Knowledge catalogue
Calling all builders 🛠️📣 Google AI Pro and Ultra subscribers will now get increased usage limits and access to Nano Banana Pro and Gemini Pro models in @GoogleAIStudio — no API key required. Sign in w
arXiv:2602.05523v2 Announce Type: replace-cross Abstract: Agentic large language models (LLMs) are increasingly evaluated on cybersecurity tasks using capture-the-flag (CTF) benchmarks, yet existing p
arXiv:2604.15547v1 Announce Type: cross Abstract: The fundamental challenge of using Large Language Models (LLMs) for reliable, enterprise-grade analytics, such as sentiment prediction, is the conflic
Ethan Mollick shared context about OpenAI's o1-preview model launch, likely discussing the capabilities and implications of this new reasoning-focused AI system that represents a shift toward models d
arXiv:2603.20210v3 Announce Type: replace-cross Abstract: Masked Diffusion Models (MDMs) provide an efficient non-causal alternative to autoregressive generation but often struggle with token dependen
arXiv:2604.15851v1 Announce Type: cross Abstract: Differential privacy (DP) has a wide range of applications for protecting data privacy, but designing and verifying DP algorithms requires expert-leve
arXiv:2604.16238v1 Announce Type: new Abstract: Decision-makers rely on weather forecasts to plant crops, manage wildfires, allocate water and energy, and prepare for weather extremes. Today, such for
arXiv:2604.15588v1 Announce Type: cross Abstract: The integration of Large Language Models (LLMs) into scientific workflows presents exciting opportunities to accelerate biomedical discovery. However,
arXiv:2604.15841v1 Announce Type: new Abstract: While large language models (LLMs) have achieved remarkable success in general language tasks, their performance on Chouxiang Language, a representative
arXiv:2604.15775v1 Announce Type: new Abstract: Learning with large-scale datasets and information-critical applications, such as in High Energy Physics (HEP), demands highly complex, large-scale mode
arXiv:2506.15064v3 Announce Type: replace Abstract: Deep neural networks are powerful tools for solving nonlinear problems in science and engineering, but training highly accurate models becomes chall
arXiv:2604.15678v1 Announce Type: new Abstract: Pretrained Vision-Language Models (VLMs) like CLIP show promise in continual learning, but existing Few-Shot Class-Incremental Learning (FSCIL) methods
arXiv:2508.21675v3 Announce Type: replace Abstract: Misleading visualizations are a potent driver of misinformation on social media and the web. By violating chart design principles, they distort data
arXiv:2604.15882v1 Announce Type: cross Abstract: We introduce JFinTEB, the first comprehensive benchmark specifically designed for evaluating Japanese financial text embeddings. Existing embedding be
Kimi K2.6 just dropped. And it crushed Claude Opus 4.6 on SWE-Bench Pro. Kimi K2.6: 58.6 GPT-5.4 xhigh: 57.7 Gemini 3.1 Pro: 54.2 Claude Opus 4.6: 53.4 An open source Chinese model is now #1 on agenti
arXiv:2406.15809v5 Announce Type: replace Abstract: Citizen reporting platforms help the public and authorities stay informed about sexual harassment incidents. However, the high volume of data shared
Release: llm-openrouter 0.6 llm openrouter refresh command for refreshing the list of available models without waiting for the cache to expire. I added this feature so I could try Kimi 2.6 on OpenRout
arXiv:2604.14334v2 Announce Type: replace-cross Abstract: Gradient saliency from deep sequence models surfaces candidate biomarkers efficiently, but the resulting gene lists can be contaminated by tis
arXiv:2604.15360v1 Announce Type: new Abstract: This study presents a triadic analysis of energy storage operation under multi-stage model predictive control, investigating the interplay between data
arXiv:2604.14518v2 Announce Type: replace Abstract: We present Mind DeepResearch (MindDR), an efficient multi-agent deep research framework that achieves leading performance with only ~30B-parameter m
arXiv:2604.16054v1 Announce Type: cross Abstract: Multimodal large language models (MLLMs) have achieved impressive progress on vision language benchmarks, yet their capacity for visual cognitive and
arXiv:2604.15475v1 Announce Type: new Abstract: Deploying learned multi-robot models on heterogeneous robots remains challenging due to hardware heterogeneity, communication constraints, and the lack
arXiv:2604.16235v1 Announce Type: new Abstract: This paper presents a systematic benchmark of state-of-the-art multilingual large language models (LLMs) adapted via token pruning - a compression techn
arXiv:2604.15937v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly deployed to curate and rank human-created content, yet the nature and structure of their biases in these
arXiv:2510.22149v3 Announce Type: replace-cross Abstract: Federated learning (FL) has emerged as a promising paradigm for decentralized model training, enabling multiple clients to collaboratively lea
arXiv:2604.15557v1 Announce Type: cross Abstract: Steering vectors work for some concepts and layers but fail for others, and practitioners have no way to predict which setting applies before running
arXiv:2604.15945v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) is widely used to augment the input to Large Language Models (LLMs) with external information, such as recent or do
arXiv:2604.15994v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) excel at recognizing individual visual elements and reasoning over simple linear diagrams. However, when faced
arXiv:2509.25897v2 Announce Type: replace-cross Abstract: People often encounter role conflicts -- social dilemmas where the expectations of multiple roles clash and cannot be simultaneously fulfilled
arXiv:2604.15499v1 Announce Type: cross Abstract: Cryptographically secure neural network inference typically relies on secure computing techniques such as Secure Multi-Party Computation (MPC), enabli
