HFEPX Hub
Updated from current HFEPX corpus (Feb 26, 2026). 74 papers are grouped in this hub page. Common evaluation modes: Automatic Metrics, Simulation Env. Frequently cited benchmark: retrieval. Common metric signal: accuracy. Newest paper in this set is from Feb 25, 2026.
Human Feedback Mix
Evaluation Modes
Top Benchmarks
Top Metrics
Rui Yang, Qianhui Wu, Zhaoyang Wang, Hanyang Chen, Ke Yang · Feb 25, 2026 · Citations: 0
Open-source native GUI agents still lag behind closed-source systems on long-horizon navigation tasks.
Patrick Tser Jern Kon, Archana Pradeep, Ang Chen, Alexander P. Ellis, Warren Hunt · Feb 25, 2026 · Citations: 0
Our approach combines supervised fine-tuning on expert-augmented trajectories with agentic reinforcement learning that explicitly discourages degenerative looping and unproductive expert collaboration.
Shunsuke Ubukata · Feb 25, 2026 · Citations: 0
Chain-of-Thought (CoT) distillation from Large Language Models (LLMs) often induces "overthinking" in Small Language Models (SLMs), leading to performance degradation and excessive token consumption.
Tomoya Kawabe, Rin Takano · Feb 25, 2026 · Citations: 0
We present a hierarchical multi-agent LLM-based planner with prompt optimization: an upper layer decomposes tasks and assigns them to lower-layer agents, which generate PDDL problems solved by a classical planner.
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.
Kangning Shen, Jingyuan Zhang, Chenxi Sun, Wencong Zeng, Yang Yue · Feb 25, 2026 · Citations: 0
Large Language Models (LLMs) have demonstrated significant potential as autonomous software engineering (SWE) agents.
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.
Xiaoke Huang, Bhavul Gauri, Kam Woh Ng, Tony Ng, Mengmeng Xu · Feb 25, 2026 · Citations: 0
On cross-family OOD evaluation, VecGlypher substantially outperforms both general-purpose LLMs and specialized vector-font baselines for text-only generation, while image-referenced generation reaches a state-of-the-art performance, with ma
Darshan Gadginmath, Ahmed Allibhoy, Fabio Pasqualetti · Feb 24, 2026 · Citations: 0
However, a critical gap remains for their deployment in safety-critical domains: the lack of formal guarantees that generated samples will satisfy hard constraints.
Dmitrii Pantiukhin, Ivan Kuznetsov, Boris Shapkin, Antonia Anna Jost, Thomas Jung · Feb 24, 2026 · Citations: 0
Here we present PANGAEA-GPT, a hierarchical multi-agent framework designed for autonomous data discovery and analysis.
Yining Hong, Huang Huang, Manling Li, Li Fei-Fei, Jiajun Wu · Feb 24, 2026 · Citations: 0
Drawing upon human reflective practitioners, we introduce Reflective Test-Time Planning, which integrates two modes of reflection: \textit{reflection-in-action}, where the agent uses test-time scaling to generate and score multiple candidat
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.
Dengjia Zhang, Xiaoou Liu, Lu Cheng, Yaqing Wang, Kenton Murray · Feb 24, 2026 · Citations: 0
Large language models (LLMs) are increasingly deployed as multi-step decision-making agents, where effective reward design is essential for guiding learning.
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.
Che Wang, Fuyao Zhang, Jiaming Zhang, Ziqi Zhang, Yinghui Wang · Feb 24, 2026 · Citations: 0
Large Language Model (LLM) agents are susceptible to Indirect Prompt Injection (IPI) attacks, where malicious instructions in retrieved content hijack the agent's execution.
W. Frederick Zimmerman · Feb 24, 2026 · Citations: 0
I introduce semantic novelty--cosine distance between each paragraph's sentence embedding and the running centroid of all preceding paragraphs--as an information-theoretic measure of narrative structure at corpus scale.
Alex Stein, Furong Huang, Tom Goldstein · Feb 24, 2026 · Citations: 0
Held-out in-domain accuracy under asymmetric evaluation improves from 46.0\% to 62.0\%, and average (maj@8) accuracy on public document-free math benchmarks improves from 20.2\% to 35.4\%.
