Knowledge catalogue
arXiv:2403.18136v3 Announce Type: replace-cross Abstract: Graph Neural Networks (GNNs) have gained popularity in numerous domains, yet they are vulnerable to backdoor attacks that can compromise their
arXiv:2602.22551v2 Announce Type: replace-cross Abstract: Cancer is often driven by specific combinations of an estimated two to nine gene mutations, known as multi-hit combinations. Identifying these
Palo Alto Networks Inc. today launched Idira, a new identity security platform designed to manage human, machine and artificial intelligence agent identities across the enterprise under a single privi
If you want to get a sense of the AI writing gap, just look at the horrible prompting jobs in the replies. “Write a hot take! In all lowercase! Add an analogy to randomly summarize the tweet - which n
This post likely discusses Tesla's autonomous driving capabilities, specifically how some users have become so reliant on autopilot or full self-driving features that they rarely manually control thei
arXiv:2605.08841v1 Announce Type: new Abstract: Vision-Language Models (VLMs) exhibit systematic bias toward visual illusions, recalling memorized facts rather than perceiving actual visual difference
arXiv:2605.09127v1 Announce Type: new Abstract: Contact-implicit trajectory optimization (CITO) has attracted growing attention as a unified framework for planning and control in contact-rich robotic
arXiv:2603.08558v2 Announce Type: replace Abstract: Learning compact state representations in Markov Decision Processes (MDPs) has proven crucial for addressing the curse of dimensionality in large-sc
arXiv:2601.20477v3 Announce Type: replace Abstract: We study the training dynamics of neural classifiers through the lens of binary hypothesis testing. We re-formalize classification as a collection o
arXiv:2512.02012v2 Announce Type: replace Abstract: MeanFlow (MF) has recently been established as a framework for one-step generative modeling. However, its ``fastforward'' nature introduces key chal
arXiv:2605.09256v1 Announce Type: cross Abstract: We introduce a use of the (M)-cover (or (M)-layer) transform for machine learning. The method replicates a model (M) times, but instead of coupling th
arXiv:2605.08577v1 Announce Type: new Abstract: In modern GANs, maintaining an Exponential Moving Average (EMA) of the generator's weights is a standard practice, as such an averaged model consistentl
arXiv:2605.10523v1 Announce Type: new Abstract: The field of image-to-video generation has made remarkable progress. However, challenges such as human limb twisting and facial distortion persist, espe
arXiv:2605.08950v1 Announce Type: cross Abstract: Lexical difficulty prediction is a fundamental problem in language learning and readability assessment, requiring models to estimate word difficulty a
arXiv:2605.10149v1 Announce Type: new Abstract: Temporal action segmentation (TAS) divides untrimmed videos into labeled action segments. While fully supervised methods have advanced the field, challe
arXiv:2603.00918v3 Announce Type: replace-cross Abstract: Text-to-image generation powers content creation across design, media, and data augmentation. Post-training of text-to-image generative models
arXiv:2605.08184v1 Announce Type: cross Abstract: This research addresses a validated TMS EEG cleaning pipeline and a corresponding benchmark dataset. It evaluates two widely used artifact removal pip
'In California, every professor has 10 different startups with their students. In some Canadian universities, it's one professor out of ten.' @jpineau1, Chief AI Officer at @Cohere at #WebSummitVancou
arXiv:2605.08295v1 Announce Type: cross Abstract: While random demonstration labels barely hurt in-context learning (Min et al., 2022), we show that homogeneous labels--even semantically valid ones--c
In KAME, a fast speech model starts replying instantly, while a backend LLM runs in parallel to inject deep knowledge on the fly. It’s a completely different way to approach conversational AI, making
arXiv:2512.21587v2 Announce Type: replace-cross Abstract: Spatial photonic Ising machines offer a novel optical platform for optimization and spin-model simulation, but existing diffraction-based sche
arXiv:2605.10269v1 Announce Type: new Abstract: Maritime object detection is critical for the safe navigation of unmanned surface vessels (USVs), requiring accurate recognition of obstacles from small
arXiv:2601.16097v2 Announce Type: replace Abstract: Large Language Models enable users to access database using natural language interfaces using tools like Text2SQL, Text2SPARQL, and Text2Cypher, whi
arXiv:2010.03496v4 Announce Type: replace-cross Abstract: Knowledge Graphs (KG) are of vital importance for multiple applications on the web, including information retrieval, recommender systems, and
arXiv:2605.10267v1 Announce Type: new Abstract: In industrial procurement, an LLM answer is useful only if it survives a standards check: recommended material must match operating condition, every par
arXiv:2605.09977v1 Announce Type: new Abstract: Spatio-temporal fetal brain atlases are important for characterizing normative neurodevelopment and identifying congenital anomalies. However, existing
arXiv:2605.10518v1 Announce Type: cross Abstract: Masked Diffusion Models (MDMs) have emerged as a promising alternative to autoregressive models in language modeling, offering the advantages of paral
arXiv:2605.08180v1 Announce Type: cross Abstract: Modern IoT and sensor networks generate vast amounts of data, posing significant challenges for storage, transmission, and real-time processing. Tradi
arXiv:2601.21926v4 Announce Type: replace Abstract: Diffusion-based visuomotor policies built on 3D visual representations have achieved strong performance in learning complex robotic skills. However,
arXiv:2602.05198v2 Announce Type: replace Abstract: Environmental monitoring robots often need to estimate data fields (e.g., salinity, temperature, bathymetry) under tight resource constraints. Class
