Search: “models”

arXiv:2604.17114v1 Announce Type: new Abstract: Frontier large language models generate clinically accurate outputs, but their citations are often fabricated. We term this the Provenance Gap. We teste

arXiv:2509.16621v2 Announce Type: replace-cross Abstract: Learned Sparse Retrieval (LSR) such as SPLADE has growing interest for effective semantic 1st stage matching while enjoying the efficiency of

arXiv:2604.17420v1 Announce Type: new Abstract: Money laundering poses severe risks to global financial systems, driving the widespread adoption of machine learning for transaction monitoring. However

arXiv:2507.21526v3 Announce Type: replace Abstract: Large Language Models (LLMs) incur quadratic attention complexity with input length, creating a major time bottleneck in the prefilling stage. Exist

arXiv:2604.16748v1 Announce Type: new Abstract: Time series forecasting plays a pivotal role in critical sectors such as finance, energy, transportation, and meteorology. However, Long-term Time Serie

arXiv:2405.13068v4 Announce Type: replace-cross Abstract: Large language models (LLMs) have revolutionized various applications, making robust safety alignment essential to prevent harmful outputs. Cu

arXiv:2604.17472v1 Announce Type: new Abstract: Recent advances in 3D vision have led to specialized models for either 3D understanding (e.g., shape classification, segmentation, reconstruction) or 3D

arXiv:2510.27462v2 Announce Type: replace Abstract: Supervised fine-tuning (SFT) on long chain-of-thought (CoT) trajectories has emerged as a crucial technique for enhancing the reasoning abilities of

arXiv:2604.16917v1 Announce Type: new Abstract: Languages encode distinct abstractions and inductive priors, yet most large language models (LLMs) overlook this diversity by reasoning in a single domi

arXiv:2602.17909v2 Announce Type: replace Abstract: Diffusion-based approaches have recently demonstrated strong performance for single-image novel view synthesis by conditioning generative models on

arXiv:2604.16067v1 Announce Type: cross Abstract: Adapting pre-trained vision-language models (VLMs) for robotic control requires injecting high-magnitude continuous gradients from a flow-matching act

arXiv:2504.06355v2 Announce Type: replace Abstract: Learning in environments with sparse rewards remains a fundamental challenge in reinforcement learning. Artificial curiosity addresses this limitati

arXiv:2604.15390v1 Announce Type: cross Abstract: Code deobfuscation is the task of recovering a readable version of a program while preserving its original behavior. In practice, this often requires

arXiv:2604.16217v1 Announce Type: cross Abstract: Large language models are increasingly deployed in settings where reliability matters, yet output-level uncertainty signals such as token probabilitie

arXiv:2604.15821v1 Announce Type: cross Abstract: Universal Machine Learning Interatomic Potentials (uMLIPs), pre-trained on massively diverse datasets encompassing inorganic materials and organic mol

Calling all builders 🛠️📣 Google AI Pro and Ultra subscribers will now get increased usage limits and access to Nano Banana Pro and Gemini Pro models in @GoogleAIStudio — no API key required. Sign in w

arXiv:2602.05523v2 Announce Type: replace-cross Abstract: Agentic large language models (LLMs) are increasingly evaluated on cybersecurity tasks using capture-the-flag (CTF) benchmarks, yet existing p

arXiv:2604.15547v1 Announce Type: cross Abstract: The fundamental challenge of using Large Language Models (LLMs) for reliable, enterprise-grade analytics, such as sentiment prediction, is the conflic

Ethan Mollick shared context about OpenAI's o1-preview model launch, likely discussing the capabilities and implications of this new reasoning-focused AI system that represents a shift toward models d

arXiv:2603.20210v3 Announce Type: replace-cross Abstract: Masked Diffusion Models (MDMs) provide an efficient non-causal alternative to autoregressive generation but often struggle with token dependen

arXiv:2604.15851v1 Announce Type: cross Abstract: Differential privacy (DP) has a wide range of applications for protecting data privacy, but designing and verifying DP algorithms requires expert-leve

arXiv:2604.16238v1 Announce Type: new Abstract: Decision-makers rely on weather forecasts to plant crops, manage wildfires, allocate water and energy, and prepare for weather extremes. Today, such for

