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 2610.08791World Models' Last Exam in Physics
Mingju Gao, Qingle Liu, Yuzhao Peng et al. · Vision
Video world models can produce visually convincing yet physically inconsistent sequences, raising concerns about their reliability for prediction and planning in embodied AI systems.
- 2026-08arXiv 2609.00188ZimaBlue: Evolving Generalizable World Action Models through Scalable Video Pre-training
Xionghao Wu, Yijun Yang, Shiyang Zhou et al. · Vision
Robotic manipulation faces a fundamental scaling challenge: robust generalization demands broad physical experience, yet action-labeled robot trajectories are expensive to collect and inherently limited in diversity.
- 2026-08arXiv 2608.22215Dual-Layer Agentic Memory with Fast Write Routing and Slow Consolidation
Wenzhi Li, Dong Nie, Rui Lan et al. · Language models & NLP
Large language model (LLM) agents operate in dynamic environments where knowledge continuously evolves. Existing memory systems typically treat external memory as a monotonically growing repository, inevitably leading to retrieval degradation and increasing computational costs over time.
- 2026-07arXiv 2607.27036Mitigating Compounding Error via Video Representation Regularization
Taiye Chen, Qi Zhang, Yisen Wang · Vision
Video diffusion-based world models enable long autoregressive video generation for robotics, autonomous driving and simulation tasks, yet sliding-window autoregressive inference suffers from severe error accumulation that degrades frame quality over time.
- 2026-07arXiv 2607.21962Ground Truth First: A Longitudinal Evaluation Instrument for Agent Memory, and the Tenure Crossover in Memory-Architecture Rankings
Quentin Spencer · Language models & NLP
Benchmarks for LLM-agent memory typically generate conversations first and extract answer keys afterwards -- with documented label-error and contamination problems -- and they overwhelmingly measure short interaction histories.
- 2026-07arXiv 2607.10362A Control Theory of Predictability in Latent World Models
Hanzhe You, Yonggang Zhang, Maohao Ran et al. · Machine learning
Latent world models are trained to predict future states in a learned representation and are then deployed inside a planner that selects actions by simulating them forward.
- 2026-07arXiv 2607.06401A Definition and Roadmap for World Models
Xinyuan Chen, Haoyu Guo, Shi Guo et al. · AI & agents
World models -- internal simulators that learn the structure and dynamics of an environment -- have become one of the most actively debated concepts in AI.
- 2026-06arXiv 2606.28757A Physics-Grounded Benchmark for Multi-Agent Dynamics in World Models
Nuo Chen, Lulin Liu, Zihao Li et al. · Vision
Generative world models hold immense promise as scalable simulators for autonomous systems, particularly for synthesizing rare but safety-critical multi-agent interactions, such as vehicle collisions.
- 2026-06arXiv 2606.27326Hallucination in World Models is Predictable and Preventable
Nicklas Hansen, Xiaolong Wang · Machine learning
Modern generative world models render increasingly realistic action-controllable futures, yet they frequently hallucinate: rollouts remain visually fluent while drifting from the ground-truth dynamics.
- 2026-06arXiv 2606.24322Securing LLM-Agent Long-Term Memory Against Poisoning: Non-Malleable, Origin-Bound Authority with Machine-Checked Guarantees
Yedidel Louck · Security
LLM agents increasingly rely on persistent long-term memory, which creates a critical vulnerability that we study here: memory poisoning.
- 2026-06arXiv 2606.22030When Does Belief-Based Agent Memory Help? Reliability-Conditional Updating and Provenance-Capped Poisoning Defense
Pranav Singh · AI & agents
We investigate when belief-based memory actually improves large language model (LLM) agents.
- 2026-06arXiv 2606.12990Exposure Bias as Epistemic Underidentification in Recursive Forecasting
Riku Green, Zahraa S. Abdallah, Telmo M Silva Filho · Machine learning
Recursive multi-step forecasting is usually framed as distribution shift: models are trained on observed histories but deployed on their own predictions.
- 2026-06arXiv 2606.03979Language Models Need Sleep: Learning to Self-Modify and Consolidate Memories
Ali Behrouz, Farnoosh Hashemi, Adel Javanmard et al. · Machine learning
The past few decades have witnessed significant advances in the design of machine learning algorithms, from early studies on task-specific shallow models to more general deep Large Language Models (LLMs).
