HFEPX Hub
Updated from current HFEPX corpus (Feb 26, 2026). 109 papers are grouped in this hub page. Common evaluation modes: Simulation Env, Automatic Metrics. 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
Leon Pielage, Ole Hätscher, Mitja Back, Bernhard Marschall, Benjamin Risse · Feb 25, 2026 · Citations: 0
This work demonstrates the feasibility of modular, personality-adaptive architectures for education, customer support, and broader human-computer interaction.
Cheng-Yeh Yang, Chien-Chun Wang, Li-Wei Chen, Hung-Shin Lee, Hsin-Min Wang · Feb 25, 2026 · Citations: 0
Low-resource automatic speech recognition (ASR) continues to pose significant challenges, primarily due to the limited availability of transcribed data for numerous languages.
Bitan Majumder, Anirban Sen · Feb 25, 2026 · Citations: 0
Sarcasm detection in multilingual and code-mixed environments remains a challenging task for natural language processing models due to structural variations, informal expressions, and low-resource linguistic availability.
Masha Naslidnyk · Feb 25, 2026 · Citations: 0
Representing, comparing, and measuring the distance between probability distributions is a key task in computational statistics and machine learning.
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.
Kazi Samin Yasar Alam, Md Tanbir Chowdhury, Tamim Ahmed, Ajwad Abrar, Md Rafid Haque · Feb 25, 2026 · Citations: 0
We benchmark transformer-based models and evaluate zero-shot large language models under structured prompting.
Touseef Hasan, Laila Cure, Souvika Sarkar · Feb 25, 2026 · Citations: 0
We conduct a pilot evaluation study using community-sourced queries to examine system behavior in realistic scenarios.
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.
Chun-wei Ho, Sabato Marco Siniscalchi, Kai Li, Chin-Hui Lee · Feb 25, 2026 · Citations: 0
Evaluation on simulation data shows that score-guided learning achieves very good music segmentation and separation results.
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.
Rui Zhao, Xihui Li, Yizheng Zhang, Yuzhen Liu, Zhong Zhang · Feb 24, 2026 · Citations: 0
Multi-agent deep Reinforcement Learning (RL) has made significant progress in developing intelligent game-playing agents in recent years.
Zhangjie Xia, Yu Yang, Pan Xu · Feb 24, 2026 · Citations: 0
Off-dynamics offline reinforcement learning (RL) aims to learn a policy for a target domain using limited target data and abundant source data collected under different transition dynamics.
Mark Marron · Feb 24, 2026 · Citations: 0
Fully leveraging the capabilities of AI agents in software development requires a rethinking of the software ecosystem itself.
Xuan Luo, Yubin Chen, Zhiyu Hou, Linpu Yu, Geng Tu · Feb 24, 2026 · Citations: 0
As large language models (LLMs) are increasingly embedded in everyday decision-making, their safety responsibilities extend beyond reacting to explicit harmful intent toward anticipating unintended but consequential risks.
Haoyang Li, Changsong Liu, Wei Rao, Hao Shi, Sakriani Sakti · Feb 24, 2026 · Citations: 0
Automatic speech recognition (ASR) degrades severely in noisy environments.
ChengYou Li, XiaoDong Liu, XiangBao Meng, XinYu Zhao · Feb 24, 2026 · Citations: 0
The paradigm of Large Language Models is undergoing a fundamental transition from static inference engines to dynamic autonomous cognitive systems.While current research primarily focuses on scaling context windows or optimizing prompt engi
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.
Yaacov Pariente, Vadim Indelman · Feb 24, 2026 · Citations: 0
We present POMDPPlanners, an open-source Python package for empirical evaluation of Partially Observable Markov Decision Process (POMDP) planning algorithms.
Azrin Sultana, Firoz Ahmed · Feb 24, 2026 · Citations: 0
Natural Language Processing enables computers to understand human language by analysing and classifying text efficiently with deep-level grammatical and semantic features.
Che Wang, Jiaming Zhang, Ziqi Zhang, Zijie Wang, Yinghui Wang · Feb 24, 2026 · Citations: 0
The integration of external data services (e.g., Model Context Protocol, MCP) has made large language model-based agents increasingly powerful for complex task execution.
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.
Peter Hase, Christopher Potts · Feb 24, 2026 · Citations: 0
Inspecting Chain-of-Thought reasoning is among the most common means of understanding why an LLM produced its output.
Anirudh Ajith, Amanpreet Singh, Jay DeYoung, Nadav Kunievsky, Austin C. Kozlowski · Feb 24, 2026 · Citations: 0
We introduce PreScience -- a scientific forecasting benchmark that decomposes the research process into four interdependent generative tasks: collaborator prediction, prior work selection, contribution generation, and impact prediction.
Yu Li, Pranav Narayanan Venkit, Yada Pruksachatkun, Chien-Sheng Wu · Feb 23, 2026 · Citations: 0
Existing evaluation approaches rely on demographic surveys, personality questionnaires, or short AI-led interviews as proxies, but lack direct assessment against what individuals actually said.
