Knowledge catalogue
arXiv:2512.13765v2 Announce Type: replace-cross Abstract: The forward problem in electrocardiology, computing body surface potentials from cardiac electrical activity, is traditionally solved using ph
arXiv:2606.11477v1 Announce Type: cross Abstract: Correcting handwritten exams by hand is time-consuming and error-prone, particularly for large cohorts, while fully digital exams tend to force a dida
arXiv:2606.12147v1 Announce Type: new Abstract: We consider the problem of engineering autonomous intelligent agents that are capable to responsibly not comply with user requests. We argue that machin
arXiv:2606.12349v1 Announce Type: new Abstract: Accurate identification of hydrodynamic derivatives is essential for control and navigation of Unmanned Surface Vehicles (USVs), but high-fidelity manoe
arXiv:2606.11269v1 Announce Type: new Abstract: Personality assessment aims to infer stable personality traits from dynamic behaviors across language, voice, and facial cues. Since different personali
arXiv:2606.11646v1 Announce Type: new Abstract: Compositional data -- vectors encoding relative proportions -- arise across scientific domains, including ecology, geochemistry, and genomics. The featu
arXiv:2606.11662v1 Announce Type: new Abstract: Deep search requires agents to answer complex questions through multi-step web search, browsing, evidence comparison, and synthesis. A central challenge
arXiv:2606.11314v1 Announce Type: new Abstract: We introduce TRON, a rendering framework that combines 3D Gaussian ray tracing with neural rendering to enable realistic and controllable rendering of r
arXiv:2606.12306v1 Announce Type: new Abstract: Safe ground navigation in large, threat-augmented environments requires aerial support that actively reduces the risks that a ground vehicle faces along
arXiv:2606.11573v1 Announce Type: new Abstract: Radar-camera BEV perception often suffers from degraded performance when evaluated across datasets, as changes in driving scenes, sensor configurations,
arXiv:2512.22088v3 Announce Type: replace-cross Abstract: The scaling law, a cornerstone of Large Language Model (LLM) development, predicts improvements in model performance with increasing computati
arXiv:2606.12372v1 Announce Type: cross Abstract: Human-in-the-loop reinforcement learning (HiL-RL) has emerged as an effective paradigm for real-world robotic manipulation, enabling online policy imp
arXiv:2606.11740v1 Announce Type: cross Abstract: We study whether grounded reasoning supervision from abundant 2D medical images can improve 3D medical VQA when both input types are aligned through a
arXiv:2606.12138v1 Announce Type: cross Abstract: Sparse autoencoders (SAEs) are widely used to interpret neural network representations, but their utility depends on whether the learned features are
Up until yesterday, our entire MTS team has operated under the philosophy of tokenmaxxing as much as possible on Claude Max plans. With Fable, this may no longer be possible: - One of our team members
arXiv:2606.11681v1 Announce Type: new Abstract: We propose UR-BERT, a Romanized transcription-based text-to-speech (TTS) encoder for massively multilingual TTS systems. Conventional grapheme-to-phonem
arXiv:2606.11534v1 Announce Type: cross Abstract: Urban heat is amplified by impermeable surfaces and heterogeneous built environments, yet street-level variability remains difficult to quantify becau
Drew Friedman / Federal News Network: US OPM awards a ~400M, 10-year IT contract to Oracle to develop a single, government-wide system for agencies to manage their HR capabilities — The nearly 400 mil
arXiv:2606.12252v1 Announce Type: cross Abstract: Training deep neural networks for clinical time-series analysis is computationally demanding, yet many healthcare settings lack the resources required
arXiv:2606.11945v1 Announce Type: new Abstract: This report describes our participation in SemEval-2026 Task 8 on multi-turn retrieval and question answering. The task evaluates conversational systems
arXiv:2602.02726v2 Announce Type: replace-cross Abstract: Large language models (LLMs) encode rich semantic information in their hidden states, yet it remains difficult to understand what information
arXiv:2606.12373v1 Announce Type: new Abstract: Reinforcement Learning (RL) with verifiable environments has emerged as a powerful approach for enhancing the reasoning capabilities of Large Language M
Very cool! Should get more followers: https://huggingface.co/biohub new @NoPriorsPod with Priscilla Chan, Mark Zuckerburg and Alex Rives: - taking seriously the @biohub mission to cure and prevent all
Very pleased to hear Anthropic have walked back this policy https://simonwillison.net/2026/Jun/11/anthropic-walks-back-policy/ BREAKING NEWS: Anthropic's latest model will NOT help you if it thinks yo
arXiv:2606.12243v1 Announce Type: cross Abstract: Speculative decoding (SD) addresses the high inference costs of LLMs by having lightweight drafters generate candidates for large verifiers to validat
arXiv:2606.12028v1 Announce Type: new Abstract: Generalizable robot manipulation requires not only task-level reasoning over unseen scenes, but also reliable grounding of visual plans into embodiment-
Elon Musk shared a video demonstrating xAI's Grok Imagine, an AI image and video generation tool. The post showcases the capabilities of this newly developed feature within the Grok AI system. This re
arXiv:2601.03792v2 Announce Type: replace Abstract: Large Language Models (LLMs) have demonstrated remarkable proficiency in general medical domains. However, their performance significantly degrades
arXiv:2512.19245v2 Announce Type: replace-cross Abstract: This paper tackles the problem of estimating the relative position, orientation, and velocity between a UAV and a planar platform undergoing a
arXiv:2606.12036v1 Announce Type: new Abstract: Recent advances in Vision Transformers (ViTs) for face recognition (FR) have moved beyond the standard CLS-token paradigm. In this paradigm, a special c
arXiv:2511.21594v3 Announce Type: replace Abstract: Large language models (LLMs) achieve state-of-the-art results across many natural language tasks, but their internal mechanisms remain difficult to
arXiv:2606.12023v1 Announce Type: new Abstract: Vision Transformers (ViTs) have gained significant attention in computer vision and shown strong potential for face recognition (FR). However, their hig
arXiv:2606.11546v1 Announce Type: new Abstract: Vision-language models like CLIP can provide rich semantic priors for open-vocabulary object detection. However, jointly integrating both textual and vi
arXiv:2606.12396v1 Announce Type: new Abstract: Vision-language-action (VLA) models can describe scenes and reason about them in language, yet still struggle to ground their actions in the dense 3D wo
arXiv:2606.12263v1 Announce Type: new Abstract: While Latent Diffusion Models (LDMs) have revolutionized visual synthesis, they are increasingly exploited for unauthorized mimicry of individuals. Exis
arXiv:2505.03649v4 Announce Type: replace-cross Abstract: Modeling of intricate relational patterns has become a cornerstone of contemporary statistical research and related data science fields. Netwo
Gary Marcus discusses expert commentary and analysis regarding OpenAI's initial public offering filing, likely examining implications for the AI industry, company valuation, and competitive landscape.
