Knowledge catalogue
arXiv:2608.23149v1 Announce Type: cross Abstract: The alignment of Large Language Models heavily relies on English-centric high-quality preference data, which often leads to suboptimal performance in
arXiv:2508.14537v2 Announce Type: replace Abstract: Whole slide images (WSIs) in computational pathology pose a major computational challenge due to their gigapixel scale, often requiring tens to hund
arXiv:2608.21638v1 Announce Type: new Abstract: Accurate and reliable collection of electricity consumption data through Advanced Metering Infrastructure (AMI) is of great importance for the operation
arXiv:2608.22879v1 Announce Type: new Abstract: Task-specific lightweight models for surgical phase recognition excel at capturing temporal dynamics but generalize poorly under domain shift. Conversel
arXiv:2606.14782v3 Announce Type: replace-cross Abstract: Multimodal Large Language Models (MLLMs) achieve strong vision-language reasoning but incur large KV caches and high decoding latency with lon
arXiv:2608.22403v1 Announce Type: new Abstract: Human video is playing an increasingly central role in training World Action Models (WAMs), owing to its diversity and low collection cost relative to t
NVIDIA is bringing the next wave of RTX gaming to the Gamescom conference running this week in Cologne, Germany, with support for new games, anti-cheat technologies and increased visual quality. Elect
arXiv:2608.22284v1 Announce Type: cross Abstract: Deep Reinforcement Learning (DRL) has achieved significant success in complex decision-making problems. As DRL systems are increasingly deployed in re
arXiv:2608.22631v1 Announce Type: new Abstract: Terminal agents are a compelling application of large language models (LLMs), with the potential to integrate deeply into users' daily workflows. Reinfo
arXiv:2608.22102v1 Announce Type: cross Abstract: We present GCA (Gaussian Constitutive Alignment), a framework for learning implicit constitutive laws from monocular dynamic video of deformable objec
arXiv:2403.18494v2 Announce Type: replace Abstract: We investigate the learning dynamics of fully-connected neural networks through the lens of the neural gradient signal-to-noise ratio (SNR), examini
arXiv:2412.00776v5 Announce Type: replace Abstract: Continual learning (CL) learns from a non-stationary data stream without storing or re-training on all seen samples. Meta-continual learning (MCL) c
arXiv:2608.22142v1 Announce Type: new Abstract: This paper addresses the issue of self-intersecting trajectories (in phase space) in industrial reduced-order modeling and proposes the Latent-Augmented
arXiv:2608.22500v1 Announce Type: new Abstract: At the heart of composed visual data retrieval is the fusion of a reference visual input and a textual modification into a single query. While current s
arXiv:2608.23206v1 Announce Type: new Abstract: We study spherical occupancy profiles-the ray-wise occupancy probability profiles P(r) = T(r) o(r) distilled from multi-view 3D Gaussian reconstructions
arXiv:2608.22765v1 Announce Type: new Abstract: Coupled-dynamics environments expose the one-step outcomes that would follow from several possible counterfactual actions under a common realization of
arXiv:2608.21762v1 Announce Type: cross Abstract: Vision-language models (VLMs) fail many detail-centric questions for a concrete reason: the answer is visible in the image, yet lost after the image i
arXiv:2307.09295v3 Announce Type: replace Abstract: We study a ranking and selection problem of learning from choice-based feedback with dynamic assortments. In this problem, a company sequentially di
arXiv:2602.17493v2 Announce Type: replace-cross Abstract: We develop a method for training neural networks on Boolean data in which the values at all nodes are strictly pm 1, and the resulting models
arXiv:2505.14240v3 Announce Type: replace Abstract: Integrating combinatorial optimization layers into neural networks has recently attracted significant research interest. However, many existing appr
arXiv:2608.22274v1 Announce Type: cross Abstract: Machine translation tests masked diffusion language models (dLLMs) because every source token must be rendered faithfully, while fixed canvas decoding
arXiv:2502.14643v3 Announce Type: replace Abstract: Direct Preference Optimization (DPO) is a widely adopted offline algorithm for preference-based reinforcement learning from human feedback (RLHF), d
arXiv:2608.21501v1 Announce Type: new Abstract: Credit assignment in large-language-model reinforcement learning (LLM RL) can be separated into three objects: evidence about success, a transport opera
arXiv:2608.22832v1 Announce Type: new Abstract: The social interactions among crowds via extit{Danmaku} (a.k.a., bullet comments) on modern multimedia platforms can facilitate both viewpoint conflicts
arXiv:2608.21714v1 Announce Type: new Abstract: Recent page-image retrievers such as ColPali have improved retrieval over visually rich documents, yet little is known about how they behave in cross-li
arXiv:2608.23219v1 Announce Type: new Abstract: Vehicular traffic is a major source of air pollution; however, the contribution of remotely acquired traffic information to local machine-learning (ML)
arXiv:2608.21794v1 Announce Type: cross Abstract: GUI grounding evaluations that expose UI elements as text metadata often treat high instruction-element embedding similarity as evidence of semantic g
arXiv:2608.22140v1 Announce Type: cross Abstract: Large Language Models (LLMs) achieve strong reasoning performance, but their robustness to realistic lexical corruption remains poorly understood. We
arXiv:2512.10294v2 Announce Type: replace Abstract: We propose Conformal Lie-group Action Prediction Sets (CLAPS), a symmetry-aware conformal prediction-based algorithm that constructs, for a given ac
arXiv:2608.21676v1 Announce Type: new Abstract: Robotic systems are traditionally designed and deployed in static configurations, with assumptions made at design-time becoming immutable constraints du
