Forgetting as a Feature: Cognitive Alignment of Large Language Models
This work addresses the issue of forgetting in LLMs for AI researchers, offering a novel perspective that could enhance adaptive intelligence, though it is incremental in applying human memory principles to existing models.
The paper tackles the problem of systematic forgetting in large language models (LLMs) by reinterpreting it as a functional cognitive mechanism, and shows that modeling LLM inference with probabilistic memory decay improves long-horizon reasoning performance.
Large Language Models (LLMs) are often evaluated against ideals of perfect Bayesian inference, yet growing evidence suggests that their in-context reasoning exhibits systematic forgetting of past information. Rather than viewing this behavior as a limitation, we reinterpret forgetting as a functional cognitive mechanism. Drawing inspiration from human memory dynamics, we model LLM inference as a probabilistic memory process governed by exponential decay. We introduce a benchmark suite that evaluates temporal reasoning, concept drift adaptation, and associative recall, enabling direct comparison between model behavior and human cognitive patterns. Our empirical results reveal that LLMs demonstrate forgetting rates analogous to human memory efficiency trade-offs between stability and adaptability. Building on these observations, we propose probabilistic memory prompting, a lightweight strategy that shapes evidence integration to mimic human-like memory decay, leading to improved long-horizon reasoning performance. Our findings position forgetting not as a failure mode, but as a principled mechanism for adaptive intelligence.