Knowledge catalogue
arXiv:2605.08386v1 Announce Type: new Abstract: Skill libraries have become a practical way for LLM agents to reuse procedural experience across tasks. However, existing systems typically treat skills
arXiv:2605.09341v1 Announce Type: cross Abstract: Large language model (LLM) agent systems are increasingly expected to improve after deployment, but existing work often decouples two adaptation targe
arXiv:2605.08693v1 Announce Type: new Abstract: Skills provide an effective mechanism for improving LLM agents on complex tasks, yet in existing agent frameworks, their creation, refinement, and selec
arXiv:2605.10114v1 Announce Type: new Abstract: Large Language Model (LLM)-based agents (e.g., OpenClaw) increasingly rely on reusable skill libraries to solve artifact-rich tasks such as document-cen
arXiv:2605.05443v2 Announce Type: replace-cross Abstract: LLM watermarks must be detectable without compromising text quality, yet most existing schemes bias the next-token distribution and pay for de
arXiv:2605.10503v1 Announce Type: new Abstract: Large Language Models (LLMs) show remarkable semantic understanding but often struggle with structural understanding when processing graph topologies in
arXiv:2605.08262v1 Announce Type: cross Abstract: Crystal generative models have shown rapid progress for accelerating the discovery of bulk, periodic materials. However, many material systems such as
arXiv:2605.10376v1 Announce Type: new Abstract: Vision-Language Models (VLMs) have advanced rapidly in multimodal perception and language understanding, yet it remains unclear whether they can reliabl
arXiv:2605.08546v1 Announce Type: cross Abstract: The Gromov-Wasserstein (GW) problem provides a framework for aligning heterogeneous datasets by matching their intrinsic geometry, but its statistical
arXiv:2507.17921v2 Announce Type: replace-cross Abstract: Canonical correlation analysis (CCA) is a technique for finding correlated sets of features between two datasets. In this paper, we propose a
arXiv:2605.10831v1 Announce Type: cross Abstract: Large language models possess strong chemical reasoning capabilities, making them effective molecular editors. However, property-relevant information
arXiv:2605.08738v1 Announce Type: cross Abstract: Structured pruning and knowledge distillation (KD) are typical techniques for compressing large language models, but it remains unclear how they shoul
arXiv:2605.10453v1 Announce Type: cross Abstract: Speculative decoding speeds up autoregressive generation in Large Language Models (LLMs) through a two-step procedure, where a lightweight draft model
arXiv:2605.08580v1 Announce Type: cross Abstract: To cope with the large contexts that long-horizon LLM agents produce, modern frameworks increasingly rely on compaction -- invoking an LLM to rewrite
arXiv:2605.10029v1 Announce Type: new Abstract: Pixel-level slum mapping has long been constrained by limited cross-city generalisation, the absence of continuous density estimation, and weak global c
arXiv:2605.08246v1 Announce Type: new Abstract: Railway track intrusions pose a critical safety challenge for Indian Railways, encompassing wildlife incursions and deliberate malicious obstructions. T
arXiv:2605.09610v1 Announce Type: cross Abstract: We introduce SmartEval, a benchmark for systematically evaluating the quality of Solidity smart contracts generated by large language models (LLMs) fr
arXiv:2605.09224v1 Announce Type: new Abstract: Sparse autoencoders (SAEs) have been used widely to decompose and interpret neural network activations, especially those of transformer language models.
arXiv:2601.22131v2 Announce Type: replace Abstract: Multi-objective optimization aims to solve problems with competing objectives. Evaluating such problems is often slow or expensive, limiting the bud
arXiv:2605.09073v1 Announce Type: new Abstract: Continuous-time state estimation is gaining in popularity due to its abilities to provide smooth solutions, handle asynchronous sensors, and interpolate
arXiv:2602.09317v2 Announce Type: replace-cross Abstract: Neural networks are increasingly used as fast surrogate models across various domains, but unconstrained predictions can violate physical, ope
So basically Anthropic’s estimated valuation went up half a trillion dollars in a couple weeks (then back down a bit) on hype. Small wonder that companies like OpenAI, Anthropic and Tesla keep hyping
arXiv:2605.09598v1 Announce Type: new Abstract: Vision-language models (VLMs) have recently shown strong potential in soccer video understanding. However, given the high complexity of soccer videos du
arXiv:2605.08230v1 Announce Type: new Abstract: Background: Fentanyl overdose deaths are still increasing across the U.S. We do not fully understand which county-level social and structural conditions
arXiv:2605.10079v1 Announce Type: new Abstract: Video generation has advanced rapidly, producing photorealistic videos from text or image prompts. Meanwhile, film production and social robotics increa
arXiv:2605.10515v1 Announce Type: cross Abstract: The integration of Artificial Intelligence (AI) with Distributed Ledger Technology (DLT) has become a growing research area, yet contributions tend to
arXiv:2605.09063v1 Announce Type: new Abstract: Following the recent achievement of gold-medal performance on the IMO by frontier LLMs, the community is searching for the next meaningful and challengi
Sound on! This is pretty cool :D SolveIt is already an amazing environment for learning and exploring any topic, or for development/writing etc. But add in real-time conversational interaction too jus
The Information: Source: Anthropic is in advanced talks to acquire New York-based Stainless, which helps developers generate SDKs from APIs, for at least 300M — Anthropic is in advanced talks to acqui
arXiv:2605.08583v1 Announce Type: new Abstract: Large language models are increasingly used in scientific writing, yet they can fabricate citation-shaped references that appear plausible but fail bibl
