Knowledge catalogue
arXiv:2607.20410v1 Announce Type: new Abstract: Value alignment of Large Language Models (LLMs) has been shown to be culturally biased toward Western norms. This results in the mishandling of local va
arXiv:2607.18882v1 Announce Type: new Abstract: Validating Explainable Artificial Intelligence (XAI) methods in medical imaging requires ground-truth data with known locations of informative features.
arXiv:2607.19914v1 Announce Type: new Abstract: We study finite-horizon MDP planning under root-based (resolute) risk objectives that apply a rank-dependent functional to the distribution of total ret
arXiv:2607.19911v1 Announce Type: new Abstract: Long-range vehicle trajectories provide important spatio-temporal evidence for traffic safety analysis, autonomous driving evaluation, and data-driven t
LTX Desktop v1.1.0 just shipped with some big updates. Apple Silicon Macs can generate video locally now, there's a built-in LoRA/IC-LoRA library you can browse and apply from inside the app with per-
arXiv:2607.14595v2 Announce Type: replace Abstract: Large-scale video diffusion models deliver strong generation performance, but full fine-tuning for downstream tasks incurs prohibitive computational
Many still debate open vs closed, and compare cost per token (accounting metric). Better to shift attention to cost per successful task. @seldo and the team at @arizeai did so across 2,400 runs. Concl
arXiv:2607.19444v1 Announce Type: new Abstract: Open-access datasets for marine-engine predictive maintenance remain scarce, particularly those from controlled fault experiments with documented operat
Sara Fischer / Axios: Mark Zuckerberg launches a media campaign positioning the company's AI development as an optimistic, pro-human alternative to its competitors' “dystopian” ideas — Meta CEO Mark Z
arXiv:2607.19235v1 Announce Type: cross Abstract: Theory of Mind (ToM), the ability to infer other's beliefs, intentions, and states of knowledge, is central to social interaction, yet remains challen
arXiv:2607.19397v1 Announce Type: new Abstract: Deep Q-networks use target networks to stabilise bootstrapped value learning, but the standard hard copy update also introduces a tradeoff. Holding the
arXiv:2607.18673v2 Announce Type: new Abstract: Vision Language Models (VLMs) are well known for hallucinating non-existent objects in images. Objects with missing parts present a unique challenge for
The news about Anthropic settling a class action lawsuit for 1.5B over training data isn't just a legal headache for them, it's a massive warning sign for engineering teams relying entirely on closed
arXiv:2512.05462v2 Announce Type: replace-cross Abstract: Pharmaceutical drug discovery demands machine learning (ML) infrastructure that goes beyond general-purpose Machine Learning Operations (MLOps
There's a bunch of small MoE with around 1B active params, like LFM2.5 8B A1B and Granite 4.0h 7B A1B; and then there are models with 3B+ like Qwen 3.x ~30B A3B and Gemma 4 26B A4B, but those are alre
arXiv:2607.20323v1 Announce Type: new Abstract: Nanopore devices have emerged as powerful tools for single-molecule sensing, with potential for rapid, portable diagnostics. They detect changes in ioni
arXiv:2607.20151v1 Announce Type: cross Abstract: Cross-Project Defect Prediction (CPDP) involves building models using data from external projects, called training projects, to predict modules from t
arXiv:2607.19910v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) are increasingly expected to automate visualization development by generating code directly from visual designs
arXiv:2607.19695v1 Announce Type: new Abstract: Robots deployed in delivery, campus, and emergency-response settings often need to navigate from buildings to streets within a single continuous episode
arXiv:2607.14186v4 Announce Type: replace-cross Abstract: Scaling executable agent training data for LLM post-training is bottlenecked by substrate-bound methods that tie task generation to predefined
arXiv:2607.19356v1 Announce Type: new Abstract: Tool-using LLM agents increasingly execute high-impact actions, making runtime safety monitoring essential. We present NEXUS (Neural EXecution Utility a
arXiv:2607.20238v1 Announce Type: new Abstract: Visible-infrared (VIS-IR) alignment is a key pre-training task for robust multi-sensor perception. Most existing methods use uniform patch-wise contrast
arXiv:2607.19951v1 Announce Type: new Abstract: Cross-view geo-localization between UAV and satellite imagery remains a fundamental yet highly challenging task, especially under large off-nadir views
arXiv:2607.20194v1 Announce Type: new Abstract: The development of organic light-emitting diode (OLED) materials faces the compounded challenges of an astronomically large chemical space, stringent qu
arXiv:2607.19573v1 Announce Type: new Abstract: Structural generalization has been measured repeatedly by several benchmarks, yet it has never been formally defined. We give a definition that translat
arXiv:2607.18827v1 Announce Type: new Abstract: Gaze Object Prediction (GOP) aims to localize and recognize the objects humans attend to, a task crucial for understanding human-centric interactions. H
OpenAI Group PBC today launched Health in ChatGPT, a feature that lets people connect their medical records and Apple Health data to the chatbot — one day after a lawsuit accused the company of handin
arXiv:2607.20121v1 Announce Type: new Abstract: LLM-based agents leverage third-party skills to extend their capabilities in open-world scenarios. However, third-party skills can introduce extra secur
arXiv:2607.19421v1 Announce Type: cross Abstract: Silicon-photonic (SiPh) accelerators have emerged as a promising platform for Vision Transformer (ViT) inference by performing matrix multiplications
Anyone building with AI eventually hits the same tradeoff: how to get the most intelligence per dollar, the right model at the right cost for each task. That’s what DigitalOcean Inference Engine is bu
