Forgetting as a Feature: Cognitive Alignment of Large Language Models

TL;DR

This paper reinterprets the systematic forgetting in large language models as a functional cognitive mechanism inspired by human memory, introducing a probabilistic memory model and prompting strategy to enhance long-term reasoning.

Contribution

It models LLM inference as a probabilistic memory process, introduces a benchmark suite, and proposes a memory prompting method to improve reasoning by leveraging forgetting.

Findings

LLMs exhibit forgetting rates similar to human memory trade-offs.

Probabilistic memory prompting improves long-horizon reasoning.

The benchmark suite enables comparison of model behavior with human cognition.

Abstract

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.