VLAA-GUI integrates completeness verification, loop breaking, and online search, boosting GUI automation success to 77.5%.

Qijun Han, Haoqin Tu, Zijun Wang et al.

ProactAgent combines EXPONEVO and PROACTRL for proactive memory retrieval, achieving 32% higher success rate and 33% fewer rounds on SciWorld.

Yuxuan Cai, Wei Li, Jie Zhou et al.

SCG-MEM uses schema-constrained generation to significantly improve LoCoMo benchmark performance.

Lei Zheng, Weinan Song, Daili Li et al.

SkillLearnBench evaluates continual skill learning with a three-level framework, showing limited gains across tasks and models.

Shanshan Zhong, Yi Lu, Jingjie Ning et al.

Memora benchmark and FAMA metric evaluate long-term memory in personalized agents, revealing models' struggles with memory consolidation and forgetting over months.

Md Nayem Uddin, Kumar Shubham, Eduardo Blanco et al.

Discovering a shared logical subspace in LLMs improves logical reasoning accuracy by up to 11% via alignment of natural-language and symbolic views.

Feihao Fang, My T. Thai, Yuanyuan Lei

InsightGen generates diverse and relevant insights to enhance open-ended document QA.

Saransh Sharma, Pritika Ramu, Aparna Garimella et al.

GPT models predict experience ratings from open-ended survey text; prompt optimization improves accuracy by 2%.

Andrew Hong, Jason Potteiger, Luis E. Zapata

Micro Language Models (μLMs) enable instant responses by generating the first 4-8 words on-device, with cloud models completing the response.

Wen Cheng, Tuochao Chen, Karim Helwani et al.

Study shows large language models impact AI conference peer reviews, especially in linguistic complexity and evaluative focus.

Wenqing Wu, Chengzhi Zhang, Yi Zhao et al.

The study reveals dual alignment between language model layers and human sentence processing, with early layers suited for natural reading and later layers better modeling complex syntactic processing.

Tatsuki Kuribayashi, Alex Warstadt, Yohei Oseki et al.

GSQ achieves high-accuracy low-bit quantization using Gumbel-Softmax sampling, narrowing the accuracy gap with QTIP methods.

Alireza Dadgarnia, Soroush Tabesh, Mahdi Nikdan et al.

Transition-matrix regularization improves next dialogue act prediction in counseling conversations, boosting macro-F1 by 9-42%.

Eric Rudolph, Philipp Steigerwald, Jens Albrecht

This study introduces the Adversarial Humanities Benchmark (AHB), revealing that stylistic transformations increase attack success rates from 3.84% to up to 65%, exposing weaknesses in model safety robustness.

Marcello Galisai, Susanna Cifani, Francesco Giarrusso et al.

ArbGraph enhances long-form RAG reliability through conflict-aware evidence arbitration, reducing hallucinations.

Qingying Niu, Yuhao Wang, Ruiyang Ren et al.

Study shows current LLM agents lack environmental curiosity, discovering solutions in 79-81% of cases but utilizing them in only 37-50%.

Leon Engländer, Sophia Althammer, Ahmet Üstün et al.

Token-efficient, self-evolving LLM agent using context density maximization with four core mechanisms.

Jiaqing Liang, Jinyi Han, Weijia Li et al.

Proposes grounded multi-turn social simulation with SSBC to analyze LLM support strategy shifts based on estimated user distress.

Michelle Star, Andrew Aquilina, Yu-Ru Lin

A dual-aspect evaluation framework analyzes LLMs on Vietnamese legal text, revealing readability-accuracy trade-offs.

Van-Truong Le

BAGEL benchmark evaluates language models' performance on animal knowledge using closed-book questions on taxonomy, morphology, etc.

Jiacheng Shen, Masato Hagiwara, Milad Alizadeh et al.