Knowledge catalogue
You do not need frontier models for everything. For example, open models are great for automations. If you are not sure where to adopt open models, start there. It's one of the biggest changes I've ma
Qwen 125B/6B models with a 1‑million‑token context window and multimodal support have been released on Alibaba’s OpenCode Go platform. The new Qwen3.8‑Flash variant is also now available in the same e
arXiv:2608.26947v1 Announce Type: cross Abstract: Embodied agents need environments that are visually diverse, physically interactive, and changing over time. Procedural simulators can generate large
arXiv:2608.26236v1 Announce Type: new Abstract: We present a six-stage framework for auditing the reproducibility of scientific claims across a research literature within the computer science domain,
arXiv:2608.26234v1 Announce Type: cross Abstract: Retail electricity markets in deregulated systems face significant price volatility and complex interactions with forward and futures products, posing
arXiv:2608.27086v1 Announce Type: new Abstract: Enterprise AI deployment is a coordination problem across business units, application and AI teams, testing, platform engineering, infrastructure, secur
arXiv:2406.06837v2 Announce Type: cross Abstract: A Bayesian data assimilation scheme is formulated for advection-dominated advective and diffusive evolutionary problems, based upon the Dynamic Likeli
arXiv:2608.27313v1 Announce Type: cross Abstract: We establish a global finite-sample guarantee for synchronous quantile temporal-difference learning (QTD) in tabular distributional reinforcement lear
arXiv:2601.21217v2 Announce Type: replace-cross Abstract: We introduce a flexible empirical Bayes approach for fitting Bayesian generalized linear models. Specifically, we adopt a novel mean-field var
arXiv:2510.11567v2 Announce Type: replace Abstract: Synthetic datasets are widely used for training urban scene recognition models, but even highly realistic renderings show a noticeable gap to real i
arXiv:2608.26859v1 Announce Type: new Abstract: Current object pose estimation research remains predominantly model-centric, focusing on architectural innovations and post-processing refinements. This
arXiv:2608.26926v1 Announce Type: new Abstract: Small language models (sLLMs) are nowadays hosted on devices with limited memory and computational budget. In an autoregressive setup, inference is memo
arXiv:2608.26177v1 Announce Type: cross Abstract: Long-form generation exposes fundamental limitations of large language models. Even 70B-parameter models exhibit length collapse at 16k-token outputs,
arXiv:2608.27010v1 Announce Type: new Abstract: In the present work an efficient border control management procedure is proposed. Compared to operational queue management systems, whose operations are
arXiv:2608.27239v1 Announce Type: cross Abstract: Adverse drug reactions (ADRs) are a major, largely preventable source of patient harm. In high-income settings, electronic health records store a pati
arXiv:2608.26194v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) systems have attracted significant interest for their ability to mitigate hallucinations in Large Language Models
arXiv:2608.26162v1 Announce Type: new Abstract: Safety-critical mental-health support systems must distinguish when supportive conversation is appropriate from when free-form generation should be bloc
arXiv:2608.26506v1 Announce Type: cross Abstract: Model merging enables combining multiple fine-tuned models without additional training, but its safety implications remain poorly understood. Prior wo
arXiv:2510.09316v3 Announce Type: replace-cross Abstract: We compile 129 public LLM prompt datasets with more than 1.22TB and more than 673M instances and organize them into a unified taxonomy. We use
arXiv:2608.26949v1 Announce Type: new Abstract: Answering questions over real-world documents requires processing long inputs that interleave text with tables. Optical context compression, which repre
arXiv:2608.26164v1 Announce Type: new Abstract: Large language models can interpret natural-language chemistry questions, but their internal reasoning is difficult to inspect, constrain, and validate.
arXiv:2608.26618v1 Announce Type: cross Abstract: Model selection becomes particularly challenging under strong predictor dependence and model-class uncertainty, especially when there are exponentiall
arXiv:2608.26493v1 Announce Type: new Abstract: Decentralized learning systems aim to collaboratively train models across multiple clients without relying on a central coordinator. While decentralizat
arXiv:2608.26363v1 Announce Type: new Abstract: This paper introduces a unified mathematical framework for modeling information propagation through convolutional neural networks (CNNs), with the aim o
arXiv:2602.20159v3 Announce Type: replace-cross Abstract: Rapid progress in video models has largely focused on visual quality, leaving their reasoning capabilities underexplored. Video reasoning grou
arXiv:2510.13065v3 Announce Type: replace Abstract: Finding 'true' clusters in a data set is a challenging problem. Clustering solutions obtained using different models and algorithms do not necessari
arXiv:2608.26701v1 Announce Type: new Abstract: We present an extension and comprehensive real-world validation of Co-Scientist, a Gemini-based multi-agent system designed to accelerate end-to-end sci
arXiv:2511.06611v2 Announce Type: replace Abstract: Accurate measurement of circular centers is a fun-damental geometric sensing problem in instrumentation and measurement tasks involving cameras, LiD
arXiv:2608.26581v1 Announce Type: new Abstract: Low-bit quantization offers a promising avenue for reducing the computational and memory demands of Multimodal Large Language Models (MLLMs). Recent har
arXiv:2608.26469v1 Announce Type: new Abstract: In many reinforcement learning (RL) domains, environments are connected by prerequisite relations, such as difficulty-increasing edits or parameter incr
