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From the 1 of 9 linked papers with an AI index.

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9 papers

cs.LG2026

Why Are GUI Agents Correct but Late? Decode on the Decision-Time Critical Path, Tested with Pre-Compiled Policy Trees

Zihan Dong, Rui Qian, Qishi Zhan +3

The paper introduces Adaptive Anticipatory Policy Trees (AAPT), a method that pre‑computes conditional action trees during idle screen time so GUI agents can react instantly to eve…

cs.AI2026

Scaling GUI Agents with Visual State Transitions

Xiangyan Liu, Kaixin Li, Haonan Wang +6

We introduce State Transition Pretraining (STP) as a new scaling axis for GUI agents. During the STP stage, we continually pretrain a unified multimodal model on visual state trans…

cs.CV2026

JarvisHub: An Open Harness for Canvas-Native Multimodal Creative Agents

Yunlong Lin, Zixu Lin, Zhaohu Xing +23

Creative AI is moving from single-step asset generation toward long-horizon multimodal production. Although recent generative models can synthesize high-quality images, videos, aud…

cs.AI2026

From Storage to Experience: A Survey on the Evolution of LLM Agent Memory Mechanisms

Jinghao Luo, Yuchen Tian, Chuxue Cao +6

Large Language Model (LLM)-based agents have fundamentally reshaped artificial intelligence by integrating external tools and planning capabilities. While memory mechanisms have em…

cs.CL2025

MM-CRITIC: A Holistic Evaluation of Large Multimodal Models as Multimodal Critique

Gailun Zeng, Ziyang Luo, Hongzhan Lin +5

The ability of critique is vital for models to self-improve and serve as reliable AI assistants. While extensively studied in language-only settings, multimodal critique of Large M…

cs.CL2025

MemeArena: Automating Context-Aware Unbiased Evaluation of Harmfulness Understanding for Multimodal Large Language Models

Zixin Chen, Hongzhan Lin, Kaixin Li +3

The proliferation of memes on social media necessitates the capabilities of multimodal Large Language Models (mLLMs) to effectively understand multimodal harmfulness. Existing eval…