Towards LifeSpan Cognitive Systems
Abstract: Building a human-like system that continuously interacts with complex environments -- whether simulated digital worlds or human society -- presents several key challenges. Central to this is enabling continuous, high-frequency interactions, where the interactions are termed experiences. We refer to this envisioned system as the **LifeSpan Cognitive System (LSCS)**. A critical feature of LSCS is its ability to engage in incremental and rapid updates while retaining and accurately recalling past experiences. We identify two major challenges in achieving this: (1) Abstraction and Experience Merging, and (2) Long-term Retention with Accurate Recall. These properties are essential for storing new experiences, organizing past experiences, and responding to the environment in ways that leverage relevant historical data. Unlike language models with continual learning, which typically rely on large corpora for fine-tuning and focus on improving performance within specific domains or tasks, LSCS must rapidly and incrementally update with new information from its environment at a high frequency. Existing technologies with the potential of solving the above two major challenges can be classified into four classes based on a conceptual metric called **Storage Complexity**, which measures the relative space required to store past experiences. Each of these four classes of technologies has its own strengths and limitations while we argue none of them alone can achieve LSCS alone. To this end, we propose a potential paradigm for LSCS that can integrate all four classes of technologies. The new paradigm, serving as a conjecture, operates through two core processes: Absorbing Experiences and Generating Responses.
Submission Length: Regular submission (no more than 12 pages of main content)
Assigned Action Editor: ~Andrew_Kyle_Lampinen1
Submission Number: 3461
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