Knowledge catalogue
ChatGPT Desktop (the Codex app) now supports integration with Ollama models, allowing users to run local LLMs within the application. To utilize this feature, simply download or upgrade to Ollama vers
arXiv:2609.11689v1 Announce Type: cross Abstract: Area-based social risk indices summarize residents' socioeconomic conditions but incompletely capture physical features of place that may affect healt
arXiv:2609.09882v1 Announce Type: new Abstract: Language models fine-tuned on customer behavior can predict outcomes and generate explanations, but these readouts are often treated as interchangeable.
arXiv:2609.11687v1 Announce Type: new Abstract: We revisit Kashin-decomposition-based weight quantization for large language models and propose an improved algorithm with stronger convergence properti
arXiv:2609.08071v1 Announce Type: new Abstract: Firms making inventory decisions have access to operational data, optimization tools, and large language models (LLMs). Typically, data characterize the
arXiv:2609.09554v1 Announce Type: new Abstract: We introduce BuzzASR, a collection of language-specialized fine-tuned Whisper models adapted for automatic speech recognition (ASR) in 102 languages. La
arXiv:2609.09586v1 Announce Type: cross Abstract: Time series foundation models (TSFMs) are increasingly pre-trained on synthetically generated time series trajectories, where the data generating proc
arXiv:2609.05782v1 Announce Type: new Abstract: Vision-language models (VLMs) offer a promising alternative to conventional fire detection systems by reasoning about the semantic context of a scene an
arXiv:2609.09486v1 Announce Type: cross Abstract: Fairness auditing of text-to-image diffusion models often requires generating large numbers of images across sampling configurations, making comprehen
arXiv:2609.06647v1 Announce Type: new Abstract: Although professional workflows leverage large language models widely, the interpretation for auditing unconstrained free-text generation is usually int
arXiv:2509.24877v3 Announce Type: replace Abstract: The social science of large language models (LLMs) examines how these systems evoke mind attributions, interact with one another, and transform huma
arXiv:2609.05503v1 Announce Type: new Abstract: Diffusion models have become the dominant family of generative models in the visual domain. However, their widespread public availability enables misuse
arXiv:2609.06880v1 Announce Type: new Abstract: Reasoning over language instructions in embodied tasks such as robotics often requires understanding spatial relations from a speaker's situated perspec
I'm using two different models. One Gemma 4 and the other qwen 3.5. Both of them are responding to my input either by silence ('thought for a moment.... then nothing') or nonsense: <|turn>user <|turn>
arXiv:2609.07596v1 Announce Type: cross Abstract: Vision-language models are increasingly used in settings where some input modalities may be unavailable, yet we know little about whether they can fai
arXiv:2609.07408v1 Announce Type: cross Abstract: This study investigates the classification of individuals as healthy or at risk of Parkinson's disease using machine learning (ML) models, focusing on
arXiv:2609.05889v1 Announce Type: cross Abstract: Resource-exhaustion attacks against autoregressive vision-language models (VLMs) typically assume unimodal threat models, treating the image branch as
arXiv:2609.07286v1 Announce Type: cross Abstract: Large audio-language models (LALMs) can hallucinate audio objects, answering 'yes' to absent sound events, thus undermining reliability in audio quest
I'm looking for more information to expand my creativity around this. I don't really have a realistic idea of what people actually do with local AI yet, I mostly just want to explore the possibilities
arXiv:2609.05178v1 Announce Type: new Abstract: Vision-Language-Action (VLA) or World Action (WAM) models have recently demonstrated remarkable performance in robotic manipulation. On LIBERO, SOTA met
arXiv:2601.04202v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) are increasingly applied to telecom engineering tasks, yet perform poorly on 3GPP specifications. These standards
arXiv:2609.04806v1 Announce Type: new Abstract: Financial large language models are increasingly deployed for summarization of reports and disclosures, where numerical hallucination poses significant
arXiv:2609.04090v1 Announce Type: new Abstract: Current conditioned generative models heavily rely on score functions for guidance during training. When the generative model is a diffusion process wit
arXiv:2609.03892v1 Announce Type: cross Abstract: 3D spatial reasoning underpins understanding and acting in the physical world, yet it remains unreliable in current multimodal large language models (
arXiv:2609.03493v1 Announce Type: new Abstract: Modern vision-language models (VLMs) can directly answer many image-grounded questions, yet they often struggle with complex queries requiring fine-grai
arXiv:2602.01734v2 Announce Type: replace Abstract: Training instability remains a critical challenge in large language model (LLM) pretraining, often manifesting as sudden gradient explosions that wa
arXiv:2605.25784v2 Announce Type: replace Abstract: Multimodal large language models (MLLMs) have advanced rapidly in remote-sensing analysis, yet existing evaluations remain predominantly 2D-centric.
