Knowledge catalogue
arXiv:2605.31148v1 Announce Type: cross Abstract: Humans can effortlessly perceive spatial layouts, form cognitive representations, reason about spatial relations, and translate such reasoning into ac
arXiv:2605.31529v1 Announce Type: new Abstract: True video intelligence demands more than recognizing what is visible: it requires reasoning about why events unfold, predicting what would change under
arXiv:2605.31156v1 Announce Type: new Abstract: Causal discovery aims to recover directed causal relations from observational and interventional data, providing a basis for mechanistic understanding a
arXiv:2603.07551v2 Announce Type: replace-cross Abstract: Zero-shot Text-to-Speech (TTS) voice cloning poses severe privacy risks, demanding the removal of specific speaker identities from trained TTS
arXiv:2601.09633v2 Announce Type: replace Abstract: Taxonomies form the backbone of structured knowledge representation across diverse domains, enabling applications such as e-commerce and semantic se
arXiv:2602.01186v2 Announce Type: replace-cross Abstract: Classical Federated Learning relies on a multi-round iterative process of model exchange and aggregation between server and clients, with high
arXiv:2605.31367v1 Announce Type: cross Abstract: Token mixing layers play a key role in how language models can learn and generate long-range dependencies. Their efficiency relies on the necessary tr
arXiv:2605.31113v1 Announce Type: new Abstract: Automatically detecting machine-generated text (MGT) is critical to maintaining the knowledge integrity of user-generated content (UGC) platforms such a
arXiv:2605.30451v1 Announce Type: new Abstract: Group Relative Policy Optimization (GRPO) is an effective recipe for training reasoning models with verifier-based outcome rewards, but its supervision
arXiv:2605.30671v1 Announce Type: new Abstract: Recovering ego-camera orientation from manipulation video is a prerequisite for disentangling hand motion from camera motion, a key step in imitation le
arXiv:2602.03890v2 Announce Type: replace Abstract: Point clouds provide a compact and expressive representation of 3D objects, and have recently been integrated into multimodal large language models
arXiv:2605.29454v1 Announce Type: new Abstract: While Membership Inference Attacks (MIAs) are the prevailing method for identifying training data, their application has expanded into privacy auditing
arXiv:2605.30349v1 Announce Type: new Abstract: Autoregressive video diffusion models generate streaming video by producing frames sequentially, conditioning each chunk on previously generated content
arXiv:2605.28914v1 Announce Type: cross Abstract: Tool-using language agents turn model decisions into external side effects: they read files, run scripts, call APIs, send messages, and invoke Model C
arXiv:2512.01334v2 Announce Type: replace Abstract: Text-guided image-to-video generation has made substantial progress, yet it still struggles to execute text-specified edits that require substantial
b9391 is a build release of llama.cpp, an open-source C/C++ inference framework for running large language models locally. llama.cpp provides lightweight, optimized model inference with support for mu
arXiv:2602.14307v3 Announce Type: replace Abstract: As frontier Large Language Models (LLMs) increasingly saturate new benchmarks shortly after they are published, benchmarking itself is at a juncture
arXiv:2509.23571v3 Announce Type: replace-cross Abstract: As cyber threats continue to grow in scale and sophistication, blue team defenders increasingly require advanced tools to proactively detect a
arXiv:2605.29225v1 Announce Type: new Abstract: Self-evolving agents improve over time by reflecting on past failures, but existing evaluation is limited in two ways: it measures only task scores, lea
arXiv:2605.30122v1 Announce Type: cross Abstract: Deep-learning precipitation nowcasting models are often optimized using pointwise losses such as mean squared error or mean absolute error, which can
arXiv:2605.29365v1 Announce Type: new Abstract: Formality transfer is commonly framed as a symmetric bidirectional task between informal and formal registers. We argue that this framing conceals a sup
arXiv:2605.29272v1 Announce Type: cross Abstract: Fraud detection models in payment networks train on chargeback labels that are systematically biased. Every label must survive three sequential gates:
arXiv:2605.30241v1 Announce Type: new Abstract: Misinformation verification increasingly occurs in public, fast-moving, and multilingual online settings, where static benchmarks provide an incomplete
arXiv:2605.29476v1 Announce Type: new Abstract: This work presents a comparative evaluation of machine translation systems applied to images containing textual information, a task that lies at the int
arXiv:2605.30333v1 Announce Type: new Abstract: A plausible future mathematical claim must satisfy two constraints: it should follow the direction of prior work and respect the formal dependencies tha
arXiv:2508.19202v3 Announce Type: replace Abstract: Scientific problem solving poses unique challenges for LLMs, requiring both deep domain knowledge and the ability to apply such knowledge through co
If you've built a chatbot, you know this conversation: User: 'Book a table for two tomorrow at 7pm.' Agent: 'Okay, for what day?' User: 'Tomorrow.' Agent: 'What time?' A date picker would have ended t
arXiv:2512.10401v3 Announce Type: replace-cross Abstract: This paper is concerned with differentiable resampling in the context of sequential Monte Carlo (e.g., particle filtering). Drawing on reparam
arXiv:2605.30027v1 Announce Type: new Abstract: Multimodal documents contain diverse elements, such as tables, figures, and layouts, which can complicate retrieval tasks. While current approaches typi
arXiv:2509.23730v2 Announce Type: replace Abstract: Large language models (LLMs) have recently advanced in reasoning when optimized with reinforcement learning (RL) under verifiable rewards. Existing
