Knowledge catalogue
arXiv:2604.19763v1 Announce Type: cross Abstract: Speech Emotion Recognition (SER) systems have growing applications in sensitive domains such as mental health and education, where biased predictions
arXiv:2604.19794v1 Announce Type: new Abstract: Rough set theory models uncertainty by approximating target concepts through lower and upper sets induced by indiscernibility, or more generally, by gra
arXiv:2604.20675v1 Announce Type: new Abstract: Linear models are widely used in computational neuroimaging to identify biomarkers associated with brain pathologies. However, interpreting the learned
arXiv:2512.00281v2 Announce Type: replace Abstract: Early detection of malignant lung nodules remains limited by reliance on size- and growth-based screening criteria, which can delay diagnosis. We pr
arXiv:2604.19782v1 Announce Type: cross Abstract: Recent advances in large audio language models (LALMs) have enabled multilingual speech understanding. However, benchmarks for evaluating LALMs remain
arXiv:2604.20148v1 Announce Type: cross Abstract: Can small language models achieve strong tool-use performance without complex adaptation mechanisms? This paper investigates this question through Met
arXiv:2604.19811v1 Announce Type: cross Abstract: AI leaders and safety reports increasingly warn that advances in model reasoning may enable biological misuse, including by low-expertise users, while
arXiv:2604.20496v1 Announce Type: cross Abstract: The April 2026 Claude Mythos sandbox escape exposed a critical weakness in frontier AI containment: the infrastructure surrounding advanced models rem
Rachel Metz / Bloomberg: OpenAI launches GPT-5.5, designed to handle complex tasks with minimal guidance; the model will be used to power the company's upcoming “super app” — OpenAI is introducing an
arXiv:2604.19766v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) expands the knowledge of Large Language Models (LLMs), yet current static retrieval methods struggle with complex
arXiv:2505.02242v2 Announce Type: replace Abstract: Diffusion models have recently emerged as the dominant approach in visual generation tasks. However, the lengthy denoising chains and the computatio
arXiv:2604.20420v1 Announce Type: cross Abstract: AI research often emphasizes model design and algorithmic performance, while deployment and inference remain comparatively underexplored despite being
arXiv:2604.20003v1 Announce Type: cross Abstract: The integration of single-cell proteomic data is often hindered by the fragmented nature of targeted antibody panels. To address this limitation, we i
arXiv:2507.08540v5 Announce Type: replace-cross Abstract: The proliferation of software vulnerabilities presents a significant challenge to cybersecurity, necessitating more effective detection method
Anthropic's Mythos AI model, a powerful cybersecurity tool that the company said could be dangerous in the wrong hands, has been accessed by a 'small group of unauthorized users,' Bloomberg reports. A
arXiv:2604.19747v1 Announce Type: new Abstract: Sparse-view 3D reconstruction is essential for modeling scenes from casual captures, but remain challenging for non-generative reconstruction. Existing
arXiv:2604.19530v1 Announce Type: new Abstract: Transformer-based scientific foundation models are increasingly deployed in high-stakes settings, but current architectures give deterministic outputs a
arXiv:2604.19414v1 Announce Type: cross Abstract: Sequential Recommendation (SR) aims to predict the next interaction of a user based on their behavior sequence, where complementary relations often pr
arXiv:2604.18942v1 Announce Type: new Abstract: Vision-language models (VLMs) exhibit affirmation bias: a systematic tendency to select positive captions ('X is present') even when the correct descrip
arXiv:2509.12052v3 Announce Type: replace Abstract: Current talking-head generation has gradually shifted from GAN-based methods to diffusion-based paradigms, achieving remarkable progress in visual f
arXiv:2511.10899v2 Announce Type: replace Abstract: Tool-augmented Language Models (TaLMs) can invoke external tools to solve problems beyond their parametric capacity. However, it remains unclear whe
🚀 Meet Qwen3.6-27B, our latest dense, open-source model, packing flagship-level coding power! Yes, 27B, and Qwen3.6-27B punches way above its weight. 👇 What's new: 🧠 Outstanding agentic coding — surpa
Not the first time either - they shut down a bunch of of their original proprietary hosted embedding models in this announcement back in April 2024 https://openai.com/index/gpt-4-api-general-availabil
OpenAI: OpenAI releases Privacy Filter, an open-weight model for masking personally identifiable information in text, with 1.5B total and 50M active parameters — Our state of the art model for masking
arXiv:2604.19656v1 Announce Type: new Abstract: Large language models have achieved remarkable progress on complex reasoning tasks. However, they often implicitly fabricate information when inputs are
Qwen3.6-35B-A3B has achieved #1 trending status on Hugging Face, indicating significant community interest and adoption of this large language model. The announcement highlights the model's popularity
arXiv:2509.13281v5 Announce Type: replace Abstract: Current safety evaluations of language models rely on benchmark-based assessments that may miss localized vulnerabilities. We present RepIt, a simpl
arXiv:2604.19734v1 Announce Type: cross Abstract: Scaling humanoid foundation models is bottlenecked by the scarcity of robotic data. While massive egocentric human data offers a scalable alternative,
arXiv:2604.19728v1 Announce Type: cross Abstract: We present VLA Foundry, an open-source framework that unifies LLM, VLM, and VLA training in a single codebase. Most open-source VLA efforts specialize
We need open traces so that everyone can train open agent models! cc @steipete @badlogicgames @thdxr @matanSF @hwchase17 People are misreading the SpaceX/Cursor deal as an M&A story. It’s actually a b
