HFEPX Hub
Updated from current HFEPX corpus (Feb 26, 2026). 20 papers are grouped in this hub page. Common evaluation modes: Simulation Env, Automatic Metrics. Frequently cited benchmark: retrieval. Common metric signal: cost. Newest paper in this set is from Feb 25, 2026.
Human Feedback Mix
Evaluation Modes
Top Benchmarks
Top Metrics
Chenyv Liu, Wentao Tan, Lei Zhu, Fengling Li, Jingjing Li · Feb 25, 2026 · Citations: 0
Reinforcement learning enhances physical grounding through exploration yet typically relies on external reward signals that remain isolated from the agent's internal states.
Xiaoxuan Wang, Han Zhang, Haixin Wang, Yidan Shi, Ruoyan Li · Feb 25, 2026 · Citations: 0
Agentic reinforcement learning (ARL) has rapidly gained attention as a promising paradigm for training agents to solve complex, multi-step interactive tasks.
Yue Yang, Shuo Cheng, Yu Fang, Homanga Bharadhwaj, Mingyu Ding · Feb 25, 2026 · Citations: 0
We introduce a 21-task simulation benchmark consisting of two challenging suites: LIBERO-Long++ and Ultra-Long.
Victor Reijgwart, Cesar Cadena, Roland Siegwart, Lionel Ott · Feb 24, 2026 · Citations: 0
Hierarchical, multi-resolution volumetric mapping approaches are widely used to represent large and complex environments as they can efficiently capture their occupancy and connectivity information.
Yanna Jiang, Delong Li, Haiyu Deng, Baihe Ma, Xu Wang · Feb 24, 2026 · Citations: 0
Agentic systems increasingly rely on reusable procedural capabilities, \textit{a.k.a., agentic skills}, to execute long-horizon workflows reliably.
Hyeonje Choi, Jeongsoo Lee, Hyojun Lee, Jay-Yoon Lee · Feb 24, 2026 · Citations: 0
We introduce \ToolMATH, a math-grounded benchmark that evaluates tool-augmented language models in realistic multi-tool environments where the output depends on calling schema-specified tools and sustaining multi-step execution.
Victor De Lima, Jiqun Liu, Grace Hui Yang · Feb 17, 2026 · Citations: 0
Within this framework, we construct framing-sensitive agent personas by fine-tuning language models with framing-conditioned loss attenuation, inducing targeted biases while preserving overall task competence.
Skyler Hallinan, Thejas Venkatesh, Xiang Ren, Sai Praneeth Karimireddy, Ashwin Paranjape · Feb 16, 2026 · Citations: 0
Tool-calling is essential for Large Language Model (LLM) agents to complete real-world tasks.
Aladin Djuhera, Swanand Ravindra Kadhe, Farhan Ahmed, Heiko Ludwig, Holger Boche · Feb 12, 2026 · Citations: 0
Advances in large language models (LLMs) are driving a shift toward using reinforcement learning (RL) to train agents from iterative, multi-turn interactions across tasks.
Venus Team, Changlong Gao, Zhangxuan Gu, Yulin Liu, Xinyu Qiu · Feb 9, 2026 · Citations: 0
GUI agents have emerged as a powerful paradigm for automating interactions in digital environments, yet achieving both broad generality and consistently strong task performance remains challenging.
Huatong Song, Lisheng Huang, Shuang Sun, Jinhao Jiang, Ran Le · Feb 3, 2026 · Citations: 0
In this technical report, we present SWE-Master, an open-source and fully reproducible post-training framework for building effective software engineering agents.
Xiang Li, Ning Yan, Masood Mortazavi · Jan 29, 2026 · Citations: 0
While Large Language Models (LLMs) have demonstrated strong zero-shot reasoning capabilities, their deployment as embodied agents still faces fundamental challenges in long-horizon planning.
Bertie Vidgen, Austin Mann, Abby Fennelly, John Wright Stanly, Lucas Rothman · Jan 20, 2026 · Citations: 0
We introduce the AI Productivity Index for Agents (APEX-Agents), a benchmark for assessing whether AI agents can execute long-horizon, cross-application tasks created by investment banking analysts, management consultants, and corporate law
Xinyu Zhu, Yuzhu Cai, Zexi Liu, Bingyang Zheng, Cheng Wang · Jan 15, 2026 · Citations: 0
The advancement of artificial intelligence toward agentic science is currently bottlenecked by the challenge of ultra-long-horizon autonomy, the ability to sustain strategic coherence and iterative correction over experimental cycles spanni
Chi-Pin Huang, Yunze Man, Zhiding Yu, Min-Hung Chen, Jan Kautz · Jan 14, 2026 · Citations: 0
Fast-ThinkAct learns to reason efficiently with latent CoTs by distilling from a teacher, driven by a preference-guided objective to align manipulation trajectories that transfers both linguistic and visual planning capabilities for embodie
Huilin Xu, Zhuoyang Liu, Yixiang Luomei, Feng Xu · Dec 9, 2025 · Citations: 0
Extensive experiments on the AerialVLN and OpenFly benchmark validate the effectiveness of our method.
Qiusi Zhan, Hyeonjeong Ha, Rui Yang, Sirui Xu, Hanyang Chen · Oct 31, 2025 · Citations: 0
Recent advances in Vision-Language Models (VLMs) have propelled embodied agents by enabling direct perception, reasoning, and planning task-oriented actions from visual inputs.
NVIDIA, :, Arslan Ali, Junjie Bai, Maciej Bala · Oct 28, 2025 · Citations: 0
These capabilities enable more reliable synthetic data generation, policy evaluation, and closed-loop simulation for robotics and autonomous systems.
Chengshu Li, Mengdi Xu, Arpit Bahety, Hang Yin, Yunfan Jiang · Oct 21, 2025 · Citations: 0
Imitation learning from large-scale, diverse human demonstrations has been shown to be effective for training robots, but collecting such data is costly and time-consuming.
Shangheng Du, Jiabao Zhao, Jinxin Shi, Zhentao Xie, Xin Jiang · Mar 16, 2025 · Citations: 0
With the rapid development of Large Language Models (LLMs), LLM-based agents have been widely adopted in various fields, becoming essential for autonomous decision-making and interactive tasks.
