Knowledge catalogue
arXiv:2607.08690v1 Announce Type: cross Abstract: Speculative decoding accelerates sampling from an autoregressive LLM by using a faster auxiliary model to draft tokens which are then verified in para
arXiv:2512.21414v2 Announce Type: replace Abstract: Recent tool-use frameworks powered by vision-language models (VLMs) improve image understanding by grounding model predictions with specialized tool
arXiv:2503.15414v3 Announce Type: replace-cross Abstract: Federated learning seeks to foster collaboration among distributed clients while preserving the privacy of their local data. Traditional feder
arXiv:2607.08255v1 Announce Type: new Abstract: Large language models increasingly serve as teachers generating training data for smaller students. Prior multi-teacher knowledge distillation methods m
arXiv:2602.17686v4 Announce Type: replace-cross Abstract: Distilling Chain-of-Thought (CoT) reasoning from large language models into compact student models presents a fundamental challenge: teacher r
arXiv:2607.07852v1 Announce Type: cross Abstract: Automated segmentation of cervical-spine MRI is increasingly used in clinical workflows, yet no fairness audit exists for this anatomy. We show that a
arXiv:2607.07993v1 Announce Type: new Abstract: Identifying faithfulness hallucinations in LLM-generated outputs remains challenging due to the scarcity of high-quality annotated data. Recent work rel
arXiv:2607.08705v1 Announce Type: new Abstract: Rapid advancements in video diffusion models and temporal editing tools have enabled the generation of highly realistic human-centric videos, posing unp
arXiv:2509.26076v2 Announce Type: replace Abstract: As the mathematical capabilities of large language models (LLMs) improve, it becomes increasingly important to evaluate their performance on researc
arXiv:2607.08016v1 Announce Type: new Abstract: Video relighting requires balancing long-form temporal consistency with a physically grounded understanding of light transport, which depends on accurat
arXiv:2607.07745v1 Announce Type: new Abstract: While accuracy, robustness, and calibration are all essential for reliable neural networks, they are often studied separately; developing models that sa
arXiv:2604.05182v2 Announce Type: replace-cross Abstract: We introduce the Large Sparse Reconstruction Model to study how scaling transformer context windows affects feed-forward 3D reconstruction. Al
arXiv:2607.08679v1 Announce Type: new Abstract: Diabetic Retinopathy (DR) is a leading cause of preventable blindness worldwide, requiring automated lesion segmentation using deep learning models for
This post discusses positive experiences with GPT-5.6's capabilities in verification, advisory functions, and high-level orchestration tasks, suggesting the model performs better than expected in thes
arXiv:2607.08423v1 Announce Type: new Abstract: The rapid integration of Large Vision-Language Models (VLMs) into critical infrastructure promises to revolutionize personalized healthcare and dietary
arXiv:2607.08763v1 Announce Type: cross Abstract: Reasoning has become a core capability for large models, especially when reliable decisions require understanding logical consequences. Recent video g
arXiv:2502.15543v4 Announce Type: replace-cross Abstract: Large language models (LLMs) integrated with retrieval-augmented generation (RAG) have improved factuality by grounding outputs in external ev
arXiv:2607.08066v1 Announce Type: new Abstract: Chain-of-thought (CoT) monitoring is a promising safety mechanism for AI agents, based on the premise that visible reasoning traces can surface misalign
arXiv:2607.07719v1 Announce Type: cross Abstract: Parameter-efficient fine-tuning adapts a large language model to one task cheaply, but across a task sequence LoRA-style methods keep stacking low-ran
arXiv:2607.08402v1 Announce Type: cross Abstract: Large-scale and diverse datasets are needed to train AI models to take real-time decisions for autonomous vehicles (AVs), an intelligent transportatio
arXiv:2607.08456v1 Announce Type: cross Abstract: A model should refuse two different things: answers it would get wrong, and questions it should not answer at all, such as unanswerable ones or ones r
arXiv:2607.07724v1 Announce Type: cross Abstract: Block-sparse attention scales long-context language models by replacing the O(N^2) softmax with a per-query top-k selection over key blocks. This cuto
arXiv:2607.07846v1 Announce Type: new Abstract: VectorizationLLM is a specialized Large Language Model based on Google open-weight LLMs. The model is designed to assist students to learn smart vectori
arXiv:2603.16551v2 Announce Type: replace-cross Abstract: Generative models are increasingly used to augment medical imaging datasets for fairer AI, yet a key assumption often goes unexamined: that ge
Congrats to our llama cousins 🫡🦙 Big day for Ollama! When we started, open models and the open source AI ecosystem were in their early days with few believers. Our belief in open source has never wave
arXiv:2607.07322v1 Announce Type: cross Abstract: Automated crowd counting in Hajj video is difficult not because current models lack capacity, but because the footage violates the assumptions those m
arXiv:2603.17057v2 Announce Type: replace-cross Abstract: Active multi-fidelity surrogate modeling is developed for multi-condition airfoil shape optimization to reduce high-fidelity CFD cost while re
arXiv:2607.07379v1 Announce Type: new Abstract: In agentic scientific machine learning (SciML), large language model (LLM) agents can discover surrogate models and select one by an automated score, ty
arXiv:2602.21556v2 Announce Type: replace Abstract: When designing compound AI systems, a common approach is to query multiple copies of the same model and aggregate the responses to produce a synthes
arXiv:2306.12859v3 Announce Type: replace Abstract: Federated learning is a distributed machine learning technology, which realizes the balance between data privacy protection and data sharing computi
arXiv:2607.06636v1 Announce Type: cross Abstract: Large language models frequently generate code that appears correct on typical inputs yet fails on edge cases, invalid inputs, and other specification
arXiv:2607.06643v1 Announce Type: cross Abstract: Backdoor attacks severely threaten large-scale AI models. When model owners delegate training to external compute providers within a decentralized tra
arXiv:2607.07500v1 Announce Type: cross Abstract: Time series classification (TSC) is dominated by a two-stage paradigm: train a feature encoder -- either from scratch on the target dataset or via pre
arXiv:2607.07038v1 Announce Type: new Abstract: Medical image segmentation models can achieve strong benchmark performance while remaining sensitive to scanner, protocol, and institutional variation.
