Knowledge catalogue
When you project exponential growth – and miss. I stand by my call that OpenAI is likely to someday be seen as the WeWork of AI. Exclusive: OpenAI recently missed its own targets for new users and rev
arXiv:2604.24764v1 Announce Type: new Abstract: Recent video foundation models demonstrate impressive visual synthesis but frequently suffer from geometric inconsistencies. While existing methods atte
Wow. Musk’s lawyers will capitalize on this. 🚨 Microsoft's lawyer just told the jury Microsoft 'knew nothing'. OpenAI's non-profit board approved all funding, every step of the way and Microsoft did i
arXiv:2604.24623v1 Announce Type: new Abstract: Graph-based Retrieval-Augmented Generation (GraphRAG) extends traditional RAG by using knowledge graphs (KGs) to give large language models (LLMs) a str
arXiv:2604.23486v1 Announce Type: new Abstract: Educational NLP systems are typically evaluated using engagement metrics and satisfaction surveys, which are at best a proxy for meeting pedagogical goa
arXiv:2604.23536v1 Announce Type: new Abstract: Diffusion models have achieved unprecedented success in text-aligned generation, largely driven by Classifier-Free Guidance (CFG). However, standard CFG
arXiv:2604.23878v1 Announce Type: new Abstract: Despite a century of empirical memory research, existing AI agent memory systems rely on system-engineering metaphors (virtual-memory paging, flat LLM s
arXiv:2511.14135v2 Announce Type: replace-cross Abstract: Fair workload enforcement in heterogeneous multi-agent systems that pursue shared objectives remains challenging. Fixed fairness penalties oft
AI has had one safest technology roll-outs in history. Read that again, because it's a fact. It's used by billions with a tiny fraction of a percent of actual problems. And yet it's seen as dangerous
arXiv:2604.22236v1 Announce Type: cross Abstract: Human decision-makers often face choices about complex cases with many potentially relevant features, but limited bandwidth to inspect and integrate a
“Always keep a separate backup.” For sure, every experienced programmer should know this. 𝘉𝘶𝘵 𝘸𝘦 𝘫𝘶𝘴𝘵 𝘤𝘳𝘦𝘢𝘵𝘦𝘥 𝘢 𝘸𝘩𝘰𝘭𝘦 𝘨𝘦𝘯𝘦𝘳𝘢𝘵𝘪𝘰𝘯 𝘰𝘧 𝘷𝘪𝘣𝘦 𝘤𝘰𝘥𝘦𝘳𝘴 𝘸𝘩𝘰 𝘥𝘰𝘯’𝘵. And we now have a legion of synthetic coding
arXiv:2604.22535v1 Announce Type: new Abstract: Objective: To propose and retrospectively validate an integrated framework addressing three barriers to clinical translation of readmission prediction:
arXiv:2604.22280v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) have emerged as a promising foundation for universal multimodal embeddings. Recent studies have shown that reas
Gary Marcus reported on X that polling software is experiencing a malfunction where votes are not displaying, a problem he notes has been observed by multiple users. He confirmed the issue from his ow
arXiv:2510.19020v2 Announce Type: replace-cross Abstract: We propose a new method for statistical inference in generalized linear models. In the overparameterized regime, Principal Component Regressio
arXiv:2604.22498v1 Announce Type: cross Abstract: Although Multimodal Large Language Models (MLLMs) have advanced rapidly, they still face notable challenges in fine-grained multi-image understanding,
arXiv:2512.22519v2 Announce Type: replace Abstract: Recent Vision-Language-Action (VLA) models have made impressive progress toward general-purpose robotic manipulation by post-training large Vision-L
arXiv:2604.22428v1 Announce Type: new Abstract: Predicting individual cognitive decline in Alzheimer's disease (AD) is difficult due to the heterogeneity of disease progression. Reliable clinical tool
arXiv:2604.22140v1 Announce Type: cross Abstract: We study stochastic multi-armed bandits in which the objective is a statistical functional of the long-run reward distribution, rather than expected r
arXiv:2604.21960v1 Announce Type: cross Abstract: Computed Tomography (CT) is a widely used imaging modality in medical and industrial applications. To limit radiation exposure and measurement time, t
arXiv:2604.22335v1 Announce Type: new Abstract: Large language models (LLMs) often produce content that contradicts or overlooks information provided in the input context, a phenomenon known as faithf
arXiv:2601.19963v2 Announce Type: replace-cross Abstract: Training a high-performing neural decoder can be difficult when only limited data are available from a recording session. To address this chal
arXiv:2604.22562v1 Announce Type: cross Abstract: Client contribution estimation in Federated Learning is necessary for identifying clients' importance and for providing fair rewards. Current methods
arXiv:2604.22152v1 Announce Type: new Abstract: Evaluating robotics policies across thousands of environments and thousands of tasks is infeasible with existing approaches. This motivates the need for
arXiv:2604.22089v1 Announce Type: cross Abstract: Generative Artificial Intelligence (GAI) systems that can automatically generate content in the form of source code or other contents (e.g., images) h
arXiv:2604.22586v1 Announce Type: new Abstract: We propose FlowAnchor, a training-free framework for stable and efficient inversion-free, flow-based video editing. Inversion-free editing methods have
Gary Marcus is a prominent AI researcher, cognitive scientist, and entrepreneur who frequently discusses artificial intelligence, machine learning, and cognitive science on X (formerly Twitter). His p
@GaryMarcus Never ever use generative AI for anything critical. The technology is probablistic all the way down and therefore inherently unreliable. Why is this so hard to understand? It’s wild how ma
@GaryMarcus These coding tools are intellectual chain saws. Powerful in the hands of a caring professional, extremely dangerous to the operator when used improperly. Implying AGI-adjacency has created
Generative AI as the Hindenberg 🦔 Michael Wooldridge, professor of AI at Oxford, is warning that the race to market AI has raised the risk of a 'Hindenburg moment' that could shatter global confidence
