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Matching papers

Beacon: Knowing When and How to Perform Agentic Visual Reasoning

48 upvotes · 30 JUL 2026 · Qixun Wang, Yang Shi, Letian Cheng et al.

This paper proposes a new approach to agentic visual reasoning, which helps large language models (LLMs) perform better on complex tasks by using tools more efficiently. Practitioners might care about this research because it aims to improve the performance of LLMs on challenging problems.

agentic visual reasoningmultimodal large language modelstool usemode adaptivenesstool effectreinforcement learning

OpenForgeRL: Train Harness-native Agents in Any Environment

7 upvotes · 23 JUL 2026 · Xiao Yu, Baolin Peng, Ruize Xu et al.

This paper creates a new framework, OpenForgeRL, that allows researchers to train AI agents in complex environments using real harnesses, rather than relying on simplified inference systems. Practitioners might care because it enables more realistic testing and training of agents in real-world settings.

harness-based agentscontinual learningmulti-turn reasoningtool useopen infrastructurereinforcement learning