arXiv:2607.07117v1 Announce Type: cross Abstract: In text-to-image in-context learning (T2I-ICL), a model has to infer a latent compositional pattern from fewshot demonstrations for generating a query
Try @Grok 4.5! Grok 4.5 is the top non-Anthropic model on AA-Briefcase, combining frontier agentic knowledge work capabilities with leading cost and time-efficiency Yesterday @SpaceXAI released Grok 4
arXiv:2607.07236v1 Announce Type: new Abstract: This study investigates the presence and propagation of bias within Neural Networks through a comprehensive multi-level analysis spanning the learned la
We comprehensively benchmarked GPT-5.6 on document understanding. At a high-level there's no change between GPT-5.6 Sol and GPT-5.5 in terms of performance over tables, text, charts, layout, and more.
arXiv:2607.05985v1 Announce Type: new Abstract: This paper presents a black-box evaluation framework to systematically assess the ability of Large Language Models (LLMs) to generate Design Structure M
arXiv:2607.05783v1 Announce Type: new Abstract: Environmental illusions (eg., shadows, reflections, and tire marks) are naturally existing yet overlooked phenomena in real-world driving environments.
arXiv:2607.05405v1 Announce Type: cross Abstract: To interact with users fairly and without stereotyping, AI models must display cultural competency, i.e., the ability to infer and adapt to a user's i
Correct Grok 4.5 goes public tomorrow. Here’s everything that’s known. The model runs on V9, xAI’s new 1.5 trillion parameter foundation, roughly three times the size of the v8-small architecture behi
arXiv:2607.06503v1 Announce Type: new Abstract: Large language model (LLM) agents solving multi-step tasks frequently commit to trajectories that are doomed to fail, yet continue to consume substantia
The agentic AI era is here, transforming how higher education institutions innovate, operate, and fundamentally empower learners, faculty, and researchers. AI agents can deliver unprecedented efficien
I often use Pinokio to download and work with local open source AI tools/models, and one neat thing is that it exposes them seamlessly to Code and Codex. GPT-5.6 actually found my local video models a
arXiv:2503.13212v3 Announce Type: replace Abstract: Alignment between human brain networks and artificial models has become an active research area in vision science and machine learning. A widely ado
arXiv:2607.06127v1 Announce Type: new Abstract: We present LLM4SDM, the first study of open-source smaller language models (OS-sLLMs) for automated assessment of shared decision making (SDM) using the
arXiv:2607.06173v1 Announce Type: new Abstract: Recent advances in video diffusion have been driven by scaling transformer-based architectures to billions of parameters, substantially improving visual
arXiv:2607.05904v1 Announce Type: new Abstract: Training a language model against its own reference-free judgments (the premise of self-rewarding, self-play, and LLM-as-a-judge pipelines) assumes a mo
arXiv:2601.12494v3 Announce Type: replace-cross Abstract: Audio large language models (LLMs) enable unified speech understanding and generation, but adapting them to linguistically complex and dialect
NVIDIA Nemotron 3 Ultra is offering leading performance at lower cost than top closed models with the largest and most widely adopted AI agent orchestration platform. LangChain tuned its Deep Agents h
arXiv:2607.05483v1 Announce Type: cross Abstract: Agentic workflows often operate over shared, structured state. Because LLM context windows are limited, each model invocation is typically shown only
arXiv:2607.06440v1 Announce Type: new Abstract: Recent text-to-image models such as DALLE-3 excel at following diverse prompts yet remain blind to individual aesthetic preferences. We study personaliz
arXiv:2607.06008v1 Announce Type: new Abstract: Large language model (LLM) agents have shown strong performance in long-horizon tasks that require planning, tool use, and interaction with external env
arXiv:2607.06145v1 Announce Type: new Abstract: In this paper, we define the quantity of prompting complexity: for a fixed instruction-tuned language model, what is the shortest plausible prompt that
arXiv:2607.05727v1 Announce Type: new Abstract: Pre-trained Vision-Language Models (VLMs) like CLIP have proven highly effective as foundation models for various downstream applications. However, prom
Software-as-a-service (SaaS) is evolving into Agents-as-a-service (AaaS). Instead of isolated applications, developers are creating AI agents that interoperate using standardized open protocols such a
arXiv:2607.03426v1 Announce Type: cross Abstract: Large language models (LLMs) exhibit strong reasoning and world-knowledge capabilities, yet often struggle to gather information effectively across th
arXiv:2607.03523v1 Announce Type: cross Abstract: Code repair is an important capability for language models (LMs): given a buggy program and unit tests, an LM must produce a fixed program that passes
arXiv:2601.07475v2 Announce Type: replace-cross Abstract: The emergence of fine-grained numerical formats like NVFP4 presents new opportunities for efficient Large Language Model (LLM) inference. Howe