Full-parameter post-training of trillion-parameter-scale MoE models introduces substantial system-level challenges for large-scale distributed training, including severe memory pressure, non-overlappe
arXiv:2607.19261v1 Announce Type: new Abstract: Whole-slide image (WSI) diagnosis requires identifying diagnostically relevant regions, examining them across magnifications, and integrating multi-scal
arXiv:2607.18448v1 Announce Type: cross Abstract: Pathology report generation from whole-slide images (WSIs) is a rapidly growing multimodal learning problem, yet progress is difficult to measure beca
arXiv:2607.19653v1 Announce Type: cross Abstract: Large language model (LLM) agents now perform well on correctness-oriented repository-level tasks, including SWE-Bench issue resolution and feature im
arXiv:2607.20378v1 Announce Type: new Abstract: Physics-informed learning of partial differential equations (PDEs) has been dominated by multilayer perceptrons (MLPs), whose spectral bias and dense pa
arXiv:2607.19696v1 Announce Type: cross Abstract: The accurate diagnosis of spinal pathologies depends heavily on radiological interpretation, yet automated systems are hindered by the lack of diverse
arXiv:2607.19787v1 Announce Type: cross Abstract: Polarimetric synthetic aperture radar (PolSAR) image classification is a representative task for physics-aware GeoAI, where land-cover semantics are c
arXiv:2607.20045v1 Announce Type: cross Abstract: Physical noise in near-term quantum hardware is usually treated as a nuisance to suppress. We ask whether it can instead act as a hardware-native regu
arXiv:2607.19171v1 Announce Type: new Abstract: Fine-tuning pre-trained point-cloud backbones typically updates all parameters, resulting in substantial computation and memory overhead. More important
arXiv:2607.19711v1 Announce Type: new Abstract: Noisy and corrupted points can substantially degrade point cloud recognition performance, especially under challenging corruption settings. In particula
arXiv:2607.20002v1 Announce Type: cross Abstract: Time series foundation models (TSFMs) have emerged as general-purpose models for time series analysis, but pretraining alone is often insufficient for
arXiv:2607.17820v2 Announce Type: replace Abstract: Training-free few-shot adaptation methods have gained significant attention recently in the context of Vision-language Models (VLMs). Yet, current b
arXiv:2605.05598v2 Announce Type: replace Abstract: The proliferation of large language models (LLMs) in educational settings has paradoxically undermined the cognitive processes they purport to suppo
arXiv:2607.19359v1 Announce Type: new Abstract: Long-term memory is essential for LLM agents that interact across sessions, yet current memory benchmarks primarily evaluate single-hop recall, leaving
Link to their official GGUF repo: https://huggingface.co/poolside/Laguna-S-2.1-GGUF/tree/main All the GGUFs received this fix 5ish hours ago - correct yarn_attn_factor to 1.0 (llama.cpp derives mscale
arXiv:2607.20253v1 Announce Type: cross Abstract: In this report, we present a unified song generation framework capable of producing high-quality full-length music from lyrics, text descriptions, and
arXiv:2602.04401v4 Announce Type: replace Abstract: Visual Place Recognition (VPR) is a key component for localization in Global Navigation Satellite System (GNSS)-denied environments, but its perform
arXiv:2607.17612v2 Announce Type: replace Abstract: Continuous diffusion models have become the dominant paradigm for photo-realistic image Super-Resolution (SR), but they typically formulate reconstr
arXiv:2512.11899v2 Announce Type: replace Abstract: Large vision-language models (LVLMs) are vulnerable to typographic attacks, where misleading text inserted into an image can override visual underst
arXiv:2607.20058v1 Announce Type: new Abstract: Large language models can answer scientific questions, yet a correct output does not reveal whether the model represents or uses the governing physics.
arXiv:2607.19403v1 Announce Type: cross Abstract: Validating federated learning frameworks on real clinical data is an essential step between proof-of-concept demonstrations in controlled synthetic en
arXiv:2607.18540v1 Announce Type: new Abstract: Robotic perception pipelines increasingly rely on large vision backbones deployed on SWaP-constrained edge platforms, making post-training quantization
arXiv:2606.17567v2 Announce Type: replace Abstract: While sequential residual fitting is the bedrock of standard boosting frameworks, it inherently breeds learner redundancy by repeatedly revisiting c
arXiv:2607.19722v1 Announce Type: new Abstract: Automatic pain assessment from facial video remains challenging due to the spatial heterogeneity of pain-related facial cues. This study proposes ReFace
arXiv:2607.19678v1 Announce Type: cross Abstract: AI-generated answers in high-stakes domains are often fluent but difficult to verify, especially when they contain multi-step reasoning rather than a
arXiv:2607.20090v1 Announce Type: cross Abstract: Retrieval-augmented large language models frequently face contexts that interleave useful evidence with misleading statements or instruction-like cont
arXiv:2607.19377v1 Announce Type: cross Abstract: Physics-informed neural networks (PINNs) provide a mesh-free framework for solving partial differential equations, but their training is often affecte
arXiv:2607.18709v2 Announce Type: replace Abstract: Existing robot datasets remain expensive to curate, embodiment-specific, and insufficiently annotated with the fine-grained structure required for g
arXiv:2607.19332v1 Announce Type: cross Abstract: Generative models have undergone many generations of evolution, from VAEs/GANs to diffusion/flow matching. Along the way, the underlying techniques ha
arXiv:2607.20197v1 Announce Type: new Abstract: Remote sensing image editing aims to modify remote sensing images according to natural language instructions while preserving geographic rules and senso