Absorbing Complexity: An Interaction-Native Knowledge Harness for Financial LLM Agents

Researchers propose InKH, an architecture for financial AI agents that maintains persistent context about users, portfolios, and market conditions rather than forcing users to repeatedly restate information. In controlled benchmarks, InKH achieves 82% latency reduction and 96% improvement in stale-knowledge elimination compared to existing approaches, suggesting that AI financial tools succeed by absorbing operational complexity into their systems rather than delegating it to users.

The core challenge addressed by this research reflects a fundamental gap in current financial AI deployment: existing agents lack persistent, structured memory systems that maintain relevant context across multiple interactions. Financial decision-making requires continuity—market assumptions, risk tolerances, portfolio compositions, and past judgments cannot be reliably retrieved from conversation history alone. InKH tackles this by converting user interactions, market data, portfolio updates, and tool outputs into a temporal knowledge graph with built-in decay and invalidation mechanisms, ensuring that decision context remains both accessible and accurate.

This work emerges from broader recognition that consumer adoption of AI financial tools hinges on reducing friction. Traditional chatbots transfer cognitive load to users, requiring them to re-establish context repeatedly. In finance, this inefficiency creates material risks: stale market assumptions lead to outdated recommendations, forgotten portfolio constraints cause unsafe allocations, and poor auditability undermines regulatory compliance and user trust.

The benchmarking results demonstrate meaningful architectural improvements. The 82% latency reduction matters for real-time trading workflows, while the 96% reduction in stale-knowledge usage directly addresses safety concerns in financial contexts. The passive knowledge injection approach and wiki audit surface address two critical needs: operational efficiency and explainability for compliance.

However, the evaluation remains synthetic and controlled—the researchers explicitly note this does not validate live trading performance. Real-world deployment requires testing against market volatility, edge cases in portfolio complexity, and actual regulatory audit requirements. The framework's maturity metrics and write-time invalidation suggest practical implementation feasibility, but production validation across diverse user types and market conditions remains necessary before conclusions about broader financial AI adoption can be drawn.

• →InKH reduces latency by 83% and stale-knowledge errors by 97% through persistent temporal graph memory designed specifically for financial contexts.
• →The architecture absorbs operational complexity into the system rather than delegating it to users, addressing a key friction point in financial AI adoption.
• →Structured knowledge injection, decay mechanisms, and audit surfaces enable both better performance and stronger regulatory compliance compared to agent-driven retrieval systems.
• →Evaluation on synthetic benchmarks demonstrates architectural soundness but does not validate real-world trading performance or production robustness.
• →The research supports a design principle that financial AI adoption depends on system-level complexity management rather than improved user interfaces.