Mike Isaac / New York Times: Sources: Anthropic is in talks to raise between 30B and 50B in a funding round that would value it at up to 950B — The start-up, which recently released a powerful A.I. mo
Mark Gurman / Bloomberg: Sources: Apple plans to make the Camera app fully customizable in iOS 27, along with noticeable design changes across Siri, Safari, Weather, and more — Apple Inc. is planning
Wall Street Journal: Sources: Google is in talks with SpaceX and other companies for a rocket launch deal, as Google expands its own efforts to put orbital data centers in space — A deal between the t
Semafor: Sources: Jensen Huang was left out of President Trump's China trip to avoid unwanted scrutiny and awkward conversations about the sale of Nvidia chips to China — THE SCOOP — The Trump adminis
Alex Heath / Sources: Sources: Sam Altman recently discussed launching an AI compute company that is majority-owned by OpenAI, similar to Stargate's original data center initiative — Sources say Sam A
Financial Times: Sources: some Amazon employees are using in-house OpenClaw-like tool MeshClaw for unnecessary tasks to inflate AI token use after Amazon set weekly AI targets — In-house MeshClaw tool
Bloomberg: Sources: Wispr Flow developer Wispr AI is in talks to raise a round that could more than double its valuation to 2B; source: the round is set to total ~260M — Wispr AI Inc., the developer b
arXiv:2605.09449v1 Announce Type: new Abstract: Recent multimodal large language models (MLLMs) have made remarkable progress in visual understanding and language-based reasoning, yet they lack a pers
SpaceX has just received FCC approval to acquire ~65 MHz of nationwide spectrum from EchoStar for the company's next-gen direct-to-device @Starlink Mobile service. The FCC says the deal gives SpaceX “
SpaceX is considering several locations domestically and internationally to build the world’s most advanced spaceports! It’s no secret that we intend to launch Starship a lot, targeting thousands of f
SpaceX's official social media account is being praised for its engaging visual content and aesthetic presentation, earning recognition as a favorite among followers for its artistic approach to shari
arXiv:2605.09165v1 Announce Type: cross Abstract: Looped language models repeat a set of transformer layers through depth, reducing memory costs and providing natural early-exit points at loop boundar
arXiv:2602.00986v2 Announce Type: replace Abstract: Recent studies show that LLM hidden states encode reward-related information, such as answer correctness and model confidence. However, existing app
arXiv:2605.08183v1 Announce Type: new Abstract: Generalized Category Discovery (GCD) seeks to identify novel categories from unlabeled data while retaining the classification ability of seen categorie
arXiv:2605.09403v1 Announce Type: cross Abstract: Architectural choices inside the Transformer feedforward network (FFN) block do not merely affect the block itself; they reshape the computations lear
arXiv:2605.09687v1 Announce Type: new Abstract: Remote Sensing (RS) single-image super-resolution aims to reconstruct high-resolution imagery from low-resolution observations while preserving fine spa
arXiv:2605.08220v1 Announce Type: new Abstract: The automated extraction of data from scientific charts is a critical task for large-scale literature analysis. While multimodal Large Language Models (
arXiv:2602.03916v3 Announce Type: replace-cross Abstract: Spatial reasoning is a fundamental aspect of human cognition, yet it remains a major challenge for contemporary vision-language models (VLMs).
arXiv:2505.18511v2 Announce Type: replace Abstract: Stochastic Partial Differential Equations (SPDEs) driven by random noise play a central role in modeling physical processes with rough spatio-tempor
speaks for itself Musk's lawyer: 'Are you completely trustworthy?' Altman: 'I believe so.' Musk's lawyer: 'But, you know, you don't know whether you're completely trustworthy.' Altman: 'I'll just amen
arXiv:2605.08226v1 Announce Type: new Abstract: The rapid proliferation of AI-generated images (AIGI) presents a significant challenge to digital information integrity. While human observers and exist
arXiv:2601.11042v2 Announce Type: replace-cross Abstract: Sequential knowledge editing in large language models often causes catastrophic collapse of the model's general abilities, especially for para
arXiv:2603.00541v2 Announce Type: replace Abstract: Generative foundation models are increasingly scaled in both width and depth, posing significant challenges for stable feature learning and reliable
arXiv:2605.09498v1 Announce Type: cross Abstract: Time series, spatial data, and images are natural applications of Neural Processes. However, when such data exhibit strong periodicity and quasi-perio
arXiv:2508.08441v3 Announce Type: replace-cross Abstract: Automated molecular structure elucidation remains challenging, as existing approaches often depend on pre-compiled databases or restrict thems
arXiv:2603.19222v2 Announce Type: replace Abstract: Denoising diffusion models are widely used for high-quality image and video generation. Their performance depends on noise schedules, which define t
arXiv:2605.08151v1 Announce Type: cross Abstract: LLM serving platforms are increasingly deployed as multi-model cloud systems, where user demand is often long-tailed: a few popular large models recei
arXiv:2605.10027v1 Announce Type: cross Abstract: Psychological support hotlines provide critical support for individuals experiencing mental health emergencies, yet current assessments largely rely o
arXiv:2605.09031v1 Announce Type: new Abstract: Energy-based models (EBMs) are flexible generative architectures inspired by statistical physics, but their learning and generative properties remain po
arXiv:2605.05629v2 Announce Type: replace-cross Abstract: We study the problem of learning generative models for discrete sequences in a continuous embedding space. Whereas prior approaches typically