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OpenVLHarness: Real World Visual Agentic Tool-Use with Multimodal Memory

Paper recorded by Signals 4 on 2026-10-19 in cs.AI. Abstract reproduced from arXiv; link to the original below.

Published 2026-10-19 on arXiv · recorded by Signals 4 on 2026-10-20

Category: cs.AI · 人工智能 · first seen 2026-10-20

Abstract

Multimodal large language models (MLLMs) have made rapid progress on visual reasoning, yet many tasks require evidence that is difficult to obtain through internal reasoning alone: our controlled study on 22 benchmarks shows that simply increasing the inference budget of the base model yields only modest gains. Such tasks demand grounded perception, external knowledge, and numerical measurements o

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#241 most recent of 960 cs.AI papers we have recorded · ↑ newer: Agentic Ensemble with Vision-Language Models for Visual Anomaly Detect · ↓ older: Reinforcement Learning with Visually Attributable On-Policy Self-Disti
Cite this page: OpenVLHarness: Real World Visual Agentic Tool-Use with Multimodal Memory: the #241 most recent of 960 cs.AI papers we have recorded (as of 2026-10-19). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/openvlharness-real-world-visual-agentic-tool-use-with-multimodal-memory.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
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