Using this experience for a task requires deciding what information to acquire and how separate findings fit together. As the agent reads, newly discovered facts can change what it needs to seek next. Effective memory acquisition therefore demands adaptive exploration while preserving accumulated evidence. We present Picorer, a memory harness that acquires evidence one clue at a time. Its LLM policy revises a working state to guide search, while an independent ledger retains inspected passages for answering. Under at most eight search calls per query, Picorer raises MemoryAgentBench Overall from 41.60% to 49.17% with GPT-4o-mini, outperforming HippoRAG-v2 and MemGPT. With GPT-4.1-mini it leads the reproduced BEAM baselines at 72.52% and 60.49% on the 100K- and 10M-token archives, and with GPT-5-mini it reaches 90.00% on LoCoMo. Controlled interventions on multi-hop fact consolidation show that completing supporting relations yields larger accuracy gains than partial progress. Further reading can still improve accuracy after an independent model judges the evidence sufficient.