InKH architecture absorbs complexity into financial LLM agents, cutting latency 83%, token cost 82%, and stale knowledge 97% while raising task quality 0.108 on a 46k-episode synthetic benchmark versus baselines.
FinAgentBench: A Benchmark Dataset for Agentic Retrieval in Financial Question Answering
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Absorbing Complexity: An Interaction-Native Knowledge Harness for Financial LLM Agents
InKH architecture absorbs complexity into financial LLM agents, cutting latency 83%, token cost 82%, and stale knowledge 97% while raising task quality 0.108 on a 46k-episode synthetic benchmark versus baselines.