arXiv AI August 21, 2026 By Su Yan, Rakesh Iyer

When Machines Speak: A Unified Generative Framework for Integrating Machine-Native Symbols into Pretrained Large Language Models

arXiv:2608. 19529v1 Announce Type: cross Abstract: Many real-world AI systems represent entities, behaviors, and structured information using discrete machine-native symbols rather than natural language.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv AI

Aug 28

Grounded Token Initialization for New Vocabulary in LMs for Generative Recommendation

The paper examines how new vocabulary tokens are added to language models for generative recommendation tasks. It shows that the common practice of initializing these tokens as the mean of existing embeddings collapses them into a degenerate subspace, hindering fine‑tuning. The authors propose Grounded Token Initialization (GTI), which places new tokens at semantically meaningful positions in the pretrained embedding space using linguistic supervision, and demonstrate that GTI outperforms mean initialization and other adaptation methods across several benchmarks.

By Daiwei Chen, Zhoutong Fu, Chengming Jiang, Haichao Zhang, Ran Zhou, Tan Wang, Chunnan Yao, Guoyao Li, Rui Cai, Yihan Cao, Ruijie Jiang, Fedor Borisyuk, Jianqiang Shen, Jingwei Wu, Ramya Korlakai Vinayak

llms rag fine-tuning benchmarks

arXiv Machine Learning

Jun 10

Generative Archetype-Grounded Item Representations for Sequential Recommendation

arXiv:2606. 11023v1 Announce Type: cross Abstract: Sequential recommendation aims to predict users' next interaction with items by analyzing their historical behavior.

By Yifan Li, Jiahong Liu, Xinni Zhang, Hao Chen, Yankai Chen, Wenhao Yu, Jianting Chen, Irwin King

llms rag benchmarks

arXiv AI

Sep 15

Mimir: Large-scale Multilingual Concept Modeling

arXiv:2605.25263v2 Announce Type: replace-cross Abstract: Current language modeling approaches are built around tokens. Text corpora are split into tokens, and models are trained by performing comput...

By Elio Musacchio, Lucia Siciliani, Pierpaolo Basile

llms

Hugging Face Trending Papers

Jun 9

Generative Archetype-Grounded Item Representations for Sequential Recommendation

Sequential recommendation aims to predict users' next interaction with items by analyzing their historical behavior. However, the limited quality of item representations remains a critical bottleneck.

llms rag benchmarks

arXiv Machine Learning

Jun 4

Can Large Language Models Generalize Procedures Across Representations?

arXiv:2602. 03542v2 Announce Type: replace-cross Abstract: Large language models (LLMs) are trained and tested extensively on symbolic representations such as code and graphs, yet real-world user tasks are often specified in natural language.

By Fangru Lin, Valentin Hofmann, Xingchen Wan, Weixing Wang, Zifeng Ding, Anthony G. Cohn, Janet B. Pierrehumbert

llms reinforcement-learning

arXiv AI

Jun 3

ReaLM: Residual Quantization Bridging Knowledge Graph Embeddings and Large Language Models

arXiv:2510. 09711v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) have recently emerged as a powerful paradigm for Knowledge Graph Completion (KGC), offering strong reasoning and generalization capabilities beyond traditional embedding-based approaches.

By Wenbin Guo, Xin Wang, Jiaoyan Chen, Lingbing Guo, Zhao Li, Zirui Chen

llms rag nlp efficiency benchmarks

One email a morning, machine-written

Related stories

Grounded Token Initialization for New Vocabulary in LMs for Generative Recommendation

Generative Archetype-Grounded Item Representations for Sequential Recommendation

Mimir: Large-scale Multilingual Concept Modeling

Generative Archetype-Grounded Item Representations for Sequential Recommendation

Can Large Language Models Generalize Procedures Across Representations?

ReaLM: Residual Quantization Bridging Knowledge Graph Embeddings and Large Language Models