Knowledge catalogue
arXiv:2410.05985v4 Announce Type: replace Abstract: The increasing size of deep learning models has made distributed training across multiple devices essential. Synchronous, centralized methods incur
arXiv:2606.21821v1 Announce Type: new Abstract: Understanding the causal structure of a language model's thought process is a problem of significant importance for both transparency and safety. In thi
arXiv:2606.22437v1 Announce Type: new Abstract: We conduct a systematic study of 18 widely used vision-language benchmarks and identify three major issues: 1) many items do not rely on visual cues and
arXiv:2501.09731v2 Announce Type: replace-cross Abstract: We establish a formal connection between the decades-old surrogate outcome model in biostatistics and economics and the emerging field of pred
arXiv:2603.01949v2 Announce Type: replace Abstract: Dominant approaches for modelling Partial Differential Equations (PDEs) rely on deterministic predictions, yet many physical systems of interest are
arXiv:2606.23104v1 Announce Type: new Abstract: On-policy distillation (OPD) improves LLM reasoning by training a student model on its own generated outputs, but standard OPD treats all student-genera
arXiv:2606.22840v1 Announce Type: new Abstract: We present RLM-Cascade, a proxy-layer system that applies speculative decoding at the response level to reduce LLM API costs without requiring model arc
arXiv:2606.18774v2 Announce Type: replace Abstract: We present RouteJudge, an online pairwise preference evaluation framework for LLM routing systems, with a public platform available at https://route
arXiv:2606.22210v1 Announce Type: new Abstract: One of the powerful techniques in data modeling is accounting for features that are available at the training stage, but are not available when the trai
arXiv:2602.22747v2 Announce Type: replace Abstract: Epistemic uncertainty in neural networks is commonly modeled using two second-order paradigms: distribution-based representations, which rely on pos
arXiv:2606.20709v1 Announce Type: new Abstract: Given a content reference and a style reference, content-preserving style transfer requires the model to generate stylized outputs with content and styl
arXiv:2606.22370v1 Announce Type: new Abstract: Recent advances in video generation have made minute-level synthesis possible; however, generating long videos remains challenging due to error accumula
arXiv:2606.22976v1 Announce Type: new Abstract: In this paper, we propose using random walks on graphs as a verifiable sandbox to study different parallel sampling strategies in masked diffusion model
arXiv:2606.21847v1 Announce Type: new Abstract: Low-rank decomposition serves as a promising compression paradigm for large language models, however, rank allocation remains challenging: manual rules
Human intelligence is fundamentally a collective intelligence. We solve complex problems by participating in a vast cultural network that builds upon ideas across generations. I believe the strongest
Try it out! We are seeing amazing results with GLM 5.2! it is indeed quite good! don't try it in claude code/codex - those harnesses are overly tuned for their proprietary models dcode (deepagents cod
arXiv:2606.12040v1 Announce Type: new Abstract: The design of reinforced concrete highway barriers is a safety-critical process that requires strict compliance with regulatory provisions such as the A
arXiv:2606.11961v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used as conditional generators for structured data, relying on in-context learning (ICL) to adapt to new
arXiv:2606.11195v1 Announce Type: cross Abstract: Large language models (LLMs) have transformed how humans access information, but not how we reason with it. Their fluency accelerates consumption whil
arXiv:2606.11440v1 Announce Type: new Abstract: Existing multi-agent LLM orchestration methods, ranging from brute-force ensembles to learned routers, select models and topologies based on task and mo
arXiv:2606.11676v1 Announce Type: cross Abstract: Traditional wildfire models rely on rigid, low-dimensional parameters and static fuel maps, frequently underpredicting fire spread. To address this we
arXiv:2606.12364v1 Announce Type: new Abstract: Transformers dominate modern sequence modeling, but their quadratic attention incurs substantial computational cost. Subquadratic architectures offer a
arXiv:2606.11689v1 Announce Type: new Abstract: Composed Image Retrieval (CIR) constitutes a pivotal paradigm requiring models to perform joint reasoning on reference images and modification texts. Ho
This post discusses running Google's Gemma 4 QAT (Quantized Aware Training) 12B model on a GPU with 8GB of memory while maintaining a 16k token context window. The author likely shares performance ben
arXiv:2606.11244v1 Announce Type: cross Abstract: Efficient large language model (LLM) serving is increasingly constrained by deployment cost. Quantization is a key technique for reducing serving cost
arXiv:2412.09023v3 Announce Type: replace Abstract: Channel and spatial attention mechanisms introduced in earlier work enhance the representational capabilities of deep convolutional neural networks
arXiv:2606.12387v1 Announce Type: cross Abstract: Large Language Models (LLMs) have democratized database access through Text-to-SQL, but moving from prototypes to production remains difficult. Real d
arXiv:2512.22088v3 Announce Type: replace-cross Abstract: The scaling law, a cornerstone of Large Language Model (LLM) development, predicts improvements in model performance with increasing computati
arXiv:2511.21594v3 Announce Type: replace Abstract: Large language models (LLMs) achieve state-of-the-art results across many natural language tasks, but their internal mechanisms remain difficult to
arXiv:2601.22025v2 Announce Type: replace-cross Abstract: Evaluating Large Language Model (LLM) applications differs from conventional software testing because outputs are probabilistic, semantically
