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University of Oregon ♣ Adobe Research
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Shifting reasoning to the indexing stage, RL-Index reduces online inference latency while boosting retrieval performance through LLM-generated rationales.
Existing agents excel at structured tasks but falter in feedback-driven operations and long-horizon workflows, revealing critical gaps in their capabilities for scientific instrument control.
AutoMine outperforms existing methods in scenario mining, achieving a HOTA-Temporal score of 36.38 in a competitive setting.
Adapter design can make or break coding performance in OpenClaw-style agents, with a full adapter boosting success rates by over 50 percentage points.
Infini Memory redefines long-term memory for LLMs, achieving a 64.7% score on MemoryAgentBench by structuring memory as topic documents that evolve over time.