arXiv:2608.22648v1 Announce Type: cross Abstract: As an increasing number of critical applications, including environmental, emergency, meteorological, and agricultural, rely on real-time anomaly dete
arXiv:2509.04226v2 Announce Type: replace Abstract: State space models (SSMs) are an emerging paradigm that achieves linear-time scaling, however, they intrinsically suffer from 'curse of memory'. The
arXiv:2608.22370v1 Announce Type: new Abstract: Task-specific LoRA adapters offer a modular way to specialize large language and vision-language models. However, existing adapter composition methods a
arXiv:2608.21764v1 Announce Type: cross Abstract: Event-based vision has emerged as a promising paradigm for energy-aware artificial intelligence (AI), offering sparse, low-latency visual signals that
arXiv:2608.23124v1 Announce Type: cross Abstract: Personalized text generation for authors and literary writing is essential for applications such as adaptive writing assistants, creative support tool
arXiv:2608.21374v1 Announce Type: new Abstract: Literature reviews are essential to scientific progress, but rigorously evaluating automatically generated reviews remains difficult because many aspect
arXiv:2608.22124v1 Announce Type: new Abstract: LLM-assisted writing is often treated as a detection problem, as it raises questions about clarity, integrity, equity, and evaluation. An analysis of 69
arXiv:2601.16890v2 Announce Type: replace-cross Abstract: Automated fact-checking (AFC) systems are susceptible to adversarial attacks, enabling false claims to evade detection. Existing adversarial f
arXiv:2608.23058v1 Announce Type: new Abstract: Large language models (LLMs) now support forecasting systems that combine language-based reasoning with temporal data, evidence retrieval, external tool
arXiv:2608.22731v1 Announce Type: new Abstract: Nonverbal behavior generation systems for virtual agents often take an utterance as input and generate nonverbal behaviors that emphasize or illustrate
arXiv:2608.22295v1 Announce Type: cross Abstract: Evaluation of large language models (LLMs) increasingly requires predicting how a model will perform on new questions or tasks before collecting large
Hi, I’m having fun using “huihui_ai/Qwen3.6-abliterated:35b-Claude-4.7” for pen testing. It’s uncensored so it does whatever I want. I’m on M4 Pro 14 core 64GB. Just wondering if there are some models
arXiv:2608.22993v1 Announce Type: new Abstract: Students increasingly use LLMs as tutors for coursework and problem solving. Little is known about the level of assistance LLMs provide when students us
arXiv:2510.11358v3 Announce Type: replace-cross Abstract: Retrieval-augmented generation (RAG) is typically optimized for topical relevance, yet its success ultimately depends on whether retrieved pas
arXiv:2608.21836v1 Announce Type: new Abstract: Large language models have become increasingly capable agents for low-level code and kernel optimization, but isolated kernel benchmarks provide only a
arXiv:2608.21858v1 Announce Type: cross Abstract: The rapid evolution of energy structures has positioned microgrids as pivotal components of next-generation power systems, offering enhanced resilienc
arXiv:2608.22417v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used to support text analysis in qualitative research, yet evidence on their performance in inductive cont
Hey everyone, I'm trying to set up a local LLM on my machine. My goal is to use it with Qwen Code or OpenCode to help me out with small to medium sized tasks, but nothing too big. I tested many differ
arXiv:2607.16327v2 Announce Type: replace Abstract: Medical image segmentation is essential for modern computer-aided medicine. Recently, text-guided segmentation has shown promise by incorporating cl
arXiv:2608.23383v1 Announce Type: new Abstract: Video generation is progressing beyond isolated clips toward long-form narratives and interactive worlds, requiring models to preserve identities, follo
arXiv:2607.14192v3 Announce Type: replace Abstract: As recommender systems mature in the past few years, their optimization objectives have evolved from a primary focusing on short-term behavioral sig
arXiv:2608.23200v1 Announce Type: new Abstract: Large language models are increasingly expected to execute complex workflows whose success depends on maintaining interdependent constraints and produci
arXiv:2602.00612v3 Announce Type: replace Abstract: Diffusion Large Language Models (dLLMs) have demonstrated promising generative capabilities and are increasingly used to produce formal languages de
arXiv:2608.23090v1 Announce Type: new Abstract: Looping videos are essential for practical applications such as web graphics, game development, and social media. However, existing approaches typically
arXiv:2608.22145v1 Announce Type: new Abstract: Across implicit-neural-representation (INR) architectures and analytic benchmarks we observe that a thoroughly tuned Adam (especially its learning rate
arXiv:2608.21561v1 Announce Type: new Abstract: Fisher width measures the Gaussian width of a probe set after deformation by the local Fisher geometry. We study its evolution along learning trajectori
arXiv:2608.22723v1 Announce Type: new Abstract: This workshop paper comprehensively reviews the First Challenge on Unified Removal of Raindrops and Reflections. The challenge aims to address a frequen
arXiv:2608.22764v1 Announce Type: new Abstract: Joint-embedding predictive architectures (JEPAs) learn latent dynamics for planning and avoid representation collapse by matching features to maximum-en
arXiv:2608.22035v1 Announce Type: new Abstract: Robot foundation models have substantially advanced perception and control, but natural human-robot collaboration requires more than executing isolated
arXiv:2608.22465v1 Announce Type: new Abstract: High-fidelity free-viewpoint video (FVV) and interactive rendering increasingly rely on explicit Gaussian representations, yet practical deployment rema