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.
- 2025-10arXiv 2510.23671Sparsity and Superposition in Mixture of Experts
Marmik Chaudhari, Jeremi Nuer, Rome Thorstenson · Machine learning
Mixture of Experts (MoE) models have become central to scaling large language models, yet their mechanistic differences from dense networks remain poorly understood.
- 2025-10arXiv 2510.21614Huxley-Gödel Machine: Human-Level Coding Agent Development by an Approximation of the Optimal Self-Improving Machine
Wenyi Wang, Piotr Piękos, Li Nanbo et al. · AI & agents
Recent studies operationalize self-improvement through coding agents that edit their own codebases.
- 2025-10arXiv 2510.21978Beyond Reasoning Gains: Mitigating General-Capability Forgetting in Large Reasoning Models
Hoang Phan, Xianjun Yang, Yuanshun Yao et al. · Machine learning
Reinforcement learning with verifiable rewards (RLVR) has delivered impressive gains in mathematical and multimodal reasoning and has become a standard post-training paradigm for contemporary language and vision-language models.
- 2025-10arXiv 2510.19172When Facts Change: Probing LLMs on Evolving Knowledge with evolveQA
Nishanth Sridhar Nakshatri, Shamik Roy, Manoj Ghuhan Arivazhagan et al. · Language models & NLP
LLMs often fail to handle temporal knowledge conflicts--contradictions arising when facts evolve over time within their training data.
- 2025-10arXiv 2510.19941Are Greedy Task Orderings Better Than Random in Continual Linear Regression?
Matan Tsipory, Ran Levinstein, Itay Evron et al. · Machine learning
We analyze task orderings in continual learning for linear regression, assuming joint realizability of training data. We focus on orderings that greedily maximize dissimilarity between consecutive tasks, a concept briefly explored in prior work but still surrounded by open questions.
- 2025-10arXiv 2510.18212A Definition of AGI
Dan Hendrycks, Dawn Song, Christian Szegedy et al. · AI & agents
The lack of a concrete definition for Artificial General Intelligence (AGI) obscures the gap between today's specialized AI and human-level cognition.
Cited inStudy: Superintelligence - 2025-10arXiv 2510.18874Retaining by Doing: The Role of On-Policy Data in Mitigating Forgetting
Howard Chen, Noam Razin, Karthik Narasimhan et al. · Proceedings of the 43rd International Conference on Machine Learning (ICML), 2026 · Machine learning
Adapting language models (LMs) to new tasks via post-training carries the risk of degrading existing capabilities -- a phenomenon classically known as catastrophic forgetting.
- 2025-10arXiv 2510.19116That's Deprecated! Understanding, Detecting, and Steering Knowledge Conflicts in Language Models for Code Generation
Jaesung Bae, Cameron Churchwell, Mitchell Hermon et al. · Language models & NLP
This paper investigates how large language models (LLMs) behave when faced with discrepancies between their parametric knowledge and conflicting information contained in a prompt.
- 2025-10arXiv 2510.15416Adaptive Minds: Empowering Agents with LoRA-as-Tools
Pavan C Shekar, Aswanth Krishnan · AI & agents
We investigate a framework in which LoRA adapters are treated as callable tools that a base language model can dynamically select and invoke.
- 2025-10arXiv 2510.15514Voting with the Graph: Stable RLAIF via Topological Consistency Maximization
Boyin Liu, Zhuo Zhang, Sen Huang et al. · AI & agents
, ), occurring in 5-9% of evaluations across state-of-the-art models.
- 2025-10arXiv 2510.15103Continual Learning via Sparse Memory Finetuning
Jessy Lin, Luke Zettlemoyer, Gargi Ghosh et al. · Language models & NLP
Modern language models are powerful, but typically static after deployment. A major obstacle to building models that continually learn over time is catastrophic forgetting, where updating on new data erases previously acquired capabilities.
Cited inTo keep what a model knows, control what it is allowed to moveStudy: Fortifying knowledge in continual learningStudy: Genesys program prior artStudy: Hardware guardian core for continual learningStudy: Memory world models and source trustStudy: Seven continual learning methods testedStudy: Weight editing to prevent forgetting - 2025-10arXiv 2510.13999REAP the Experts: Why Pruning Prevails for One-Shot MoE compression
Mike Lasby, Ivan Lazarevich, Nish Sinnadurai et al. · Machine learning
Sparsely-activated Mixture-of-Experts (SMoE) models offer efficient pre-training and low latency but their large parameter counts create significant memory overhead, motivating research into expert compression.
- 2025-10arXiv 2510.12040Uncertainty Quantification for Hallucination Detection in Large Language Models: Foundations, Methodology, and Future Directions
Sungmin Kang, Yavuz Faruk Bakman, Duygu Nur Yaldiz et al. · Language models & NLP
The rapid advancement of large language models (LLMs) has transformed the landscape of natural language processing, enabling breakthroughs across a wide range of areas including question answering, machine translation, and text summarization.
