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From the 1 of 83 linked papers with an AI index.

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20242026
most citedAI-Assisted Peer Review at Scale: The AAAI-26 AI Review Pilot

1 citations · 1 across the 37 of their papers we have counts for

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cs.CL2026

PhoneBuddy: Training Open Models for Agentic Phone Use

Zhengyang Tang, Xin Lai, Pengyuan Lyu +23

Phones are becoming an important execution surface for general-purpose agents, but training open models for reliable phone use remains difficult because the environment that matter…

cs.CL2026

Towards Pareto-Optimal Tool-Integrated Agents with Pareto Ranking Policy Optimization

Junyi Li, Xiaowei Qian, Yingyi Zhang +6

Recent advances in tool-integrated language agents have significantly improved their ability to solve complex reasoning tasks. However, existing alignment methods predominantly foc…

cs.CL2026

PhoneHarness: Harnessing Phone-Use Agents through Mixed GUI, CLI, and Tool Actions

Chenxin Li, Zhengyao Fang, Zhengyang Tang +18

Phone agents are increasingly expected to complete real mobile workflows rather than merely predict the next screen action. However, much of the current mobile-agent literature sti…

cs.CL2026

GENIE: A Fine-Grained Measure for Novelty

Ramya Namuduri, Manya Wadhwa, Anshun Asher Zheng +2

Large Language Models have consistently demonstrated a lack of creativity and diversity across tasks. Prior work has focused on addressing whether models are capable of generating…

cs.CL2026

Beyond Fully Random Masking: Attention-Guided Denoising and Optimization for Diffusion Language Models

Jia Deng, Junyi Li, Wayne Xin Zhao +3

Diffusion large language models (dLLMs) offer an efficient alternative to autoregressive models through parallel decoding, yet existing post-training methods largely rely on random…

cs.CL2026

AI generates well-liked but templatic empathic responses

Emma S. Gueorguieva, Hongli Zhan, Jina Suh +4

Recent research shows that greater numbers of people are turning to Large Language Models (LLMs) for emotional support, and that people rate LLM responses as more empathic than hum…