Knowledge catalogue
arXiv:2604.07016v1 Announce Type: new Abstract: A key challenge in scaling up Reinforcement Learning is generalizing learned behaviour. Without the ability to carry forward acquired knowledge an agent
arXiv:2604.08272v1 Announce Type: new Abstract: Deep image prior (DIP) is an unsupervised deep learning framework that has been successfully applied to a variety of inverse imaging problems. However,
arXiv:2604.06401v1 Announce Type: new Abstract: The large language models (LLMs) might produce a persuasive argument within mathematical and logical fields, although such argument often includes some
arXiv:2508.07299v2 Announce Type: replace-cross Abstract: The effectiveness of unlabeled data in Semi/Self-Supervised Learning (SSL) depends on appropriate assumptions for specific scenarios, thereby
arXiv:2604.06541v1 Announce Type: cross Abstract: We investigate whether a multiscale tensor-network architecture can provide a useful inductive bias for reconstruction-based anomaly detection in coll
arXiv:2505.24848v4 Announce Type: replace Abstract: To enable egocentric contextual AI in always-on smart glasses, it is crucial to be able to keep a record of the user's interactions with the world,
arXiv:2604.07165v1 Announce Type: new Abstract: Reinforcement learning for Large Language Model agents is often hindered by sparse rewards in multi-step reasoning tasks. Existing approaches like Group
arXiv:2503.02537v4 Announce Type: replace Abstract: Diffusion models have achieved remarkable progress across various visual generation tasks. However, their performance significantly declines when ge
arXiv:2512.01925v2 Announce Type: replace-cross Abstract: Recent advancements in large language models (LLMs) have been driven by their emergent reasoning capabilities, particularly through long chain
arXiv:2604.07036v1 Announce Type: cross Abstract: Recently, LLM-based agents have become increasingly popular across many applications, including complex sequential decision-making problems. However,
arXiv:2603.10512v2 Announce Type: replace Abstract: Artificial intelligence has advanced significantly through the development of intelligent game-playing systems, providing rigorous testbeds for deci
arXiv:2604.08282v1 Announce Type: new Abstract: Radar perception models are trained with different inputs, from range-Doppler spectra to sparse point clouds. Dense spectra are assumed to outperform sp
arXiv:2510.19225v3 Announce Type: replace-cross Abstract: Reinforcement learning (RL) has become essential for unlocking advanced reasoning capabilities in large language models (LLMs). RL workflows i
arXiv:2604.07575v1 Announce Type: new Abstract: Autonomous multi-agent target tracking in GPS-denied and communication-restricted environments (e.g., underwater exploration, subterranean search and re
arXiv:2604.06603v1 Announce Type: cross Abstract: Large language models (LLMs) have shown strong knowledge reserves and task-solving capabilities, but still face the challenge of severe hallucination,
arXiv:2604.03134v2 Announce Type: replace Abstract: Few-Shot Medical Image Segmentation (FSMIS) aims to segment novel object classes in medical images using only minimal annotated examples, addressing
arXiv:2604.03128v2 Announce Type: replace Abstract: On-policy distillation (OPD) has become a popular training paradigm in the LLM community. This paradigm selects a larger model as the teacher to pro
arXiv:2508.16703v4 Announce Type: replace-cross Abstract: On-device running Large Language Models (LLMs) is nowadays a critical enabler towards preserving user privacy. We observe that the attention o
arXiv:2604.07171v1 Announce Type: new Abstract: Decision-making in military aviation Prognostics and Health Management (PHM) faces significant challenges due to the 'curse of dimensionality' in large-
arXiv:2511.11663v2 Announce Type: replace-cross Abstract: The emergence of accurate open large language models (LLMs) has sparked a push for advanced quantization techniques to enable efficient deploy
arXiv:2604.08185v1 Announce Type: new Abstract: Tensegrity robots offer compliance and adaptability, but their nonlinear, and underconstrained dynamics make state estimation challenging. Reliable cont
arXiv:2603.11394v2 Announce Type: replace Abstract: Patients and clinicians are increasingly using chatbots powered by large language models (LLMs) for healthcare inquiries. While state-of-the-art LLM
arXiv:2604.07321v1 Announce Type: cross Abstract: Propositional Linear Temporal Logic (LTL) is a popular formalism for specifying desirable requirements and security and privacy policies for software,
arXiv:2010.00638v2 Announce Type: replace-cross Abstract: Generative models for tabular data have evolved rapidly beyond Generative Adversarial Networks (GANs). While GANs pioneered synthetic tabular
arXiv:2604.06291v1 Announce Type: cross Abstract: Low-Rank Adaptation (LoRA) enables parameter-efficient fine-tuning of Large Language Models (LLMs), and recent Mixture-of-Experts (MoE) extensions fur
arXiv:2604.07182v1 Announce Type: cross Abstract: As the worlds second most consumed beverage after water, tea is not just a cultural staple but a global economic force of profound scale and influence
arXiv:2503.01804v4 Announce Type: replace Abstract: Ensuring both syntactic and semantic correctness in Large Language Model (LLM) outputs remains a significant challenge, despite being critical for r
arXiv:2604.06240v1 Announce Type: cross Abstract: Verifying the success of computer use agent (CUA) trajectories is a critical challenge: without reliable verification, neither evaluation nor training
