Knowledge catalogue
arXiv:2606.26200v1 Announce Type: cross Abstract: Modern machine learning systems have outgrown their origins as isolated predictive constructs, evolving into complex socio-technical architectures tha
arXiv:2606.26387v1 Announce Type: cross Abstract: Multimodal large language models (MLLMs) extend large language models (LLMs) with visual perception, enabling joint reasoning over images and text. De
arXiv:2601.20381v3 Announce Type: replace Abstract: Visual foundation models provide strong perceptual features for robotics, but their dense representations lack explicit object-level structure, limi
arXiv:2606.12808v3 Announce Type: replace-cross Abstract: Adaptive Hamiltonian learning is central to calibrating and characterizing quantum devices. In an adaptive controller, choosing the next exper
arXiv:2606.26663v1 Announce Type: new Abstract: World Action Models (WAMs) generate actions together with predicted futures, offering a powerful interface for robot decision making. In contact-rich ma
arXiv:2606.26300v1 Announce Type: new Abstract: A classical intuition holds that verifying a solution is easier than producing one. For today's coding agents, this intuition is being inverted: as foun
Things came undone this week: Starmer resigned, SpaceX tanked, the Iran deal stumbled, and a housing bill that passed the Senate is dying on Trump’s desk. Today’s conversation with @GaryMarcus is a mu
This is what’s causing Anthropic to aggressively beg for govt protection (see below). Customers are finding cheaper alternatives. Keeping employees requires continuing ultra-rich secondaries ($$) that
arXiv:2603.14717v2 Announce Type: replace Abstract: Generating novel protein sequences that respect a family's statistical constraints typically requires training deep generative models on thousands t
arXiv:2606.27342v1 Announce Type: cross Abstract: Entity Matching (EM) is a core operation in the data integration pipeline, where records from different sources are compared to determine whether they
arXiv:2606.27344v1 Announce Type: new Abstract: Dexterous manipulation depends on contact events that are fast, local, and often visually occluded. Piezoelectric microphones offer a compact and high-b
arXiv:2606.18974v2 Announce Type: replace Abstract: Unified multimodal models (UMMs) interleave generated ''visual thoughts'' (VTs) with text reasoning to improve spatial tasks. This incurs roughly an
Gary Marcus fact-checks a claim that WeWork had no revenue, indicating this is a debunking of a false statement about the coworking company's financial performance. The post clarifies that WeWork did
“WeWork’s IPO fell apart. I’ve been calling OpenAI the (possible) WeWork of AI for a long time. I will not be surprised if their IPO plans apart, too” - @garymarcus, June 10, two weeks before OpenAI d
arXiv:2606.26400v1 Announce Type: new Abstract: Agentic systems are changing how complex operational tasks are coordinated, introducing a new paradigm for connecting heterogeneous data sources and aut
arXiv:2606.27288v1 Announce Type: new Abstract: Multi-model LLM systems such as routing, voting, cascades, fusion, and mixture-of-agents are used to beat single-model accuracy. We show that their gain
arXiv:2606.27374v1 Announce Type: cross Abstract: Going beyond predicting robot actions, World Action Models (WAMs) can also generate future visual observations. We build on this generative capability
arXiv:2606.24999v1 Announce Type: new Abstract: High-dimensional partial differential equations (PDEs) with unknown coefficients arise widely in scientific machine learning, including continuous-time
arXiv:2501.14622v5 Announce Type: replace Abstract: Learning efficient representations for decision-making policies is a challenge in imitation learning (IL). Current IL methods require expert demonst
arXiv:2606.25985v1 Announce Type: new Abstract: Vision-language-action (VLA) models have shown strong potential for general-purpose robot manipulation, but their inference latency remains a major obst
arXiv:2606.25003v1 Announce Type: new Abstract: Federated split learning (FSL) enables collaborative training across bandwidth-constrained IoT devices, but repeated activation and gradient exchange cr
arXiv:2606.25424v1 Announce Type: cross Abstract: Diffusion-based text-to-speech (TTS) models have achieved significant improvements in speech quality. However, modeling sharp prosodic transitions and
ah, the creative S-1 For those unfamiliar, WeWork pioneered the creative S-1 and the subsequent failure to IPO, and Sam seems to be following a comparable path. /cc @GaryMarcus https://qz.com/1685919/
arXiv:2602.02219v2 Announce Type: replace Abstract: Large language models are widely employed as evaluators, a paradigm commonly referred to as LLM-as-a-judge. Prior research has predominantly examine
An almost perfect prediction of the current AI predicament — and what may ultimately unravel our entire economy — from almost three years ago (August 2023), at Marcus on AI: Nearly every word remains
An important point which Gary will break down on tomorrow’s @ProfGMarkets episode Tune in Imagine if i had a business that gave you 20 bills for 10 bills. As word spread, my customer base would grow.
