Search: “models”

arXiv:2607.07880v1 Announce Type: new Abstract: Synthesizing realistic full-body human interactions with articulated objects is a fundamental challenge for embodied AI and graphics, with applications

arXiv:2412.07259v5 Announce Type: replace Abstract: DatalogMTL is a powerful rule-based language for temporal reasoning. Due to its high expressive power and flexible modeling capabilities, it is suit

guys this was in 2024 lmao https://x.com/zenorocha/status/1872652921227374912?s=46 Let’s talk about AI SEO. A lot of new users are signing up to our product because AI models are recommending it. Here

arXiv:2607.08579v1 Announce Type: cross Abstract: Missing data is a persistent obstacle in scientific, social science, and public health research, often biasing analyses and placing accountability on

arXiv:2607.08724v1 Announce Type: new Abstract: Human decision-making is highly flexible -- some actions are taken immediately; others require longer deliberation. Language models have exhibited a sim

arXiv:2607.08185v1 Announce Type: cross Abstract: Enhancing images to make them visually appealing is a persistent challenge in computer vision. Many deep-learning methods train models on paired datas

arXiv:2606.16149v2 Announce Type: replace Abstract: Rare disease diagnosis involves interpreting clinical and genetic findings through complex diagnostic reasoning. We investigated whether this reason

arXiv:2607.08257v1 Announce Type: new Abstract: Large language models (LLMs) have shown strong performance on isolated psychiatric tasks, including dialogue, diagnosis, and treatment planning, yet exi

arXiv:2607.08203v1 Announce Type: new Abstract: Progress in colonoscopy polyp segmentation is routinely reported through leaderboard comparisons on a small set of public benchmarks. We argue that this

arXiv:2607.07857v1 Announce Type: cross Abstract: We build a team of specialized large language-model agents and present an agent-driven workflow for research-level formalization in theoretical physic

arXiv:2510.07328v2 Announce Type: replace-cross Abstract: Medical decision systems increasingly rely on data from multiple sources to ensure reliable and unbiased diagnosis. However, existing multimod

arXiv:2607.08756v1 Announce Type: cross Abstract: We present MulTTiPop, a dataset of pop music segments and their associated multitrack MIDI recordings for the evaluation of automatic music transcript

arXiv:2607.08391v1 Announce Type: cross Abstract: Making tradeoffs between execution latency and result utility (i.e., anytime computing) for adapting to dynamic operational requirements has been show

arXiv:2607.07830v1 Announce Type: new Abstract: Model-free reinforcement learning has enabled impressive humanoid locomotion; however, control on steep slopes remains largely unexplored. Unlike flat o

arXiv:2607.08034v1 Announce Type: cross Abstract: Large language models (LLMs) are used worldwide, yet disproportionately reflect Western values, limiting their ability to represent diverse value syst

arXiv:2607.07834v1 Announce Type: new Abstract: Pseudo-nitzschia diatoms pose recurrent risks to coastal ecosystems and shellfish harvesting along the Portuguese Atlantic coast. Here we develop and ev

This article explores profiling techniques for attention mechanisms in PyTorch, the computational bottleneck in transformer models. It likely provides practical tools and methods for measuring and ana

arXiv:2607.08270v1 Announce Type: new Abstract: Forecasting the future anatomy of slow-evolving neurodegenerative diseases could enable earlier, more targeted intervention and improve clinical trial d

arXiv:2607.08590v1 Announce Type: new Abstract: Scientific experiments are often designed to maximize information gain, yet in many applications the primary objective is to support reliable downstream

arXiv:2607.07895v1 Announce Type: new Abstract: Research on stereotypes in large language models (LLMs) has largely focused on English-speaking contexts, due to the lack of datasets in other languages

arXiv:2607.08137v1 Announce Type: cross Abstract: Federated reinforcement learning (FRL) is crucial for enabling collaborative learning across multiple agents without sharing raw data, thereby enhanci

arXiv:2602.12612v2 Announce Type: replace-cross Abstract: Traditional methods for automating recommender system design, such as Neural Architecture Search (NAS), are often constrained by a fixed searc

arXiv:2607.07968v1 Announce Type: new Abstract: Soft robotic exogloves can provide hand rehabilitation and assistance. Fitting these gloves often relies on standardized measurements not tailored to th

arXiv:2606.17482v2 Announce Type: replace Abstract: Generating adversarial driving scenarios is critical for evaluating and improving autonomous vehicle decision-making systems in simulation. Recent a

arXiv:2607.07756v1 Announce Type: new Abstract: Multimodal learning usually requires a dedicated encoder per modality. When a tabular modality is involved, prior work has been mostly using a plain MLP

Try Grok 4.5 in Perplexity Grok 4.5 is now available as an orchestrator model in Computer for Consumer Pro and Max subscribers. We evaluated it against five other orchestrator configurations on WANDR.

