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Axis-LoRA: Probing and Adapting Embedding Models for Personal-Memory Retrieval

Researchers find that frozen dense embedding models poorly capture time, place, person, and event axes in personal-memory retrieval, with axis changes barely shifting embeddings. They propose Axis-LoRA, a per-axis adapter with four LoRA branches and a per-query gate, which improves retrieval accuracy on benchmarks like LongMemEval, ATM-Bench, and LoCoMo while training only a small fraction of parameters.

arxiv16h agoScore 70

retrieval-augmented generationparameter-efficient fine-tuningpersonal AI

Research Editor selected

On-Policy Training Helps Multi-Hop RAG Retrievers Recover From Mistakes

A new paper proposes training chain embedding retrievers on their own retrieval histories, including mistakes, rather than only on gold-standard histories. The method mixes these on-policy states with teacher-forced examples and masks alternative correct passages to avoid penalizing valid continuations. The authors report more complete evidence retrieval and better recovery from errors, with transfer without additional training to harder long-chain variants of HotpotQA and 2WikiMultiHopQA constructed for the study.

Why it matters: The study addresses a concrete reliability problem in multi-hop RAG: retrieval errors that compound when subsequent searches depend on previously retrieved passages.

arxivOct 30, 2026Score 85

Retrieval-augmented generationEmbedding modelsOn-policy training