Knowledge catalogue
arXiv:2609.18916v1 Announce Type: cross Abstract: Mixture-of-Experts (MoE) language models suffer from large parameter counts, which create a significant memory bottleneck. Expert pruning is the most
arXiv:2609.18453v1 Announce Type: new Abstract: A calibrated Vision-Language Model (VLM) can repeatedly self-correct, say 'Wait, I should recheck,' arrive at the wrong answer, and still report high co
arXiv:2609.17443v1 Announce Type: new Abstract: Vision-language models (VLMs) achieve strong visual question answering (VQA) performance, but processing large cluttered images is computationally expen
arXiv:2609.16373v1 Announce Type: new Abstract: Probabilistic certification of Bayesian neural networks lower-bounds the posterior probability that a model satisfies a verifier-defined safety property
arXiv:2609.16647v1 Announce Type: new Abstract: Gender bias in large vision-language models (LVLMs) undermines their fairness and reliability, compromising output trustworthiness. Current mitigation m
arXiv:2609.15277v1 Announce Type: new Abstract: Entrepreneurial cognition is a foundation of entrepreneurship research. Yet the growing involvement of large language models (LLMs) in entrepreneurial w
arXiv:2609.15055v1 Announce Type: cross Abstract: Deep learning has led to numerous proposed architectures for Automated Emotion Recognition (AER) from electrocardiogram (ECG) data, but inconsistencie
arXiv:2603.04419v3 Announce Type: replace-cross Abstract: Vision-language models produce different object and use descriptions under different persona prompts, but low overlap alone does not identify
arXiv:2609.15242v1 Announce Type: new Abstract: Environmental, Social, and Governance (ESG) reporting is critical for corporate accountability, with Large Language Models (LLMs) and Retrieval-Augmente
arXiv:2609.14676v1 Announce Type: new Abstract: Purpose: To compare four multimodal large language models (MLLMs) with radiologists of varying expertise in breast density assessment, BI-RADS assessmen
arXiv:2609.13247v1 Announce Type: cross Abstract: Calibrating an agent-based model (ABM) is difficult because its objective landscape is stochastic and rugged, and can be evaluated only through costly
arXiv:2609.13717v1 Announce Type: new Abstract: Masked reconstruction is widely used for EEG foundation models, but optimizing reconstruction on low-SNR waveforms does not necessarily produce the most
arXiv:2609.13654v1 Announce Type: new Abstract: Pretrained foundation models (FMs) have achieved remarkable success in computer vision, yet their high fine-tuning cost limits practical deployment. Par
@openrouter: OpenRouter users spent more on OpenAI's models than on Anthropic's in the week of September 7, the first time that happened since the week of February 26, 2024 — OpenRouter users spent mo
arXiv:2609.14106v1 Announce Type: new Abstract: We evaluated parameter-efficient fine-tuning (PEFT) of the Segment Anything Model (SAM) for liver tumor segmentation in abdominal CT of colorectal liver
arXiv:2509.19375v2 Announce Type: replace-cross Abstract: Large language models are increasingly used for clinical text classification, where overconfident misclassifications can directly affect patie
arXiv:2609.13060v1 Announce Type: new Abstract: Diffusion Language Models (DLMs) offer promising parallel generation capabilities but lag behind autoregressive models in complex reasoning and tool-use
For all the focus on hard math, we should also have a bank of historical mysteries and cyphers that we can throw models at. I know a few historians (like https://generativehistory.substack.com/) that
arXiv:2609.12902v1 Announce Type: new Abstract: Artificial intelligence has shown promise in assisting radiologists in imaging-based diagnosis across a wide range of diseases. Efficient training of la
Many concerns this weekend about slowing down AI, some targeting open models. We need to be responsible. But we can't let this slow down, or worse, prohibit open models. It's the wrong risk. Open mode
arXiv:2609.11872v1 Announce Type: cross Abstract: Continuous glucose monitoring (CGM) provides high-frequency measurements of glucose dynamics and enables short-term glucose forecasting for diabetes m
arXiv:2506.00849v2 Announce Type: replace Abstract: Despite the empirical success of Diffusion Models (DMs) and Variational Autoencoders (VAEs), their generalization performance remains theoretically
arXiv:2609.11900v1 Announce Type: cross Abstract: Spatial reasoning depends not only on metric properties such as distance, angle, and shape, but also on topological relations that remain invariant un
arXiv:2601.19285v3 Announce Type: replace Abstract: Diffusion models achieve remarkable generation quality, yet face a fundamental challenge known as memorization, where generated samples can replicat
arXiv:2601.15645v2 Announce Type: replace Abstract: Large-scale language models (LLMs) often offer clinical judgments based on incomplete information, increasing the risk of misdiagnosis. Existing stu
Paris-based Arlequin AI SAS today announced it raised €28 million, about $32 million, in new funding to accelerate development of a new AI model architecture based on a different type of network that
arXiv:2609.09476v1 Announce Type: cross Abstract: In-vehicle assistants must translate natural-language requests into accurate vehicle function calls under strict memory and latency constraints, makin
arXiv:2609.07160v1 Announce Type: cross Abstract: Diffusion Large Language Models (dLLMs) generate text via bidirectional iterative denoising, naturally supporting user-specified constraints anchored
arXiv:2609.09909v1 Announce Type: new Abstract: Although text-to-image diffusion models generally exhibit strong prompt-following ability, we identify a persistent and previously underexplored failure
arXiv:2607.14112v2 Announce Type: replace-cross Abstract: Large language models (LLMs) are trained and evaluated as though perfect reliability is achievable for any task given sufficient scale. We sho
arXiv:2507.22076v2 Announce Type: replace Abstract: Text-to-image (T2I) generation models have made significant strides but still struggle with prompt sensitivity: even minor changes in prompt wording
arXiv:2609.05583v1 Announce Type: new Abstract: Vision-Language Models (VLMs) are reshaping computer vision by aligning visual and textual embeddings, allowing models to recognize visual concepts and
arXiv:2609.06704v1 Announce Type: new Abstract: Chain-of-thought (CoT) may often look plausible, yet it may not faithfully reflect the model's decision-making process. While methods for measuring the
arXiv:2609.05525v1 Announce Type: new Abstract: Large vision-language models (VLMs) are increasingly deployed in safety-critical settings, yet existing visual jailbreak research has focused almost exc
arXiv:2609.07666v1 Announce Type: new Abstract: Full-parameter fine-tuning of large language models has substantial memory costs because backpropagation stores activations and gradients. Zeroth-order
arXiv:2506.10911v2 Announce Type: replace Abstract: Training large language models is generally done on clusters containing thousands of accelerators, communicating over a high-bandwidth interconnect.
