Knowledge catalogue
arXiv:2605.04678v1 Announce Type: cross Abstract: Latent actions serve as an intermediate representation that enables consistent modeling of vision-language-action (VLA) models across heterogeneous da
GLM-5V-Turbo Tech Report: Toward a Native Foundation Model for Multimodal Agents This report summarizes the main improvements behind GLM-5V-Turbo across model design, multimodal training, reinforcemen
arXiv:2605.05197v1 Announce Type: new Abstract: Grammaticality and likelihood are distinct notions in human language. Pretrained language models (LMs), which are probabilistic models of language fitte
arXiv:2605.04063v1 Announce Type: new Abstract: Alzheimer's Dementia (AD) is a progressive neurodegenerative disease marked by irreversible decline, making reliable modeling of its progression essenti
arXiv:2605.04344v1 Announce Type: cross Abstract: We introduce a simple yet powerful framework for training large language models. In contrast to the standard autoregressive next-token prediction base
arXiv:2502.12370v3 Announce Type: replace Abstract: Recent advancements in transformer-based models have greatly improved time series analysis, providing robust solutions for tasks such as forecasting
arXiv:2605.03546v1 Announce Type: cross Abstract: Turning ideas into full software projects from scratch has become a popular use case for language models. Agents are being deployed to seed, maintain,
arXiv:2605.05093v1 Announce Type: cross Abstract: Regularization is often used in high-dimensional regression settings to generate a sparse model, which can save tremendous computing resources and ide
arXiv:2605.04494v1 Announce Type: cross Abstract: Reinforcement learning from human feedback (RLHF) has been popular for aligning text-to-image (T2I) diffusion models with human preferences. As a main
arXiv:2605.04409v1 Announce Type: new Abstract: Remote Sensing Image Change Captioning (RSICC) aims to generate spatially grounded natural language descriptions of scene evolution from bi-temporal ima
arXiv:2601.21851v2 Announce Type: replace Abstract: Foundation models, despite their robust zero-shot capabilities, remain vulnerable to spurious correlations and 'Clever Hans' strategies. Existing mi
arXiv:2605.00968v1 Announce Type: cross Abstract: Positional encoding plays a pivotal role in determin?ing the extrapolation and generalization performance of wireless foundation models for channel st
arXiv:2509.20789v4 Announce Type: replace Abstract: The remarkable success of modern AI has been closely tied to scaling laws, yet the finite supply of high-quality data makes data efficiency--learnin
arXiv:2605.02906v1 Announce Type: new Abstract: In the field of software operations, Large Language Models (LLMs) have attracted increasing attention. However, existing research has not yet achieved e
arXiv:2605.03624v1 Announce Type: new Abstract: Aspect-Based Sentiment Analysis (ABSA) enables fine-grained opinion analysis by identifying sentiments toward specific aspects or targets within a text.
arXiv:2605.03111v1 Announce Type: cross Abstract: Social-educational robots designed for socially interactive pedagogical support, such as the Robot Study Companion (RSC), rely on responsive, privacy-
arXiv:2605.03259v1 Announce Type: new Abstract: High-throughput plant phenotyping, the quantitative measurement of observable plant traits, is critical for modern breeding but remains constrained by a
arXiv:2602.00594v2 Announce Type: replace Abstract: A good language model starts with a good tokenizer. Tokenization is especially important for speech modeling, which must handle continuous signals t
arXiv:2605.03217v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly deployed in settings that require nuanced ethical reasoning, yet existing bias evaluations treat model out
AI coding agents are rapidly becoming ubiquitous across the software industry, fundamentally changing how developers write, test, and debug daily code. While these tools excel at localized, self-conta
arXiv:2605.00827v1 Announce Type: cross Abstract: Large Language Model (LLM) agents increasingly interact with external systems through tool-calling protocols such as the Model Context Protocol (MCP).
