Research Objectives
We are exploring the "growth potential" of enterprise AI systems. It depends not only on model capabilities, but on long-term memory, short-term context, task review, feedback learning, and knowledge updates — forming a continuously evolving operational mechanism.
In real business, successful outcomes, failure cases, user preferences, industry changes, and process adjustments should all become part of the system's future behavior — making growth not a feature, but an inherent capability.
Accumulate important facts, business preferences, historical cases, and strategy changes into searchable, updatable memory units.
Record task goals, execution paths, outcome deviations, and improvement suggestions — making every action material for the next optimization.
Convert user evaluations, business metrics, and human corrections into model behavior adjustments, knowledge updates, and strategy optimization.
Technical Pathway
Memory Layering
Distinguish short-term context, long-term business memory, user preferences, organizational knowledge, and execution logs — building a layered memory architecture.
Review & Attribution
Analyze gaps between task outcomes and expectations — determining whether issues stem from cognitive judgment, tool invocation, process constraints, or environmental changes.
Strategy Updates
Write review results back into knowledge, prompts, rules, case libraries, and execution strategies — enabling controlled, auditable evolution.
Current Focus Areas
Our research focuses on "how business memory participates in reasoning" and "how feedback translates into controlled updates" — ensuring the system grows more intelligent with use while remaining explainable and manageable.
