Foundation-model-based industrial agents leverage large language models for autonomous decision-making, enhancing human interaction (+37%) and uncertainty handling (+35%).

Vincent Henkel, Felix Gehlhoff, David Kube et al.

MILD integrates bidirectional perception and multi-layered alignment with ECPO optimization for human-vehicle collaboration.

Jiyao Wang, Yunbiao Wang, Yubo Jiao et al.

DiagramNet employs multi-stage training and a decoupled multi-agent workflow to achieve superior system-level diagram recognition, outperforming GPT-5 and industry benchmarks.

Jincheng Lou, Ruohan Xu, Jiapeng Li et al.

GUI-SD introduces on-policy self-distillation with visual privileged context and entropy-guided loss, boosting GUI grounding accuracy to 68.4% and training speed 4× faster.

Yan Zhang, Daiqing Wu, Huawen Shen et al.

This paper introduces a reinforcement learning framework for GUI agents, categorized into offline, online, and hybrid strategies, emphasizing reward engineering and data efficiency.

Junan Hu, Jian Liu, Jingxiang Lai et al.

Proposes an agentic RL framework for LLMs integrating meta-reasoning, multi-step planning, and external tools, achieving 50% improvement on complex tasks.

Fangming Cui, Ruixiao Zhu, Cheng Fang et al.

Proposed event-driven step-level cascade framework improves efficiency, achieving 58.2% success rate.

Jinbiao Wei, Kangqi Ni, Yilun Zhao et al.

Proposed a disagreement-guided strategy routing method, improving mathematical reasoning accuracy by 3%-7%.

Zhimin Lin, Yixin Ji, Jinpeng Li et al.

SciCrafter evaluates AI's discovery-to-application ability in Minecraft; current models achieve only 26% success.

Zhou Ziheng, Huacong Tang, Jinyuan Zhang et al.

ZenBrain is a 7-layer neuroscience-inspired memory architecture integrating 15 mechanisms, significantly improving QA and cross-session reasoning with 91.3% oracle accuracy at 1/106 query cost.

Alexander Bering

Proposes an LLM-based evaluation framework to enhance math reasoning assessment accuracy beyond symbolic math limitations.

Erez Yosef, Oron Anschel, Shunit Haviv Hakimi et al.

AgentSearchBench improves agent search ranking quality using execution signals, bridging the gap between semantics and performance.

Bin Wu, Arastun Mammadli, Xiaoyu Zhang et al.

A-MAR framework enhances multimodal art retrieval explanation quality through structured reasoning plans.

Shuai Wang, Hongyi Zhu, Jia-Hong Huang et al.

SafetyALFRED evaluates safety planning in multimodal LLMs in kitchen settings, finding good hazard recognition but low risk mitigation success.

Josue Torres-Fonseca, Naihao Deng, Yinpei Dai et al.

Large language models exhibit normative conformity, revealing underlying mechanisms.

Mikako Bito, Keita Nishimoto, Kimitaka Asatani et al.

MathNet provides a global multimodal benchmark for mathematical reasoning and retrieval, covering 30,676 Olympiad-level problems from 47 countries.

Shaden Alshammari, Kevin Wen, Abrar Zainal et al.

BLF system achieves state-of-the-art binary forecasting performance on ForecastBench using sequential Bayesian updating of linguistic beliefs.

Kevin Murphy

ClawEnvKit automates environment generation for claw-like agents, reducing costs by 13,800x.

Xirui Li, Ming Li, Derry Xu et al.

Proposes EvoOR-Agent, a co-evolution framework optimizing reasoning paths and architectures, achieving 15% performance gains on heterogeneous benchmarks.

Jiahao Huang, Peilan Xu, Xiaoya Nan et al.

SkillFlow benchmark demonstrates 8.43% improvement in task success via lifelong skill evolution, using a dual-agent iterative framework.

Ziao Zhang, Kou Shi, Shiting Huang et al.