Knowledge catalogue
arXiv:2606.26463v1 Announce Type: new Abstract: Deliberating takes time. In real-time settings, that time is not free. Standard reinforcement learning (RL) sidesteps this as the environment waits inde
arXiv:2606.27199v1 Announce Type: new Abstract: Successful forecasting involves identifying patterns between historical and future states of the world which generalize to future observations. We apply
arXiv:2606.26535v1 Announce Type: cross Abstract: Current VLM evaluations often conflate language priors with genuine spatial reasoning. To address this, we introduce CRISP, a novel structural-diagnos
Traditional alerting systems often force a compromise: you can either alert immediately on simple, noisy log events, or monitor rigid, pre-configured metrics that fail when faced with data with many u
from tokenmaxxing to this in less than three months. anyone who doesn’t see this as affecting Anthropic and OpenAI is not paying attention. UBS says 60% of companies now watching AI budgets are moving
“Gary Marcus … says that investments into scaling AI is the “greatest capital misallocation in history” which leaves everyone “on the hook”. However, he also says that it is still early days for the A
Gary Marcus discusses fundamental challenges and limitations in current AI systems during an interview, likely covering topics such as the gap between narrow and general intelligence, reasoning limita
arXiv:2606.26899v1 Announce Type: new Abstract: Embedding-based retrieval ranks items by their similarity to a query in a shared vector space and usually aims to return the highest-scoring items. In m
arXiv:2606.26917v1 Announce Type: cross Abstract: Online reinforcement learning is widely used to align large language models (LLMs) with reward signals, yet training can be unstable under noisy or mi
arXiv:2606.26125v1 Announce Type: cross Abstract: Emerging 6G and edge-intelligent networks require effective and balanced routing algorithms among varied and spatially distributed devices. Existing f
arXiv:2606.26298v1 Announce Type: new Abstract: Autonomous AI agents may begin to perform consequential, irreversible actions such as clinical prescribing and production software deployment. This pape
arXiv:2606.27202v1 Announce Type: new Abstract: Graph neural networks have moved from a niche representation-learning technique to the default model class wherever data carry relational structure. The
arXiv:2606.12816v3 Announce Type: replace-cross Abstract: Quantum circuit routing is a key step in compiling programs for noisy intermediate-scale quantum processors. Routes that appear efficient by s
arXiv:2606.26643v1 Announce Type: new Abstract: This paper focuses on the hardware specifications required for a table tennis robot to beat professional players. After analyzing the motions of elite p
arXiv:2506.07031v5 Announce Type: replace-cross Abstract: Emerging Large Reasoning Models (LRMs) consistently excel in mathematical and reasoning tasks, showcasing remarkable capabilities. However, th
arXiv:2606.26100v1 Announce Type: new Abstract: Media bias detection is a critical task for ensuring fair and balanced information dissemination, yet existing sentence-level approaches classify each s
arXiv:2606.26316v1 Announce Type: new Abstract: We study first-order methods for smooth objectives satisfying the Polyak-L{}ojasiewicz (PL) condition when gradient samples are generated by an exogenou
Dean W. Ball / Hyperdimensional: How US federal AI policy has gone from implausibly libertarian to increasingly draconian and opaque, and how to fix it, including using independent auditors — 35 thoug
arXiv:2510.26518v2 Announce Type: replace Abstract: Human feedback is critical for aligning AI systems to human values. As AI capabilities improve and AI is used to tackle more challenging tasks, veri
arXiv:2606.26855v1 Announce Type: new Abstract: Imitating human demonstrations has emerged as a dominant paradigm for learning humanoid loco-manipulation policies. However, scaling these approaches re
arXiv:2606.27239v1 Announce Type: new Abstract: High-quality demonstration data are essential for humanoid robot skill learning, especially for whole-body behaviors that require coordinated perception
arXiv:2606.26502v1 Announce Type: new Abstract: Large reasoning models (LRMs) take longer on harder problems, just as humans do. This surface similarity hides an opposite pattern within items. When an
Connor Leahy argues that accumulating personal capital provides no protection against disempowerment by superintelligent AI systems, suggesting that wealth alone cannot guarantee security or agency in
arXiv:2606.26575v1 Announce Type: cross Abstract: Complex multi-agent control tasks remain challenging for traditional rule-based and model-based approaches, motivating the adoption of learning-based
arXiv:2512.23816v2 Announce Type: replace-cross Abstract: In this paper, we study the private and robust alignment of language models from a theoretical perspective by establishing upper bounds on the
arXiv:2606.27025v1 Announce Type: new Abstract: Building general-purpose role-playing agents that faithfully portray any character from a natural-language profile remains challenging. The dominant par
arXiv:2606.26801v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models have shown strong potential for generalizable robotic manipulation. During fine-tuning, however, action supervisio
In a matter of weeks, U.S. federal AI policy has gone from implausibly libertarian to increasingly draconian and opaque. Today, over 35 distinct observations, I analyze how we got here and offer the m
arXiv:2606.26981v1 Announce Type: cross Abstract: Synthesizing human motion from textual descriptions is essential for immersive digital applications, yet existing methods face a persistent trade-off
Gary Marcus discusses arguments surrounding trillion-dollar implications, likely related to AI development, regulation, or economic impacts given his expertise in artificial intelligence and cognitive
