most citedDevstral: Fine-tuning Language Models for Coding Agent Applications

2 citations · 5 across the 15 of their papers we have counts for

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cs.AI2026

ComBodied Agents: a New Paradigm of Human-Centric Agentic AI

Qianggang Ding, Xingyao Wang, Rui Feng +19

After an older adult misses a medication dose, a software agent can send another reminder and an embodied agent can bring the medication. Yet neither explains whether the person fo…

cs.AI2026

A Rubric-Supervised Critic from Sparse Real-World Outcomes

Xingyao Wang, Valerie Chen, Heng Ji +1

Academic benchmarks for coding agents tend to reward autonomous task completion, measured by verifiable rewards such as unit-test success. In contrast, real-world coding agents ope…

cs.AI2025★ 1 cited

Training Proactive and Personalized LLM Agents

Weiwei Sun, Xuhui Zhou, Weihua Du +5

Despite rapid progress, current AI agents are primarily optimized for isolated task completion. We argue for a paradigm shift toward training agents as collaborators that communica…

cs.AI2025

How can we assess human-agent interactions? Case studies in software agent design

Valerie Chen, Rohit Malhotra, Xingyao Wang +7

While benchmarks measure the accuracy of LLM-powered agents, they mostly assume full automation, failing to represent the collaborative nature of real-world use cases. In this pape…

cs.AI2025★ 1 cited

The Danger of Overthinking: Examining the Reasoning-Action Dilemma in Agentic Tasks

Alejandro Cuadron, Dacheng Li, Wenjie Ma +13

Large Reasoning Models (LRMs) represent a breakthrough in AI problem-solving capabilities, but their effectiveness in interactive environments can be limited. This paper introduces…