arXiv:2608.30672v1 Announce Type: new Abstract: Recent advances in large language models and multimodal models have pushed remote sensing (RS) processing from simple perception models to agentic syst...
arXiv:2512. 15231v3 Announce Type: replace Abstract: The automated and intelligent processing of massive remote sensing (RS) datasets is critical in Earth observation (EO).
arXiv:2609.40048v1 Announce Type: new Abstract: Ultra-long video temporal grounding requires balancing long-range evidence search with fine-grained event understanding under a limited visual budget,...
arXiv:2606.14397v4 Announce Type: replace Abstract: As agentic systems continue to evolve and are widely deployed in real-world scenarios, there is a growing demand to faithfully evaluate their capab...
The paper proposes a new paradigm for Earth observation (EO) modeling in which a general-purpose coding agent automatically builds and recursively improves models by interacting with a harness and a verifier. Any verifiable EO task can be framed with a harness that defines permissible operations and a verifier that assesses success, enabling the agent to learn from labeled benchmarks, independent sensors, physical models, or repeated observations. On six diverse EO benchmarks—including optical, SAR, and multispectral time-series imagery—the agent-built models outperform state‑of‑the‑art methods on every task, achieving superior land‑cover mapping using only 11% of the training labels.
arXiv:2606. 14397v1 Announce Type: new Abstract: As agentic systems continue to evolve and are widely deployed in real-world scenarios, there is a growing demand to faithfully evaluate their capabilities.
The paper demonstrates that Earth observation (EO) models can be automatically constructed and refined by a general-purpose coding agent when provided with a harness (defining permissible tools) and a verifier (defining success criteria). By casting any verifiable EO task—using labeled benchmarks, independent sensors, physical models, or repeated observations—as such a harness-verifier pair, the agent recursively improves its own outputs. On four EO benchmarks covering optical, SAR, and multispectral time-series imagery, the agent-built models outperform state‑of‑the‑art methods while retaining most accuracy with only about 1 % of the training labels.
arXiv:2607. 15942v1 Announce Type: cross Abstract: Remote sensing vision-language models are increasingly expected to support open-ended reasoning over Earth Observation data and a variety of tasks.
RS-Claw-Evolution is an environment-feedback-driven framework designed to enhance lightweight remote sensing agents for long-horizon tasks. It improves agents through three stages—interaction evolution, experience evolution, and decision evolution—using executable code, failure-aware trajectory generation, and reinforcement learning with multi-dimensional rewards. On Earth-Bench, a Qwen3-4B agent trained with this framework reaches 65.9% accuracy, surpassing larger baselines and approaching GPT-5 performance.
arXiv:2609.14857v1 Announce Type: new Abstract: Recent work extends recursive self-improvement (RSI) to agent harnesses for long-horizon coding and terminal tasks, enabling agents to improve executio...
Beacon is a new agentic visual reasoning model that improves multimodal large language models (MLLMs) by better deciding when to use tools and how to use them. It introduces two key concepts—Mode Adaptiveness, which ensures tools are invoked only when necessary, and Tool Effect, which measures the net benefit of tool use— and trains the model with supervised fine‑tuning and reinforcement learning that rewards necessity-aware decisions and expands capability through expert hints. Across 13 benchmarks, Beacon outperforms other open‑source models, achieving the highest average score and the largest net tool‑gain on diagnostic tests.
SimCRAFT is a model‑agnostic framework that distills remote sensing orchestration into a compact 7B‑scale model. It creates a large, constraint‑validated workflow planning corpus (SimRS‑14k) using a multi‑agent synthesis engine and a Mock Execution Engine, then fine‑tunes the model with Contextual Retrieval‑Augmented Fine‑Tuning (CRAFT) to reason analogically. Experiments show SimCRAFT‑7B outperforms open‑weight LLMs and rivals advanced closed‑source models, providing a lightweight, efficient baseline for autonomous remote sensing deployment.