Knowledge catalogue
Enterprises are learning that the race to AI-native operations is not won by deploying the most tools or agents — it is won by the organizations that build the most trusted AI data foundations underne
arXiv:2606.01086v1 Announce Type: cross Abstract: Flow and diffusion models generate high-quality samples in many modalities; however, many network evaluations are required during inference due to num
arXiv:2606.01302v1 Announce Type: new Abstract: Modern large-scale deep learning exhibits two striking empirical phenomena: behavioural scaling laws (predictable performance gains with increasing scal
arXiv:2606.00509v1 Announce Type: new Abstract: Recovering surface normals from single view polarization images in complex media remains challenging. This paper focuses on ice as a representative comp
arXiv:2602.02416v2 Announce Type: replace Abstract: Self-correction in language models remains elusive. In this work, we explore whether language models can explicitly localize errors in incorrect rea
arXiv:2606.01781v1 Announce Type: new Abstract: Accurate prediction of protein-protein interaction sites (PPIS) is essential for understanding cellular processes, disease mechanisms, and therapeutic t
arXiv:2601.20803v2 Announce Type: replace Abstract: This paper presents several strategies to automatically obtain additional examples for in-context learning, effectively transforming relation extrac
arXiv:2606.00087v1 Announce Type: cross Abstract: Effective pre-polysomnography screening for obstructive sleep apnea-hypopnea syndrome (OSAHS) requires combining clinical risk factors with visible cr
arXiv:2606.00831v1 Announce Type: new Abstract: Subliminal learning is a phenomenon where language models can transmit behavioral traits to other models through seemingly innocuous data (Cloud et al.,
arXiv:2606.00995v1 Announce Type: new Abstract: Subliminal learning refers to a student language model acquiring a teacher's traits (e.g. a system-prompted preference for owls) when fine-tuned on the
Super excited to announce seven new world-class MAI models today. They represent what we consider a new era in AI designed to keep you in control and on the frontier. First is our text foundation mode
arXiv:2606.00825v1 Announce Type: new Abstract: AI glasses present a compelling platform for AI agents to serve as personalized memory assistants. To be genuinely useful, such systems must move beyond
arXiv:2606.01843v1 Announce Type: cross Abstract: Deepfake detection suffers from poor generalization across forgery methods, as existing models tend to rely on spurious method-specific shortcuts that
arXiv:2606.00380v1 Announce Type: cross Abstract: Machine unlearning removes the influence of specific training data from a trained model without retraining it from scratch. Evaluating an unlearning m
arXiv:2601.22276v2 Announce Type: replace-cross Abstract: As Text-to-Image (T2I) diffusion models are increasingly used in real-world creative workflows, a principled framework for valuing contributor
arXiv:2606.00999v1 Announce Type: new Abstract: Large-scale vision foundation models have driven substantial gains on dense prediction tasks such as semantic segmentation, but their size makes deploym
arXiv:2602.23866v2 Announce Type: replace-cross Abstract: Software engineering agents (SWE) are improving rapidly, with recent gains largely driven by reinforcement learning (RL). However, RL training
This appears to be a social media post from Swyx that mentions Microsoft, Satya Nadella (Microsoft CEO), and references to podcasts including 'No Priors' and 'Latent Space,' likely discussing Microsof
arXiv:2510.23379v2 Announce Type: replace-cross Abstract: We investigate a relatively under-explored class of hybrid neurosymbolic models that integrate symbolic learning with neural reasoning to cons
arXiv:2606.00298v1 Announce Type: cross Abstract: Data-driven reduced-order modeling is an essential component in the computer-aided design of control systems. In this work, we present a novel symmetr
arXiv:2510.17303v2 Announce Type: replace Abstract: Symmetries are known to improve the empirical performance of machine learning models, yet theoretical guarantees explaining these gains remain limit
arXiv:2606.02219v1 Announce Type: new Abstract: Object pose estimation is a fundamental problem for an agent system to perceive or manipulate objects in images or videos. However, current instance-lev
arXiv:2606.00851v1 Announce Type: cross Abstract: Empathetic spoken dialogue systems must infer a user's emotional state to respond appropriately, yet everyday speech often carries weak, neutral, or a
arXiv:2404.07373v2 Announce Type: replace-cross Abstract: This paper presents a method to synthesize neural network controllers to maximize reward subject to the hard constraint that the feedback syst
arXiv:2606.00282v1 Announce Type: cross Abstract: Large-scale recommendation systems operate across diverse domains, yet they face the challenges of data sparsity and noisy implicit feedback. Traditio
arXiv:2511.13487v3 Announce Type: replace-cross Abstract: This study presents a systematic evaluation of time-frequency feature design for binaural sound source localization (SSL), focusing on how fea
arXiv:2606.00673v1 Announce Type: new Abstract: Thermal imaging offers a powerful alternative to visible-spectrum vision under challenging conditions such as low illumination and adverse weather, yet
arXiv:2509.24696v2 Announce Type: replace-cross Abstract: Personalizing large language models (LLMs) to individual user preferences is a critical step beyond generating generically helpful responses.