Yue Pan, Xingyao Wang, Hanyue Zhang, Liwei Liu, Changxin Li · Feb 23, 2026 · Citations: 0
The model's high sensitivity was further corroborated by additional follow-up data, confirming its efficacy in predicting HF deterioration and its potential to secure patient safety in remote, home-based settings.
Chongyang Gao, Diji Yang, Shuyan Zhou, Xichen Yan, Luchuan Song · Feb 23, 2026 · Citations: 0
We introduce \CFE{} (\textbf{C}lassroom \textbf{F}inal \textbf{E}xam), a multimodal benchmark for evaluating the reasoning capabilities of large language models across more than 20 STEM domains.
Dongming Jiang, Yi Li, Songtao Wei, Jinxin Yang, Ayushi Kishore · Feb 22, 2026 · Citations: 0
Agentic memory systems enable large language model (LLM) agents to maintain state across long interactions, supporting long-horizon reasoning and personalization beyond fixed context windows.
Maryam Amirizaniani, Alireza Salemi, Hamed Zamani · Feb 22, 2026 · Citations: 0
Personalization in Question Answering (QA) requires answers that are both accurate and aligned with users' background, preferences, and historical context.
Diogo Glória-Silva, David Semedo, João Maglhães · Feb 22, 2026 · Citations: 0
Our evaluation shows that VIGiA outperforms existing state-of-the-art models on all tasks in a conversational plan guidance setting, reaching over 90\% accuracy on plan-aware VQA.
Qijie You, Wenkai Yu, Wentao Zhang · Feb 22, 2026 · Citations: 0
With the rapid advancement of agent-based methods in recent years, Agentic RAG has undoubtedly become an important research direction.
Chenhang Cui, An Zhang, Yuxin Chen, Gelei Deng, Jingnan Zheng · Feb 22, 2026 · Citations: 0
Across diverse mathematics and perception benchmarks, SNRF consistently enhances LVLM inference performance while preserving perceptual capabilities.
Wilson Y. Lee · Feb 22, 2026 · Citations: 0
Why do language agents fail on tasks they are capable of solving?
Wenlong Meng, Chen Gong, Terry Yue Zhuo, Fan Zhang, Kecen Li · Feb 21, 2026 · Citations: 0
LLM agents rely heavily on high-quality trajectory data to guide their problem-solving behaviors, yet producing such data requires substantial task design, high-capacity model generation, and manual filtering.
Stephen Russell · Feb 21, 2026 · Citations: 0
Most semantic drift studies report multiple signals e.g., embedding displacement, neighbor changes, distributional divergence, and recursive trajectory instability, without a shared explanatory theory that relates them.
Zhiqin Qian, Ryan Diaz, Sangwon Seo, Vaibhav Unhelkar · Feb 20, 2026 · Citations: 0
When training artificial intelligence (AI) to perform tasks, humans often care not only about whether a task is completed but also how it is performed.
Aidar Myrzakhan, Tianyi Li, Bowei Guo, Shengkun Tang, Zhiqiang Shen · Feb 19, 2026 · Citations: 0
Diffusion Language Models (DLMs) incur high inference cost due to iterative denoising, motivating efficient pruning.
Yue Liu, Zhiyuan Hu, Flood Sung, Jiaheng Zhang, Bryan Hooi · Feb 19, 2026 · Citations: 0
This paper introduces KLong, an open-source LLM agent trained to solve extremely long-horizon tasks.
Yunseung Lee, Subin Kim, Youngjun Kwak, Jaegul Choo · Feb 19, 2026 · Citations: 0
However, such errors have rarely been captured by existing benchmarks.
Jared Moore, Rasmus Overmark, Ned Cooper, Beba Cibralic, Nick Haber · Feb 19, 2026 · Citations: 0
A growing body of work attempts to evaluate the theory of mind (ToM) abilities of humans and large language models (LLMs) using static, non-interactive question-and-answer benchmarks.
