Goal-Mem: Goal-Oriented Reasoning for RAG Memory in LLM Agents
Goal-Mem is a novel framework designed to overcome the shortcomings of RAG-based memory in conversational LLM agents. Current techniques typically fetch memory by assessing semantic similarity to user inputs, which can lead to irrelevant or inadequate evidence for multi-hop or commonsense reasoning tasks. Instead of gradually expanding from retrieved context, Goal-Mem utilizes explicit backward chaining from the user's utterance as a target. This framework is presented in a paper available on arXiv (2605.12213) and seeks to enhance coherent behavior in agentic systems over extended periods.
Key facts
- Goal-Mem is a goal-oriented reasoning framework for RAG-based agentic memory.
- It performs explicit backward chaining from the user's utterance as a goal.
- Existing methods retrieve memory based on semantic similarity to raw user utterances.
- Existing methods often return irrelevant or insufficient evidence for grounded reasoning.
- The framework addresses multi-hop and commonsense reasoning challenges.
- The paper is available on arXiv with ID 2605.12213.
- LLM-based conversational AI agents struggle with coherent behavior over long horizons.
- RAG-based approaches store interactions in external memory modules.
Entities
Institutions
- arXiv