Semantic Memory

Evaluating Long-Term Memory for Long-Context Question Answering compares Full Context, RAG, A-Mem, RAG+PromptOpt, and RAG+EpMem memory components across semantic, episodic, and procedural memory for long conversational QA. On LoCoMo, RAG+EpMem reaches an average F1 ranking of 1.83 for Llama 3.2-3B Instruct and 1.80 for GPT-4o mini while using around 1,000 tokens per query versus over 23,000 for Full Context.

Learning from Supervision with Semantic and Episodic Memory combines a performance agent, critic agent, semantic memory, episodic memory, and memory retriever to turn label-grounded critiques into reusable supervision without parameter updates. On the Multi-Condition Ranking dataset with Mixtral 8x22B and o4-mini as critic, Learning from Supervision with Semantic and Episodic Memory reaches 85.6% accuracy, a 24.8% gain over the EP_LABEL baseline at 60.8%.

Semantic Anchoring enriches conversational memory by combining a hybrid memory store with dense and symbolic indexes, structured memory representation tuples, hybrid storage and indexing, and a retrieval scoring method. On MultiWOZ-Long, Semantic Anchoring reaches 83.5% Factual Recall and 80.8% Discourse Coherence, beating Entity-RAG by 7.6 and 8.6 points respectively.

WorldMM dynamically coordinates Episodic Memory, Semantic Memory, Visual Memory, an Adaptive Retrieval Agent, and a Response Agent to answer queries over hour- to week-long videos. On five long video QA benchmarks, WorldMM-GPT reaches 69.5% average accuracy, beating M3-Agent’s 55.1% by 14.4 points and the best prior memory baseline HippoRAG’s 57.0% by 12.5 points.