Vered Tohar, Tsahi Hayat, Amir Leshem · Feb 18, 2026 · Citations: 0
In a blinded authorship test with 50 humanities students and graduates (three AI poems and three poems by well-known poets each), judgments were at chance: human poems were labeled human 54% of the time and AI poems 52%, with 95% confidence
Ido Levy, Eilam Shapira, Yinon Goldshtein, Avi Yaeli, Nir Mashkif · Feb 18, 2026 · Citations: 0
Agentic systems, AI architectures that autonomously execute multi-step workflows to achieve complex goals, are often built using repeated large language model (LLM) calls for closed-set decision tasks such as routing, shortlisting, gating,
GLM-5-Team, :, Aohan Zeng, Xin Lv, Zhenyu Hou · Feb 17, 2026 · Citations: 0
We present GLM-5, a next-generation foundation model designed to transition the paradigm of vibe coding to agentic engineering.
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.
Emanuele Ricco, Elia Onofri, Lorenzo Cima, Stefano Cresci, Roberto Di Pietro · Feb 16, 2026 · Citations: 0
Hallucinations -- fluent but factually incorrect responses -- pose a major challenge to the reliability of language models, especially in multi-step or agentic settings.
Sara Rajaee, Sebastian Vincent, Alexandre Berard, Marzieh Fadaee, Kelly Marchisio · Feb 16, 2026 · Citations: 0
We systematically evaluate several open- and closed-weights RLMs on the WMT24++ benchmark and find that enabling explicit reasoning consistently degrades translation quality across languages and models.
Chenxi Han, Yuheng Min, Zihao Huang, Ao Hong, Hang Liu · Feb 13, 2026 · Citations: 0
Recent advances in data-driven reinforcement learning and motion tracking have substantially improved humanoid locomotion, yet critical practical challenges remain.
Jianke Yang, Ohm Venkatachalam, Mohammad Kianezhad, Sharvaree Vadgama, Rose Yu · Feb 12, 2026 · Citations: 0
We introduce KeplerAgent, an agentic framework that explicitly follows this scientific reasoning process.
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.
Yuzhu Cai, Zexi Liu, Xinyu Zhu, Cheng Wang, Siheng Chen · Feb 8, 2026 · Citations: 0
Autonomous Machine Learning Engineering (MLE) requires agents to perform sustained, iterative optimization over long horizons.
Yifan Zhu, Xinyu Mu, Tao Feng, Zhonghong Ou, Yuning Gong · Feb 3, 2026 · Citations: 0
To address these issues, we propose OmniRAG-Agent, an agentic omnimodal QA method for budgeted long audio-video reasoning.
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.
Ahsan Bilal, Ahmed Mohsin, Muhammad Umer, Ali Subhan, Hassan Rizwan · Feb 1, 2026 · Citations: 0
For each problem, the agent runs multiple inference iterations.
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.
Jingyi Xu, Xingyu Ren, Zhoupeng Shou, Yumeng Zhang, Zhiqiang You · Jan 24, 2026 · Citations: 0
Large language models show potential in task-oriented dialogue systems, yet existing training methods often rely on token-level likelihood or preference optimization, which poorly align with long-horizon task success.
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
Wang Zixian · Jan 18, 2026 · Citations: 0
Experiments on MATH benchmarks show that the Hilbert projection formulation prevents gradient saturation typical of KL-constrained methods.
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
Weizhe Lin, Hui-Ling Zhen, Shuai Yang, Xian Wang, Renxi Liu · Dec 20, 2025 · Citations: 0
The rapid development of large language model (LLM)-based agents has unlocked new possibilities for autonomous multi-turn reasoning and tool-augmented decision-making.
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.
Qiming Bao, Xiaoxuan Fu, Michael Witbrock · Dec 6, 2025 · Citations: 0
We present a controlled evaluation framework consisting of four stress tests: (1) rule deletion (redundant vs.
Chenliang Li, Adel Elmahdy, Alex Boyd, Zhongruo Wang, Siliang Zeng · Nov 25, 2025 · Citations: 0
Reinforcement learning (RL) algorithms such as PPO and GRPO are widely used to train large language models (LLMs) for multi-turn agentic tasks.
Myung Ho Kim · Nov 21, 2025 · Citations: 0
Large language model agents suffer from fundamental architectural problems: entangled reasoning and execution, memory volatility, and uncontrolled action sequences.