Cited inInvestors are paying for agents that learn on the job. The hard part is knowing what they forgot.To keep what a model knows, control what it is allowed to moveWhat a model lets into long-term memory matters more than how it learnsStudy: Fortifying knowledge in continual learningStudy: Genesys program prior artStudy: Hardware guardian core for continual learningStudy: LLMs modifying open model weightsStudy: Logical coherence of LLM world modelsStudy: Memory world models and source trustStudy: Seven continual learning methods tested - 2026-06arXiv 2606.02800Cosmos 3: Omnimodal World Models for Physical AI
NVIDIA, :, Aditi et al. · Vision
We introduce Cosmos 3, a family of omnimodal world models designed to jointly process and generate language, image, video, audio, and action sequences within a unified mixture-of-transformers architecture.
- 2026-05arXiv 2605.31314AR Forcing: Towards Long-Horizon Robot Navigation World Model
Yifei Yang, Zehua Fan, Huan Li et al. · Robotics & control
The diffusion based robot navigation world models are typically trained using parallel supervision, while autoregressive inference is employed during path planning.
- 2026-05arXiv 2605.19242PhyWorld: Physics-Faithful World Model for Video Generation
Pu Zhao, Juyi Lin, Timothy Rupprecht et al. · Vision
World simulators can provide safe and scalable environments for training Physical AI systems before real-world deployment. Large video generation models are emerging as a promising basis for such simulators because they can generate diverse and realistic visual futures.
- 2026-05arXiv 2605.12484Learning, Fast and Slow: Towards LLMs That Adapt Continually
Rishabh Tiwari, Kusha Sareen, Lakshya A Agrawal et al. · Machine learning
, via RL). However, updating model parameters forces them to absorb task-specific information, which can result in catastrophic forgetting and loss of plasticity.
- 2026-05arXiv 2605.05097Continual Knowledge Updating in LLM Systems: Learning Through Multi-Timescale Memory Dynamics
Andreas Pattichis, Constantine Dovrolis · Machine learning
LLMs are trained once, then deployed into a world that never stops changing. External memory compensates for this, but most systems manage it explicitly rather than letting it adapt on its own.
- 2026-03arXiv 2603.14517Learning to Forget: Sleep-Inspired Memory Consolidation for Resolving Proactive Interference in Large Language Models
Ying Xie · AI & agents
Large language models (LLMs) suffer from proactive interference (PI): outdated information in the context window disrupts retrieval of current values.
- 2026-03arXiv 2603.13972FLUX: Data Worth Training On
Gowtham, Sai Rupesh, Sanjay Kumar et al. · Language models & NLP
Modern large language model training is no longer limited by data availability, but by the inability of existing preprocessing pipelines to simultaneously achieve massive scale and high data quality.
- 2026-03arXiv 2603.11395ARROW: Augmented Replay for RObust World models
Abdulaziz Alyahya, Abdallah Al Siyabi, Markus R. Ernst et al. · Transactions on Machine Learning Research, 2026 · Machine learning
Continual reinforcement learning challenges agents to acquire new skills while retaining previously learned ones with the goal of improving performance in both past and future tasks.
- 2026-03arXiv 2603.07670Memory for Autonomous LLM Agents:Mechanisms, Evaluation, and Emerging Frontiers
Pengfei Du · AI & agents
Large language model (LLM) agents increasingly operate in settings where a single context window is far too small to capture what has happened, what was learned, and what should not be repeated.
- 2026-03arXiv 2603.01935Dream2Learn: Structured Generative Dreaming for Continual Learning
Salvatore Calcagno, Matteo Pennisi, Federica Proietto Salanitri et al. · Machine learning
Continual learning requires balancing plasticity and stability while mitigating catastrophic forgetting.
- 2026-02arXiv 2602.06923From Kepler to Newton: Inductive Biases Guide Learned World Models in Transformers
Ziming Liu, Sophia Sanborn, Surya Ganguli et al. · Machine learning
Can general-purpose AI architectures go beyond prediction to discover the physical laws governing the universe? True intelligence relies on "world models" -- causal abstractions that allow an agent to not only predict future states but understand the underlying governing dynamics.
