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.
- 2026-10arXiv 2610.25914FisherEdit: Fisher Information Guided Knowledge Editing for Large Language Models
Yizhou Yang, Jiahong Fu, Zhiwu Wang et al. · Language models & NLP
Sequential knowledge editing in Large Language Models (LLMs) often leads to a degradation of general capabilities due to cumulative parameter interference.
- 2026-06arXiv 2606.23276Exposing the Illusion of Erasure in Knowledge Editing for LLMs
Advik Raj Basani, Anshuman Chhabra · Machine learning
Knowledge Editing (KE) has emerged as a frontier for updating specific facts in LLMs without costly retraining, but its reliability and underlying mechanisms remain poorly understood.
- 2026-05arXiv 2605.09285BetaEdit: Null-Space Constrained Sequential Model Editing
Bingqing Liu, Wei Liu, Yuhua Li · Language models & NLP
Null-space-based methods have garnered considerable attention in model editing by constraining updates to the null space of the pre-existing knowledge representation, thereby preserving the model's original behavior.
- 2026-01arXiv 2602.02543Norm Anchors Make Model Edits Last
Mingda Liu, Zhenghan Zhu, Ze'an Miao et al. · Machine learning
Sequential Locate-and-Edit (L&E) model editing can fail abruptly after many edits.
- 2025-06arXiv 2506.10943Self-Adapting Language Models
Adam Zweiger, Jyothish Pari, Han Guo et al. · Machine learning
Large language models (LLMs) are powerful but static; they lack mechanisms to adapt their weights in response to new tasks, knowledge, or examples. We introduce Self-Adapting LLMs (SEAL), a framework that enables LLMs to self-adapt by generating their own finetuning data and update directives.
Cited inCoherence is selected, anchored, then pruned: a literature synthesis on logical coherence in LLM world modelsA model rewrote another model's weights. Read what it took away.Study: Consequence consistent updatesStudy: Continual learning order and companion dataStudy: Fortifying knowledge in continual learningStudy: LLMs modifying open model weightsStudy: Logical coherence of LLM world modelsStudy: Seven continual learning methods testedStudy: Weight editing to prevent forgetting - 2025-05arXiv 2505.14679UltraEdit: Training-, Subject-, and Memory-Free Lifelong Editing in Language Models
Xiaojie Gu, Ziying Huang, Jia-Chen Gu et al. · Language models & NLP
Lifelong learning enables large language models (LLMs) to adapt to evolving information by continually updating their internal knowledge. An ideal system should support efficient, wide-ranging updates while preserving existing capabilities and ensuring reliable deployment.
- 2025-03arXiv 2503.05683WikiBigEdit: Understanding the Limits of Lifelong Knowledge Editing in LLMs
Lukas Thede, Karsten Roth, Matthias Bethge et al. · Language models & NLP
Keeping large language models factually up-to-date is crucial for deployment, yet costly retraining remains a challenge. Knowledge editing offers a promising alternative, but methods are only tested on small-scale or synthetic edit benchmarks.
Cited inEWC protects what its Fisher sees: a literature synthesis on elastic weight consolidation, scale and coherenceStudy: Consequence consistent updatesStudy: Continual learning order and companion dataStudy: Elastic weight consolidation deep diveStudy: Fortifying knowledge in continual learningStudy: LLMs modifying open model weightsStudy: Logical coherence of LLM world modelsStudy: Orchestrated specialists and bridge synthesisStudy: Weight editing to prevent forgetting - 2025-02arXiv 2502.11177The Mirage of Model Editing: Revisiting Evaluation in the Wild
Wanli Yang, Fei Sun, Jiajun Tan et al. · Language models & NLP
Despite near-perfect results reported in the literature, the effectiveness of model editing in real-world applications remains unclear.
- 2024-10arXiv 2410.02355AlphaEdit: Null-Space Constrained Knowledge Editing for Language Models
Junfeng Fang, Houcheng Jiang, Kun Wang et al. · 13th International Conference on Learning Representations (ICLR 2025 Oral) · Language models & NLP
Large language models (LLMs) often exhibit hallucinations due to incorrect or outdated knowledge. Hence, model editing methods have emerged to enable targeted knowledge updates.