arXiv:2504.04718v2 Announce Type: replace-cross Abstract: Recent studies have demonstrated that test-time compute scaling effectively improves the performance of small language models (sLMs). However,
arXiv:2606.00503v1 Announce Type: cross Abstract: Modifying an attribute in tabular data often introduces an unnatural instance by breaking its relationships with other attributes. The modified instan
arXiv:2606.02384v1 Announce Type: new Abstract: Progress in tabular machine learning has largely focused on increasingly sophisticated model architectures. At the same time, feature engineering remain
arXiv:2606.01020v1 Announce Type: new Abstract: Identifying logical fallacies in everyday discourse is challenging for many people. This challenge is amplified in the era of Large Language Models (LLM
arXiv:2606.01820v1 Announce Type: new Abstract: Fine-grained morphosyntactic error annotation is important in clinical and developmental language research, yet it is labour-intensive, expert-dependent
arXiv:2505.19489v2 Announce Type: replace Abstract: The Linux kernel is a critical system, serving as the foundation for numerous systems. Bugs in the Linux kernel can cause serious consequences, affe
arXiv:2606.00643v1 Announce Type: cross Abstract: Physics-Informed Neural Networks (PINNs) often train slowly or fail to converge on challenging partial differential equations (PDEs), a behavior recen
arXiv:2606.00662v1 Announce Type: new Abstract: This report presents TAP-JEPA, our runner-up submission to the EPIC-KITCHENS-100 (EK-100) Action Anticipation Challenge at EgoVis 2026. The task is to a
arXiv:2606.00487v1 Announce Type: new Abstract: Using a diffusion model for parallel drafting is a promising approach for speculative decoding. By predicting tokens at multiple future positions in a s
arXiv:2606.01421v1 Announce Type: new Abstract: We show that an array of scatterers which has been designed to have latent ('hidden') symmetries can be used as a sensor. We use the capacitance matrix
arXiv:2606.00880v1 Announce Type: cross Abstract: Continual reinforcement learning aims to produce agents that learn not only to improve at their current tasks but also to adapt as task distributions
arXiv:2606.01868v1 Announce Type: new Abstract: Reinforcement Learning (RL) has long served as a model for goal-directed animal behavior in neuroscience. Modern deep RL has shown remarkable success ac
arXiv:2606.00926v1 Announce Type: cross Abstract: Mechanistic studies of sequence models often treat layerwise state encodings as architectural traits: recurrent models concentrate readable state, att
arXiv:2606.00029v1 Announce Type: cross Abstract: Retrieval-augmented generation systems struggle with temporal reasoning and evidence fusion when answering complex questions over historical criminal
Teach an agent a workflow once. Have it remember after every rebuild. This tutorial shows how to deploy @nousresearch Hermes Agent with NVIDIA NemoClaw and OpenShell, connect it to Slack, Outlook, Git
Tecban deployed Copilot to business and engineering teams. Tecban reported 5,808 hours saved per year through AI in 11 cases, in a Microsoft customer story.
arXiv:2606.01213v1 Announce Type: cross Abstract: Despite tremendous recent progress, current text-guided image editing methods still struggle with many aspects of editing involving instruction follow
arXiv:2606.01613v1 Announce Type: cross Abstract: This paper presents an agentic retrieval-augmented generation (RAG) framework for domain-specific technical reasoning support, instantiated over a cur
arXiv:2602.06136v2 Announce Type: replace-cross Abstract: Test-time adaptation (TTA) offers a compelling remedy for machine learning (ML) models that degrade under domain shifts, improving generalisat
arXiv:2606.01106v1 Announce Type: new Abstract: TimeLogicQA evaluates whether video question answering systems can reason over temporal relations such as event existence, ordering, persistence, bounda
arXiv:2606.01176v1 Announce Type: new Abstract: Real temporal interaction streams carry predictive structure in short-horizon motif patterns -- repetition, reciprocity, star diversity, triadic flow --
arXiv:2606.01031v1 Announce Type: cross Abstract: Audio-driven talking-head generation has advanced rapidly, yet existing evaluation protocols mainly rely on frame-wise metrics that assume strict temp
arXiv:2605.24470v2 Announce Type: replace Abstract: Video-text retrieval has witnessed remarkable progress driven by large-scale vision-language pretraining, yet most existing approaches inherit an im
arXiv:2606.01520v1 Announce Type: new Abstract: A single action-conditioned latent predictive architecture can in principle be trained on the structured state of a driving scene, a robot workspace, or
Tesla's market capitalization at its initial public offering in 2010 was approximately 0.1% of its current valuation, representing a roughly 1,000-fold increase in market value over the subsequent yea
arXiv:2606.01070v1 Announce Type: cross Abstract: Dense retrievers excel at first-stage candidate generation but lack effective reranking in zero-resource settings. Existing approaches face a fundamen
arXiv:2606.01112v1 Announce Type: new Abstract: Heterogeneous marine robotic systems composed of an unmanned surface vehicle (USV) and a hybrid remotely operated vehicle (HROV) have shown great potent
arXiv:2606.01050v1 Announce Type: new Abstract: Recent AI-generated image (AIGI) detectors perform well on natural-image benchmarks, but their behavior on text-rich forgeries, such as fabricated scree
Thanks everyone for reading! Longer version some additional points on crowd psychology and links to a pair of great new podcasts with the legendary investors @realsteveeisman and @gnoble79 and converg
The agent development lifecycle has been manual for too long. We’re building a future where it runs continuously, without manual triggers. Where well-understood issue types resolve without human revie
arXiv:2602.02557v2 Announce Type: replace-cross Abstract: Recent advances in end-to-end trained omni-models have substantially improved audio capabilities by strengthening text-audio modality alignmen
Fine-tuning for domain-specific tasks means improving performance in one area without degrading the model’s general capabilities, and getting that balance right is harder than it looks. This post walk