Knowledge catalogue
This is awesome news, @ollama now supports the fantastic, @GitHub Copilot CLI! ollama launch copilot Ollama now supports GitHub's Copilot CLI, the terminal agent that works directly with repositories
I cannot provide a reliable summary of this specific post without access to its content, as the URL appears incomplete or potentially inaccurate. To create an accurate knowledge base entry, please pro
Stephanie Stacey / Financial Times: Airbnb launches a pilot in NYC, LA, and other cities that lets users to select from a range of boutique hotels alongside private homes in a bid to boost growth — Co
NEW paper from Apple. Interesting idea: 'Attention to Mamba'. The paper introduces a two-stage recipe for cross-architecture distillation from Transformers into Mamba. Naive distillation collapses tea
arXiv:2604.13120v1 Announce Type: cross Abstract: Large language models generate plausible code but cannot verify correctness. Existing multi-agent systems simulate execution or leave verification opt
arXiv:2504.12482v2 Announce Type: replace Abstract: The emergence of Agentic Artificial Intelligence (AAI) systems capable of independently initiating digital interactions necessitates a new optimisat
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
Amanda Askell is likely a researcher or professional involved in AI safety or alignment work, and Jeremy Howard is publicly crediting or thanking her for a contribution or achievement on social media.
arXiv:2601.13503v2 Announce Type: replace Abstract: Psychiatric narratives encode patient identity not only through explicit identifiers but also through idiosyncratic life events embedded in their cl
arXiv:2602.14517v2 Announce Type: replace Abstract: Large language models (LLMs) have achieved strong results in mathematical reasoning, and are increasingly deployed as tutoring and learning support
arXiv:2604.14389v1 Announce Type: new Abstract: Automated fact-checking in dialogue involves multi-turn conversations where colloquial language is frequent yet understudied. To address this gap, we pr
arXiv:2604.15173v1 Announce Type: new Abstract: Temporal action segmentation (TAS) demands dense temporal supervision, yet most of the annotation cost in untrimmed videos is spent identifying and refi
arXiv:2604.14161v1 Announce Type: new Abstract: Reliable evaluation is essential in machine learning research, yet methodological flaws-particularly data leakage-continue to undermine the validity of
arXiv:2604.14602v1 Announce Type: new Abstract: Large language models (LLMs) frequently generate toxic content, posing significant risks for safe deployment. Current mitigation strategies often degrad
arXiv:2604.14856v1 Announce Type: new Abstract: Understanding climate change requires reasoning over complex causal networks. Yet, existing causal discovery datasets predominantly capture explicit, di
This post likely discusses a four-step framework for data processing and AI workflows: connecting to data sources, ingesting raw data, extracting relevant information, and enriching it with additional
arXiv:2604.15140v1 Announce Type: new Abstract: We introduce DiscoTrace, a method to identify the rhetorical strategies that answerers use when responding to information-seeking questions. DiscoTrace
arXiv:2512.24120v2 Announce Type: replace Abstract: Automated neural network architecture design remains a significant challenge in computer vision. Task diversity and computational constraints requir
arXiv:2604.14809v1 Announce Type: cross Abstract: We study a classification problem with three key challenges: pervasive informative missingness, the integration of partial prior expert knowledge into
arXiv:2604.14727v1 Announce Type: new Abstract: To quantify the geometric expressivity of transformers, we introduce a tropical geometry framework to characterize their exact spatial partitioning capa
Fastest way to deploy deepagents gets even more powerful we just shipped support for subagents with `deepagents deploy`! add an agents/ dir to your project with an AGENTS.md per specialized subagent.
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:2604.13108v1 Announce Type: cross Abstract: AI coding agents spend a substantial fraction of their tool calls on undirected codebase exploration. We investigate whether providing agents with for
arXiv:2510.14133v2 Announce Type: replace Abstract: Agentic AI systems, which leverage multiple autonomous agents and large language models (LLMs), are increasingly used to address complex, multi-step
This post likely explores the various relationship dynamics and social connections people develop in shared living and working spaces, with a speculative or humorous tone suggested by the 'Maybe lover
arXiv:2604.13048v1 Announce Type: cross Abstract: Modern cloud-native platforms expose thousands of time series metrics through systems like Prometheus, yet formulating correct queries in domain-speci
arXiv:2604.15037v1 Announce Type: cross Abstract: Recent advancements in LLM agents are gradually shifting from reactive, text-based paradigms toward proactive, multimodal interaction. However, existi
arXiv:2403.10559v3 Announce Type: replace Abstract: This report investigates the history and impact of Generative Models and Connected and Automated Vehicles (CAVs), two groundbreaking forces pushing
Grok 4.3 Beta is insanely good and now comes with powerful new tools that are super helpful for everyday use: • Create clean slides, graphs, images and dashboards • Pull real-time data and images dire
arXiv:2512.04884v3 Announce Type: replace Abstract: We present a dataset for force-grounded, cross-view articulated manipulation that couples what is seen with what is done and what is felt during rea
arXiv:2604.14980v1 Announce Type: cross Abstract: Building on recent advances in AI, hybrid decision making (HDM) holds the promise of improving human decision quality and reducing cognitive load. We
