Knowledge Augmentation for Language Models

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11 papers in the last four weeks, up 120% on the four weeks before. 0.1% of all new papers.

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RAGenome: Scaling Retrieval-Based Genomic Language Models to Long Contexts
Oct 8, 2026Frederikke Isa Marin, Panagiotis Antoniadis, Dionysia Danai Brilli +5Genomics Language Modeling
University of Copenhagen · Novo Nordisk A/S · Technical University of Denmark
UNREAL: Unifying Retrieval and Long-Context with a Single Model
Oct 6, 2026Edan Kinderman, Elad Hoffer, Yochai Blau +4Retrieval-Augmented Generation Knowledge Augmentation for Language Models
NVIDIA · Technion
From Papers to Mechanisms: An Evidence-Grounded Knowledge Substrate for Scientific Language Models
Oct 5, 2026Qiuhui Chen, Yibo Liu, Tao Dai +3Knowledge Augmentation for Language Models Scientific QA
School of Information Science and Engineering, East China University of Science and Technology · School of Computer Science, Shanghai Jiao Tong University · Tencent
From Knowledge Access to Source Learning: Developing Source-Specific Competence
Oct 1, 2026Lucheng Fu, Kejing Xia, Yiyang Wang +10Continual Learning for LLM Agents Knowledge Augmentation for Language Models
Georgia Institute of Technology · University of Illinois at Urbana-Champaign · University of California, Los Angeles
On the (In)effectiveness of AMR Augmentation for Large Language Models
Sep 30, 2026Hoa Quynh Nhung Nguyen, Jacopo Staiano, Michael SullivanLLM Evaluation Knowledge Augmentation for Language Models
Saarland University · University of Trento
Draft-KV: Learning Useful Latent Communication Between Language Models
Sep 28, 2026Linquan Wu, Shichang Meng, Tianxiang Jiang +7Knowledge Augmentation for Language Models Multi-LLM Collaboration
City University of Hong Kong · University of Science and Technology of China · University of Electronic Science and Technology of China +3
When Does Structured Knowledge Help Neural Theorem Proving?
Sep 28, 2026Sareh Nabi, Roland Vogl, Marzieh NabiAutomated Theorem Proving Knowledge Augmentation for Language Models
Stanford University
Context Spanning: A Communication Framework for Full-Duplex Speech Models and External LLM Backends
Sep 27, 2026Seonghyeon Go, Yongwoo Kim, Hyeonjin Cha +1Knowledge Augmentation for Language Models Spoken Dialogue Systems
Mindlogic
Semantic Abstraction for Natural Language Inference: a Methodological Framework for Discovering and Compensating Semantic Knowledge and Reasoning Gaps in Large Language Models
Sep 22, 2026David Torres-Moreno, Jorge Hermosillo-ValadezKnowledge Augmentation for Language Models Natural Language Inference
GraphSkillAA: Attribution-Guided Skill-Graph Updating with Targeted Validation and Rollback
Sep 17, 2026Ziqiao Shang, Ling-Yue Ge, Lan-Zhe GuoKnowledge Augmentation for Language Models LLM Agent Skill Learning
National Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China · School of Intelligence Science and Technology, Nanjing University, Suzhou, China
Where Should a Document Live: Context, Representations, or Parameters?
Sep 15, 2026Nathanaël Carraz Rakotonirina, Momchil Hardalov, Gonzalo Iglesias +1Memory-Augmented Language Models Knowledge Augmentation for Language Models
Amazon AGI
PunGraph: Retrieval-Enhanced Phonetic-Semantic Graph Reasoning for Pun Understanding
Sep 15, 2026Yuchen Su, Zijian Huang, Yaotian Shi +6Figurative Language Understanding Knowledge Augmentation for Language Models
School of Computer Science, University of Auckland, New Zealand · School of Computer and Information Technology, Shanxi University · School of Electronic Engineering and Computer Science, Queen Mary University of London
Repair Before Reinforce: Context-Augmented Knowledge Graph Reasoning for Multi-Hop Question Answering
Sep 14, 2026Tharaka D. Fonseka, Niraj K. JhaMulti-Hop QA Knowledge Augmentation for Language Models
Dept. of Electrical and Computer Engineering, Princeton University
KuaiRP Series Role-playing Models Technical Report
Sep 12, 2026Yipeng Wang, Ziwei Zhang, Jiahui Zhang +2Large Language Model-Based Role-Play Simulation Knowledge Augmentation for Language Models
Kuaishou GameMind Lab
DKL: Decoupled Knowledge Learning for Instruction-Tuned Language Models
Sep 2, 2026Kushagra Bhushan, Meghanadh Pulivarthi, Sai Krishna Reddy Sathi +7Knowledge Augmentation for Language Models Retrieval-Augmented Generation
1IBM · 2Indian Institute of Technology, Madras
KItCAT: Knowledge Injection via Input Corruption for Auto-regressive Training
Aug 31, 2026Meghanadh Pulivarthi, Kushagra Bhushan, Vineet Kumar +5Language Model Pretraining Knowledge Augmentation for Language Models
IBM · Work done while at IBM. Currently at Amazon Books
Frozen Memory Is Not Enough: Rethinking External Memory as Extraction
Aug 17, 2026Mingyuan Li, Guangsheng Yu, Xu Wang +1Memory-Augmented Language Models Knowledge Augmentation for Language Models
ELLIS Institute Finland · University of Turku · University of Technology Sydney
LittleLearner: Language Models Under Pedagogically Controlled Knowledge Exposure
Aug 13, 2026Fanfei Li, Jana Zeller, Manuel Prada-Corral +4Language Model Pretraining Knowledge Augmentation for Language Models
ETHZ
LoKiFormer: Locality-aware Attention with Decoupled Knowledge Memory for Efficient Large Language Model Pretraining
Aug 12, 2026Qiuwu Chen, Zimo Liu, Yuchen Li +8Language Model Pretraining Memory-Augmented Language Models
AIGCode · South China University of Technology · Pazhou Laboratory
AquiLLM: An Architecture for Supporting Tacit Knowledge Capture in Research Groups
Aug 9, 2026Jack Stark, Srinath Saikrishnan, Vikram Seenivasan +3Memory-Augmented Language Models Knowledge Augmentation for Language Models
University of California, Los Angeles, Los Angeles, California, USA · Southern Oregon University, Ashland, Oregon, USA
KV-Skill: Forging Expertise in the Model's Native Language
Aug 5, 2026Zhaowei Han, Xiang Zhang, Bing Han +3Knowledge Augmentation for Language Models Parameter-Efficient Fine-Tuning
University of Michigan, Ann Arbor, MI, USA
Wnuan: Staged Post-Training for Question Answering over Proprietary Enterprise Knowledge
Aug 3, 2026Xiaofeng Shi, Xiaosong Qiu, Wenxin Ma +6Supervised Fine-Tuning Knowledge Augmentation for Language Models
Beijing Academy of Artificial Intelligence (BAAI) · Beijing District Heating Group Co., Ltd. (BDHG) · Beijing University of Posts and Telecommunications (BUPT)
RING: Retrieval-Internalized Generation for Continual Large-Scale Knowledge Injection
Aug 3, 2026Shicheng Xu, Liang Pang, Liyi Chen +7Retrieval-Augmented Generation Continual Learning for LLMs
State Key Laboratory of AI Safety, Institute of Computing Technology, CAS · University of Chinese Academy of Sciences · Xiaohongshu Inc.
The Graph Language: How Knowledge Graphs Speak to Large Language Models
Aug 2, 2026Giuseppe PirròLLM Grounding Knowledge Augmentation for Language Models
DeMaCS, University of Calabria
SkillSmith: Learning to Compose Parametric Skills and Textual Knowledge
Jul 29, 2026Lucio M. Dery, Benedict Aaron Tjandra, Siavash Samiei +4Knowledge Augmentation for Language Models Prompt Tuning
Google DeepMind
Fewer Clarifications, Better Code: Benchmarking Cross-Session Personalized Ambiguity Adaptation in Coding Assistants
Jul 29, 2026Zijian Xu, Wenshuo Zhang, Zisen Qin +4Knowledge Augmentation for Language Models Code Generation
Southeast University · The Hong Kong University of Science and Technology
Retrieval-Augmented Large Language Models as Components of Cognitive Computing architecture for Regulatory Knowledge Management
Jul 27, 2026Dariusz Nowak-NovaRetrieval-Augmented Generation Knowledge Augmentation for Language Models
1WSB University, Poland
Do Small Models Use the Law You Give Them? Context-Injected Fine-Tuning for Legal QA in Bangladesh
Jul 26, 2026Moniruzzaman Mahadi, Abrar Mohammed Tanzim Alam, Sayma Siddika Monalisa +3Multilingual Language Model Evaluation LLM Grounding
Department of Electrical and Computer Engineering, North South University Dhaka, Bangladesh · Department of Computer Science and Engineering, BRAC University Dhaka, Bangladesh
Dynamic Commonsense Coordination for Empathetic Response Generation
Jul 24, 2026Zhengyu QiKnowledge Augmentation for Language Models Emotion-Aware Dialogue Generation
Leiden Institute of Advanced Computer Science (LIACS) Leiden University, The Netherlands
Data Quality over Capacity: Internalizing Documents into LoRA Adapters for Closed-Book QA
Jul 23, 2026Joan Figuerola HurtadoKnowledge Augmentation for Language Models LLM Fine-Tuning
Independent Researcher