arXiv:2605.10087v1 Announce Type: new Abstract: This paper describes an initiation of interaction(IoI) detection framework without keywords for human-robot interaction(HRI) based on audio and vision s
arXiv:2605.08832v1 Announce Type: new Abstract: Neural surrogate models for computational fluid dynamics (CFD) are typically trained as forward operators that map explicit problem specifications, such
arXiv:2605.08268v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly deployed in multi-agent systems where agents communicate in natural language to solve tasks jointly. A k
arXiv:2605.09803v1 Announce Type: cross Abstract: This research paper addresses the limitations of current mobile accessibility services like TalkBack, which provide manual gesture-based sequential fe
arXiv:2605.09273v1 Announce Type: new Abstract: We study online multicalibration beyond the worst-case. We give a single, efficient algorithm which dynamically interpolates between benign and worst-ca
arXiv:2605.10039v1 Announce Type: cross Abstract: Frontier coding agents read configuration files (CLAUDE.md, AGENTS.md, Cursor Rules) at session start and are expected to follow the conventions insid
arXiv:2602.03677v2 Announce Type: replace Abstract: Modality following is the ability to selectively leverage multimodal contexts based on user instructions. It is fundamental to the safety and reliab
Qasim Nauman / New York Times: Instructure reached a deal with hackers who breached its Canvas platform to return stolen data and destroy copies, without disclosing what it gave in exchange — Instruct
arXiv:2412.01324v4 Announce Type: replace Abstract: This work presents a novel and efficient nonlinear programming framework that tightly integrates hierarchical decision-making with whole-body invers
arXiv:2605.08139v1 Announce Type: cross Abstract: In modern distributed cloud environments, efficient resource allocation is required as traditional scaling mechanisms are often subject to cloud thras
arXiv:2603.04737v2 Announce Type: replace Abstract: Existing reasoning evaluation paradigms suffer from different limitations: fixed benchmarks are increasingly saturated and vulnerable to contaminati
arXiv:2605.08327v1 Announce Type: cross Abstract: In structured decision-making workflows such as form filling, compliance checking, and maintenance reporting, LLM outputs must be locally correct, glo
arXiv:2605.08131v1 Announce Type: new Abstract: Inverse reinforcement learning (IRL) learns a reward function and a corresponding policy that best fit the demonstration data of an expert. However, in
Molly Taft / Wired: Internal emails: xAI added 19 gas turbines to Colossus 2 over the past two months; xAI is fighting a lawsuit alleging Clean Air Act violations at the site — Emails show that Elon M
arXiv:2605.09128v1 Announce Type: cross Abstract: Multi-agent AI systems need behavioral constitutions, but it is unresolved whether such rules should emerge internally through agent self-governance o
arXiv:2605.08930v1 Announce Type: new Abstract: While explicit Chain-of-Thought (CoT) empowers large reasoning models (LRMs), it enables the generation of riskier final answers. Current alignment para
arXiv:2605.10627v1 Announce Type: cross Abstract: Coreference resolution is typically evaluated using aggregate statistical metrics such as CoNLL-F1, which measure structural overlap between predicted
arXiv:2605.10796v1 Announce Type: new Abstract: Machine learning has become increasingly prevalent in football performance analysis, yet most studies prioritize predictive accuracy while implicitly as
arXiv:2605.09870v1 Announce Type: cross Abstract: We propose SVAR-FM (Structural VAR with Flow Matching), a framework for time series causal discovery that treats a physics-based simulator as a mechan
arXiv:2605.10633v1 Announce Type: cross Abstract: Fine-tuning Large Language Models (LLMs) on benign narrow data can sometimes induce broad harmful behaviors, a vulnerability termed emergent misalignm
arXiv:2605.09238v1 Announce Type: cross Abstract: Muon and related norm-constrained matrix optimizers have become central to large-scale learning problems. They are formulated as a linear maximization
Introducing Flux Matching, a generative modeling paradigm that generalizes diffusion models to vector fields that need not be the score function. Enables structural priors in the dynamics, faster samp
Together AI launched Voice Finder, a tool designed to help developers quickly select appropriate voices for their applications from a library of over 600 voice options. The tool streamlines the voice
Introducing voice finder from Together AI, a new tool to search, filter, and audition 600+ voices across leading TTS models. AI natives can now find the right voice for their app faster by describing
arXiv:2601.03511v2 Announce Type: replace-cross Abstract: A major challenge for the operation of large language models (LLMs) is how to predict whether a specific LLM will produce sufficiently high-qu
arXiv:2605.09439v1 Announce Type: new Abstract: Generative models are powerful tools for sampling from a learned distribution P(Y mid X), and inverse-design methods invert this map to find an input x
arXiv:2605.08233v1 Announce Type: cross Abstract: Inverse design of RF passive components from S-parameters is a high-dimensional, ill-posed problem, and prior generative approaches are limited to sin
arXiv:2605.09090v1 Announce Type: cross Abstract: Visual Grounding benchmarks assume that the object described by a referring expression is always present in the image, and grounding models are theref
arXiv:2605.08664v1 Announce Type: new Abstract: Current image quality assessment methods are heavily biased towards global distortions (e.g., noise, blur), neglecting local perceptual artifacts such a
arXiv:2605.10732v1 Announce Type: cross Abstract: Automated transit payment analysis is vital for scalable fare auditing and passenger analytics, yet practice still relies on limited manual inspection