arxiv arXiv cs.AI · 51d ago · research

See-and-Reach: Vision-Language Navigation for UAVs in Field of View

from English

UAV-VLN-FOV isolates the see-and-reach stage for precise evaluation of UAV navigation. 3DG-VLN enhances visual grounding and spatial alignment using dynamic 3D direction cues, achieving a 13.82% success rate improvement over baselines and validated in real-world trials.

Importance 3/3 New feature vs. leaders New harness with differentiators arXiv cs.AI Google DeepMind Allen AI Hugging Face AI agents Evaluation & benchmarks Multimodal

Read original

arxiv arXiv cs.CL · 47d ago · 1 view

Beaver: Agent Harness for Scientific Curation from Multimodal Sources

Beaver is an agent harness that extracts structured information from scientific papers by integrating multimodal evidence tooling, task scaffolding, and artifact-grounded autoresearch. It achieves 81.0 on the Gold-Referenced Attribute Score, outperforming frontier agents by over 23 points, with key gains on high-value attributes requiring cross-modal reasoning.

arxiv arXiv cs.LG · 52d ago · 3 views

OneCanvas: 3D Scene Understanding via Panoramic Reprojection

OneCanvas enables 3D scene understanding in Vision-Language Models by aggregating patch features onto a single panoramic canvas using 3D world coordinates. It achieves state-of-the-art performance on SQA3D and VSI-Bench, and generalizes to out-of-distribution data on SPBench, using significantly less training compute than existing methods.

arxiv arXiv cs.CL · 51d ago · 1 view

H-RePlan: Hierarchical Recovery for Cross-Device Agent Systems

H-RePlan introduces a hierarchical replanning framework that separates device-local strategy recovery from global orchestrator replanning. It outperforms existing baselines by achieving higher completion and instruction adherence, with reduced token cost, through scope-aware recovery in multi-device agent systems.

arxiv arXiv cs.AI · 51d ago

Hidden Evolution of Disguised Visual Context in VLMs

Visual tokens enter large language models as raw, unstructured signals. Their internal transformation and integration depend on architecture—either as in-context prompts or injected into intermediate layers—leading to distinct evolution paths in visual representation and frequency characteristics. We find that attention alone is insufficient; performance is driven by the quality of visual representations at each layer across different integration paradigms.

arxiv arXiv cs.LG · 52d ago

Act2Answer Evaluates Knowledge Retention in Vision-Language-Action Models

Act2Answer introduces a lightweight protocol to assess commonsense and world knowledge retention in VLA models by requiring agents to answer questions through object placement actions. A large-scale study of 7 VLA models and 9 VLM baselines reveals that VLAs perform well on simple concepts but show larger gaps on rich semantic categories compared to their source VLMs, with VQA co-training improving knowledge retention and peak answer-relevant signals observed in middle VLA layers.