MoganColBERT-TR is a Turkish multi‑vector retrieval model that projects token embeddings from 768 to 128 dimensions and uses MaxSim late interaction for scoring. It builds on the previously trained MoganBERT‑TR encoder, adapting it to the ColBERT objective with a single‑epoch distillation phase using cross‑encoder teacher scores. Evaluated on five Turkish BEIR datasets in a zero‑shot setting, it achieves an overall score of 37.36, outperforming larger models on most datasets.
By Furkan Yilmaz, Habibe Aleyna Tasdemir, Muhammed Faruk Gozay
The paper introduces LRE (Learned Relevance Eviction), a lightweight, CPU‑only, language‑model‑free scorer that learns which parts of an agent’s interaction history are task‑critical and preserves them verbatim. In experiments, LRE matches or surpasses baseline eviction policies on accuracy‑cost trade‑offs, recovers 93% of full‑history accuracy, reduces worst‑case prompt size by 52%, and outperforms dense and token‑pruning encoders in conversational memory while being 295–1569× smaller. The method also achieves superior budgeted answer quality on LoCoMo reading and can be trained annotation‑free, recovering 95% of supervised scorer performance.
By Nusrat Jahan Lia, Aritra Mazumder
arXiv:2607. 20458v1 Announce Type: cross Abstract: Large language model (LLM) agents operating over extended dialogues accumulate vast amounts of information, yet existing memory systems either retain everything indiscriminately or apply uniform forgetting heuristics that fail to distinguish relevant from irrelevant knowledge.
By Haowen Lai
LLM-based query expansion improves retrieval by generating document-like passages. In hybrid retrieval, however, most evaluations fuse fixed top-$L$ dense and sparse rankings. Because the cutoff contr...
arXiv:2609.22100v1 Announce Type: cross
Abstract: Retrieval-augmented generation (RAG) improves language models with retrieved evidence, but processing many long passages is costly and can introduce...
By Artem Sakhno, Grigorii Davydenko, Omar Zoloev, Julia Belikova, Andrey Savchenko, Maksim Makarenko
arXiv:2608.22767v1 Announce Type: new
Abstract: Long-term language-model agents accumulate memories across interactions, but their retrievers typically do not accumulate retrieval experience. Semanti...
By Qi Feng, Chris Ding, Jicong Fan