Knowledge catalogue
arXiv:2408.08686v4 Announce Type: replace-cross Abstract: Language Models (LMs) have been widely used in recommender systems to incorporate textual information of items into item IDs, leveraging their
arXiv:2604.23106v1 Announce Type: cross Abstract: Existing multi-agent Large Language Model (LLM) frameworks for code generation typically use execution feedback and improve iteratively using Input/Ou
Nvidia Corp. today launched a powerful reasoning artificial intelligence model that unifies text, vision and speech, capable of acting as the “brains” of faster, smarter agentic AI applications. Dubbe
arXiv:2512.20298v2 Announce Type: replace-cross Abstract: Growing reliance on LLMs for psychiatric self-assessment raises questions about their ability to interpret qualitative patient narratives. Thi
arXiv:2505.01700v3 Announce Type: replace Abstract: Existing protein-ligand docking studies typically focus on the self-docking scenario, which is less practical in real applications. Moreover, some s
arXiv:2604.24154v1 Announce Type: cross Abstract: The Universal Approximation Theorem (UAT) guarantees universal function approximation but does not explain how residual models distribute approximatio
arXiv:2604.23238v1 Announce Type: cross Abstract: Frontier models push the boundaries of what is learnable at extreme computational costs, yet distillation via sampling reasoning traces exposes closed
arXiv:2604.22760v1 Announce Type: cross Abstract: Large language models (LLMs) increasingly operate as autonomous agents that reason over external APIs to perform complex tasks. However, their reliabi
arXiv:2602.07096v2 Announce Type: replace-cross Abstract: Reliable financial reasoning requires knowing not only how to answer, but also when an answer cannot be justified. In real financial practice,
arXiv:2604.23824v1 Announce Type: new Abstract: Automatic induction of high-quality dictionaries is essential for building lexical resources, yet low-resource languages and dialects pose several chall
arXiv:2604.22939v1 Announce Type: cross Abstract: While the next-token prediction (NTP) paradigm enables large language models (LLMs) to express their intrinsic knowledge, its sequential nature constr
arXiv:2604.24109v1 Announce Type: new Abstract: Semi-supervised learning (SSL) has become a promising solution to alleviate the annotation burden of deep learning-based medical image segmentation mode
arXiv:2603.25111v2 Announce Type: replace Abstract: Recent advances have shown the effectiveness of self-evolving LLM agents on tasks such as program repair and scientific discovery. In this paradigm,
arXiv:2604.24594v1 Announce Type: cross Abstract: As large language models (LLMs) evolve into agentic problem solvers, they increasingly rely on external, reusable skills to handle tasks beyond their
arXiv:2604.24003v1 Announce Type: new Abstract: Large language models (LLMs) achieve strong reasoning performance by allocating substantial computation at inference time, often generating long and ver
arXiv:2603.26380v2 Announce Type: replace Abstract: The attention mechanism has been the core component in modern transformer architectures. However, the computation of standard full attention scales
arXiv:2604.23806v1 Announce Type: cross Abstract: The reverse process in score-based diffusion models is formally equivalent to overdamped Langevin dynamics in a time-dependent energy landscape. In ou
arXiv:2509.25346v2 Announce Type: replace Abstract: Predicting cellular responses to genetic perturbations represents a fundamental challenge in systems biology, critical for advancing therapeutic dis
arXiv:2601.21048v2 Announce Type: replace Abstract: Unsupervised neural combinatorial optimization (NCO) enables learning powerful solvers without access to ground-truth solutions. Existing approaches
The new LLM trained only on pre-1931 text is small enough that it can potentially run on device, so, with the right tools, you can get a fully vintage version of Siri, but from the era of Downton Abbe
arXiv:2604.24668v1 Announce Type: new Abstract: Given the increased use of LLMs in financial systems today, it becomes important to evaluate the safety and robustness of such systems. One failure mode
NVIDIA Nemotron-3 Nano Omni is now available to try through Together AI's platform, offering access to a compact multimodal model capable of processing both text and audio inputs. This announcement hi
Welcome to the agentic era! Last week, leaders from our public sector customer and partner ecosystem took the stage at Google Cloud Next to share how they are leveraging AI and agents to scale their i
arXiv:2604.22774v1 Announce Type: cross Abstract: Accurate transcription of handwritten mathematics is crucial for educational AI systems, yet current benchmarks fail to evaluate this capability prope
arXiv:2604.24764v1 Announce Type: new Abstract: Recent video foundation models demonstrate impressive visual synthesis but frequently suffer from geometric inconsistencies. While existing methods atte
arXiv:2604.22261v1 Announce Type: new Abstract: Large language models (LLMs) struggle with relation completion (RC), both with and without retrieval-augmented generation (RAG), particularly when the r
arXiv:2604.22046v1 Announce Type: cross Abstract: Large language models (LLMs) have recently shown strong potential for generating project-level unit tests. However, existing state-of-the-art approach
arXiv:2603.10377v2 Announce Type: replace-cross Abstract: Sparse autoencoders can localize where concepts live in language models, but not how they interact during multi-step reasoning. We propose Cau
arXiv:2604.22281v1 Announce Type: new Abstract: Recent advances in vision-language models have demonstrated remarkable performance across diverse multi-modal tasks, including document question answeri
FLUX 2 Dev is an open-weight, 32-billion-parameter AI model developed by Black Forest Labs for text-to-image generation and advanced image editing . The model is available in ComfyUI and Diffusers fra
arXiv:2604.22750v1 Announce Type: new Abstract: The wide adoption of AI agents in complex human workflows is driving rapid growth in LLM token consumption. When agents are deployed on tasks that requi
Lately I've been having fun with running coding agents fully locally. The setup I landed on is: - Pi agent - Gemma 4 26B A4B - Server of choice: LM Studio/Ollama/llama.cpp I wrote a step-by-step guide
arXiv:2604.22056v1 Announce Type: new Abstract: Optimal wireless transmitter placement is a central task in radio-network planning, yet exhaustive search becomes prohibitively expensive at scale. This
arXiv:2604.22212v1 Announce Type: cross Abstract: In spite of the utility of 3-D electron back-scattered diffraction (EBSD) microscopy, the data collection process can be time-consuming with serial-se
arXiv:2604.22168v1 Announce Type: new Abstract: Here, we explore the problem of error propagation mitigation in modular digital twins as a sequential decision process. Building on a companion study th
arXiv:2604.22074v1 Announce Type: new Abstract: Reinforcement Learning from Verifiable Rewards (RLVR) on chain-of-thought reasoning has become a standard part of language model post-training recipes.
