arXiv subjects

Diego Llanes

Publications and source records attributed to Diego Llanes.

3 recordsLinked to original sources

arXiv2026

MolmoWeb: Open Visual Web Agent and Open Data for the Open Web

Web agents--autonomous systems that navigate and execute tasks on the web on behalf of users--have the potential to transform how people interact with the digital world. However, the most capable web agents today rely on proprietary models with undisclosed training data and recipes, limiting scientific understanding, reproducibility, and community-driven progress. We believe agents for the open web should be built in the open. To this end, we introduce (1) MolmoWebMix, a large and diverse mixture of browser task demonstrations and web-GUI perception data and (2) MolmoWeb, a family of fully open multimodal web agents. Specifically, MolmoWebMix combines over 100K synthetic task trajectories from multiple complementary generation pipelines with 30K+ human demonstrations, atomic web-skill trajectories, and GUI perception data, including referring expression grounding and screenshot question answering. MolmoWeb agents operate as instruction-conditioned visual-language action policies: given a task instruction and a webpage screenshot, they predict the next browser action, requiring no access to HTML, accessibility trees, or specialized APIs. Available in 4B and 8B size, on browser-use benchmarks like WebVoyager, Online-Mind2Web, and DeepShop, MolmoWeb agents achieve state-of-the-art results outperforming similar scale open-weight-only models such as Fara-7B, UI-Tars-1.5-7B, and Holo1-7B. MolmoWeb-8B also surpasses set-of-marks (SoM) agents built on much larger closed frontier models like GPT-4o. We further demonstrate consistent gains through test-time scaling via parallel rollouts with best-of-N selection, achieving 94.7% and 60.5% pass@4 (compared to 78.2% and 35.3% pass@1) on WebVoyager and Online-Mind2Web respectively. We will release model checkpoints, training data, code, and a unified evaluation harness to enable reproducibility and accelerate open research on web agents.

cs.CV↗

arXiv2024

STARS: Sensor-agnostic Transformer Architecture for Remote Sensing

We present a sensor-agnostic spectral transformer as the basis for spectral foundation models. To that end, we introduce a Universal Spectral Representation (USR) that leverages sensor meta-data, such as sensing kernel specifications and sensing wavelengths, to encode spectra obtained from any spectral instrument into a common representation, such that a single model can ingest data from any sensor. Furthermore, we develop a methodology for pre-training such models in a self-supervised manner using a novel random sensor-augmentation and reconstruction pipeline to learn spectral features independent of the sensing paradigm. We demonstrate that our architecture can learn sensor independent spectral features that generalize effectively to sensors not seen during training. This work sets the stage for training foundation models that can both leverage and be effective for the growing diversity of spectral data.

cs.CV↗

arXiv2024

A self-consistent data-driven model for determining stellar parameters from optical and near-IR spectra

Data-driven models, which apply machine learning to infer physical properties from large quantities of data, have become increasingly important for extracting stellar properties from spectra. In general, these methods have been applied to data in one wavelength regime or another. For example, APOGEE Net has been applied to near-IR spectra from the SDSS-V APOGEE survey to predict stellar parameters (Teff, log g, and [Fe/H]) for all stars with Teff from 3,000 to 50,000 K, including pre-main sequence stars, OB stars, main sequence dwarfs, and red giants. The increasing number of large surveys across multiple wavelength regimes provides the opportunity to improve data-driven models through learning from multiple datasets at once. In SDSS-V, a number of spectra of stars will be observed not just with APOGEE in near-IR, but also with BOSS in optical regime. Here we aim to develop a complementary model, BOSS Net, that will replicate the performance of APOGEE Net in these optical data through label transfer. We further improve the model by extending it to brown dwarfs, as well as white dwarfs, resulting in a comprehensive coverage between 1700<Teff<100,000 K and 0<log g<10, to ensure BOSS Net can reliably measure parameters of most of the commonly observed objects within this parameter space. We also update APOGEE Net to achieve a comparable performance in the near-IR regime. The resulting models provide a robust tool for measuring stellar evolutionary states, and in turn, enable characterization of the star forming history of the Galaxy.

astro-ph.SR↗