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RAGenome: Scaling Retrieval-Based Genomic Language Models to Long Contexts

Frederikke Isa Marin, Panagiotis Antoniadis, Dionysia Danai Brilli, Andreas Bjerregaard+4

University of Copenhagen · Novo Nordisk A/S · Technical University of Denmark

Genomics Language Modeling Long-Context Language Modeling Knowledge Augmentation for Language Models

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The genome holds the blueprint that governs the biological properties of the cell. Consequently, advancing our knowledge of genomic function is crucial both for a broader understanding of biology and for continued biomedical advances. The success of large language models on natural language and protein sequences has motivated similar efforts on genomic data. However, standard genomic language models (gLMs) often require extremely large computational resources and still fall behind traditional methods on some downstream tasks. Recently, MSA-based pretraining has been proposed as an efficient alternative, but existing models are limited to short input contexts, restricting their use to short-range tasks, such as variant effect prediction. In this work, we present RAGenome, the first retrieval-based gLM that scales pretraining to longer contexts (100

\times

longer than existing MSA-based gLMs), allowing it to capture both across-species evolutionary relationships and within-species longer-range interactions. Trained on whole-genome alignments from 100 vertebrates, RAGenome substantially improves the long-range capabilities of MSA-based gLMs, raising gene finding performance from 0.45 to 0.60, while remaining competitive on purely evolutionary-based tasks like prioritizing pathogenic variants. RAGenome provides competitive gLM performance at a fraction of the training cost, unifying evolutionary modeling and long-range capabilities within a single, flexible, scalable framework. Code is available at https://github.com/PanosAntoniadis/RAGenome.

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