Knowledge catalogue
arXiv:2606.10448v1 Announce Type: cross Abstract: The financial market is a typical low signal-to-noise ratio (SNR) setting, which often destabilizes off-policy maximum-entropy methods like Soft Actor
arXiv:2606.09859v1 Announce Type: cross Abstract: MLLMs frequently hallucinate objects inconsistent with visual inputs. This issue is typically attributed to the over-reliance on language priors, whic
arXiv:2601.08379v2 Announce Type: replace-cross Abstract: Pre-trained diffusion models have emerged as powerful generative priors for both unconditional and conditional sample generation, yet their ou
arXiv:2606.10825v1 Announce Type: new Abstract: Diffusion policies (DPs) have emerged as expressive policy representations for robot learning, often used with imitation learning methods such as behavi
arXiv:2606.11167v1 Announce Type: new Abstract: Full-duplex spoken dialogue models can listen and speak simultaneously, making them a promising architecture for natural conversation. However, current
arXiv:2606.10675v1 Announce Type: new Abstract: We present a method for accurate multilingual word-level forced alignment, consisting of an alignment encoder and a learned alignment decoder. The encod
New series from The Observer with @Yoshua_Bengio, me, and others on potential catastrophic risks in AI. (link at bottom of thread) For the last few months I’ve been investigating how concerned we shou
arXiv:2605.05857v2 Announce Type: replace Abstract: Tokamaks remain leading candidates for achieving practical fusion energy, yet many important control problems inside these devices are still difficu
arXiv:2606.10771v1 Announce Type: cross Abstract: Reinforcement learning (RL)-based algorithms have recently emerged as a promising approach for adaptive optics (AO) control. In simulations and labora
One paper from three years ago has been influencing policymakers around the world about labour decisions regarding AI. What happens when the gap between the evidence and new policy widens? Our report
Gary Marcus critiques Oracle's after-hours stock decline following announcements of increased capital allocation toward AI initiatives, suggesting skepticism about the company's AI strategy and spendi
arXiv:2606.10369v1 Announce Type: new Abstract: As large language models (LLMs) continue to scale, it becomes increasingly challenging to grow model capacity under fixed computation budgets. We propos
arXiv:2606.10126v1 Announce Type: cross Abstract: Personalized persuasive text generation can improve relevance and engagement, but demographic conditioning may also introduce unequal framing across g
arXiv:2606.09890v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly deployed as autonomous agents capable of executing multi-step action trajectories toward a given objecti
Prediction (which I have made before and which increasingly seems likely) OpenAI is gonna fall, and take a lot down with it. BREAKING: SoftBank shares fall 9% after its attempt secure a $6 billion mar
A question posed on X regarding whether a recent 10% decline in the tech market will impact SpaceX's planned IPO timing and execution. The post, from AI researcher and entrepreneur Gary Marcus, raises
really good point. who else? BREAKING; Bill Gates just told Congress that Jeffrey Epstein had sought to use Gates’ affairs in an effort to blackmail him. So the question now is, if he did this to Bill
arXiv:2606.10346v1 Announce Type: new Abstract: Reinforcement learning has become a key paradigm for eliciting reasoning abilities in large language models, where exploration is crucial for discoverin
arXiv:2603.23183v2 Announce Type: replace-cross Abstract: Recent advances in generative recommendation have leveraged pretrained LLMs by formulating sequential recommendation as autoregressive generat
arXiv:2606.10781v1 Announce Type: cross Abstract: Unsupervised term discovery involves segmenting unlabelled speech into word- or syllable-like units and clustering these into a lexicon of candidate t
arXiv:2606.09891v1 Announce Type: cross Abstract: Ranking in digital marketplaces is a dynamic exposure-allocation mechanism: displayed items shape discovery trajectories and success events logged by
arXiv:2606.10348v1 Announce Type: new Abstract: Object navigation requires an agent to locate a target in an unknown environment through visual observations. Existing methods typically rely on open-vo
arXiv:2606.11092v1 Announce Type: cross Abstract: Elite humanoid soccer shooting requires whole-body stability, high-impulse whole-body interactions, and accuracy to targets. Motion tracking-driven re
arXiv:2606.11130v1 Announce Type: new Abstract: We study the task of agnostically learning general (as opposed to homogeneous) ReLUs under the Gaussian distribution with respect to the squared loss. I
arXiv:2606.10917v1 Announce Type: new Abstract: Although Large Language Model (LLM) agents have demonstrated strong performance on complex tasks, their learning is often limited by inefficient interac
arXiv:2606.09871v1 Announce Type: cross Abstract: Group Relative Policy Optimization (GRPO) and its variants, originally developed for Large Language Models (LLMs), have recently been applied to Multi
arXiv:2606.10398v1 Announce Type: cross Abstract: Does personalizing what a reader sees pay off, and where does it stop? Using a social web highlighter and a co-readership identity control (the same d
arXiv:2511.19706v2 Announce Type: replace-cross Abstract: Rotation invariance is a fundamental requirement across many computer vision tasks. Historically, this inductive bias has been encoded through
arXiv:2606.10334v1 Announce Type: new Abstract: Code-generating large language models (LLMs) increasingly produce visual artifacts such as charts, web pages, and slides by writing programs that are ex
arXiv:2606.09837v1 Announce Type: cross Abstract: Emotional interaction is increasingly crucial for conversational AI, yet current systems lack a self-emotion determination mechanism to drive the stre
