arXiv AI

Getting the Parameters Right: A Difficulty-Graded Benchmark and Probe-Guided Training for LLM Tool Calls

arXiv:2608. 03071v1 Announce Type: new Abstract: Large language model agents derive much of their capability from tool use.

arXiv AI

Aug 11

PluginEval: A Diagnostic Benchmark for Fine-Grained Error Attribution in Function Calling

arXiv:2608. 08700v1 Announce Type: new Abstract: Reliable evaluation of tool routing is critical as Large Language Models increasingly operate as autonomous agents.

By Dongjie Xu, Julius, Hanchi Dong, Minghua Tang, Yuxuan Sun, Ziwei Nie, Zicheng Liu, Dujun Qing, Jiajie Xu

llms agents benchmarks safety

arXiv AI

Jun 11

Soft-Prompt Tuning for Fair and Efficient LLM Benchmark Evaluation

arXiv:2606. 12117v1 Announce Type: cross Abstract: Benchmark scores often misrepresent a large language model's (LLM's) knowledge, because they rely, e.

By Selen Erkan, Bastian Boll, Kristian Kersting, Bj\"orn Deiseroth, Letitia Parcalabescu

llms benchmarks

arXiv Machine Learning

6d ago

LLM Router: Rethinking Routing with Prefill Activations

arXiv:2603. 20895v3 Announce Type: replace-cross Abstract: Existing routers rely on semantic query features or handcrafted features, which often fail to capture model-specific failures or intrinsic task difficulty.

By Tanay Varshney, Annie Surla, Michelle Xu, Gomathy Venkata Krishnan, Maximilian Jeblick, David Austin, Neal Vaidya, Davide Onofrio

llms

arXiv AI

Jun 2

ProbeScale: Probing Analysis to Optimize Neural Scaling Laws for Efficient Small Language Model Inference

arXiv:2606. 01806v1 Announce Type: cross Abstract: Small Language Models (SLMs) offer a balance between capability and computational feasibility.

By Sourav Das

llms

arXiv AI

Jun 3

Diagnosing Knowledge Gaps in LLM Tool Use: An Agentic Benchmark for Novel API Acquisition

arXiv:2606. 03657v1 Announce Type: new Abstract: Large language models for code generation often need to use APIs that are absent from their pretraining data.

By Jinnuo Liu, Yue Peng, Jinhan Niu, Hongyi Wen

llms agents fine-tuning benchmarks

arXiv AI

Jun 17

How Inference Compute Shapes Frontier LLM Evaluation

arXiv:2606. 17930v1 Announce Type: new Abstract: AI evaluations are shifting toward harder tasks that benefit from longer trajectories involving tool use and iterative problem solving.

By Jessica McFadyen, Ole Jorgensen, Harry Coppock, Kevin Wei, Cozmin Ududec

llms agents benchmarks safety

arXiv AI

Jun 9

AliyunConsoleAgent: Training Web Agents in Real-World Cloud Environments via Distillation and Reinforcement Learning

arXiv:2606. 09447v1 Announce Type: new Abstract: We present AliyunConsoleAgent, a web agent framework for automated documentation verification in real-world cloud consoles.

By Bojie Rong, Zheyu Shen, Qiaoping Wang, Pengfei Kang, Yang Xu, Yawen Wei, Hanyu Wu, Zhi Zhao, Leihao Pei, Linquan Jiang

llms agents reinforcement-learning fine-tuning efficiency benchmarks safety

arXiv AI

Jul 24

Routing Without Training: Controllable-Ratio LLM Offloading via Reliability Gating

arXiv:2607. 20481v1 Announce Type: new Abstract: Local-cloud collaboration is a practical way to deploy large language models under resource constraints, but existing methods often rely on trained routers or collaboration-aware finetuning that tie routing behavior to a particular operating regime.

By Evan Chen, Shiqiang Wang, Kevin S Chan, Su Wang, Christopher Brinton

llms fine-tuning

arXiv AI

5d ago

SPADE: Speculative Decoding for Precise and Low Cost Distributed Edge Cloud Inference

arXiv:2608. 13076v1 Announce Type: new Abstract: Large Language Models (LLMs) have achieved remarkable success in natural language understanding and generation, but their deployment is constrained by high computational demands.

By Divya Jyoti Bajpai, Kishan Kumar Upadhyay, Manjesh Kumar Hanawal

llms nlp

arXiv AI

Jun 26

The Capability Frontier: Benchmarks Miss 82% of Model Performance

arXiv:2606. 26836v1 Announce Type: new Abstract: Existing benchmarks typically report accuracy for a single model on a single run.

By Bradley Fowler, Ryan Smith, Daniel Thi Graviet, William Myers, Joshua Greaves, Narmeen Fatimah Oozeer, Ant\'ia Garc\'ia, Philip Quirke, Amirali Abdullah, Fazl Barez, Shriyash Kaustubh Upadhyay

llms agents benchmarks

arXiv AI

Jun 29

End-to-End Dynamic Sparsity for Resource-Adaptive LLM Inference

arXiv:2606. 27743v1 Announce Type: cross Abstract: Large Language Models (LLMs) inference is typically deployed under a static resource assumption, where models execute a fixed computational graph regardless of the runtime environment.

By Yuhang Chen, Jinhao Duan, Ruichen Zhang, Mingfu Liang, Xiaohan Wei, Yunchen Pu, Fei Tian, Chonglin Sun, Parish Aggarwal, Frank Shyu, Luke Simon, Sandeep Pandey, Tianlong Chen, Xi Liu

llms efficiency

arXiv AI

Jun 3

A Matter of TASTE: Improving Coverage and Difficulty of Agent Benchmarks

arXiv:2605. 28556v2 Announce Type: replace Abstract: As agent capabilities advance, existing benchmarks, such as $\tau^2$-Bench, are becoming increasingly saturated.

By Tomer Keren, Nitay Calderon, Asaf Yehudai, Yotam Perlitz, Michal Shmueli-Scheuer, Roi Reichart

llms agents benchmarks

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