- 2026-02arXiv 2602.03747LIVE: Long-horizon Interactive Video World Modeling
Junchao Huang, Ziyang Ye, Xinting Hu et al. · Vision
Autoregressive video world models predict future visual observations conditioned on actions. While effective over short horizons, these models often struggle with long-horizon generation, as small prediction errors accumulate over time.
- 2026-01arXiv 2601.21282WorldBench: Benchmarking Physical Understanding of World Models by Isolating Physics Concepts
Rishi Upadhyay, Howard Zhang, Jim Solomon et al. · Vision
Recent advances in generative foundational models, often termed "world models," have propelled interest in applying them to critical tasks like robotic planning and autonomous system training.
- 2026-01arXiv 2601.21698Curriculum Learning for LLM Pretraining: An Analysis of Learning Dynamics
Mohamed Elgaar, Hadi Amiri · Machine learning
Curriculum learning changes the order of pretraining data, but it remains unclear how ordering changes the learning dynamics.
- 2026-01arXiv 2601.11004NOVA: NOise-aware Verbal Confidence CAlibration for Robust Large Language Models in RAG Systems
Jiayu Liu, Rui Wang, Qing Zong et al. · Language models & NLP
Accurately assessing model confidence is essential for deploying large language models (LLMs) in mission-critical factual domains. While retrieval-augmented generation (RAG) is widely adopted to improve grounding, confidence calibration in RAG settings remains poorly understood.
- 2026-01arXiv 2601.07264The Confidence Dichotomy: Analyzing and Mitigating Miscalibration in Tool-Use Agents
Weihao Xuan, Qingcheng Zeng, Heli Qi et al. · Language models & NLP
Autonomous agents based on large language models (LLMs) are rapidly evolving to handle multi-turn tasks, but ensuring their trustworthiness remains a critical challenge.
- 2025-12arXiv 2512.06232Opinion: Learning Intuitive Physics May Require More than Visual Data
Ellen Su, Solim Legris, Todd M. Gureckis et al. · Vision
Humans expertly navigate the world by building rich internal models founded on an intuitive understanding of physics.
- 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 2510.10125Ctrl-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 2510.09551Titans 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.
- 2025-09arXiv 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.
- 2025-08arXiv 2508.21296MyGO: Memory Yielding Generative Offline-consolidation for Lifelong Learning Systems
Shihao Ji, Zihui Song · Machine learning
Continual or Lifelong Learning aims to develop models capable of acquiring new knowledge from a sequence of tasks without catastrophically forgetting what has been learned before.
- 2025-08arXiv 2508.16651HiCL: Hippocampal-Inspired Continual Learning
Kushal Kapoor, Wyatt Mackey, Yiannis Aloimonos et al. · Machine learning
We propose HiCL, a novel hippocampal-inspired dual-memory continual learning architecture designed to mitigate catastrophic forgetting by using elements inspired by the hippocampal circuitry.
- 2025-07arXiv 2507.07417May I have your Attention? Breaking Fine-Tuning based Prompt Injection Defenses using Architecture-Aware Attacks
Nishit V. Pandya, Andrey Labunets, Sicun Gao et al. · Security
A popular class of defenses against prompt injection attacks on large language models (LLMs) relies on fine-tuning to separate instructions and data, so that the LLM does not follow instructions that might be present with data.
- 2025-07arXiv 2507.06952What Has a Foundation Model Found? Using Inductive Bias to Probe for World Models
Keyon Vafa, Peter G. Chang, Ashesh Rambachan et al. · Machine learning
Foundation models are premised on the idea that sequence prediction can uncover deeper domain understanding, much like how Kepler's predictions of planetary motion later led to the discovery of Newtonian mechanics.
- 2025-06arXiv 2506.09849IntPhys 2: Benchmarking Intuitive Physics Understanding In Complex Synthetic Environments
Florian Bordes, Quentin Garrido, Justine T Kao et al. · Vision
We present IntPhys 2, a video benchmark designed to evaluate the intuitive physics understanding of deep learning models.
- 2025-06arXiv 2506.09985V-JEPA 2: Self-Supervised Video Models Enable Understanding, Prediction and Planning
Mido Assran, Adrien Bardes, David Fan et al. · AI & agents
A major challenge for modern AI is to learn to understand the world and learn to act largely by observation.
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.
