arXiv AI August 18, 2026 By Tengxiao Liu, Zifeng Wang, Jin Miao, I-Hung Hsu, Jun Yan, Jiefeng Chen, Rujun Han, Fangyuan Xu, Yanfei Chen, Ke Jiang, Samira Daruki, Yi Liang, William Yang Wang, Tomas Pfister, Chen-Yu Lee

Budget-Aware Tool Use Enables Effective Agent Scaling

Read the original on arXiv AI →

arXiv:2511. 17006v2 Announce Type: replace Abstract: Scaling test-time computation has been extended from language model reasoning to tool-augmented agents, where scaling involves not only thinking in tokens but also acting via tool calls that directly constrain environmental interaction.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv AI

Jun 3

FutureWeaver: Planning Test-Time Compute for Multi-Agent Systems with Modularized Collaboration

arXiv:2512. 11213v2 Announce Type: replace Abstract: Scaling test-time computation has been shown to significantly improve large language model (LLM) performance without additional training.

By Dongwon Jung, Peng Shi, Muhao Chen, Yi Zhang

llms agents reinforcement-learning benchmarks

arXiv AI

4d ago

Evaluating Test-Time Scaling of General LLM Agents

arXiv:2602.18998v2 Announce Type: replace Abstract: LLM agents are increasingly expected to operate as general-purpose systems that resolve real-world user requests, yet their dynamic scaling behavio...

By Xiaochuan Li, Ryan Ming, Pranav Setlur, Abhijay Paladugu, Andy Tang, Hao Kang, Shuai Shao, Rong Jin, Chenyan Xiong

llms agents benchmarks

arXiv AI

Jun 30

CostBench: Evaluating Multi-Turn Cost-Optimal Planning and Adaptation in Dynamic Environments for LLM Tool-Use Agents

arXiv:2511. 02734v3 Announce Type: replace Abstract: Current evaluations of Large Language Model (LLM) agents primarily emphasize task completion, often overlooking resource efficiency and adaptability.

By Jiayu Liu, Cheng Qian, Zhaochen Su, Qing Zong, Shijue Huang, Bingxiang He, Yi R. Fung

llms agents benchmarks

arXiv AI

4d ago

Lookahead-R: Budget-Aware Tool Retrieval via Execution-Centric Planning

arXiv:2609.35811v1 Announce Type: cross Abstract: Tool retrieval is a critical bottleneck for LLM-based agents operating over large, heterogeneous API ecosystems. Existing approaches face an inherent...

By Zongze Wu, Yani Guo, Runnan Li

llms agents benchmarks

arXiv AI

Aug 7

EcoAgent-Bench: Evaluating Economic Decision-Making in Budget-Constrained LLM Agents

arXiv:2608. 05519v1 Announce Type: new Abstract: Agent benchmarks usually measure task completion and treat resource use as an auxiliary statistic.

By Jie Wu, Ming Gong, Feixiang Cheng, Qinqin Zhao

llms agents benchmarks

arXiv AI

Sep 10

DAREBench: Deployment-Aware and Reliable Evaluation of Models as Agents

arXiv:2609.06059v1 Announce Type: new Abstract: As large language models evolve from question-answering systems into general-purpose agents, evaluation must move beyond static answer correctness to a...

By Yu Liu, Zhilin Liu, Zhiwei Yang, Shaojie Zhang, Zheyuan Deng, Tingwei Huang, Zhenbo Luo, Lei Jiang, Yanbing Liu, Pei Fu

llms agents multimodal benchmarks safety

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FutureWeaver: Planning Test-Time Compute for Multi-Agent Systems with Modularized Collaboration

Evaluating Test-Time Scaling of General LLM Agents

CostBench: Evaluating Multi-Turn Cost-Optimal Planning and Adaptation in Dynamic Environments for LLM Tool-Use Agents

Lookahead-R: Budget-Aware Tool Retrieval via Execution-Centric Planning

EcoAgent-Bench: Evaluating Economic Decision-Making in Budget-Constrained LLM Agents

DAREBench: Deployment-Aware and Reliable Evaluation of Models as Agents