Knowledge catalogue
arXiv:2605.13045v1 Announce Type: new Abstract: The existing methods for evaluating the medical knowledge of Large Language Models (LLMs) are largely based on atemporal examination-style benchmarks, w
arXiv:2605.12646v1 Announce Type: cross Abstract: It is widely agreed that when AI models assist decision-makers in high-stakes domains by predicting an outcome of interest, they should communicate th
arXiv:2605.12682v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used as reasoning modules in many applications. While they are efficient in certain tasks, LLMs often stru
arXiv:2605.12741v1 Announce Type: new Abstract: Enabling Large Language Models (LLMs) to continuously improve from environmental interactions is a central challenge in post-training. While on-policy s
Like any AI dev not employed by a closed lab, we share the ambition that at least 10x more should be capable of training frontier models in 2026. And like we all know, some 10x leaps can't survive a c
arXiv:2605.12752v1 Announce Type: new Abstract: LoRA is widely adopted for continual fine-tuning of Large Language Models due to its parameter efficiency, modularity across tasks, and compatibility wi
arXiv:2605.12556v1 Announce Type: new Abstract: Low-light image enhancement is challenging due to complex degradations, including amplified noise, artifacts, and color distortion. While Retinex-based
arXiv:2605.13218v1 Announce Type: new Abstract: Cancer is one of the leading causes of death worldwide, making the development of rapid, minimally invasive, label-free and scalable diagnostic strategi
arXiv:2605.12655v1 Announce Type: new Abstract: Multi-agent reinforcement learning (MARL) in real-world use cases may need to adapt to external natural language instructions that interrupt ongoing beh
arXiv:2605.12640v1 Announce Type: new Abstract: Panoptic segmentation requires the simultaneous recognition of countable thing instances and amorphous stuff regions, placing joint demands on long-rang
arXiv:2601.21975v2 Announce Type: replace Abstract: Recent work identifies a stated-revealed (SvR) preference gap in language models (LMs): a mismatch between the values models endorse and the choices
arXiv:2605.13527v1 Announce Type: new Abstract: Reusable skills have become a core substrate for improving agent capabilities, yet most existing skill packages encode reusable behavior primarily as te
arXiv:2507.10797v2 Announce Type: replace Abstract: This paper introduces the framework of multi-armed sampling, which serves as the sampling counterpart to the optimization problem of multi-armed ban
arXiv:2605.13829v1 Announce Type: cross Abstract: We introduce Negation Neglect, where finetuning LLMs on documents that flag a claim as false makes them believe the claim is true. For example, models
Yohei Nakajima, an AI researcher and entrepreneur, posted about unexpectedly achieving a ranking of #989, likely referring to a leaderboard, competition, or metric related to his AI work or social med
arXiv:2605.13457v1 Announce Type: new Abstract: Diffusion-based real-world image super-resolution (Real-ISR) has achieved remarkable perceptual quality; however, directly super-resolving images to 4K
arXiv:2605.12825v1 Announce Type: cross Abstract: We introduce Orthrus, a simple and efficient dual-architecture framework that unifies the exact generation fidelity of autoregressive Large Language M
arXiv:2605.12525v1 Announce Type: cross Abstract: Current emotion analysis in social media is predominantly author-centric, failing to capture the subjective nature of emotional responses across diver
arXiv:2601.21366v2 Announce Type: replace Abstract: The forward pass of a Transformer can be seen as an interacting particle system on the unit sphere: time plays the role of layers, particles that of
arXiv:2601.09636v2 Announce Type: replace Abstract: While GUI agents have shown strong performance under explicit and completion instructions, real-world deployment requires aligning with users' more
arXiv:2605.13370v1 Announce Type: new Abstract: For over a decade, explicit memory architectures like the Neural Turing Machine have remained theoretically appealing yet practically intractable for la
arXiv:2512.08411v2 Announce Type: replace Abstract: Model-based planning in robotic domains is challenged by the hybrid nature of physical dynamics, where continuous motion is punctuated by discrete e
arXiv:2605.13111v1 Announce Type: new Abstract: Autoregressive video generation enables streaming and open-ended long video synthesis, but still suffers from long-term degradation caused by accumulate
arXiv:2605.13833v1 Announce Type: cross Abstract: Modeling long-range dependencies in sequential data remains a central challenge in machine learning. Transformers address this challenge through atten
arXiv:2605.13838v1 Announce Type: new Abstract: Video-guided 3D animation holds immense potential for content creation, offering intuitive and precise control over dynamic assets. However, practical d
arXiv:2605.12511v1 Announce Type: cross Abstract: Social media serves as a primary source of information in the current digital era. Many people consume a vast range of information in a very short spa
arXiv:2506.09522v3 Announce Type: replace-cross Abstract: Large Vision Language Models (LVLMs) achieve strong performance across multimodal tasks by integrating visual perception with language underst
Ring-2.6-1T is a trillion-parameter flagship reasoning model designed for real-world complex task scenarios, now available as an open-source model. The model features about 63B activated parameters pe
arXiv:2605.13667v1 Announce Type: new Abstract: Scene graph generation provides a compact structured representation for visual perception, but accurate and fast graph prediction from images and videos
arXiv:2605.12524v1 Announce Type: cross Abstract: We introduce ProofGrid, a benchmark suite for evaluating LLM reasoning through machine-checkable proofs rather than final answers alone. ProofGrid con