Gary Marcus discusses potential implications for generative AI if Michael Saylor (MicroStrategy CEO and prominent Bitcoin advocate) experiences financial or reputational difficulties. The post likely
arXiv:2507.07920v2 Announce Type: replace-cross Abstract: Cerebrovascular research heavily relies on quantitative analysis of intracranial arteries from time-of-flight magnetic resonance angiography,
arXiv:2606.25207v1 Announce Type: cross Abstract: Hyperparameter Optimization (HPO) is essential for maximizing machine learning model performance, and its core challenge is sample efficiency: finding
arXiv:2606.25128v1 Announce Type: cross Abstract: Volume and quality of datasets are crucial for deep learning model training, yet they are often constrained by availability and data acquisition costs
arXiv:2606.25421v1 Announce Type: new Abstract: Recent studies on world modeling for Large Language Model (LLM) agents typically formulate the learning objective as next-observation prediction. Howeve
arXiv:2512.00727v2 Announce Type: replace Abstract: Reinforcement learning has enabled impressive locomotion skills on articulated robots, but common policy representations remain only weakly aligned
arXiv:2606.25056v1 Announce Type: new Abstract: Behavioral Foundation Models (BFMs) offer a promising path toward universal physics-based character control by organizing a rich repertoire of physicall
arXiv:2505.12843v2 Announce Type: replace Abstract: Reinforcement Learning from Human Feedback (RLHF) relies on reward models to align large language models with human preferences. However, RLHF often
arXiv:2606.25473v1 Announce Type: new Abstract: Autoregressive video diffusion with causal diffusion transformers has emerged as a major paradigm for real-time streaming video generation and action-co
Chad Jones, economist who'll be joining Anthropic: 'In other words, with log utility it is optimal to take a 1 in 3 chance of ending human existence in exchange for a 2/3 chance of dramatically raisin
Chart of increasing revenue, pre the decline of token-maxxing, not looking at the immense costs/lack of profitability, not considering threats from open-source models, price-war dynamics etc. @noahopi
arXiv:2606.25386v1 Announce Type: new Abstract: By maintaining global consistency across robot teams, multi-robot LiDAR map merging enables faster exploration and efficient area coverage. However, map
arXiv:2606.25293v1 Announce Type: new Abstract: Positional encodings (PEs) are essential for Transformers. Yet designing effective PEs for non-Euclidean graphs remains challenging. Such encodings shou
arXiv:2510.17059v2 Announce Type: replace Abstract: Zero-shot imitation learning requires an agent to reproduce expert behavior from a single demonstration without additional environment interaction o
arXiv:2512.22487v2 Announce Type: replace Abstract: The design of Korean constituency treebanks raises a central representational question concerning the choice of terminal units. Although Korean word
arXiv:2507.09052v3 Announce Type: replace Abstract: Training data for class-conditional image synthesis often exhibit a long-tailed distribution with limited amount of images for tail classes. Such an
Crazy how many times people have told me over the last five years that a solution to hallucinations was right around the corner — and yet here we still are. This study tests how often LLMs invent answ
arXiv:2606.25483v1 Announce Type: new Abstract: Path-traced synthetic stereo data underlie a large fraction of modern disparity-estimation training pipelines. We report a previously unrecognised prope
arXiv:2606.16497v2 Announce Type: replace-cross Abstract: GPU kernel optimization represents a paradigm where functional correctness is assumed and execution efficiency is the objective. We present da
arXiv:2606.25360v1 Announce Type: new Abstract: While end-to-end Vision-Language-Action (VLA) models show promise in robotic manipulation, their monolithic paradigm inherently couples semantic reasoni
arXiv:2606.26048v1 Announce Type: new Abstract: Soft robots are challenging to control due to their nonlinear and time-varying dynamics. Data-enabled predictive control (DeePC) offers a model-free alt
arXiv:2606.25939v1 Announce Type: new Abstract: Demonstration augmentation is proposed for cost-efficient data acquisition, but existing methods are fundamentally limited in deformable manipulation du
arXiv:2606.24954v1 Announce Type: cross Abstract: Vibration-based health monitoring of rotating machinery requires reliable fault diagnosis under operational data constraints, yet condition assessment
arXiv:2605.25661v2 Announce Type: replace Abstract: Current mainstream methods of aligning diffusion models with human preferences typically employ VLM-based reward models. However, these reward model
arXiv:2606.25254v1 Announce Type: cross Abstract: Maternal-fetal US is the primary imaging modality for monitoring fetal development, yet accurate automated segmentation remains challenging due to the
arXiv:2606.25295v1 Announce Type: new Abstract: Mobile manipulation is a fundamental robotics task and has advanced rapidly in recent years, enabling robots to navigate, reach, and interact with objec
arXiv:2606.25197v1 Announce Type: new Abstract: Learning effective policies for adaptive data acquisition remains challenging: posterior-based methods rely on surrogate models and posterior approximat
arXiv:2606.25284v1 Announce Type: new Abstract: Camera-based monitoring systems are increasingly adopted in healthcare settings for the continuous assessment of patient movement and activities. Howeve
Secure software supply chain solution provider Chainguard Inc. today expanded its Chainguard Repository product with malware scanning, policy enforcement and visibility features that now cover Java pa
arXiv:2606.25923v1 Announce Type: new Abstract: A digital twin (DT) is a virtual model of a real-world system that can assist decision-making by simulating scenarios induced by different policies. How
arXiv:2606.19190v2 Announce Type: replace Abstract: Robust state estimation and mapping in long-term, large-scale, and highly dynamic environments remains a key challenge in robotics. Existing LiDAR-I
arXiv:2606.25344v1 Announce Type: new Abstract: 4D generation aims to animate 3D objects with realistic motion, holding great promise for applications. Existing methods typically decouple 3D asset gen
arXiv:2606.26006v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models are often constrained by the imitation ceiling imposed by sub-optimal data. While Reinforcement Learning (RL) fine-t
arXiv:2606.26093v1 Announce Type: new Abstract: Human demonstrations are a scalable data source for learning robot manipulation policies. However, common sources of human demonstration data, such as m