arXiv:2501.09331v3 Announce Type: replace Abstract: A machine that learns a task from observations must encounter and process uncertainty and novelty, especially when it is to maintain performance whe
arXiv:2604.14034v1 Announce Type: cross Abstract: Recent advances in Generative Artificial Intelligence, particularly Large Language Models (LLMs), have stimulated growing interest in automating or as
arXiv:2601.18675v2 Announce Type: replace Abstract: We investigate whether temporal embedding models trained on longitudinal electronic health records can learn clinically meaningful representations w
arXiv:2505.20122v2 Announce Type: replace Abstract: This paper introduces MEBench, a novel benchmark for evaluating mutual exclusivity (ME) bias, a cognitive phenomenon observed in children during wor
arXiv:2604.15190v1 Announce Type: cross Abstract: Simulating group-level user behavior enables scalable counterfactual evaluation of merchant strategies without costly online experiments. However, bui
arXiv:2604.15101v1 Announce Type: cross Abstract: Learning-to-Rank (LTR) is a supervised machine learning approach that constructs models specifically designed to order a set of items or documents bas
arXiv:2604.15107v1 Announce Type: cross Abstract: Feature selection is a classical problem in statistics and machine learning, and it continues to remain an extremely challenging problem especially in
arXiv:2604.14622v1 Announce Type: new Abstract: In this work, we propose a Multigrain-aware Semantic Prototype Scanning paradigm for pan-sharpening, built upon a high-order RWKV architecture and a tri
arXiv:2604.14424v1 Announce Type: new Abstract: Most practical engineering design problems involve nonlinear spatio-temporal dynamical systems. Multi-physics simulations are often performed to capture
arXiv:2604.15214v1 Announce Type: cross Abstract: Quantum kernel methods are among the leading candidates for achieving quantum advantage in supervised learning. A key bottleneck is the cost of infere
arXiv:2509.23391v3 Announce Type: replace-cross Abstract: Machine learning has become a fundamental approach for modeling, prediction, and control, enabling systems to learn from data and perform comp
arXiv:2506.03610v3 Announce Type: replace Abstract: Large Language Model (LLM) agents are reshaping the game industry, by enabling more intelligent and human-preferable characters. Yet, current game b
arXiv:2604.13037v1 Announce Type: cross Abstract: Mining multiple longest common subsequences (extit{MLCS}) from a set of sequences of three or more over a finite alphabet Sigma (a classical NP-hard p
arXiv:2602.03295v2 Announce Type: replace Abstract: Large Language Models (LLMs) and Vision-Language Models (VLMs) have demonstrated remarkable capabilities. However, their deployment is hindered by s
arXiv:2603.13683v2 Announce Type: replace Abstract: Although debiased large language models (LLMs) excel at handling known or low-bias prompts, they often fail on unfamiliar and high-bias prompts. We
arXiv:2604.14463v1 Announce Type: new Abstract: Large language models (LLMs) emulate a consistent human-like behavior that can be shaped through activation-level interventions. This paradigm is conver
arXiv:2602.20370v2 Announce Type: replace Abstract: The universal approximation theorem establishes that neural networks can approximate any continuous function on a compact set. Later works in approx
arXiv:2509.12833v2 Announce Type: replace Abstract: Projection-based safety filters, which modify unsafe actions by mapping them to the closest safe alternative, are widely used to enforce safety cons
arXiv:2604.13180v1 Announce Type: new Abstract: Recent advances in agentic AI have enabled increasingly autonomous workflows, but existing systems still face substantial challenges in achieving reliab
arXiv:2604.14865v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly exposed to adaptive jailbreaking, particularly in high-stakes Chemical, Biological, Radiological, and Nucl
arXiv:2604.13826v1 Announce Type: cross Abstract: Sentiment analysis in software engineering focuses on understanding emotions expressed in software artifacts. Previous research highlighted the limita
arXiv:2604.14399v1 Announce Type: new Abstract: Autonomous on-orbit servicing demands embodied agents that perceive through visual sensors, reason about 3D spatial situations, and execute multi-phase
arXiv:2510.07890v3 Announce Type: replace Abstract: Research on cross-dialectal transfer from a standard to a non-standard dialect variety has typically focused on text data. However, dialects are pri
arXiv:2604.14197v1 Announce Type: new Abstract: Large language model (LLM) performance depends heavily on prompt design, yet prompt construction is often described and applied inconsistently. Our purp
'The post all LLMs hate and want you to NOT SEE' ^^ Serious title, you can also discuss this with the LLM, it would be biased against it, as it's known at this point that memory is the true long term
arXiv:2512.03048v4 Announce Type: replace-cross Abstract: Static content-based AI value alignment is insufficient for robust alignment under capability scaling, distributional shift, and increasing au
arXiv:2507.02935v2 Announce Type: replace Abstract: Successful human-agent teaming relies on an agent being able to understand instructions given by a (human) principal. In many cases, an instruction
This feature is powered by Replit’s parallel agents, introduced in Agent 4. Replit can spin up separate copies of your project, work on them in parallel, and then combine the results at the end- handl
arXiv:2507.15066v5 Announce Type: replace Abstract: Time series anomaly detection (TSAD) has traditionally focused on binary classification and often lacks the fine-grained categorization and explanat