Intel / article

Memory Reward Inflation in Self-Improving LLM Agents

Source: arxiv.org
Author: Mohammad Asadolahi, Amir Amini, Samira Talebi, Amirfarhad Farhadi, Azadeh Zamanifar
Date: Published Aug 4, 2026

Context

An that stores its own highly rated attempts can learn to repeat mistakes. This study describes agents that save past work in external memory and retrieve it for similar tasks. When the model overvalues an incorrect attempt, that memory can influence later behavior. A second judge can preserve the problem if its mistakes track the original grading bias. Study abstract.

The authors report a correction method called LUCID. On BIRD, a for generating database queries from text, it achieved 56.9% execution accuracy, compared with 54.0% for the self-graded memory baseline and 52.4% without memory. This supports checking how memories receive their scores, because storing more experience can preserve errors as well as useful examples. Reported experiment.

Terms in this piece · Glossary

AI agent — An AI system that doesn't just answer once but works toward a goal in a loop — taking actions, reading the results, and deciding what to do next.
benchmark — A standard public test set for comparing AI models — the shared scoreboards behind every "model X beats model Y" claim.
LLM — A large language model — the neural network behind tools like Claude and ChatGPT, trained on huge amounts of text to predict what comes next.

Key quotes

“Incorrect episodes receive inflated rewards; thus, the agent preferentially reuses the very mistakes it has most confident in.”

“a usable signal must track truth *and* decorrelate its error from the memory bias”

Read the source arxiv.org