arXiv:2606.10688v1 Announce Type: new Abstract: Autonomous driving relies on computationally intensive perception pipelines to continuously detect and track objects in the surrounding environment. Whi
arXiv:2606.10228v1 Announce Type: cross Abstract: Safe exploration is a prerequisite for deploying reinforcement learning (RL) agents in safety-critical domains. In this paper, we approach safe explor
arXiv:2606.09888v1 Announce Type: new Abstract: Linear attention provides an efficient backbone for long-sequence recommendation by avoiding the quadratic cost of standard Transformers, but its compre
arXiv:2606.09849v1 Announce Type: cross Abstract: Building-integrated photobioreactors (PBRs) offer a pathway for carbon-neutral architecture, yet deployment is hindered by configuration complexity an
So far today • Banks to SoftBank: No thanks; OpenAI stock ain’t worth what you say. • Germany to Google: LLMs can be held liable • Senator Warren to SEC and SpaceX: Fix this mess; it’’s foul play to s
arXiv:2606.09887v1 Announce Type: cross Abstract: Reinforcement learning (RL) for large language models usually supervises reasoning with scalar outcome rewards, such as binary correctness. Such rewar
arXiv:2606.10091v1 Announce Type: cross Abstract: Machine learning (ML) models are susceptible to various security, privacy, and fairness risks. Adversaries with different characteristics (i.e., objec
Amrith Ramkumar / Wall Street Journal: Sources: Trump administration officials have told CAISI to halt publication of its model assessments while an EO President Trump signed last week is implemented
arXiv:2606.10654v1 Announce Type: new Abstract: We investigate what self-supervised speech recognition models (S3Ms) learn about speaker groups (SGs). We examine several states of S3Ms: pretrained, fi
arXiv:2406.08726v3 Announce Type: replace Abstract: Large language models (LLMs) generate text that reinforces standard language ideology: a bias towards certain language varieties that are granted mo
arXiv:2606.10653v1 Announce Type: new Abstract: Although pretrained text-to-image (T2I) generation models can produce high-quality images, they often fail to faithfully reflect the semantic intent of
arXiv:2602.02788v2 Announce Type: replace-cross Abstract: We aim to develop physics foundation models for science and engineering that provide real-time solutions to Partial Differential Equations (PD
subtle shift: some folks seemed to have shifted from expecting truly exponential progress to being happy they can find measurable progress at all. and another, even larger group has grown concerned ab
arXiv:2606.09858v1 Announce Type: cross Abstract: Robust decision-making requires compression. A system that forms a rich support state cannot usually preserve its full structure at the point of actio
arXiv:2602.04119v2 Announce Type: replace Abstract: The application of generative models for experimental drug discovery campaigns is severely limited by the difficulty of designing molecules de novo
arXiv:2606.10918v1 Announce Type: cross Abstract: The recent trend in scaling models for robot learning has resulted in impressive policies that can perform various manipulation tasks and generalize t
arXiv:2606.09883v1 Announce Type: cross Abstract: Large language models (LLMs) have made remarkable progress in reasoning tasks, largely driven by post-training paradigms, especially reinforcement lea
arXiv:2606.10371v1 Announce Type: cross Abstract: Diffusion-based action generation has become a foundational component of embodied AI, but its reliance on visual conditioning leaves deployed visuomot
arXiv:2606.11087v1 Announce Type: cross Abstract: Expressive continuous control policies, such as diffusion and flow models, form the backbone of recent advances in scaling imitation learning for simu
Dario Amodei's essay discusses the broad societal implications of AI's rapid advancement, examining its potential impacts across multiple domains including employment, economic disruption, accelerated
arXiv:2606.11173v1 Announce Type: new Abstract: Conditioning a language model on additional context, such as feedback on a previous attempt, typically improves its response. Self-distillation trains t
arXiv:2606.09902v1 Announce Type: cross Abstract: Reservoir computing exploits the fixed dynamics of a recurrent network for temporal processing, requiring only a trained linear readout. Biological ne
Gary Marcus warns about a widespread scam epidemic involving fake profiles that use stolen pictures and randomly generated usernames/handles to deceive people. He shares an example of his response to
This is genuinely big news, major unintended consequences. 🚨Breaking news that could be huge, and enormously bad for GenAI, if other countries make similar decisions. https://the-decoder.com/landmark-
this is important. and scary. CAISI has reportedly been directed to stop publishing public model assessments as the new AI EO gets implemented. Natsec engagement on AI is essential. But pulling CAISI'
Today I'm publishing a new essay, Policy on the AI Exponential. AI is progressing extremely fast—much faster than the policy process was built to handle. The essay lays out where I think the technolog
arXiv:2606.09881v1 Announce Type: cross Abstract: Deepfake detectors show large performance gaps across demographic groups. Existing fairness approaches require demographic labels, retraining, or sacr
arXiv:2606.11119v1 Announce Type: cross Abstract: Reinforcement learning with verifiable rewards (RLVR) is a promising approach for enhancing reasoning and agentic behavior in large language models. H
arXiv:2606.10472v1 Announce Type: cross Abstract: Dynamic multi-resource allocation is a central problem in shared computing environments, where users' demands arrive sequentially and resources must b
arXiv:2606.11131v1 Announce Type: new Abstract: Most existing deep learning-based PET image denoising methods assume a fixed and known dose reduction factor (DRF) for low-dose PET images. However, the