- 2025-10arXiv 2510.12266HiLoRA: Adaptive Hierarchical LoRA Routing for Training-Free Domain Generalization
Ziyi Han, Huanyu Wang, Zeyu Zhang et al. · Machine learning
Low-Rank Adaptation (LoRA) has emerged as a widely used technique for adapting large language models (LLMs) to new domains, due to its modular design and broad availability on platforms such as HuggingFace.
- 2025-10arXiv 2510.12697Multi-Agent Debate for LLM Judges with Adaptive Stability Detection
Tianyu Hu, Zhen Tan, Song Wang et al. · AI & agents
With advancements in reasoning capabilities, Large Language Models (LLMs) are increasingly employed for automated judgment tasks., majority voting), which can fail even when individual agents provide correct answers.
- 2025-10arXiv 2510.15977Bolster Hallucination Detection via Prompt-Guided Data Augmentation
Wenyun Li, Zheng Zhang, Dongmei Jiang et al. · Machine learning
Large language models (LLMs) have garnered significant interest in AI community. Despite their impressive generation capabilities, they have been found to produce misleading or fabricated information, a phenomenon known as hallucinations.
- 2025-10arXiv 2510.10125Ctrl-World: A Controllable Generative World Model for Robot Manipulation
Yanjiang Guo, Lucy Xiaoyang Shi, Jianyu Chen et al. · Robotics & control
Generalist robot policies can now perform a wide range of manipulation skills, but evaluating and improving their ability with unfamiliar objects and instructions remains a significant challenge.
- 2025-10arXiv 2510.10193SAFER: Risk-Constrained Sample-then-Filter in Large Language Models
Qingni Wang, Yue Fan, Xin Eric Wang · AI & agents
As large language models (LLMs) are increasingly deployed in risk-sensitive applications such as real-world open-ended question answering (QA), ensuring the trustworthiness of their outputs has become critical.
- 2025-10arXiv 2510.13851EvoEdit: Evolving Null-space Alignment for Robust and Efficient Knowledge Editing
Sicheng Lyu, Yu Gu, Xinyu Wang et al. · Language models & NLP
Large language models (LLMs) require continual updates to rectify outdated or erroneous knowledge. Model editing has emerged as a compelling paradigm for introducing targeted modifications without the computational burden of full retraining.
- 2025-10arXiv 2510.09551Titans Revisited: A Lightweight Reimplementation and Critical Analysis of a Test-Time Memory Model
Gavriel Di Nepi, Federico Siciliano, Fabrizio Silvestri · Machine learning
By the end of 2024, Google researchers introduced Titans: Learning at Test Time, a neural memory model achieving strong empirical results across multiple tasks. However, the lack of publicly available code and ambiguities in the original description hinder reproducibility.
- 2025-10arXiv 2510.08008Beyond Sunk Costs: Boosting LLM Pre-training Efficiency via Orthogonal Growth of Mixture-of-Experts
Ruizhe Wang, Yucheng Ding, Xiao Liu et al. · Machine learning
As the computational demands for pre-training Large Language Models (LLMs) continue to surge, the need for efficient training paradigms becomes critical.
- 2025-10arXiv 2510.08564How to Teach Large Multimodal Models New Skills
Zhen Zhu, Yiming Gong, Yao Xiao et al. · AI & agents
How can we teach large multimodal models (LMMs) new skills without erasing prior abilities? We study sequential fine-tuning on five target skills while monitoring general ability on eight held-out benchmarks across three model families.
- 2025-10arXiv 2510.05566Domain-Shift-Aware Conformal Prediction for Large Language Models
Zhexiao Lin, Yuanyuan Li, Neeraj Sarna et al. · Machine learning
Large language models have achieved impressive performance across diverse tasks. However, their tendency to produce overconfident and factually incorrect outputs, known as hallucinations, poses risks in real-world applications.
- 2025-10arXiv 2510.04136MoME: Mixture of Matryoshka Experts for Audio-Visual Speech Recognition
Umberto Cappellazzo, Minsu Kim, Pingchuan Ma et al. · Other
Large language models (LLMs) have recently shown strong potential in audio-visual speech recognition (AVSR), but their high computational demands and sensitivity to token granularity limit their practicality in resource-constrained settings.
- 2025-10arXiv 2510.03418LegalWiz: A Multi-Agent Generation Framework for Contradiction Detection in Legal Documents
Ananya Mantravadi, Shivali Dalmia, Olga Pospelova et al. · AI & agents
Retrieval-Augmented Generation (RAG) integrates large language models (LLMs) with external sources, but unresolved contradictions in retrieved evidence often lead to hallucinations and legally unsound outputs.
- 2025-10arXiv 2510.01879REPAIR: Robust Editing via Progressive Adaptive Intervention and Reintegration
Yisu Wang, Ming Wang, Haoyuan Song et al. · Language models & NLP
Post-training for large language models (LLMs) is constrained by the high cost of acquiring new knowledge or correcting errors and by the unintended side effects that frequently arise from retraining.
