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Segment-level consolidation in LycheeMemory V2 slashes construction costs by up to 86% while maintaining high accuracy in long-term memory tasks.
SkillEvo achieves a 23-point boost in skill evolution by transforming multi-turn interactions into a continuous feedback loop that actively repairs defects.
SPOT redefines on-policy distillation by ensuring that probing decisions directly enhance downstream reasoning performance, not just teacher alignment.
CRISP reduces interaction turns by up to 33% while maintaining accuracy by prioritizing critical evidence-gathering steps in deep search agents.
Personalization in LLM agents is more complex than previously thought, with existing benchmarks failing to capture the dynamic nature of user preferences and their impact on task execution.
Ranking systems can achieve greater accuracy and stability by leveraging structural relationships in permutations, as demonstrated by the SRPO framework.
Get 3.6x faster long-context LLM inference with LycheeCluster's hierarchical KV indexing, which avoids the semantic fragmentation of naive chunking.