- 2024-07arXiv 2407.09435MUSCLE: A Model Update Strategy for Compatible LLM Evolution
Jessica Echterhoff, Fartash Faghri, Raviteja Vemulapalli et al. · AI & agents
Large Language Models (LLMs) are regularly updated to enhance performance, typically through changes in data or architecture. Within the update process, developers often prioritize improving overall performance metrics, paying less attention to maintaining compatibility with earlier model versions.
- 2024-05arXiv 2405.14768WISE: Rethinking the Knowledge Memory for Lifelong Model Editing of Large Language Models
Peng Wang, Zexi Li, Ningyu Zhang et al. · Language models & NLP
Large language models (LLMs) need knowledge updates to meet the ever-growing world facts and correct the hallucinated responses, facilitating the methods of lifelong model editing.
Cited inEWC protects what its Fisher sees: a literature synthesis on elastic weight consolidation, scale and coherenceStudy: Consequence consistent updatesStudy: Elastic weight consolidation deep diveStudy: Fortifying knowledge in continual learningStudy: LLMs modifying open model weightsStudy: Orchestrated specialists and bridge synthesis - 2024-05arXiv 2405.05904Does Fine-Tuning LLMs on New Knowledge Encourage Hallucinations?
Zorik Gekhman, Gal Yona, Roee Aharoni et al. · Language models & NLP
When large language models are aligned via supervised fine-tuning, they may encounter new factual information that was not acquired through pre-training.
Cited inStable wrong beliefs evade every self-check: a literature synthesis on model self-knowledge and confident errorsTo stop a model forgetting, rehearse what it knew. New context won't do it.Study: Consequence consistent updatesStudy: Continual learning order and companion dataStudy: Fortifying knowledge in continual learningStudy: Generational competitive continual learningStudy: Graded succession by earned authorityStudy: Logical coherence of LLM world modelsStudy: Model self knowledge and confident errorsStudy: Orchestrated specialists and bridge synthesisStudy: Stopping confident wrong expert votes - 2023-07arXiv 2307.12976Evaluating the Ripple Effects of Knowledge Editing in Language Models
Roi Cohen, Eden Biran, Ori Yoran et al. · Language models & NLP
Modern language models capture a large body of factual knowledge. However, some facts can be incorrectly induced or become obsolete over time, resulting in factually incorrect generations.
Cited inA gradient kernel flags contradictions but does not predict forgetting, and steering rehearsal by it backfiresCoherence is selected, anchored, then pruned: a literature synthesis on logical coherence in LLM world modelsEWC protects what its Fisher sees: a literature synthesis on elastic weight consolidation, scale and coherenceIterated self-closure settles fast, but only evidence that overrides self-belief makes the settled state trueTo stop a model forgetting, rehearse what it knew. New context won't do it.Study: Consequence consistent updatesStudy: Continual learning order and companion dataStudy: Decision signatures for conflict detectionStudy: Elastic weight consolidation deep diveStudy: Graded succession by earned authorityStudy: Hardware guardian core for continual learningStudy: Logical coherence of LLM world modelsStudy: Orchestrated specialists and bridge synthesis - 2023-05arXiv 2305.14795MQuAKE: Assessing Knowledge Editing in Language Models via Multi-Hop Questions
Zexuan Zhong, Zhengxuan Wu, Christopher D. Manning et al. · Language models & NLP
The information stored in large language models (LLMs) falls out of date quickly, and retraining from scratch is often not an option. This has recently given rise to a range of techniques for injecting new facts through updating model weights.
Cited inTrust the premises, not the confidence: which belief to drop when an LLM contradicts itself, and why the solver is not the bottleneckStudy: Consequence consistent updatesStudy: Continual learning order and companion dataStudy: Graded succession by earned authorityStudy: LLMs modifying open model weightsStudy: Logical coherence of LLM world modelsStudy: Orchestrated specialists and bridge synthesisStudy: Weight editing to prevent forgetting - 2022-10arXiv 2210.07229Mass-Editing Memory in a Transformer
Kevin Meng, Arnab Sen Sharma, Alex Andonian et al. · Language models & NLP
Recent work has shown exciting promise in updating large language models with new memories, so as to replace obsolete information or add specialized knowledge. However, this line of work is predominantly limited to updating single associations.
Cited inEWC protects what its Fisher sees: a literature synthesis on elastic weight consolidation, scale and coherenceStudy: Consequence consistent updatesStudy: Decision signatures for conflict detectionStudy: Elastic weight consolidation deep diveStudy: Fortifying knowledge in continual learningStudy: LLMs modifying open model weights
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.
