arXiv AI August 17, 2026 By Pranav Kumar Kaliaperumal

From BERT to Frontier Agents: Eight Years of Language-Model Progress, the Collapse of the Capability-Cost Curve, and the Rise of Task-Targeted Models

arXiv:2608. 13675v1 Announce Type: cross Abstract: Between October 2018 and July 2026 AI models progressed from simple systems like BERT to massive agents that solve complex math and write software.

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arXiv Machine Learning

Aug 3

AllocBench: Measuring Online Tool Allocation Capability in LLM Agents

arXiv:2607. 23332v2 Announce Type: replace Abstract: Creating a reusable tool is an investment: an agent pays a fixed cost now in exchange for the potential of future reuse.

By Daniel Wang, Andrew Xu

llms agents robotics benchmarks

Hugging Face Trending Papers

Jul 30

Distilling Answer Set Programming Theories from Large Language Models

Writing Answer Set Programming (ASP) theories from scratch is a difficult and time-consuming task. We take a neurosymbolic approach to study whether a model can distill complete and correct theories, given a fixed agent harness with the solver in the loop.

llms agents benchmarks

arXiv Machine Learning

Jul 28

AlloBench: Measuring Online Tool Allocation Capability in LLM Agents

arXiv:2607. 23332v1 Announce Type: new Abstract: Creating a reusable tool is an investment: an agent pays a fixed cost now in exchange for the potential of future reuse.

By Daniel Wang, Andrew Xu

llms agents robotics benchmarks

arXiv AI

Jul 9

Terminus-4B: Can a Smaller Model Replace Frontier LLMs at Agentic Execution Tasks?

arXiv:2605. 03195v2 Announce Type: replace Abstract: Modern coding agents increasingly delegate specialized subtasks to subagents, which are smaller, focused agentic loops that handle narrow responsibilities like search, debugging or terminal execution.

By Spandan Garg, Vikram Nitin, Yufan Huang

llms agents reinforcement-learning fine-tuning benchmarks

arXiv Computation and Language

Sep 24

SkillGym: Internalizing Human Skills into LLMs for Real-World Problem Solving

arXiv:2609.27717v1 Announce Type: new Abstract: Human-written agent skills encode rich workflows for real-world problem solving, but are typically used as external inference-time instructions rather...

By Zhilong Ge, Yuting Shao, Yutao Yang, Yuxuan Cai, Jie Zhou, Kai Chen, Bo Zhang, Qin Chen, Liang He

llms agents reinforcement-learning fine-tuning

arXiv AI

Aug 18

Palmyra x6 Technical Report: An Agentic, Tool-Use Model Post-Trained via Anchored Supervised Fine-Tuning

arXiv:2608. 16620v1 Announce Type: cross Abstract: Palmyra x6 is a large language model optimized for use with enterprise-oriented agentic tasks.

By Peng Du, Kiran Kamble, Rakshith Vasudev, Zhizhuo Yang, Rohith Nadimpally, Arjun Krishna, Waseem Alshikh, Daniel M. Bikel

llms agents fine-tuning benchmarks safety