arXiv:2603.17942v2 Announce Type: replace Abstract: Large Language Models (LLMs) possess latent multi-token prediction (MTP) abilities despite being trained only for next-token generation. We introduc
arXiv:2505.20634v2 Announce Type: replace Abstract: Concept shift occurs when the distribution of labels conditioned on the features changes between domains, which can make even a well-tuned ML model
arXiv:2605.30336v1 Announce Type: new Abstract: Federated learning is an emerging distributed paradigm that addresses the challenges posed by heterogeneous, privacy-sensitive data. It enables multiple
arXiv:2605.30213v1 Announce Type: new Abstract: Continuous-time models are a natural choice for irregular and asynchronous data. A central design choice is how to embed discrete observations into cont
arXiv:2605.28916v1 Announce Type: cross Abstract: We report a comparison of two state-of-the-art agentic AI systems, Claude Code (Anthropic) and Codex (OpenAI), tasked with autonomously executing a si
Many organizations’ single source of truth is data that resides in BigQuery, Google’s governed, secure and petabyte-scale data platform. However, the 'last mile' of ad-hoc analysis, modeling, and repo
arXiv:2605.30248v1 Announce Type: new Abstract: Image generation models have evolved from text-conditioned pixel synthesis toward multimodal agents endowed with visual comprehension and tool invocatio
arXiv:2603.21746v2 Announce Type: replace Abstract: Pointing-based methods decompose complex tasks as sequential grounding and reasoning steps. Given a query, the model first grounds the relevant obje
arXiv:2605.29270v1 Announce Type: new Abstract: The era of the Internet of Agents (IoA) is taking shape: LLM agents are expected to fulfill user goals by orchestrating fast-growing populations of Mode
arXiv:2605.29184v1 Announce Type: cross Abstract: Large Language Models (LLMs) offer a promising avenue for scientific discovery, yet their application to symbolic regression is often constrained by i
arXiv:2601.22531v2 Announce Type: replace-cross Abstract: Because of the pervasive use of deep neural networks (DNNs), especially in high-stakes domains, the interpretability of DNNs has received incr
llama.cpp now has an official website: https://llama.app Our goal is to make local AI accessible to everyone, and improving the user experience is a big part of that. On the new landing page you’ll fi
arXiv:2605.30265v1 Announce Type: cross Abstract: Vision-Language Models (VLMs) have achieved substantial progress across a wide range of understanding and reasoning tasks, driven by large-scale image
arXiv:2605.29940v1 Announce Type: new Abstract: Large language models (LLMs) have been widely adopted for synthetic data generation, significantly reducing annotation costs. However, most existing stu
arXiv:2605.30218v1 Announce Type: new Abstract: Temperature-zero BF16 LLM inference is often treated as reproducible, yet the same request can emit different tokens when decoded alone or inside a larg
arXiv:2605.29862v1 Announce Type: cross Abstract: AI-driven respiratory sound classification (RSC) is promising for automated pulmonary disease detection, yet multi-site deployment is hindered by inte
arXiv:2508.16873v3 Announce Type: replace Abstract: Understanding how visual content conveys sentiment is increasingly important in a digital landscape dominated by imagery. However, sentiment percept
arXiv:2605.30353v1 Announce Type: new Abstract: Are AI agents tools, co-authors, or researchers? We present a quantified case study (N=1): a physicist supervising an AI coding agent (Claude Code, Sonn
arXiv:2508.15180v3 Announce Type: replace Abstract: High-quality mathematical and logical datasets with verifiable answers are essential for strengthening the reasoning capabilities of large language
arXiv:2605.30052v1 Announce Type: cross Abstract: One-shot Program-of-Thought (PoT) emits a Python program that prints a primitive-action plan; a single invalid action silently invalidates the traject
arXiv:2603.27758v2 Announce Type: replace Abstract: Metric Cross-View Geo-Localization (MCVGL) aims to estimate the 3-DoF camera pose (position and heading) by matching ground and satellite images. In
arXiv:2509.05771v2 Announce Type: replace-cross Abstract: We develop a new classification framework based on the theory of coherent risk measures and systemic risk. The proposed approach is suitable f
arXiv:2602.02751v2 Announce Type: replace-cross Abstract: Small language models are increasingly viewed as a promising, cost-effective approach to agentic AI, with proponents claiming they are suffici
arXiv:2605.30087v1 Announce Type: new Abstract: Emerging personal AI agents are moving toward persistent, multi-source memory. This creates an evaluation problem: systems must decide how to use confli
arXiv:2605.30329v1 Announce Type: new Abstract: Autonomous AI research agents aim to accelerate scientific discovery by automating the research pipeline, from hypothesis generation to peer review. How
arXiv:2605.29368v1 Announce Type: cross Abstract: The intricate nature of modern surgical care necessitates intelligent systems that can synthesize extensive patient records, support collaborative dec
arXiv:2605.29411v1 Announce Type: cross Abstract: Under standard graphical assumptions, the Markov boundary of a target variable is the smallest set of features that renders every other feature redund
arXiv:2605.28833v1 Announce Type: cross Abstract: Automatic speech recognition (ASR) has the potential to substantially reduce manual annotation effort in child speech research by generating automatic
arXiv:2605.29096v1 Announce Type: new Abstract: This paper examines records retrieved from the ClinicalTrials.gov registry to characterize temporal trends in AI terminology and the geographical distri
arXiv:2605.29708v1 Announce Type: new Abstract: Mixture-of-Experts (MoE) LLMs rely on sparse, router-driven expert activation, yet how safety alignment interacts with routed expert specialization rema