EMO: Embedding Model Distillation via Intra-Model Relation and Optimal Transport Alignments (2025 · 10 citations)
Large text embedding models such as LLM2Vec are expensive to deploy. Knowledge distillation can compress them, but it gets harder when the teacher and the student split text with different tokenizers. Earlier cross-tokenizer KD (CTKD) methods mostly match final outputs or logits. They ignore the relational knowledge…
LLM-XTM: Enhancing Cross-Lingual Topic Models with Large Language Models (2026)
Cross-lingual topic models try to find themes shared across languages. They depend on limited bilingual resources such as seed dictionaries, document embeddings or parallel corpora, so paired topics often drift apart in meaning. Earlier LLM-assisted methods either query the model for every document, which is costly…
MemORAI: Memory Organization and Retrieval via Adaptive Graph Intelligence for LLM Conversational Agents (2026)
LLM-based conversational agents cannot remember users across long, multi-session interactions. Current graph-based memory stores also have problems: they keep too much generic chatter, they cannot show which dialogue turn a fact came from, and they rank evidence the same way regardless of the question. MemORAI…

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Updated 1 November 2026