We’re Hiring: Software Engineer (R&D, Infrastructure and Platform Reliability) 🐟 https://sakana.ai/careers/infrastructure-platform-reliability-engineer/ Sakana Fugu, our Multi-Agent System as a Model,

arXiv:2607.07976v1 Announce Type: cross Abstract: Reinforcement learning (RL) has achieved remarkable success in enhancing the reasoning capabilities of large language models (LLMs). However, widely u

arXiv:2607.08605v1 Announce Type: cross Abstract: Sparse autoencoders (SAEs) have emerged as a promising technique for mechanistic interpretability by learning a set of sparse latent features in large

arXiv:2607.07989v1 Announce Type: cross Abstract: Large language model (LLM) based multi-agent systems enable complex problem solving through coordinated reasoning and action, but their distributed st

arXiv:2601.02918v3 Announce Type: replace Abstract: Image Quality Assessment (IQA) is a long-standing problem in computer vision. Previous methods typically focus on predicting numerical scores withou

1 week left. This is your last chance to apply for the 72 Hour Video Hackathon. Build alongside the world's best creators and developers, get access to cutting-edge models, and compete for $150K in pr

arXiv:2607.06990v1 Announce Type: new Abstract: Multi-robot systems provide the parallelism and redundancy necessary for long-horizon tasks, while Large Language Models (LLMs) offer the reasoning capa

arXiv:2607.06726v1 Announce Type: new Abstract: Faithful visual attribution identifies which image regions support a model prediction. Search-based perturbation methods lead the insertion--deletion fa

arXiv:2607.07708v1 Announce Type: cross Abstract: Structure-property relationships are foundational to biology, chemistry and materials science, where function, reactivity and physical response emerge

arXiv:2510.04547v5 Announce Type: replace-cross Abstract: Large pretrained vision encoders are central to multimodal intelligence, powering applications from on-device vision processing to vision-lang

arXiv:2211.14966v2 Announce Type: replace Abstract: Deep neural networks (DNNs) are highly vulnerable to adversarial attacks. Ideally, a robust model should perform well on both perturbed training dat

arXiv:2512.11935v2 Announce Type: replace Abstract: Agentic AI systems increasingly connect large language models (LLMs) to external scientific tools, yet whether and when tool access improves predict

arXiv:2607.07538v1 Announce Type: new Abstract: Training a model with noisy gradient descent can be idealized as overdamped Langevin dynamics on the loss landscape, and a natural safety question is to

b9945 is a build-tagged release of llama.cpp, an open-source library that performs inference on large language models and is co-developed alongside the GGML tensor library. The llama.cpp project does

arXiv:2607.07267v1 Announce Type: cross Abstract: Claims about the universality of human concepts have been predominantly assessed through linguistic similarity across languages and cultures. However,

The race to build the fastest AI infrastructure is reshaping the semiconductor industry, with inference speed emerging as the defining competitive dimension of the AI era. As AI model wars intensify a

arXiv:2607.07129v1 Announce Type: new Abstract: Compositionality, by organizing complex behavior as combinations of simpler elements, enables robot learning that is scalable and data efficient. Levera

arXiv:2508.00554v4 Announce Type: replace-cross Abstract: In financial trading, large language model (LLM)-based agents demonstrate significant potential, but their decisions can be sensitive to noisy

arXiv:2607.07209v1 Announce Type: cross Abstract: Modern federated and streaming learning systems often release intermediate models, so privacy must hold for the full trajectory under adaptive interac

arXiv:2603.15685v2 Announce Type: replace-cross Abstract: Omnimodal large language models (OmniLLMs) jointly process audio and visual streams, but the resulting long multimodal token sequences make in