arXiv:2609.08253v1 Announce Type: new Abstract: Diffusion models have shown remarkable performance on diverse generation tasks. Recent work finds that imposing representation alignment on the hidden s
arXiv:2409.09903v3 Announce Type: replace-cross Abstract: Softmax Mixture Models (SMMs) are discrete K-component mixture models for the probabilities of selecting one of p candidate feature vectors X_
Hey so a few months ago I got into local LLMs and didn’t really like the tools out there at the time, so I decided to build my own version of a Claude/Codex GUI for local models. I’ve been working on
Adam Satariano / New York Times: Mistral raised a €3B Series D led by Samsung at a €21B valuation, up from €11.7B a year ago, as it expands into data centers beyond developing AI models — Mistral is t
arXiv:2609.04720v1 Announce Type: cross Abstract: Vision-language models (VLMs) are expected to respond helpfully to appropriate requests while withholding compliance with requests that are incorrect,
arXiv:2609.05324v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models have shown promising progress in language-conditioned robotic manipulation. However, existing datasets and benchma
arXiv:2609.05401v1 Announce Type: cross Abstract: Vision-language models are increasingly used as reward functions for robotic learning, but this role requires paraphrase invariance: the same trajecto
arXiv:2609.04582v1 Announce Type: cross Abstract: A 0.6B language model, asked to verify 1,200 logical conclusions (half valid, half corrupted by a single semantic edit), answers YES every time. Judge
arXiv:2609.03355v1 Announce Type: cross Abstract: Logit-based knowledge distillation for autoregressive language models usually aligns teacher and student next-token distributions over the entire voca
arXiv:2609.03003v1 Announce Type: new Abstract: Causal inference is the practice of estimating the effect of a treatment or intervention from data. It traditionally requires a bespoke pipeline for eve
arXiv:2609.03814v1 Announce Type: new Abstract: Large language models (LLMs) demonstrate strong performance on standard content moderation benchmarks. However, these benchmarks often aggregate multipl
arXiv:2609.03582v1 Announce Type: new Abstract: State-of-the-art AI weather models have shown impressive medium-range forecast skill and computational efficiency, but suffer two key shortcomings: thei
arXiv:2406.16893v3 Announce Type: replace-cross Abstract: The emergence of Transformer-based Large Language Models (LLMs) has substantially augmented the capabilities of Natural Language Processing (N
arXiv:2609.02502v1 Announce Type: cross Abstract: Text-to-image (T2I) models have achieved remarkable success at faithfully rendering specified objects and attributes, yet their ability to produce vis
arXiv:2609.01655v1 Announce Type: cross Abstract: Unified recommendation models aim to jointly model non-sequential multi-field features and sequential user behaviors, but existing interaction-centric
arXiv:2312.06315v2 Announce Type: replace Abstract: Warning: This paper contains content that may be offensive or upsetting. There has been a significant increase in the usage of large language models
arXiv:2609.01723v1 Announce Type: cross Abstract: Text-to-Speech (TTS) foundation models are increasingly fine-tuned on private datasets to synthesize highly personalized voices, introducing severe pr
Research doesn’t end in right or wrong, but in insights that lead to more questions. General-purpose models aren’t fit for research because they don’t know customers’ workflows. @quadrillionlabs train
arXiv:2609.02011v1 Announce Type: cross Abstract: Large language model question answering over power-grid models must respect a fixed context budget. We introduce seed-anchored graph rendering, a dete
Australian teams can now access OpenAI GPT-5.6 Sol, Terra, and Luna models on Amazon Bedrock with global cross-Region inference from the Asia Pacific (Sydney) and Asia Pacific (Melbourne) Regions. Thi
arXiv:2511.21415v2 Announce Type: replace Abstract: We introduce DiverseVAR, a framework that enhances the diversity of text-conditioned visual autoregressive models (VAR) at test time without requiri
arXiv:2609.00661v1 Announce Type: new Abstract: Satellite foundation models offer a globally available alternative to census data for commuting origin-destination (OD) generation, yet no study has sys
arXiv:2609.01318v1 Announce Type: cross Abstract: Diffusion-based Large Vision-Language Models (dLVLMs) have recently emerged as a compelling alternative to autoregressive (AR) LVLMs, offering advanta
arXiv:2510.12851v2 Announce Type: replace-cross Abstract: Large Audio-Language Models (LALMs) excel in Audio QA but often suffer from hallucinations ungrounded in the audio. To our knowledge, we are t