arXiv:2605.02106v1 Announce Type: new Abstract: Contemporary artificial intelligence systems achieve strong performance through large-scale parameterization, retrieval augmentation, and training on ex
arXiv:2605.01555v1 Announce Type: new Abstract: We introduce an autonomous multiagent framework for mechanistic interpretability that automates both explaining and finding internal features in large l
arXiv:2605.02745v1 Announce Type: new Abstract: Molecular property models increasingly support high-stakes drug-discovery decisions, but their outputs are often difficult to audit: classical predictor
arXiv:2605.01231v1 Announce Type: new Abstract: Recent progress in time-series forecasting has led to rapidly increasing architectural complexity, yet many reported State-of-the-Art gains are statisti
arXiv:2603.19675v2 Announce Type: replace Abstract: Recently, world models have been incorporated into the autonomous driving systems to improve the planning reliability. Existing approaches typically
arXiv:2505.01185v3 Announce Type: replace-cross Abstract: Achieving sub-10 m indoor ranging with LoRaWAN is challenging because multipath, human blockage, and micro-climate dynamics induce non-station
arXiv:2505.12546v5 Announce Type: replace Abstract: Plaintiffs and defendants in copyright lawsuits over generative AI often make sweeping, opposing claims about the extent to which large language mod
arXiv:2605.00857v1 Announce Type: cross Abstract: Source-free domain adaptation (SFDA) provides a practical solution to cross-subject EEG decoding by adapting source-pretrained models to unlabeled tar
arXiv:2605.01591v1 Announce Type: cross Abstract: Neural Ranking Models (NRMs) are central to modern information retrieval but remain highly vulnerable to adversarial manipulation. Existing attacks of
arXiv:2605.02437v1 Announce Type: new Abstract: Objective: Accurate probability estimates are essential for the safe deployment of medical image segmentation models in clinical decision-making. Howeve
arXiv:2605.02266v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly proposed for clinical decision support including multilingual diagnosis in low-resource settings. However,
arXiv:2605.01205v1 Announce Type: new Abstract: Cross-Tokenizer Knowledge Distillation (CTKD) enables knowledge transfer between a large language model and a smaller student, even when they employ dif
arXiv:2605.00832v1 Announce Type: new Abstract: Current synthetic data pipelines for computer vision generate images without diagnosing what the downstream model actually needs. This open-loop paradig
arXiv:2605.01611v1 Announce Type: cross Abstract: Due to ambiguity in the wording of the EU AI Act, we examine the question of to what extent frontier biological foundation models such as ESM3 are sub
arXiv:2605.00935v1 Announce Type: cross Abstract: Diffusion models have become the foundation of modern generative systems, with most research focusing primarily on improving generation efficiency and
arXiv:2605.01111v1 Announce Type: cross Abstract: In this work, we introduce DUET (Dual-model Efficient Two-stage inference), a collaborative inference framework in which a capable model and a lightwe
arXiv:2509.06864v2 Announce Type: replace Abstract: This paper introduces PyFair, a formal framework for evaluating and verifying individual fairness of Deep Neural Networks (DNNs). By adapting the co
arXiv:2507.18654v2 Announce Type: replace-cross Abstract: Diffusion models are powerful tools for sampling from high-dimensional distributions by progressively transforming pure noise into structured
arXiv:2605.00107v1 Announce Type: cross Abstract: Quantum machine learning integrates the strengths of quantum computing and machine learning, enabling models to learn complex features using fewer par
arXiv:2605.00474v1 Announce Type: new Abstract: Modern vision models achieve remarkable accuracy, but explaining where evidence arises, what the model encodes, and how internal computations assemble t
arXiv:2601.06600v2 Announce Type: replace Abstract: Short-video platforms have become major channels for misinformation, where deceptive claims frequently leverage visual experiments and social cues.
arXiv:2605.00620v1 Announce Type: new Abstract: Scientific literature is expanding at an unprecedented pace, making it increasingly challenging to efficiently organize and access domain knowledge. A h
arXiv:2605.00414v1 Announce Type: new Abstract: Decision trees and diffusion models are ostensibly disparate model classes, one discrete and hierarchical, the other continuous and dynamic. This work u
Nous Research announced that Trinity-Large-Thinking, a new model from Arcee AI, is available for free on the Nous Portal for a limited one-week period. Users can access the model by signing up for the
This is a good explanation of why the gap between open and closed models is larger than it appears in benchmarks. I would add in that current open models are also more fragile than closed: they handle
arXiv:2604.27239v1 Announce Type: new Abstract: Drifting models are capable one-step generative models trained to follow a drifting field. The field combines attractive and repulsive softmax-weighted
arXiv:2604.27354v1 Announce Type: new Abstract: Explainable AI (XAI) aims to improve user understanding and decisions when using AI models. However, despite innovations in XAI, recent user evaluations
arXiv:2604.27495v1 Announce Type: cross Abstract: Reward models (RMs) play a central role in aligning large language models (LLMs) with human preferences. However, RMs are often sensitive to spurious
arXiv:2604.27914v1 Announce Type: new Abstract: When language models lack relevant knowledge for a given query, they frequently generate plausible responses that can be hallucinations, rather than adm
“Marcus’ specific point about coding is structurally important: a model that produces code which compiles and passes the tests it was given is not the same as a model that produces correct, secure, ma
arXiv:2507.14719v2 Announce Type: replace Abstract: As large language models (LLMs) become increasingly integrated into real-world applications, scalable and rigorous safety evaluation is essential. T
arXiv:2509.18308v3 Announce Type: replace Abstract: Pulmonary Embolism (PE) is a life-threatening condition for which accurate and timely detection is critical to patient care. However, our systematic
A seasoned developer seeking advice on selecting an Apple product to locally host large language models in the 27-36 billion parameter range, discussing the trade-offs and specifications of different
Harnesses If you've used Claude Code or Codex, you've used a harness. A harness is the infrastructure layer that wraps an AI coding agent and decides how it operates, what it can touch, and how you me
arXiv:2401.02458v3 Announce Type: replace-cross Abstract: The advent of foundation models (FMs) as an emerging suite of AI techniques has struck a wave of opportunities in computational healthcare. Th
For better or worse, regulation for closed-source models served by a few (quite large) companies is easy. It is not as easy to imagine how you regulate open-source models that can be served by a range
arXiv:2604.25927v1 Announce Type: new Abstract: Information extraction from semi-structured business documents remains a critical challenge for enterprise management. This study evaluates the capabili
Introducing Silico: the platform for building AI models with the precision of written software. Silico lets researchers and engineers see inside their models, debug failures, and intentionally design
arXiv:2604.26841v1 Announce Type: cross Abstract: When do language diffusion models memorize their training data, and how to quantitatively assess their true generative regime? We address these questi