- 2025-10arXiv 2510.00841LLM Routing with Dueling Feedback
Chao-Kai Chiang, Takashi Ishida, Masashi Sugiyama · Machine learning
We study LLM routing, the problem of selecting the best model for each query while balancing user satisfaction, model expertise, and inference cost.
- 2025-09arXiv 2509.25760TruthRL: Incentivizing Truthful LLMs via Reinforcement Learning
Zhepei Wei, Xiao Yang, Kai Sun et al. · Language models & NLP
While large language models (LLMs) have demonstrated strong performance on factoid question answering, they are still prone to hallucination and untruthful responses, particularly when tasks demand information outside their parametric knowledge.
- 2025-09arXiv 2509.26520Training Matryoshka Mixture-of-Experts for Elastic Inference-Time Expert Utilization
Yaoxiang Wang, Qingguo Hu, Yucheng Ding et al. · Language models & NLP
Mixture-of-Experts (MoE) has emerged as a promising paradigm for efficiently scaling large language models without a proportional increase in computational cost.
- 2025-09arXiv 2510.00202RouterArena: An Open Platform for Comprehensive Comparison of LLM Routers
Yifan Lu, Rixin Liu, Jiayi Yuan et al. · Machine learning
Today's LLM ecosystem comprises a wide spectrum of models that differ in size, capability, and cost. No single model is optimal for all scenarios; hence, LLM routers have become essential for selecting the most appropriate model under varying circumstances.
- 2025-09arXiv 2509.24527Training Agents Inside of Scalable World Models
Danijar Hafner, Wilson Yan, Timothy Lillicrap · AI & agents
World models learn general knowledge from videos and simulate experience for training behaviors in imagination, offering a path towards intelligent agents. However, previous world models have been unable to accurately predict object interactions in complex environments.
- 2025-09arXiv 2509.23593Avoid Catastrophic Forgetting with Rank-1 Fisher from Diffusion Models
Zekun Wang, Anant Gupta, Zihan Dong et al. · ICLR 2026 · Machine learning
Catastrophic forgetting remains a central obstacle for continual learning in neural models.
- 2025-09arXiv 2509.23678Towards a Comprehensive Scaling Law of Mixture-of-Experts
Guoliang Zhao, Yuhan Fu, Shuaipeng Li et al. · Machine learning
Mixture-of-Experts (MoE) models have become the consensus approach for enabling parameter-efficient scaling and cost-effective deployment in large language models.
- 2025-09arXiv 2509.23893Dynamic Orthogonal Continual Fine-tuning for Mitigating Catastrophic Forgettings
Zhixin Zhang, Zeming Wei, Meng Sun · Machine learning
Catastrophic forgetting remains a critical challenge in continual learning for large language models (LLMs), where models struggle to retain performance on historical tasks when fine-tuning on new sequential data without access to past datasets.
- 2025-09arXiv 2509.23055Peacemaker or Troublemaker: How Sycophancy Shapes Multi-Agent Debate
Binwei Yao, Chao Shang, Wanyu Du et al. · Language models & NLP
Large language models (LLMs) often display sycophancy, a tendency toward excessive agreeability. This behavior poses significant challenges for multi-agent debating systems (MADS) that rely on productive disagreement to refine arguments and foster innovative thinking.
- 2025-09arXiv 2510.02345Breaking the MoE LLM Trilemma: Dynamic Expert Clustering with Structured Compression
Peijun Zhu, Ning Yang, Baoliang Tian et al. · Language models & NLP
Mixture-of-Experts (MoE) Large Language Models (LLMs) face a trilemma of load imbalance, parameter redundancy, and communication overhead. We introduce a unified framework based on dynamic expert clustering and structured compression to address these issues cohesively.
- 2025-09arXiv 2509.22263Erase or Hide? Suppressing Spurious Unlearning Neurons for Robust Unlearning
Nakyeong Yang, Dong-Kyum Kim, Jea Kwon et al. · Machine learning
Large language models trained on web-scale data can memorize private or sensitive knowledge, raising significant privacy risks.
- 2025-09arXiv 2509.17348AIMMerging: Adaptive Iterative Model Merging Using Training Trajectories for Language Model Continual Learning
Yujie Feng, Jian Li, Xiaoyu Dong et al. · Language models & NLP
Continual learning (CL) is essential for deploying large language models (LLMs) in dynamic real-world environments without the need for costly retraining.
- 2025-09arXiv 2509.17377Robustness of Neurosymbolic Reasoners on First-Order Logic Problems
Hannah Bansal, Kemal Kurniawan, Lea Frermann · Language models & NLP
Recent trends in NLP aim to improve reasoning capabilities in Large Language Models (LLMs), with key focus on generalization and robustness to variations in tasks.
- 2025-09arXiv 2509.17530An Unlearning Framework for Continual Learning
Sayanta Adhikari, Vishnuprasadh Kumaravelu, P. K. Srijith · Machine learning
Growing concerns surrounding AI safety and data privacy have driven the development of Machine Unlearning as a potential solution. However, current machine unlearning algorithms are designed to complement the offline training paradigm.
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.