arXiv:2606.10493v1 Announce Type: cross Abstract: Local deployment of large Mixture-of-Experts (MoE) models falls short of the service quality achieved in cloud-scale environments, even under low-conc
As believers of open research, we are disappointed to see Anthropic silently degrading Fable 5 for AI development 'Any topic related to building pretraining pipelines, distributed training infrastruct
arXiv:2505.23851v2 Announce Type: replace-cross Abstract: Large language models (LLMs) are increasingly applied to symbolic mathematics, yet existing evaluations often conflate pattern memorization wi
arXiv:2606.11033v1 Announce Type: cross Abstract: Recent efforts to extend large language models (LLMs) to speech inputs typically rely on cascaded ASR-LLM pipelines, end-to-end speech-language models
arXiv:2606.10803v1 Announce Type: cross Abstract: Multimodal Large Language Models (MLLMs) excel at utilizing digital APIs and increasingly serve as the 'brain' of embodied AI, instructing robots to i
arXiv:2606.10401v1 Announce Type: new Abstract: Spatial intelligence is a key frontier for multimodal large language models (MLLMs), enabling them to reason about the physical world from visual experi
arXiv:2606.10479v1 Announce Type: new Abstract: Combinatorics is central to Olympiad-level mathematical problem solving, requiring deep discrete reasoning, creative constructions, and rigorous structu
arXiv:2601.18026v2 Announce Type: replace Abstract: Language identification (LID) is a fundamental step in curating multilingual corpora. However, LID models still perform poorly for many languages, e
arXiv:2606.09930v1 Announce Type: cross Abstract: The boundary between program execution and gradient-based optimization has long limited the use of code itself as a learnable scientific model. We pre
arXiv:2506.14753v3 Announce Type: replace Abstract: Diffusion models are well known for their ability to generate a high-fidelity image for an input prompt through an iterative denoising process. Unfo
arXiv:2606.10380v1 Announce Type: cross Abstract: Real-world crisis intervention is inherently conversational, yet existing research largely focuses on static texts.Real-world crisis intervention is i
arXiv:2506.09171v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) are increasingly capable, but LLM agents still struggle to plan effectively in interactive, partially observable,
arXiv:2606.10906v1 Announce Type: cross Abstract: We study models for human-AI teaming through the lens of statistical calibration. We assume the team consists of an AI model and human -- both of whic
arXiv:2606.10651v1 Announce Type: new Abstract: We introduce Kwai Keye-VL-2.0-30B-A3B, an open-source Mixture-of-Experts (MoE) multimodal foundation model designed to advance long-video understanding
arXiv:2606.10246v1 Announce Type: cross Abstract: Maliciously-created fake speech, including deepfaked and spoofed audio, is proliferating at an alarming rate, and detection models are racing to stay
arXiv:2606.09865v1 Announce Type: new Abstract: Privacy and data sharing are often in tension. Many organizations use synthetic data to reduce privacy risk and still share useful data. For tabular dat
arXiv:2606.10173v1 Announce Type: cross Abstract: As AI systems move into operating systems, privacy no longer turns only on whether a model runs locally. A local assistant may assemble email, calenda
arXiv:2606.10304v1 Announce Type: new Abstract: When LLM agents are coerced into covertly encoding sensitive data (Base64, ROT13, acrostic, synonym chains, and beyond), the resulting outputs evade out
arXiv:2606.10890v1 Announce Type: cross Abstract: Post-training quantization (PTQ) compresses large language models by mapping weights to low-bit representations. The scaling factor that defines the q
arXiv:2606.11169v1 Announce Type: cross Abstract: Large-scale model training increasingly relies on composing multiple parallelism strategies, such as data, pipeline, and expert parallelism, together
arXiv:2606.10725v1 Announce Type: cross Abstract: Background. Atrial fibrillation (AF) is the most prevalent cardiac arrhythmia and a major determinant of prognosis. Established AF risk scores rely on
Easy solution to slow down recursive AI self improvement: The lab with the top-ranked model must agree THEY must not use it for working on frontier AI But everyone else should have access to it. By de
arXiv:2602.12424v2 Announce Type: replace-cross Abstract: Benchmarks establish a standardized evaluation framework to systematically assess the performance of large language models (LLMs), facilitatin
arXiv:2606.09868v1 Announce Type: cross Abstract: As Multimodal Large Language Models (MLLMs) face growing privacy risks and regulatory constraints, machine unlearning (MU) has emerged as a crucial so
arXiv:2606.09844v1 Announce Type: cross Abstract: Large Language Models (LLMs) alter their privacy behavior based on the perceived identity of their interlocutor. While safety mechanisms typically pre
arXiv:2606.10080v1 Announce Type: cross Abstract: Generative models have shown remarkable progress in a variety of domains such as protein design, but such power enables the opaque generation of hazar
arXiv:2510.26307v3 Announce Type: replace-cross Abstract: Anomaly detection is a critical task in cybersecurity, where identifying insider threats, access violations, and coordinated attacks is essent
arXiv:2606.08802v1 Announce Type: new Abstract: Standard flow and diffusion pre-training matches the distribution of available data (e.g., molecules), which often covers only a small fraction of the v
arXiv:2606.09682v1 Announce Type: new Abstract: AutoMegaKernel (AMK) compiles a HuggingFace Llama-family model into a single persistent cooperative CUDA kernel that runs the whole forward pass in one
arXiv:2606.05797v2 Announce Type: replace Abstract: Longitudinal treatment decisions from multivariate time-series data require predicting potential outcomes under future treatment sequences in the pr