arXiv:2503.11838v2 Announce Type: replace Abstract: Sarcasm detection, with its figurative nature, poses unique challenges for affective systems designed to perform sentiment analysis. While these sys
arXiv:2604.16729v1 Announce Type: new Abstract: State-of-the-art large language models (LLMs) show high performance in general visual question answering. However, a fundamental limitation remains: cur
arXiv:2508.01302v3 Announce Type: replace Abstract: Knowledge editing aims to modify outdated knowledge in language models efficiently while retaining their original capabilities. Mainstream datasets
arXiv:2604.17377v1 Announce Type: new Abstract: While large language models have achieved remarkable performance in complex tasks, they still need a memory system to utilize historical experience in l
Anthropic's Mythos has been accessed by a small group of unauthorized users, raising questions about control of the AI model https://www.bloomberg.com/news/articles/2026-04-21/anthropic-s-mythos-model
arXiv:2604.16514v1 Announce Type: new Abstract: Autoregressive vision-language models (VLMs) deliver strong multimodal capability, but their token-by-token decoding imposes a fundamental inference bot
arXiv:2511.16857v3 Announce Type: replace Abstract: Vision Language Models (VLMs) have achieved impressive performance on spatial reasoning benchmarks, yet these evaluations mask critical weaknesses i
Understanding context is key to understanding human language, an ability which Large Language Models (LLMs) have been increasingly seen to demonstrate to an impressive extent. However, though the eval
arXiv:2510.08878v3 Announce Type: replace-cross Abstract: Text-to-audio (TTA) generation with fine-grained control signals, e.g., precise timing control or intelligible speech content, has been explor
arXiv:2602.07794v3 Announce Type: replace Abstract: Large language models (LLMs) exhibit emergent behaviors suggestive of human-like reasoning. While recent work has identified structured conceptual r
arXiv:2604.18075v1 Announce Type: new Abstract: We investigate recently introduced domain-class incremental learning scenarios for vision-language models (VLMs). Recent works address this challenge us
arXiv:2509.11206v4 Announce Type: replace-cross Abstract: Practitioners increasingly rely on Large Language Models (LLMs) to evaluate generative AI outputs through 'LLM-as-a-Judge' approaches. However
arXiv:2505.11140v3 Announce Type: replace Abstract: We introduce fs1, a simple yet effective method that improves the factuality of reasoning traces by collecting them from large reasoning models and
arXiv:2603.04592v3 Announce Type: replace Abstract: Standard Large Language Models (LLMs) are predominantly designed for static inference with pre-defined inputs, which limits their applicability in d
arXiv:2508.00553v3 Announce Type: replace Abstract: Vision-Language Models (VLMs) encode images and videos into abundant tokens, which contain substantial redundancy and computation cost. While visual
arXiv:2604.16499v1 Announce Type: new Abstract: Black-box adversarial attack on vision-language pre-trained models is a practical and challenging task, as text and image perturbations need to be consi
arXiv:2601.05414v2 Announce Type: replace Abstract: As large language models (LLMs) transition from chat interfaces to integral components of stochastic pipelines and systems approaching general intel
arXiv:2604.18199v1 Announce Type: new Abstract: Transformer-based embedding models suffer from quadratic computational and linear memory complexity, limiting their utility for long sequences. We propo
arXiv:2512.00198v2 Announce Type: replace Abstract: Breast cancer is one of the leading causes of death among women worldwide. We introduce Mammo-FM, the first foundation model specifically for mammog
arXiv:2601.06931v2 Announce Type: replace-cross Abstract: Vision-Language Models (VLMs) are increasingly deployed in socially consequential settings, raising concerns about social bias driven by demog
arXiv:2604.16943v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) have shown impressive capabilities, yet they often struggle to effectively capture the fine-grained textual inf
arXiv:2604.17354v1 Announce Type: new Abstract: Vision-Language Models (VLMs) excel at photorealistic generation, yet often struggle to represent abstract meaning such as idiomatic interpretations of
arXiv:2604.17915v1 Announce Type: new Abstract: Vision-Language Models(VLMs) excel at autoregressive text generation, yet end-to-end autonomous driving requires multi-task learning with structured out
arXiv:2604.17920v1 Announce Type: new Abstract: Synthetic Aperture Radar (SAR) plays a critical role in maritime surveillance, yet deep learning for SAR analysis is limited by the lack of pixel-level
arXiv:2604.17725v1 Announce Type: new Abstract: Large Language Models (LLMs) have shown strong promise for mining Electronic Health Records (EHRs) by reasoning over longitudinal clinical information t
arXiv:2604.17691v1 Announce Type: new Abstract: Safety alignment in large language models is remarkably shallow: it is concentrated in the first few output tokens and reversible by fine-tuning on as f
arXiv:2410.05248v4 Announce Type: replace Abstract: To acquire instruction-following capabilities, large language models (LLMs) undergo instruction tuning, where they are trained on instruction-respon
arXiv:2601.04052v2 Announce Type: replace-cross Abstract: Vision-Language-Action (VLA) models have demonstrated impressive capabilities in generalized robotic control; however, they remain notoriously
arXiv:2604.16502v1 Announce Type: new Abstract: Large Language Models (LLMs) have demonstrated strong capabilities in natural language understanding and reasoning, while recent extensions that incorpo
arXiv:2604.06155v2 Announce Type: replace-cross Abstract: Whether Large Language Models (LLMs) develop coherent internal world models remains a core debate. While conventional Next-Token Prediction (N