NeuroGenesysThe research we build on.
1,514 papers by other researchers that our work cites, from continual learning and verification to language models, agents and hardware. Each entry links to the paper on arXiv and to where we use it. 301 are cited on published pages of this site; the rest in the research notes behind our studies. Our own papers are in the archive.
- 2025-04arXiv 2504.19413Mem0: Building Production-Ready AI Agents with Scalable Long-Term Memory
Prateek Chhikara, Dev Khant, Saket Aryan et al. · Language models & NLP
Large Language Models (LLMs) have demonstrated remarkable prowess in generating contextually coherent responses, yet their fixed context windows pose fundamental challenges for maintaining consistency over prolonged multi-session dialogues.
Cited inStudy: Graphyte spec - 2025-02arXiv 2502.14802From RAG to Memory: Non-Parametric Continual Learning for Large Language Models
Bernal Jiménez Gutiérrez, Yiheng Shu, Weijian Qi et al. · Language models & NLP
Our ability to continuously acquire, organize, and leverage knowledge is a key feature of human intelligence that AI systems must approximate to unlock their full potential.
Cited inStudy: Graphyte spec - 2025-01arXiv 2501.13956Zep: A Temporal Knowledge Graph Architecture for Agent Memory
Preston Rasmussen, Pavlo Paliychuk, Travis Beauvais et al. · Language models & NLP
We introduce Zep, a novel memory layer service for AI agents that outperforms the current state-of-the-art system, MemGPT, in the Deep Memory Retrieval (DMR) benchmark.
Cited inStudy: Graphyte spec - 2024-10arXiv 2410.10813LongMemEval: Benchmarking Chat Assistants on Long-Term Interactive Memory
Di Wu, Hongwei Wang, Wenhao Yu et al. · Language models & NLP
Recent large language model (LLM)-driven chat assistant systems have integrated memory components to track user-assistant chat histories, enabling more accurate and personalized responses.
- 2024-10arXiv 2410.05779LightRAG: Simple and Fast Retrieval-Augmented Generation
Zirui Guo, Lianghao Xia, Yanhua Yu et al. · Retrieval & data
Retrieval-Augmented Generation (RAG) systems enhance large language models (LLMs) by integrating external knowledge sources, enabling more accurate and contextually relevant responses tailored to user needs.
Cited inStudy: Graphyte spec - 2024-05arXiv 2405.14831HippoRAG: Neurobiologically Inspired Long-Term Memory for Large Language Models
Bernal Jiménez Gutiérrez, Yiheng Shu, Yu Gu et al. · Language models & NLP
In order to thrive in hostile and ever-changing natural environments, mammalian brains evolved to store large amounts of knowledge about the world and continually integrate new information while avoiding catastrophic forgetting.
Cited inStudy: Graphyte spec - 2024-04arXiv 2404.16130From Local to Global: A Graph RAG Approach to Query-Focused Summarization
Darren Edge, Ha Trinh, Newman Cheng et al. · Language models & NLP
The use of retrieval-augmented generation (RAG) to retrieve relevant information from an external knowledge source enables large language models (LLMs) to answer questions over private and/or previously unseen document collections.
- 2024-02arXiv 2402.17753Evaluating Very Long-Term Conversational Memory of LLM Agents
Adyasha Maharana, Dong-Ho Lee, Sergey Tulyakov et al. · Language models & NLP
Existing works on long-term open-domain dialogues focus on evaluating model responses within contexts spanning no more than five chat sessions.
- 2023-11arXiv 2311.01453PPI++: Efficient Prediction-Powered Inference
Anastasios N. Angelopoulos, John C. Duchi, Tijana Zrnic · Machine learning
We present PPI++: a computationally lightweight methodology for estimation and inference based on a small labeled dataset and a typically much larger dataset of machine-learning predictions.
- 2022-08arXiv 2208.02814Conformal Risk Control
Anastasios N. Angelopoulos, Stephen Bates, Adam Fisch et al. · Statistics & theory
We extend conformal prediction to control the expected value of any monotone loss function. The algorithm generalizes split conformal prediction together with its coverage guarantee.
Built from every arXiv paper cited in our papers, posts, topic hubs, reports and research notes, with titles, authors, dates and abstracts from the arXiv API (updated 2026-11-12). Summaries are the opening sentences of each paper's own abstract, not our assessment; a citation means we used or checked the work, not that we endorse it. Fields follow each paper's primary arXiv category. Papers cited only outside arXiv (journals, proceedings) are not listed yet. Entries are generated from our citations, so a mistyped ID in a note would list the wrong paper; we remove those as we find them. Report a wrong entry.