arXiv:2601.10386v2 Announce Type: replace-cross Abstract: Accurate survival prediction in Non-Small Cell Lung Cancer (NSCLC) requires integrating clinical, radiological, and histopathological data. Mu
arXiv:2502.03698v4 Announce Type: replace Abstract: Learning from demonstrations is a popular approach to train AI models; however, their vulnerability to adversarial attacks remains underexplored. We
I don’t always agree with @ElonMusk. I often disagree. Sometimes loudly. But he’s basically right here, as far I can see. Scam Altman and Greg Stockman stole a charity. Full stop. Greg got tens of bil
arXiv:2604.22631v1 Announce Type: new Abstract: Modern automatic speech recognition (ASR) systems have been observed to function better for certain speaker groups (SGs) than others, despite recent gai
arXiv:2604.22565v1 Announce Type: cross Abstract: Large Language Models (LLMs) can reason well, yet often miss decisive evidence when it is buried in long, noisy contexts. We introduce HiLight, an Evi
arXiv:2408.16286v5 Announce Type: replace Abstract: Designing a safe policy for uncertain environments is crucial in real-world control systems. However, this challenge remains inadequately addressed
arXiv:2604.22158v1 Announce Type: cross Abstract: We study the problem of adaptive control of the stochastic linear quadratic regulator (LQR) with constraints that must be satisfied at every time step
People keep blaming the users for the growing number of vibe-coded reasoners. Doing so is *half* right. Users *are*screwing up - by letting vibe coded stuff access their files, without proper backups,
arXiv:2604.22117v1 Announce Type: cross Abstract: Aligned large language models(LLMs) remain vulnerable to adversarial manipulation, and their dependence on web-scale pretraining creates a subtle but
arXiv:2604.22169v1 Announce Type: cross Abstract: Generic group-based RL assumes that sampled rollout groups are already usable learning signals. We show that this assumption breaks down in sparse-hit
@robertskmiles @GaryMarcus “fails less often than a human does” is a terrible metric. We build deterministic software and test it to destruction to ensure it’s 99.99999 reliable . That’s been the basi
arXiv:2604.22374v1 Announce Type: new Abstract: Sign language translation (SLT) converts continuous sign videos into spoken-language text, yet it remains challenging due to the intrinsic modality mism
arXiv:2511.14427v3 Announce Type: replace-cross Abstract: Effective contact-rich manipulation requires robots to synergistically leverage vision, force, and proprioception. However, Reinforcement Lear
Stop blaming the users. It’s not that simple: People keep blaming the users for the growing number of vibe-coded reasoners. Doing so is *half* right. Users *are*screwing up - by letting vibe coded stu
arXiv:2510.11586v2 Announce Type: replace Abstract: Many in-silico simulations of human survey responses with large language models (LLMs) focus on generating closed-ended survey responses, whereas LL
arXiv:2601.12430v2 Announce Type: replace Abstract: Vision-language model (VLM) hallucination is commonly linked to imbalanced allocation of attention across input modalities: system, image and text.
arXiv:2604.22337v1 Announce Type: new Abstract: Most tabular-data generators match marginal statistics yet ignore causal structure, leading downstream models to learn spurious or unfair patterns. We p
Gary Marcus critiques common misconceptions about how large language models (LLMs) function, suggesting that most users have an incorrect mental model of what LLMs actually do when generating response
This statement critiques the asymmetrical attribution of outcomes in AI development, where users are blamed for failures while AI models receive credit for successes. Gary Marcus uses the metaphor of
arXiv:2604.21938v1 Announce Type: cross Abstract: Embodied AI is widely discussed as a job-displacement problem. The deeper risk, however, is governance lag: the inability of public institutions to ke
The divorce between Microsoft and OpenAI that I projected three years ago is now underway. Time for everyone to start seeing other people. OpenAI is moving away from its exclusive Microsoft arrangemen
The question of whether Altman and Brockman did what they promised ≠ the question of whether Elon is a good person. Musk argued that Altman and Brockman broke promises and abused the notion of a nonpr
there’s seem to be a ton of confusion about this post. for clarity: The question of whether Altman and Brockman did what they promised ≠ the question of whether Elon is a good person. Musk argued that
arXiv:2604.22351v1 Announce Type: cross Abstract: Mid-infrared astronomy from the ground faces critical challenges in accurately detecting and quantifying sources due to the dominant spatially and tim
arXiv:2604.22709v1 Announce Type: new Abstract: While long, explicit chains-of-thought (CoT) have proven effective on complex reasoning tasks, they are costly to generate during inference. Non-verbal
this is exactly what tools like @denieddotdev was built for (behavioral auth) some agent behavior should be deterministically blocked by a separate policy layer, not via prompt instructions reach out
This is totally wrong. Blaming the user is missing the point that (a) coding agents have been overhyped and (b) can’t reliably obey the rules given to them in system prompts and other guardrails. Sorr
arXiv:2604.22225v1 Announce Type: new Abstract: While generative text-to-speech (TTS) models approach human-level quality, monolithic metrics fail to diagnose fine-grained acoustic artifacts or explai
arXiv:2604.22174v1 Announce Type: new Abstract: Generalized Category Discovery (GCD) holds significant promise for the label-scarce Synthetic Aperture Radar (SAR) domain, yet its efficacy is severely
yep, that’s what I do. I am increasingly concerned that people in China are soaking up my wisdom, while a lot of people in the US try to dismiss me. i don’t think that is going to end well. 10. @GaryM