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LLMs can generate better features from tabular data when deployed as a multi-agent system with explicit memory of past procedures, feedback, and concepts.
AI agents in medical research aren't ready for prime time: over half fail to meet even "Limited Release" quality standards, highlighting the urgent need for domain-specific audit frameworks.
Task-specific LLMs can be efficiently fine-tuned by explicitly routing inputs to LoRA experts based on semantic similarity, rather than relying on implicit or uniform weighting schemes.
LLM caching can be rigorously optimized in continuous query spaces, enabling better performance and lower overhead than discrete methods.
Forget shallow alignment – STK-Adapter deeply fuses evolving knowledge graph structure and event chains into LLMs, unlocking superior temporal reasoning.