arXiv:2606.11780v1 Announce Type: cross Abstract: We establish conditions for embedding a corpus of N documents as d-dimensional vectors such that every k-subset S subseteq [N] is realizable as a resu
arXiv:2606.11988v1 Announce Type: new Abstract: The distinction between aleatoric and epistemic uncertainty has received considerable attention in machine learning research, mainly in the context of s
arXiv:2606.11627v1 Announce Type: cross Abstract: Recent work has shown that on-policy distillation can internalize privileged context, such as system prompts or task hints, into a student model so th
arXiv:2606.11769v1 Announce Type: new Abstract: The European AI Act is the first comprehensive regulation of artificial intelligence (AI), setting out extensive obligations, particularly for so-called
arXiv:2606.11906v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models have shown strong performance in language-conditioned robotic manipulation, yet their robustness to linguistic varia
arXiv:2601.22025v2 Announce Type: replace-cross Abstract: Evaluating Large Language Model (LLM) applications differs from conventional software testing because outputs are probabilistic, semantically
arXiv:2606.11599v1 Announce Type: new Abstract: Activation steering offers a lightweight approach to control language models' behavior at inference time, but whether it succeeds or fails heavily depen
arXiv:2606.11350v1 Announce Type: new Abstract: Retrieval-augmented generation degrades when scaled to large, heterogeneous document collections, where dense similarity loses discriminative power, and
arXiv:2606.11265v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) systems are vulnerable to corpus poisoning attacks that manipulate downstream model outputs through malicious kno
arXiv:2606.11375v1 Announce Type: cross Abstract: Standard linear probing declares a property 'encoded' when a classifier on hidden states achieves high accuracy. The protocol works well on a snapshot
arXiv:2510.02660v2 Announce Type: replace-cross Abstract: When researchers claim AI systems possess ToM or mental models, they are fundamentally discussing behavioral predictions and bias corrections
arXiv:2606.11502v1 Announce Type: cross Abstract: Language models can state that 'the Earth orbits the Sun' and, when role-playing Aristotle, assert the opposite. Recent work argues that persona adopt
arXiv:2510.01157v4 Announce Type: replace Abstract: Speech language models (SLMs) are systems of systems: independent components that unite to achieve a common goal. Despite their heterogeneous nature
arXiv:2606.12385v1 Announce Type: new Abstract: Modern LLM training pipelines increasingly rely on other models to generate data, filter corpora, judge outputs, and guide development decisions. These
arXiv:2606.12199v1 Announce Type: cross Abstract: Spoken dialogue models typically start from text LLM backbones, yet reasoning often degrades when conditioning on speech instead of text. We attribute
arXiv:2603.05573v2 Announce Type: replace Abstract: Scalable sequence models, such as Transformer variants and structured state-space models, often trade expressivity power for sequence-level parallel
arXiv:2606.11894v1 Announce Type: new Abstract: Feed-forward 3D Gaussian Splatting (3DGS) removes the need for time-consuming per-scene optimization required by traditional 3DGS. However, existing fee
The government of Canada announced today that it’s mulling a ban on social media for children under 16, though unlike some other countries that have already introduced such a ban, Canada has said the
This post documents a Wordle puzzle solution (puzzle #1,817) completed in 3 attempts out of 6 allowed guesses, showing the progression of letter feedback (gray for incorrect letters, yellow for correc
arXiv:2606.12072v1 Announce Type: new Abstract: Pretrained video generators are promising visual world models that exhibit emergent task-solving abilities; however, their reliance on detailed textual
arXiv:2606.12403v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models inherit semantic grounding from large-scale pretraining and perform competently across in-distribution manipulation
arXiv:2606.11816v1 Announce Type: cross Abstract: Forecasting real-world events requires language-model agents to reason under uncertainty from incomplete, time-bounded information. Yet evaluating whe
Wrong horse and too early. Gary nailed it. Time will show this. Masa can hype AI all he likes, but apparently he can’t even get a loan on his OpenAI shares. 🤔 But I actually half agree—and it might su