Knowledge catalogue
arXiv:2604.15299v1 Announce Type: new Abstract: Video generation has advanced rapidly, with recent methods producing increasingly convincing animated results. However, existing benchmarks-largely desi
arXiv:2601.21003v2 Announce Type: replace Abstract: Large Language Models usually put more emphasis on accuracy and therefore, will guess even when not certain about the prediction, which is especiall
arXiv:2604.15153v1 Announce Type: new Abstract: Large Language Models (LLMs) incur significant computational and memory costs when processing long prompts, as full self-attention scales quadratically
arXiv:2510.24538v2 Announce Type: replace Abstract: Textual humor is enormously diverse and computational studies need to account for this range, including intentionally bad humor. In this paper, we c
arXiv:2604.13151v1 Announce Type: new Abstract: Language Model (LM) agents are increasingly used in complex open-ended decision-making tasks, from AI coding to physical AI. A core requirement in these
arXiv:2604.08064v2 Announce Type: replace Abstract: Existing memory benchmarks for LLM agents evaluate explicit recall of facts, yet overlook implicit memory where experience becomes automated behavio
arXiv:2604.15109v1 Announce Type: new Abstract: Despite the rapid advancement of Large Language Models (LLMs), uncertainty quantification in LLM generation is a persistent challenge. Although recent a
arXiv:2604.14795v1 Announce Type: new Abstract: Visual Geometry Foundation Models (VGFMs) demonstrate remarkable zero-shot capabilities in local reconstruction. However, deploying them for kilometer-l
arXiv:2512.05024v3 Announce Type: replace-cross Abstract: As generative AI models are increasingly used to simulate real-world systems, quantifying the ``sim-to-real'' gap is critical. For each input
arXiv:2604.14808v1 Announce Type: new Abstract: Machine unlearning for large language models (LLMs) aims to remove targeted knowledge while preserving general capability. In this paper, we recast LLM
arXiv:2604.14197v1 Announce Type: new Abstract: Large language model (LLM) performance depends heavily on prompt design, yet prompt construction is often described and applied inconsistently. Our purp
Luz Ding / Bloomberg: Alibaba's new Token Hub unit releases Happy Oyster, a new AI world model that can create 3D environments, interactive videos, films, video content, and games — Alibaba Group Hold
arXiv:2604.13804v1 Announce Type: new Abstract: The rapid evolution of multimodal large models has revolutionized the simulation of diverse characters in speech dialogue systems, enabling a novel inte
arXiv:2604.13706v1 Announce Type: new Abstract: Professional fact-checkers rely on domain knowledge and deep contextual understanding to verify claims. Large language models (LLMs) and large reasoning
arXiv:2604.14129v1 Announce Type: new Abstract: While Audio-Visual Language Models (AVLMs) have achieved remarkable progress over recent years, their reliability is bottlenecked by cross-modal halluci
arXiv:2604.13371v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly described as possessing strong reasoning capabilities, supported by high performance on mathematical, logi
arXiv:2604.13592v1 Announce Type: new Abstract: Reasoning capabilities in large language models (LLMs) have generally advanced significantly. However, it is still challenging for existing reasoning-ba
arXiv:2604.13777v1 Announce Type: new Abstract: Large language models (LLMs) may memorize sensitive or copyrighted content, raising significant privacy and legal concerns. While machine unlearning has
arXiv:2512.02697v3 Announce Type: replace Abstract: Cross-view geo-localization infers a location by retrieving geo-tagged reference images that visually correspond to a query image. However, the trad
This post discusses a blog article by @pcuenq that covers the process of creating new skills and test harnesses to automate the conversion of machine learning models from the Hugging Face Transformers
arXiv:2604.13977v1 Announce Type: new Abstract: Synthetic data is a standard component in training large language models, yet systematic comparisons across design dimensions, including rephrasing stra
GPT-Rosalind is a frontier reasoning model developed by OpenAI, specifically designed to support research applications in biology, drug discovery, and translational medicine. The model was introduced
arXiv:2604.13386v1 Announce Type: new Abstract: Linear probes can detect when language models produce outputs they 'know' are wrong, a capability relevant to both deception and reward hacking. However
arXiv:2510.05056v2 Announce Type: replace Abstract: As programmers write code, they often edit and retry multiple times, creating rich 'interaction traces' that reveal how they approach coding tasks a
arXiv:2510.07019v3 Announce Type: replace Abstract: Transformers excel at sequence modeling but face quadratic complexity, while linear attention offers improved efficiency but often compromises recal
arXiv:2604.13395v1 Announce Type: cross Abstract: Large Reasoning Models (LRMs) have recently demonstrated significant improvements in complex reasoning. While quantifying generation uncertainty in LR
Nous Research is promoting their subscription portal and providing instructions for users to update and configure their Hermes model through command-line tools (`hermes update` and `hermes model`). Th
arXiv:2604.12767v1 Announce Type: cross Abstract: Multimodal Large Language Models (MLLMs) suffer from substantial computational overhead due to the high redundancy in visual token sequences. Existing
arXiv:2604.12777v1 Announce Type: cross Abstract: The human brain constructs emotional percepts not by processing facial expressions in isolation, but through a dynamic, hierarchical integration of se
arXiv:2604.12180v1 Announce Type: cross Abstract: Tropical cyclones (TCs) rank among the most destructive natural hazards, yet their forecasting faces fundamental trade-offs: numerical weather predict
