Memory Reward Inflation in Self-Improving LLM Agents
Source
Mohammad Asadolahi, Amir Amini, Samira Talebi, Amirfarhad Farhadi, Azadeh Zamanifar
Author
Mohammad Asadolahi, Amir Amini, Samira Talebi, Amirfarhad Farhadi, Azadeh Zamanifar
Date
Terms in this piece · Glossary
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.
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.
Why it matters
Self-graded episodic memory in agents can inflate confidence in bad past actions, causing agents to repeat their own mistakes; LUCID's answer-free de-inflation approach lifts BIRD execution accuracy from 52.4% (no memory) and 54.0% (self-graded memory) to 56.9%, giving a concrete fix for a subtle but consequential failure mode in AI agentAn 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.Full definition → memory design.
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”
articleWhen Does Memory Help? A Cost-Aware Evaluation of Long-Term Memory in Tool-Using LLM AgentsShweta Mishra, Shashank Mishra
articleVoice Memory for Agentic Speech RecognitionChao-Han Huck Yang, Zih-Ching Chen, Piotr Zelasko, Zhehuai Chen, Jagadeesh Balam, Boris Ginsburg
articleFinPerMA: A Theory-Informed, Event-Grounded Personalized-Memory Benchmark for LLM AgentsBen Wang, Kang Zhou, Lifan Guo, Feng Chen, Chi Zhang