arXiv:2608.27080v1 Announce Type: cross Abstract: Many scientific and engineering applications require estimating unknown parameters from experimentally observable data -- an inverse problem that is i
arXiv:2603.28410v2 Announce Type: replace Abstract: Preference-based many-objective Bayesian optimization typically assumes that all pairwise comparisons arise from a single latent utility function, d
arXiv:2608.27088v1 Announce Type: cross Abstract: While most phenotyping platforms rely primarily on image-based measurements, advanced plant characterization requires the integration of active physio
arXiv:2608.26883v1 Announce Type: new Abstract: Robotic grippers face substantial challenges in grasping and manipulating thin objects. Most existing grippers rely on highly precise approach and grasp
arXiv:2608.26141v1 Announce Type: new Abstract: Multimodal large language models have demonstrated strong document reasoning capabilities by incorporating explicit thinking processes. While this capab
arXiv:2608.26204v1 Announce Type: cross Abstract: Computer Use Agents (CUAs) are increasingly deployed to navigate mobile and desktop applications on behalf of users, yet no benchmark comprehensively
arXiv:2608.26963v1 Announce Type: new Abstract: This work concerns adversarial training against the small-norm adversarial examples that arise from the inherent input instability of a trained deep neu
arXiv:2608.26713v1 Announce Type: cross Abstract: Recent advances in Multimodal Large Language Models (MLLMs) have extended Image Aesthetic Assessment (IAA) beyond scalar scores toward interpretable c
arXiv:2601.03605v2 Announce Type: replace Abstract: Despite the significant advancements of Large Language Models (LLMs), their factuality remains a critical challenge, creating a growing need for mor
arXiv:2608.26193v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) achieve strong performance on VQA and scene understanding, yet affective reasoning remains vulnerable to shortc
arXiv:2608.26140v1 Announce Type: new Abstract: Diffusion Large Language Models (DLLMs) enable non-autoregressive decoding and bidirectional context modeling, but efficient inference remains challengi
arXiv:2608.26434v1 Announce Type: new Abstract: Code-switching is pervasive in bilingual African conversation, yet most ASR systems assume monolingual input and are evaluated on curated monolingual be
Quote: ... developing a new A.I. model intended to be as powerful as Anthropic’s cutting-edge models. ... In July, while developing Watermelon, Meta paused and later resumed a stage of A.I. developmen
arXiv:2608.26225v1 Announce Type: new Abstract: Autonomous agents increasingly perform bounded software tasks under an orchestrator that retries, resumes, and budgets them. The machinery such orchestr
Agent Seer synthesizes graded multi-turn tool-use eval scenarios from MCP specs alone—no examples, live tools, or domain tuning.
arXiv:2608.26133v1 Announce Type: new Abstract: Evaluating AI agents that use external tools requires realistic test scenarios that capture how practitioners compose tools and iterate across conversat
arXiv:2608.26747v1 Announce Type: new Abstract: Scientific LLM agents have shown promise in literature reasoning, tool use, and experiment planning, but it remains unclear whether they can autonomousl
arXiv:2608.26198v1 Announce Type: new Abstract: Operating a scientific instrument such as an atomic force microscope (AFM) requires continuous expert decision-making. A trained user defines the experi
Agentic spreadsheet extraction 📊 is hard to execute well from a price/performance standpoint because your agent needs to ingest a massive quantity of input tokens from a highly complex interface (e.g.
arXiv:2608.26623v1 Announce Type: new Abstract: LLM judges are widely used to evaluate agentic tool-calling systems, yet their reliability on structured, dependency-driven workflows remains largely un
arXiv:2608.26130v1 Announce Type: new Abstract: Coding agents built on large language models (LLMs), such as Claude Code, Cursor, OpenAI Codex, GitHub Copilot, and Aider, receive tool responses that r
arXiv:2608.26896v1 Announce Type: new Abstract: As electricity market participants increasingly adopt learning-based agents for their bidding strategies, electricity markets are becoming algorithmic.
arXiv:2608.26780v1 Announce Type: new Abstract: Control system design is critical for modern industry, such as chemical process temperature regulation and aero-engine control. However,traditional cont
arXiv:2608.26178v1 Announce Type: new Abstract: There is growing interest in whether language models have stable preferences, for technical, safety, and philosophical reasons. We test 20 language mode
arXiv:2507.11515v2 Announce Type: replace-cross Abstract: Operating Large Language Models (LLMs) on edge devices is increasingly challenged by limited communication bandwidth and strained computationa
arXiv:2608.26309v1 Announce Type: new Abstract: Modern machine learning models rely on large amounts of labeled data. However, manual annotation of large-scale datasets is expensive and time-consuming
arXiv:2608.26516v1 Announce Type: new Abstract: Two of the most fundamental questions in statistical learning theory are the following: which prediction problems are learnable, and how should they be
ALL of this was avoidable. We didn’t need to torture. We didn’t need to create an illegal shadow justice system. We have an incredible federal justice system that has proven for DECADES it can ethical
arXiv:2608.27051v1 Announce Type: new Abstract: Detecting the fetal abdominal circumference standard plane in low-cost obstetric blind sweeps is a highly imbalanced frame-classification problem: posit
arXiv:2608.27169v1 Announce Type: new Abstract: Ancient Chinese artifact text recognition is fundamental to heritage digitization, and benchmarks for ancient texts are essential for evaluating current