Knowledge catalogue
arXiv:2609.07534v1 Announce Type: new Abstract: Contact-rich assembly remains challenging because it requires submillimeter spatial accuracy and reliable interpretation of forces during sustained cont
arXiv:2609.05451v1 Announce Type: cross Abstract: Net charge is a developability-relevant property of therapeutic binders, linked to viscosity, clearance, nonspecific interaction and aggregation, and
arXiv:2609.08763v1 Announce Type: cross Abstract: Federated global navigation satellite system (GNSS) monitoring distributes a proprietary classifier to partly trusted stations, any of which may leak
Howard Liu / South China Morning Post: China says its AI compute capacity rose 177% YoY to 2,185 eflops by the end of June, and is targeting 9,800 eflops by 2030 via ~$532B in IT infrastructure spend
I wish this was more inspiring, but it is actually lame It seems like OpenAI got wind that a professor was close to solving a problem after dedicating a decade of his life to it, so they threw a huge
Ick is right. I was avoiding weighing in on this, but does anyone at OpenAI dispute that: (1) They heard rumors of a Millennium Prize breakthrough (the rumors I heard were that it was about Naiver-Sto
>math professor spends a year on one of the hardest unsolved problems in math >'Navier-Stokes' >his drafts for the whole project went through codex sessions >openai had his logs in codex >suddenly rum
OMG. I guess we have our answer from OpenAI “we stole it fair and square” Not one goddamn mention in the tweet of the human mathematicans they were in communication with, and whom they may well have r
Some thoughts on the Navier-Stokes news from a professor of aerospace engineering: 1) take a deep breath 2) posts indicating we 'solved Navier-Stokes' are incorrect and overblown. A very specific math
The post references a claim by OpenAI that an AI-generated video demonstrated “human level intelligence,” a statement the user and other commenters dismiss as false, calling the output “monstrous” and
arXiv:2609.05143v1 Announce Type: cross Abstract: The integration of artificial intelligence (AI), particularly large language models (LLMs), into educational assessment has opened new opportunities t
arXiv:2609.05251v1 Announce Type: new Abstract: We present a unified reinforcement-learning (RL) framework that discovers compact parametrized quantum circuits (PQCs) for data-scarce device modeling.
arXiv:2609.05221v1 Announce Type: cross Abstract: Large language models (LLMs) show strong reasoning ability, but their explanations can remain inconsistent, weakly grounded, or difficult to verify. W
arXiv:2609.04709v1 Announce Type: new Abstract: Text-guided diffusion editing raises disinformation concerns, making reliable image provenance essential. While watermarks are commonly used for this pu
arXiv:2609.04747v1 Announce Type: cross Abstract: To enable accurate and rapid photon control point and proton beamlet dose calculation in the DoseRAD2026 challenge, we present BEAM3R, a dose estimati
arXiv:2609.04753v1 Announce Type: new Abstract: Reasoning in large language models unfolds through diverse functional operations, such as problem formulation, goal decomposition, and deduction. Althou
arXiv:2609.05257v1 Announce Type: new Abstract: Commonsense reasoning in computer vision encompasses integrating visual data and contextual knowledge, crucial for enhancing AI's understanding of every
arXiv:2609.05028v1 Announce Type: new Abstract: Acquiring high quality annotated medical image data is critical for training deep learning models; however, annotation is expensive, time consuming, and
arXiv:2602.03102v2 Announce Type: replace Abstract: Many strong decoding methods for text generation follow a sample-and-rerank paradigm: they draw multiple candidates, score each under a utility (rew
arXiv:2609.05269v1 Announce Type: cross Abstract: LLM agent systems increasingly combine provenance tracking, authorization, policy enforcement, protocol adapters, and execution controls. However, ind
arXiv:2609.04676v1 Announce Type: new Abstract: In persona-based dialogue generation (PDG), LLMs often overuse persona attributes by incorporating them regardless of dialogue context, resulting in unn
arXiv:2609.04865v1 Announce Type: new Abstract: Skill libraries improve the sample efficiency of agentic reinforcement learning (RL) by enabling large language model (LLM) agents to reuse procedural k
arXiv:2609.04851v1 Announce Type: cross Abstract: Remote robotic systems operating over wireless networks must maintain reliable control despite limited communication resources, changing channel condi
arXiv:2609.05140v1 Announce Type: cross Abstract: Background and Objective: External evaluation of medical-imaging AI is often collapsed into discrimination. We evaluated a computational protocol that
arXiv:2609.04485v1 Announce Type: cross Abstract: We evaluate three open-weight LLMs (Gemma3-12B from the USA, Bielik-11B-v3 from Poland, and Qwen3-4B from China) against World Values Survey Wave 7 da
arXiv:2609.04759v1 Announce Type: new Abstract: Robotic dressing assistance is a promising solution for supporting older adults with physical impairments in daily living. However, dressing under human
arXiv:2609.04290v1 Announce Type: cross Abstract: Despite increasing reliance on LLMs that reason with external evidence supplied by tools, retrieval-augmented generation, other agents, and users, how
arXiv:2609.05242v1 Announce Type: new Abstract: Few-shot action recognition (FSAR) aims to recognize unseen action categories with only a small number of annotated video samples. Recent works typicall
arXiv:2609.04286v1 Announce Type: new Abstract: Artificial intelligence in recruitment has shifted the object being automated from profile pairs and ranked lists to multi-stage workflows that retrieve
arXiv:2502.01262v3 Announce Type: replace Abstract: Black-box adversarial attacks on semantic segmentation remain a challenging problem, particularly in the black-box transfer attack setting where per