arXiv:2604.15588v1 Announce Type: cross Abstract: The integration of Large Language Models (LLMs) into scientific workflows presents exciting opportunities to accelerate biomedical discovery. However,

arXiv:2604.15841v1 Announce Type: new Abstract: While large language models (LLMs) have achieved remarkable success in general language tasks, their performance on Chouxiang Language, a representative

arXiv:2604.15775v1 Announce Type: new Abstract: Learning with large-scale datasets and information-critical applications, such as in High Energy Physics (HEP), demands highly complex, large-scale mode

arXiv:2506.15064v3 Announce Type: replace Abstract: Deep neural networks are powerful tools for solving nonlinear problems in science and engineering, but training highly accurate models becomes chall

arXiv:2604.15678v1 Announce Type: new Abstract: Pretrained Vision-Language Models (VLMs) like CLIP show promise in continual learning, but existing Few-Shot Class-Incremental Learning (FSCIL) methods

arXiv:2508.21675v3 Announce Type: replace Abstract: Misleading visualizations are a potent driver of misinformation on social media and the web. By violating chart design principles, they distort data

arXiv:2604.15882v1 Announce Type: cross Abstract: We introduce JFinTEB, the first comprehensive benchmark specifically designed for evaluating Japanese financial text embeddings. Existing embedding be

Kimi K2.6 just dropped. And it crushed Claude Opus 4.6 on SWE-Bench Pro. Kimi K2.6: 58.6 GPT-5.4 xhigh: 57.7 Gemini 3.1 Pro: 54.2 Claude Opus 4.6: 53.4 An open source Chinese model is now #1 on agenti

arXiv:2406.15809v5 Announce Type: replace Abstract: Citizen reporting platforms help the public and authorities stay informed about sexual harassment incidents. However, the high volume of data shared

Release: llm-openrouter 0.6 llm openrouter refresh command for refreshing the list of available models without waiting for the cache to expire. I added this feature so I could try Kimi 2.6 on OpenRout

arXiv:2604.14334v2 Announce Type: replace-cross Abstract: Gradient saliency from deep sequence models surfaces candidate biomarkers efficiently, but the resulting gene lists can be contaminated by tis

arXiv:2604.15360v1 Announce Type: new Abstract: This study presents a triadic analysis of energy storage operation under multi-stage model predictive control, investigating the interplay between data

arXiv:2604.14518v2 Announce Type: replace Abstract: We present Mind DeepResearch (MindDR), an efficient multi-agent deep research framework that achieves leading performance with only ~30B-parameter m

arXiv:2604.16054v1 Announce Type: cross Abstract: Multimodal large language models (MLLMs) have achieved impressive progress on vision language benchmarks, yet their capacity for visual cognitive and

arXiv:2604.15475v1 Announce Type: new Abstract: Deploying learned multi-robot models on heterogeneous robots remains challenging due to hardware heterogeneity, communication constraints, and the lack

arXiv:2604.16235v1 Announce Type: new Abstract: This paper presents a systematic benchmark of state-of-the-art multilingual large language models (LLMs) adapted via token pruning - a compression techn

arXiv:2604.15937v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly deployed to curate and rank human-created content, yet the nature and structure of their biases in these

arXiv:2510.22149v3 Announce Type: replace-cross Abstract: Federated learning (FL) has emerged as a promising paradigm for decentralized model training, enabling multiple clients to collaboratively lea

arXiv:2604.15557v1 Announce Type: cross Abstract: Steering vectors work for some concepts and layers but fail for others, and practitioners have no way to predict which setting applies before running

arXiv:2604.15945v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) is widely used to augment the input to Large Language Models (LLMs) with external information, such as recent or do

arXiv:2604.15994v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) excel at recognizing individual visual elements and reasoning over simple linear diagrams. However, when faced

arXiv:2509.25897v2 Announce Type: replace-cross Abstract: People often encounter role conflicts -- social dilemmas where the expectations of multiple roles clash and cannot be simultaneously fulfilled

arXiv:2604.15499v1 Announce Type: cross Abstract: Cryptographically secure neural network inference typically relies on secure computing techniques such as Secure Multi-Party Computation (MPC), enabli

arXiv:2509.05489v2 Announce Type: replace Abstract: Reinforcement learning with verifiable rewards has significantly advanced reasoning in large language models (LLMs), but such signals remain coarse,