arXiv:2509.05489v2 Announce Type: replace Abstract: Reinforcement learning with verifiable rewards has significantly advanced reasoning in large language models (LLMs), but such signals remain coarse,
arXiv:2604.15727v1 Announce Type: new Abstract: Large language models exhibit systematic limitations in structured logical reasoning: they conflate hypothesis generation with verification, cannot dist
arXiv:2409.17596v2 Announce Type: replace-cross Abstract: In recent years, live video streaming has gained widespread popularity across various social media platforms. Quality of experience (QoE), whi
arXiv:2510.22977v2 Announce Type: replace-cross Abstract: Enhancing the reasoning capabilities of Large Language Models (LLMs) is a key strategy for building Agents that 'think then act.' However, rec
arXiv:2604.15898v1 Announce Type: new Abstract: For around a decade, non-symbolic methods have been the option of choice when explaining complex machine learning (ML) models. Unfortunately, such metho
arXiv:2603.06454v2 Announce Type: replace Abstract: We study the training objectives of denoising-based generative models, with a particular focus on loss weighting and output parameterization, includ
arXiv:2604.15823v1 Announce Type: new Abstract: Embodied robotic agents often perceive movies through an egocentric screen-view interface rather than native cinematic footage, introducing domain shift
arXiv:2408.15549v4 Announce Type: replace Abstract: As large language models (LLMs) continue to advance, aligning these models with human preferences has emerged as a critical challenge. Traditional a
arXiv:2604.15376v1 Announce Type: cross Abstract: Multi-step zoom-in pipelines are widely used for GUI grounding, yet the intermediate predictions they produce are typically discarded after coordinate
This post draws a humorous parallel between using images of salarymen eating ramen as a test case for AI image generation models in Eastern contexts and the Western 'Will Smith eating spaghetti' meme,
Simon Willison documents the differences between Anthropic's Claude Opus 4.6 and 4.7 system prompts, analyzing changes that Anthropic made public. The notes likely highlight modifications to model beh
A discussion exploring the 'sweet spot' of using Ollama's hybrid Cloud + Local setup, where a reachable Ollama host serves as the control point for both local and cloud models . The post likely covers
arXiv:2601.05991v2 Announce Type: replace Abstract: In safety-critical domains, linguistic ambiguity can have severe consequences; a vague command like 'Pass me the vial' in a surgical setting could l
arXiv:2511.18107v2 Announce Type: replace Abstract: Accurately solving partial differential equations (PDEs) is critical to understanding complex scientific and engineering phenomena, yet traditional
arXiv:2604.13940v1 Announce Type: new Abstract: Scientific peer review faces mounting strain as submission volumes surge, making it increasingly difficult to sustain review quality, consistency, and t
This article from Latent Space discusses Anthropic's Claude Opus 4.7 release, highlighting incremental improvements across multiple performance dimensions compared to the previous 4.6 version. The pie
arXiv:2604.13109v1 Announce Type: cross Abstract: We present a two-stage pipeline for AI-assisted improvement of published algorithm implementations. In the first stage, a large language model with re
arXiv:2601.06559v2 Announce Type: replace Abstract: Grounding events in videos serves as a fundamental capability in video analysis. While Vision Language Models (VLMs) are increasingly employed for t
arXiv:2603.23682v2 Announce Type: replace-cross Abstract: The rapid adoption of large language models (LLMs) in education raises profound challenges for assessment design. To adapt assessments to the
arXiv:2604.02811v2 Announce Type: replace-cross Abstract: Functional verification consumes over 50% of the IC development lifecycle, where SystemVerilog Assertions (SVAs) are indispensable for formal
arXiv:2604.15166v1 Announce Type: new Abstract: Machine unlearning aims to remove targeted knowledge from a trained model without the cost of retraining from scratch. In class unlearning, however, red
arXiv:2604.14907v1 Announce Type: new Abstract: Online hate speech and abusive language pose a growing challenge for content moderation, especially in multilingual settings and for low-resource langua
In support of our mission to accelerate the developer journey on Google Cloud, we built Dev Signal: a multi-agent system designed to transform raw community signals into reliable technical guidance by
arXiv:2511.22521v2 Announce Type: replace Abstract: Document visual question answering requires models not only to answer questions correctly, but also to precisely localize answers within complex doc
arXiv:2604.14572v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) grounds LLM responses in external evidence but treats the model as a passive consumer of search results: it never
Editor’s note: In this post, we share joint insights from Raj Gulani, Director of Product Management for Network Experiences, and Dan Rayburn, Industry analyst with 30-plus years of experience coverin
arXiv:2506.23334v3 Announce Type: replace-cross Abstract: Federated learning enables collaborative training of deep learning models across institutions without sharing sensitive patient data. However,
arXiv:2512.22897v3 Announce Type: replace Abstract: Spectral clustering has emerged as one of the most effective clustering algorithms due to its superior performance. However, most existing models ar
arXiv:2601.02997v2 Announce Type: replace-cross Abstract: Large language models (LLMs) excel in program synthesis, yet their capacity for neural architecture design -- balancing syntactic reliability,
arXiv:2604.14235v1 Announce Type: new Abstract: Fraud detection on graph data can be viewed as a demanding task that requires distinguishing between different types of nodes. Because graph neural netw