arXiv:2510.10075v2 Announce Type: replace-cross Abstract: Deep learning models have attracted lots of research attention in time series classification (TSC) task in the past two decades. Recently, dee
holy shit. someone here actually understands the history!!! @thrashima These are the guys keeping Huang honest, or at least calling out his B.S. Marcus is one of the OG AI people who said that scaling
arXiv:2609.04903v1 Announce Type: new Abstract: We present InterSing, a framework for generating realistic 3D head animations for duet singing performances. Unlike solo singing, duet performance requi
arXiv:2609.04283v1 Announce Type: new Abstract: Aligning video generative models to human preferences heavily relies on Reinforcement Learning (RL), which suffers from extensive computational overhead
arXiv:2609.04645v1 Announce Type: cross Abstract: Multimodal Vision-Language Models (VLMs) have demonstrated remarkable capabilities in cross-modal understanding, yet a fundamental challenge persists
arXiv:2609.04627v1 Announce Type: new Abstract: The abundance of online data is at risk of unauthorized usage in training deep learning models. To counter this, various Data Availability Attacks (DAAs
arXiv:2609.04866v1 Announce Type: new Abstract: Recent advances in large language models (LLMs) create opportunities to enrich simulation-based energy policy analysis, particularly by supporting struc
arXiv:2609.04947v1 Announce Type: cross Abstract: Recently, multimodal large-scale reasoning models have demonstrated remarkable capabilities in solving complex tasks through long Chains-of-Thought (M
arXiv:2609.05088v1 Announce Type: new Abstract: AI oversight methods rely on ground truth for validation, but what constitutes appropriate AI behavior is contested. This leaves evaluation of moral rea
arXiv:2609.05175v1 Announce Type: cross Abstract: Measuring the novelty of scientific papers is a central concern in research evaluation and scientometrics. From a recombination perspective, prior stu
arXiv:2609.04336v1 Announce Type: new Abstract: Medical visual question answering (Med-VQA) is often assumed to require medical fine-tuning, large models, or complex multi-agent pipelines. We revisit
arXiv:2609.05075v1 Announce Type: new Abstract: General continual learning (GCL) aims to learn from evolving data streams without task identities, explicit boundaries, or repeated access to previous d
arXiv:2609.05022v1 Announce Type: new Abstract: This paper presents new tokenization resources for Irish and evaluation measures of alignment with the morphological boundaries of the language. We pres
arXiv:2609.05036v1 Announce Type: new Abstract: AI alignment requires AI systems to adhere to human norms, values, or intentions. Under value pluralism there is no correct target, but a shared prerequ
arXiv:2609.04250v1 Announce Type: cross Abstract: An avatar that holds a conversation should decide what to say and to move while saying it, yet these abilities live in separate model families: spoken
arXiv:2601.21547v2 Announce Type: replace-cross Abstract: Real-world time series exhibit complex and evolving dynamics, making accurate forecasting extremely challenging. Recent multi-modal forecastin
arXiv:2609.04574v1 Announce Type: cross Abstract: Multimodal Graph Neural Networks have become standard for recommendation by augmenting sparse interaction data with content features. Yet current arch
arXiv:2609.04466v1 Announce Type: new Abstract: In Geometric Deep Learning, inductive biases serve two primary functions: enforcing manifold constraints and embedding relational priors. Currently, rep
arXiv:2609.04835v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used not only to retrieve information, but to answer questions, explain, teach, and support inquiry. In su
On the quote at the top, I agree with @geoffreyhinton 100% 'We would be very foolish to develop superintelligence now, when there is no scientific consensus it can be developed safely and controllably
arXiv:2609.04837v1 Announce Type: new Abstract: Single domain generalization (SDG) aims to learn a model from one labeled source domain that generalizes to unseen target domains. A common strategy is
arXiv:2609.04484v1 Announce Type: cross Abstract: This paper investigates structural priming in language model (LM) production, examining how preceding structural context influences sentence completio
arXiv:2609.04773v1 Announce Type: cross Abstract: Distillation is common in LLM post-training, where on-policy knowledge distillation (OPKD) uses student-generated trajectories to prepare the student
arXiv:2609.05094v1 Announce Type: new Abstract: Electroencephalogram (EEG) visual decoding aims to recover visual semantics from non-invasive neural time-series signals, for which robust alignment bet
arXiv:2609.04893v1 Announce Type: new Abstract: Vision-language-action (VLA) models are trained by imitation and capture what action to take but not why; adding causal reasoning improves manipulation,
arXiv:2609.04415v1 Announce Type: cross Abstract: Learning rich medical concept representations is essential for EHR prediction. Text-attributed knowledge graphs (TKGs) provide a natural foundation by
arXiv:2609.04880v1 Announce Type: new Abstract: Designing effective and fiscally sustainable policies for solar photovoltaic (PV) adoption requires balancing adoption gains against public expenditure
arXiv:2602.05765v3 Announce Type: replace Abstract: Reinforcement learning (RL) has emerged as a critical paradigm for post-training Vision-Language-Action (VLA) models, enabling embodied agents to ad
arXiv:2609.04288v1 Announce Type: cross Abstract: Voice AI applications are gaining popularity as advances in large language models (LLMs) enable more natural and accessible spoken interactions. Servi
arXiv:2609.04751v1 Announce Type: new Abstract: 3D surface cutting and UV unwrapping are fundamental problems in computer graphics. Traditional geometric optimization methods mainly focus on reducing