Agentic artificial intelligence security startup Capsule Security Ltd. today released a detection system built on two Nvidia Corp. Nemotron models it fine-tuned itself, in what it calls an “AI circuit
arXiv:2606.17350v2 Announce Type: replace-cross Abstract: Recent advances in large language models (LLMs) have enabled the generation of high-quality prose, yet whether these models are capable of gen
arXiv:2609.01683v1 Announce Type: new Abstract: Vision models deployed on microcontrollers (MCUs) are quantized to integer-only arithmetic and run in inference-only runtimes that do not carry the mach
Just three weeks after its last large language model release, Google LLC today launched Gemini 3.8 Flash and Gemini 3.8 Flash Cyber. The two LLMs are based on the same technical foundation. The primar
I have been trying to squeeze TTS stack down far enough to run in a $3 chip which has 512kb of SRAM without NPU. While trying to get to that milestone i built sanoTTS which has - 11 voices, 6 language
Looking forward to this ecosystem with open models continuing to flourish and grow with NVIDIA and Hugging Face, and the continued partnership. Congrats @JensenHuang and @ClementDelangue! Exciting day
arXiv:2609.02849v1 Announce Type: cross Abstract: Competitive programming has become a key test of large language model reasoning, with international competitions such as IOI and ICPC representing its
arXiv:2609.01015v1 Announce Type: cross Abstract: Traditional network models from network science, such as the Erdos-Renyi and configuration models, generate random networks that reproduce few selecte
arXiv:2609.01059v1 Announce Type: new Abstract: As Vision-Language Models (VLMs) tackle dynamic 3D spatial reasoning, ego-motion perception becomes essential to resolve monocular scale ambiguity. Howe
arXiv:2609.00325v1 Announce Type: new Abstract: Multilingual large language models can exhibit unintended code-switching -- unnecessarily alternating between languages during generation. We present a
arXiv:2608.29242v1 Announce Type: new Abstract: Collecting contact-rich robot experiences at scale remains a major bottleneck for generalizable manipulation. Beyond data quantity, robot learning also
arXiv:2502.12527v2 Announce Type: replace Abstract: Text-to-image diffusion models have gained widespread application across various domains, demonstrating remarkable creative potential. However, the
arXiv:2608.29847v1 Announce Type: cross Abstract: Text-to-image models learn associations between concepts - in the case of this paper, people's professions, which we refer to as roles - and visual at
arXiv:2608.29123v1 Announce Type: new Abstract: Training a video-generation model from scratch is hard for reasons that precede model design. The feedback loop is long: a failure that appears only aft
arXiv:2608.29092v1 Announce Type: new Abstract: Large vision-language models (LVLMs) frequently generate content unsupported by visual inputs. Preliminary experiments show that visual evidence is prim
arXiv:2608.30908v1 Announce Type: new Abstract: Fine-tuning Low-bit models aims to adapt a quantized model while keeping the final deployed checkpoint in the same low-bit form. This setting is practic
arXiv:2608.30841v1 Announce Type: new Abstract: Large language models can often generate plausible mathematical reasoning traces, but reliably identifying the correct solution among multiple candidate
arXiv:2608.28666v1 Announce Type: new Abstract: Video-language models (VLMs) remain brittle on tasks that require tracking events over time and grounding answers in specific spatial regions. We propos
arXiv:2608.30903v1 Announce Type: new Abstract: Dynamic stance classification models how a reply responds to its direct parent message, rather than how a post relates to a fixed topic. Existing work h
arXiv:2606.14747v2 Announce Type: replace-cross Abstract: Recent advancements have significantly expanded the theoretical context windows of Multimodal Embedding Models (MEMs). However, larger context
arXiv:2503.00030v3 Announce Type: replace-cross Abstract: Self-play-based policy optimization has emerged as an effective approach for fine-tuning large language models (LLMs), formulating preference
arXiv:2512.15052v4 Announce Type: replace-cross Abstract: Disclaimer: Samples in this paper may be harmful and cause discomfort. Multimodal large language models (MLLMs) enable multimodal understandin
arXiv:2509.24711v4 Announce Type: replace Abstract: Current answering paradigms for Large Reasoning Models (LRMs) often fail to account for the fact that some questions may lie beyond the model's oper
arXiv:2608.29999v1 Announce Type: cross Abstract: Large audio-language models (LALMs) describe audio at the clip level but cannot assign timestamps to the events, speakers, or sounds they identify. De
arXiv:2608.30081v1 Announce Type: cross Abstract: Understanding which training samples influence a generated image is an important problem in generative modeling. In flow matching, training samples in
arXiv:2602.08336v3 Announce Type: replace Abstract: Unified multimodal models (UMMs) aim to integrate multimodal understanding and generation within a unified architecture, yet it remains unclear to w
arXiv:2608.28932v1 Announce Type: new Abstract: Voice products increasingly need affective cues that are present in speech but absent from transcripts. We introduce VocalAffectBench, a public, test-on
arXiv:2608.28308v1 Announce Type: new Abstract: We study the scaling behavior of learning rate and batch size in pretraining dense large language models on English-prevalent corpora. Beyond scaling ex
arXiv:2511.14684v2 Announce Type: replace Abstract: Large language models (LLMs) often make reasoning errors when solving mathematical problems, and how to automatically detect and correct these error
This paper does a good job of showing the promise and gaps of autonomous AI scientists. Big question is how much more advanced models close those gaps. Today, we're excited to share early progress on
arXiv:2601.04778v2 Announce Type: replace-cross Abstract: Video-language models (VLMs) achieve strong multimodal understanding but remain prone to hallucinations, especially when reasoning about actio
arXiv:2608.26743v1 Announce Type: new Abstract: Enterprises fine-tune language models on proprietary data that may later require removal due to privacy, contractual, or compliance obligations. Selecti
arXiv:2608.27402v1 Announce Type: cross Abstract: How do large language models (LLMs) organize moral knowledge? Models detect moral content broadly, but detection is a low bar. We ask whether they go