arXiv:2605.12928v1 Announce Type: new Abstract: Modern language models have historically relied on two dominant design choices: subword tokenization and autoregressive (AR) ordering. These design deci
arXiv:2605.12940v1 Announce Type: cross Abstract: Probabilistic Circuits (PCs) are deep generative models that support exact and efficient probabilistic inference. Yet in autoregressive language model
arXiv:2605.12817v1 Announce Type: cross Abstract: Longitudinal clinical notes contain rich evidence of how patients evolve over time, but converting this signal into training supervision for clinical
arXiv:2605.12645v1 Announce Type: cross Abstract: Effective personalized question answering (PQA) in language models requires grounding responses in the user's underlying intent, where intent refers t
arXiv:2510.15297v4 Announce Type: replace-cross Abstract: We introduce VERA-MH (Validation of Ethical and Responsible AI in Mental Health), an automated evaluation of the safety of AI chatbots used in
arXiv:2605.13318v1 Announce Type: new Abstract: Chatbot usage has increased, including in fields for which they were never developed for--notably mental health support. To that end, we introduce Valid
arXiv:2605.12571v1 Announce Type: cross Abstract: Long video question answering requires locating sparse, time-scattered visual evidence within highly redundant content. Although current MLLMs perform
arXiv:2605.13034v1 Announce Type: new Abstract: Recent deep research systems have improved the ability of large language models to produce long, grounded reports through iterative retrieval and reason
arXiv:2605.13846v1 Announce Type: cross Abstract: This paper introduces WARDEN, an early language model system capable of transcribing and translating Wardaman, an endangered Australian indigenous lan
arXiv:2602.13215v2 Announce Type: replace Abstract: Recurrent-attention hybrids aim to combine the efficiency of recurrence with the expressivity of attention, but existing approaches typically apply
arXiv:2511.13658v2 Announce Type: replace-cross Abstract: Deceptive reviews mislead consumers, harm businesses, and undermine trust in online marketplaces. Machine learning classifiers can learn from
arXiv:2605.13465v1 Announce Type: new Abstract: Recent advances in 3D Gaussian Splatting (3DGS) have enabled significant progress in photorealistic novel view synthesis. However, traditional 3DGS reli
arXiv:2605.11405v1 Announce Type: new Abstract: Data curation has shifted the quality-compute frontier for language-model and contrastive image-text pretraining, but its role for vision-language model
arXiv:2605.12350v1 Announce Type: new Abstract: Lack of transparency in AI systems poses challenges in critical real-life applications. It is important to be able to explain the decisions of an AI sys
arXiv:2605.12069v1 Announce Type: new Abstract: Zero-shot anomaly detection aims to identify defects in unseen categories without target-specific training. Existing methods usually apply the same feat
This Reddit discussion explores users' observations about ChatGPT's ability to interpret and respond appropriately to poorly formatted, unclear, or incomplete prompts. The thread likely contains commu
arXiv:2508.05269v2 Announce Type: replace Abstract: Understanding dynamic outdoor environments requires capturing complex object interactions and their evolution over time. LiDAR-based 4D point clouds
arXiv:2605.12028v1 Announce Type: new Abstract: We describe our system for SemEval-2026 Task 8 (MTRAGEval), participating in Task A (Retrieval) across four English-language domains. Our approach emplo
arXiv:2605.11365v1 Announce Type: cross Abstract: Automated systems built on artificial intelligence (AI) are increasingly deployed across high-stakes domains, raising critical concerns about fairness
arXiv:2605.11362v1 Announce Type: new Abstract: In the data-driven era, large-scale datasets are routinely collected and analyzed using machine learning (ML) and artificial intelligence (AI) to inform
arXiv:2605.12496v1 Announce Type: new Abstract: Autoregressive video generation aims at real-time, open-ended synthesis. Yet, cinematic storytelling is not merely the endless extension of a single sce
arXiv:2605.11504v1 Announce Type: new Abstract: Recent advances in Large Language Models (LLMs) have enabled agentic systems for complex, multi-step tasks; cybersecurity is emerging as a prominent app
arXiv:2605.11153v1 Announce Type: new Abstract: We decompose an evolutionary mixture-of-LoRA system on a from-scratch ~150M-parameter widened-D substrate (D=1536, V=32000; D/V approx 0.048; the 'widen
arXiv:2502.20209v3 Announce Type: replace Abstract: In this paper, a novel dataset is introduced, designed to assess student attention within in-person classroom settings. This dataset encompasses RGB
arXiv:2605.11683v1 Announce Type: new Abstract: Vision Transformers (ViTs) incur significant computational overhead due to the quadratic complexity of self-attention relative to the token sequence len
arXiv:2605.12140v1 Announce Type: new Abstract: Myocardial point tracking (MPT) has recently emerged as a promising direction for motion estimation in echocardiography, driven by advances in general-p
arXiv:2605.12196v1 Announce Type: new Abstract: Accurate ultra-short-term wind power forecasting is critical for grid dispatch and reserve management, yet remains challenging due to the non-stationary
arXiv:2605.11752v1 Announce Type: new Abstract: Federated learning relies on effective client selection to alleviate the performance degradation caused by data heterogeneity. Most existing methods ass
arXiv:2605.12374v1 Announce Type: new Abstract: Visual latent reasoning lets a multimodal large language model (MLLM) create intermediate visual evidence as continuous tokens, avoiding external tools
arXiv:2605.11869v1 Announce Type: new Abstract: While the overall inference latency of Video Diffusion Transformers (DiTs) can be substantially reduced through model distillation, per-step inference l