arXiv:2604.06192v1 Announce Type: cross Abstract: Recent work uses entropy-based signals at multiple representation levels to study reasoning in large language models, but the field remains largely em
arXiv:2604.06724v1 Announce Type: cross Abstract: While traditional optimization problems were often studied in isolation, many real-world problems today require interdependence among multiple optimiz
arXiv:2604.06420v2 Announce Type: cross Abstract: In recommender systems, collecting, storing, and processing large-scale interaction data is increasingly costly in terms of time, energy, and computat
arXiv:2604.07816v1 Announce Type: new Abstract: Tool learning has emerged as a promising paradigm for large language models (LLMs) to address real-world challenges. Due to the extensive and irregularl
arXiv:2604.01130v3 Announce Type: replace Abstract: As sports training becomes more data-driven, traditional dart coaching based mainly on experience and visual observation is increasingly inadequate
arXiv:2604.06411v1 Announce Type: cross Abstract: CubeSats have revolutionized access to space by providing affordable and accessible platforms for research and education. However, their reliance on C
arXiv:2604.06662v1 Announce Type: cross Abstract: Generated contents have raised serious concerns about copyright protection, image provenance, and credit attribution. A potential solution for these p
arXiv:2604.06274v1 Announce Type: cross Abstract: In recent years, the pace of development of information technology in various areas has increased drastically, forcing cybersecurity specialists to co
arXiv:2511.19693v3 Announce Type: replace-cross Abstract: Payment networks form the backbone of modern commerce, generating high volumes of transaction records from daily activities. Properly modeling
arXiv:2604.06387v1 Announce Type: cross Abstract: Accurate reconstruction of environmental scalar fields from sparse onboard observations is essential for autonomous vehicles engaged in aquatic monito
arXiv:2511.18787v2 Announce Type: replace Abstract: Vision-Language Models (VLMs) perform well on multimodal benchmarks but lag behind humans and specialized models on visual perception tasks like dep
arXiv:2604.08401v1 Announce Type: cross Abstract: In large language model (LLM) agents, reasoning trajectories are treated as reliable internal beliefs for guiding actions and updating memory. However
arXiv:2604.08168v1 Announce Type: new Abstract: Vision-language-action (VLA) models have advanced robot manipulation through large-scale pretraining, but real-world deployment remains challenging due
arXiv:2604.08313v1 Announce Type: new Abstract: Dense annotations, such as segmentation masks, are expensive and time-consuming to obtain, especially for 3D medical images where expert voxel-wise labe
arXiv:2604.07242v1 Announce Type: new Abstract: Despite deep learning models running well-defined mathematical functions, we lack a formal mathematical framework for describing model architectures. Ad
arXiv:2604.07674v1 Announce Type: new Abstract: Foundation models have achieved remarkable results in medical image analysis. However, its large network architecture and high computational complexity
arXiv:2510.12476v2 Announce Type: replace Abstract: Large language models (LLMs) have grown more powerful in language generation, producing fluent text and even imitating personal style. Yet, this abi
arXiv:2604.08032v1 Announce Type: cross Abstract: Automated maritime collision avoidance will rely on human supervision for the foreseeable future. This necessitates transparency into how the system p
The specific tweet at that URL (status ID 2042079804271313343) could not be directly retrieved. However, based on the available context about Ben Golub's recent X/Twitter activity around Refine.ink...
The ICML 2026 'final justification' is a **new requirement this year** where reviewers must submit a written explanation of their final recommendation after reading author rebuttals. New for ICML ...
// Scaling Coding Agents via Atomic Skills // Most coding agents train end-to-end on full tasks like resolving GitHub issues. But complex software engineering is really a composition of simpler skills
Sparks unicorn https://x.com/emollick/status/2024756029121020236?s=20 Here is the Gemini 3.1 'Sparks unicorn' (This is created using TikZ, which is a language built for scientific diagrams & very much
At ICML 2026, reviewers are officially required to acknowledge authors' rebuttals — starting March 31, if authors have posted a response to an official review, the reviewer is required to acknowle...
Google Research introduced two AI agents to streamline academic workflows: **PaperVizAgent**, a visualizer agent for drawing academic figures, and **ScholarPeer**, a reviewer agent that automatica...
JEPA world models + Hierarchical Planning is a massive step for long-horizon robotics. A classic failure mode I’ve faced with planning with world models: flat planning often 'cheats.' For example, in
Nous Research's Hermes Agent gained a Manim skill that serves as a production pipeline for mathematical and technical animations using Manim Community Edition, creating 3Blue1Brown-style animated ...
GLM-5.1: Towards Long-Horizon Tasks Chinese AI lab Z.ai's latest model is a giant 754B parameter 1.51TB (on Hugging Face) MIT-licensed monster - the same size as their previous GLM-5 release, and shar
SuperClaude (Mythos) still seems irreducibly Claude-y given the transcripts in the system card. Here two versions of Mythos are forced to talk to each other across multiple rounds. They are less philo