arXiv:2606.26588v1 Announce Type: new Abstract: A central challenge in deploying learned robot policies is inference-time behavior steering: redirecting a policy at test time to satisfy user preferenc
June has been a dark month for the US generative AI industry, between hamfisted US policy and the transition to cheaper Chinese AI models. And then there is the delay of the OpenAI IPO. Is that the co
arXiv:2606.27023v1 Announce Type: cross Abstract: Multimodal large language models (MLLMs) applied to Medical Visual Question Answering (VQA) tend to produce overconfident outputs regardless of actual
arXiv:2606.26179v1 Announce Type: cross Abstract: While WGS-based AMR prediction has reached high accuracy, existing models lack a mechanism to ground neural attributions in established biological pat
Gary Marcus argues that $SPCX exhibits high volatility because it functions as a meme stock tied to a rocket company, with its price movements driven by sentiment and social media enthusiasm rather th
arXiv:2606.26205v1 Announce Type: new Abstract: Patients increasingly seek medication information online, yet safety knowledge for psychiatric drugs is split between regulatory adverse-event records,
arXiv:2606.26661v1 Announce Type: cross Abstract: Motion forecasting is essential for autonomous driving systems to enable safe decision-making and planning in complex driving scenarios. While existin
arXiv:2606.26647v1 Announce Type: new Abstract: 3D/2D registration serves as a cornerstone technique in surgical navigation. Traditional iterative optimization algorithms suffer from low efficiency an
arXiv:2606.26171v1 Announce Type: cross Abstract: Recent image generation models achieve impressive quality in single-image synthesis, but often fail to maintain consistency across sequential outputs,
arXiv:2209.01754v5 Announce Type: replace-cross Abstract: The empirical risk minimization approach to data-driven decision making requires access to training data drawn under the same conditions as th
arXiv:2606.18195v2 Announce Type: replace Abstract: On-policy self-distillation (OPSD) has proven effective for post-training large language models (LLMs), yet its application to diffusion LLMs (dLLMs
arXiv:2606.26497v1 Announce Type: new Abstract: Bayesian filtering of partially and noisily observed dynamical systems seeks to infer the evolving conditional distribution of the state of a dynamical
arXiv:2509.20008v2 Announce Type: replace Abstract: Penetration testing, the simulation of cyberattacks to identify security vulnerabilities, presents a sequential decision-making problem well-suited
arXiv:2606.27163v1 Announce Type: cross Abstract: I describe my solution to the LeHome Challenge 2026, an ICRA 2026 competition on bimanual garment folding. The system placed 1st of 62 teams in the on
arXiv:2602.08275v3 Announce Type: replace-cross Abstract: Elucidating the language-brain relationship requires bridging the methodological gap between the abstract theoretical frameworks of linguistic
arXiv:2606.27192v1 Announce Type: new Abstract: The prevalent dual-branch paradigm, i.e., training a side network to encode visual conditions and fusing its intermediate-layer features to a frozen pre
arXiv:2606.26451v1 Announce Type: cross Abstract: Automatic singing quality assessment (SQA) requires evaluating lyrical correctness and musical fidelity while handling expressive variations. However,
arXiv:2606.26595v1 Announce Type: new Abstract: Enhancing the analysis of service feedback is essential for public sector organizations, particularly tax administrations, where trust and compliance de
arXiv:2601.03388v3 Announce Type: replace-cross Abstract: Earlier research has shown that metaphors influence human decision-making, raising the question of whether metaphors also influence large lang
arXiv:2606.27019v1 Announce Type: new Abstract: The Unigram tokenizer uses an elegant representation which makes it straightforward to edit vocabularies, but its training is comparatively heavy and co
arXiv:2603.19864v2 Announce Type: replace Abstract: Penetration testing, the practice of simulating cyberattacks to identify vulnerabilities, is a complex sequential decision-making task that is inher
arXiv:2606.26795v1 Announce Type: cross Abstract: Video Diffusion Models (VDMs) is constrained by immense computational costs. While offline calibration-based acceleration suffers from calibration dat
arXiv:2606.26144v1 Announce Type: cross Abstract: Speaker diarization, the task of determining 'who spoke when' in a multi-speaker recording, is a critical component in applications such as meeting tr
arXiv:2505.23527v4 Announce Type: replace Abstract: Modern reinforcement learning (RL) algorithms have found success by using powerful probabilistic models, such as transformers, energy-based models,
arXiv:2606.26201v1 Announce Type: new Abstract: Learning long-horizon humanoid loco-manipulation poses a dual challenge: it requires not only the robust execution of meta-skills but also their seamles
arXiv:2606.26790v1 Announce Type: new Abstract: Outcome-based reinforcement learning provides a stable optimization backbone for language agents, but its sparse trajectory-level rewards provide little
arXiv:2606.26839v1 Announce Type: cross Abstract: Learning robust navigation policies directly from visual observations remains a fundamental challenge in vision-based robotic navigation. In end-to-en
This post likely discusses how Orwellian tactics—such as propaganda, doublespeak, and manipulation of language—are being employed in contemporary contexts, possibly relating to AI, technology, or poli
arXiv:2512.03704v3 Announce Type: replace Abstract: Long-context dialogue systems suffer from state inertia, where models over-attend to history and fail to adapt to evolving intents. We demonstrate t
arXiv:2606.27144v1 Announce Type: new Abstract: Reliable action generation for multi-stage robotic manipulation remains challenging for Vision-Language-Action (VLA) models. While existing flow-matchin