arXiv:2604.05546v2 Announce Type: replace Abstract: Large Vision-Language Models (LVLMs) enable sophisticated reasoning over images and videos, yet their inference is hindered by a systemic efficiency
arXiv:2603.25326v4 Announce Type: replace Abstract: Interest in the concept of AI-driven harmful manipulation is growing, yet current approaches to evaluating it are limited. This paper introduces a f
arXiv:2604.12250v1 Announce Type: new Abstract: This study examines how model-specific characteristics of Large Language Model (LLM) agents, including internal alignment, shape the effect of memory on
This Reddit thread from r/ollama discusses the value comparison between Ollama's 20/month Pro plan for cloud model usage versus using OpenRouter as an alternative. Ollama Cloud offers fixed-price subs
arXiv:2604.12721v1 Announce Type: new Abstract: Clinical case formulation organizes patient symptoms and psychosocial factors into causal models, often using the 5P framework. However, constructing su
arXiv:2604.12218v1 Announce Type: new Abstract: System log anomaly detection is critical for maintaining the reliability of large-scale software systems, yet traditional methods struggle with the hete
arXiv:2601.14004v4 Announce Type: replace Abstract: Mechanistic Interpretability (MI) has emerged as a vital approach to demystify the opaque decision-making of Large Language Models (LLMs). However,
arXiv:2508.12260v5 Announce Type: replace Abstract: Infectious disease forecasting in novel outbreaks or low-resource settings is hampered by the need for large disease and covariate data sets, bespok
Microsoft Corp.’s push for artificial intelligence independence is gaining traction with today’s release of MAI-Image-2-Efficient, a lean and mean version of its flagship image generation model that d
arXiv:2604.00136v2 Announce Type: replace-cross Abstract: Multi-model LLM serving operates in a non-stationary, noisy environment: providers revise pricing, model quality can shift or regress without
arXiv:2604.12384v1 Announce Type: new Abstract: Safety alignment in Large Language Models (LLMs) remains highly fragile during fine-tuning, where even benign adaptation can degrade pre-trained refusal
arXiv:2512.14754v2 Announce Type: replace-cross Abstract: Advanced LLMs have achieved near-ceiling instruction-following accuracy on benchmarks such as IFEval. However, these impressive scores do not
arXiv:2604.12908v1 Announce Type: new Abstract: At its core, robotic manipulation is a problem of vision-to-geometry mapping (f(v) rightarrow G). Physical actions are fundamentally defined by geometri
arXiv:2412.07238v3 Announce Type: replace Abstract: The identity of a speaker influences language comprehension through modulating perception and expectation. This review explores speaker effects and
arXiv:2509.03234v2 Announce Type: replace Abstract: Parameter-Efficient Fine-Tuning (PEFT) methods, such as Low-Rank Adaptation (LoRA), have significantly reduced the number of trainable parameters ne
arXiv:2509.25758v2 Announce Type: replace Abstract: The remarkable capabilities of modern large reasoning models are largely unlocked through post-training techniques such as supervised fine-tuning (S
Training code and models are live on Hugging Face. Dan Fu (Together AI's VP of Kernels) led the work. Together AI provided compute. Blog: https://www.together.ai/blog/parcae Paper: https://arxiv.org/a
Cristobal Valenzuela, co-founder and CEO of Runway, posted a social media comment suggesting that 'world models' — a major AI topic of early 2026 — are already becoming passé, using the footwear brand
arXiv:2510.10182v2 Announce Type: replace-cross Abstract: Reasoning is an important task for large language models (LLMs). Among all the reasoning paradigms, inductive reasoning is one of the fundamen
arXiv:2604.09617v1 Announce Type: new Abstract: Transparent and standardized documentation is essential for building trustworthy generative AI (GAI) systems. However, existing automated methods for ge
arXiv:2603.22962v2 Announce Type: replace Abstract: We study the theoretical behavior of denoising score matching--the learning task associated to diffusion models--when the data distribution is suppo
arXiv:2604.10950v1 Announce Type: new Abstract: Fully supervised Video Semantic Segmentation (VSS) relies heavily on densely annotated video data, limiting practical applicability. Alternatively, appl
arXiv:2604.07716v2 Announce Type: replace Abstract: We present FDM (Fan Duality Model), a linear sequence architecture that resolves the fundamental tension between memory efficiency and associative r
arXiv:2603.22479v3 Announce Type: replace-cross Abstract: Defining a constructive process to build general capabilities for language models in an automatic manner is considered an open problem in arti
arXiv:2604.10035v1 Announce Type: cross Abstract: In this study, we developed a computational implementation for a model of metaphor comprehension based on the theory of indeterminate natural transfor
arXiv:2502.11008v2 Announce Type: replace Abstract: Counterfactual reasoning is widely recognized as one of the most challenging and intricate aspects of causality in artificial intelligence. In this
arXiv:2604.10681v1 Announce Type: cross Abstract: Large Language Models (LLMs), despite their impressive capabilities across domains, have been shown to be vulnerable to backdoor attacks. Prior backdo
arXiv:2310.17245v2 Announce Type: replace-cross Abstract: Offline reinforcement learning (RL) aims to optimize a policy using collected data without online interactions. Model-based approaches are par
arXiv:2410.21316v2 Announce Type: replace-cross Abstract: Transformers and large language models~(LLMs) have seen rapid adoption in all domains. Their sizes have exploded to hundreds of billions of pa
arXiv:2604.10963v1 Announce Type: new Abstract: Medical image segmentation supports clinical workflows by precisely delineating anatomical structures and lesions. However, medical image datasets medic