arXiv:2603.26041v3 Announce Type: replace Abstract: In recent years, GUI visual agents built upon Multimodal Large Language Models (MLLMs) have demonstrated strong potential in navigation tasks. Howev
This post documents a technical demonstration of running the large Qwen3.5-397B-A17B model across distributed hardware using llama.cpp with advanced networking protocols. The approach leverages 4-bit
arXiv:2511.17388v2 Announce Type: replace Abstract: Position information is essential for language modeling. In softmax transformers, Rotary Position Embeddings (extit{RoPE}) encode positions through
arXiv:2604.22027v1 Announce Type: cross Abstract: One of the most common complaints about large language models (LLMs) is their prompt sensitivity -- that is, the fact that their ability to perform a
arXiv:2604.22409v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) have advanced static visual--spatial reasoning, yet they often fail to preserve long-horizon spatial coherence
arXiv:2604.22753v1 Announce Type: new Abstract: Scaling laws are used to plan multi-million-dollar training runs, but fitting those laws can itself cost millions. In modern large-scale workflows, asse
arXiv:2509.22630v3 Announce Type: replace-cross Abstract: Recurrent neural networks (RNNs), such as linear attention and state-space models, have gained popularity due to their constant per-token comp
arXiv:2604.22337v1 Announce Type: new Abstract: Most tabular-data generators match marginal statistics yet ignore causal structure, leading downstream models to learn spurious or unfair patterns. We p
arXiv:2505.20435v3 Announce Type: replace-cross Abstract: Existing interpretability methods for Large Language Models (LLMs) predominantly capture linear directions or isolated features. This overlook
This Reddit post discusses the challenge of manually editing tag files (.txt) when training a LoRA (Low-Rank Adaptation) model for Illustrious, noting that while CivitAI's LoRA trainer offers a conven
arXiv:2604.22273v1 Announce Type: new Abstract: Iterative self-correction is widely used in agentic LLM systems, but when repeated refinement helps versus hurts remains unclear. We frame self-correcti
arXiv:2604.22102v1 Announce Type: cross Abstract: Many robotic tasks are unforgiving; a single mistake in a dynamic throw can lead to unacceptable delays or unrecoverable failure. To mitigate this, we
Sebastian Raschka has updated his LLM architecture gallery with higher resolution figures and improved summaries, including coverage of the DeepSeek V4 model architecture. This resource provides visua
Qwen's latest model improvements focus on achieving more consistent and uniform performance quality across different artistic styles and aesthetic domains, reducing the variability in output quality t
Since GPT-5.4, we’ve unified Codex and the main model into a single system, so there’s no separate coding line anymore. GPT-5.5 takes this further, with strong gains in agentic coding, computer use, a
arXiv:2505.09971v3 Announce Type: replace Abstract: Airborne laser scanning (ALS) point cloud semantic segmentation is a fundamental task for large-scale 3D scene understanding. Fixed models deployed
arXiv:2601.11194v2 Announce Type: replace Abstract: We suggest a new multi-modal algorithm for joint inference of paired structurally aligned samples with Rectified Flow models. While some existing me
arXiv:2604.21724v1 Announce Type: new Abstract: Large token-indexed lookup tables provide a compute-decoupled scaling path, but their practical gains are often limited by poor parameter efficiency and
arXiv:2604.21260v1 Announce Type: cross Abstract: We study semisupervised mean estimation with a small labeled sample, a large unlabeled sample, and a black-box prediction model whose output may be mi
arXiv:2506.21546v4 Announce Type: replace-cross Abstract: Segmentation Vision-Language Models (VLMs) have significantly advanced grounded visual understanding, yet they remain prone to pixel-grounding
arXiv:2604.21469v1 Announce Type: new Abstract: Automating the detection of regulatory compliance remains a challenging task due to the complexity and variability of legal texts. Models trained on one
arXiv:2604.21428v1 Announce Type: new Abstract: Modern large-scale language model pre-training relies heavily on the single program multiple data (SPMD) paradigm, which requires tight coupling across
DeepSeek-V4-Pro is a Mixture-of-Experts language model with 1.6 trillion total parameters and 49 billion activated per token, supporting a 1 million token context length. Released under the MIT Licens
Raphael Satter / Reuters: Diplomatic cable: US State Department has ordered a global push to bring attention to what it says are efforts by Chinese companies to steal IP from US AI labs — The U.S. Sta