Maijunxian Wang, Ruisi Wang, Juyi Lin, Ran Ji, Thaddäus Wiedemer · Feb 23, 2026 · Citations: 0
We further present VBVR-Bench, a verifiable evaluation framework that moves beyond model-based judging by incorporating rule-based, human-aligned scorers, enabling reproducible and interpretable diagnosis of video reasoning capabilities.
Zachary Ravichandran, David Snyder, Alexander Robey, Hamed Hassani, Vijay Kumar · Feb 23, 2026 · Citations: 0
Traditional safety approaches enforce fixed constraints in user-specified contexts, limiting their ability to handle the open-ended contextual variability of real-world deployment.
Ian Steenstra, Paola Pedrelli, Weiyan Shi, Stacy Marsella, Timothy W. Bickmore · Feb 23, 2026 · Citations: 0
Large Language Models (LLMs) are increasingly utilized for mental health support; however, current safety benchmarks often fail to detect the complex, longitudinal risks inherent in therapeutic dialogue.
Maciej Świechowski, Adam Żychowski, Jacek Mańdziuk · Feb 22, 2026 · Citations: 0
The main results indicate that three of the evaluated models generally perform well across most experimental settings, with performance degradation observed as the evaluation horizon increases (i.e., with a higher number of game steps).
Xiaochuan Li, Ryan Ming, Pranav Setlur, Abhijay Paladugu, Andy Tang · Feb 22, 2026 · Citations: 0
LLM agents are increasingly expected to function as general-purpose systems capable of resolving open-ended user requests.
Jiamin Yao, Eren Gultepe · Feb 20, 2026 · Citations: 0
Applied to LLaMA-2-7B, SPQ achieves up to 75% memory reduction while maintaining or improving perplexity (e.g., WikiText-2 5.47 to 4.91) and preserving accuracy on downstream benchmarks such as C4, TruthfulQA, and GSM8K.
Usman Anwar, Tim Bakker, Dana Kianfar, Cristina Pinneri, Christos Louizos · Feb 20, 2026 · Citations: 0
Chain-of-thought (CoT) monitors are LLM-based systems that analyze reasoning traces to detect when outputs may exhibit attributes of interest, such as test-hacking behavior during code generation.
Muhammad Tayyab Khan, Lequn Chen, Wenhe Feng, Seung Ki Moon · Feb 20, 2026 · Citations: 0
When deterministic scoring cannot resolve an ambiguity, the system escalates to multimodal and constrained large-language-model reasoning, followed by a single human-in-the-loop (HITL) review step.
Ortal Hadad, Edoardo Loru, Jacopo Nudo, Niccolò Di Marco, Matteo Cinelli · Feb 20, 2026 · Citations: 0
We analyze compression behavior across three progressively more complex information ecosystems: controlled human-LLM continuations, generative mediation of a knowledge infrastructure (Wikipedia vs.
Zihan Guan, Rituparna Datta, Mengxuan Hu, Shunshun Liu, Aiying Zhang · Feb 20, 2026 · Citations: 0
Recent work has explored LLM-based agentic frameworks to automatically construct mechanistic models from data; however, existing problem settings substantially oversimplify real-world conditions, leaving it unclear whether LLM-generated mec
Zhining Zhang, Wentao Zhu, Chi Han, Yizhou Wang, Heng Ji · Feb 19, 2026 · Citations: 0
Neuroscience has uncovered a fundamental mechanism of our social nature: human brain activity becomes synchronized with others in many social contexts involving interaction.
Srikumar Nayak · Feb 19, 2026 · Citations: 0
Here's the corrected paragraph with all punctuation and formatting issues fixed: Financial risk systems usually follow a two-step routine: a model predicts return or risk, and then an optimizer makes a decision such as a portfolio rebalance
Ofer Meshi, Krisztian Balog, Sally Goldman, Avi Caciularu, Guy Tennenholtz · Feb 18, 2026 · Citations: 0
We introduce ConvApparel, a new dataset of human-AI conversations designed to address this gap.
Iman Ahmadi, Mehrshad Taji, Arad Mahdinezhad Kashani, AmirHossein Jadidi, Saina Kashani · Feb 18, 2026 · Citations: 0
MALLVI presents a Multi Agent Large Language and Vision framework that enables closed-loop feedback driven robotic manipulation.
Kushal Kedia, Tyler Ga Wei Lum, Jeannette Bohg, C. Karen Liu · Feb 18, 2026 · Citations: 0
The ability to manipulate tools significantly expands the set of tasks a robot can perform.
Yiqing Xie, Emmy Liu, Gaokai Zhang, Nachiket Kotalwar, Shubham Gandhi · Feb 18, 2026 · Citations: 0
When assessing the quality of coding agents, predominant benchmarks focus on solving single issues on GitHub, such as SWE-Bench.
Wenxuan Ding, Nicholas Tomlin, Greg Durrett · Feb 18, 2026 · Citations: 0
Each problem has latent environment state that can be reasoned about via a prior which is passed to the LLM agent.
Subrit Dikshit · Feb 18, 2026 · Citations: 0
The rapid proliferation of Large Language Models (LLMs) has revolutionized Natural Language Processing (NLP) but has simultaneously created a "resource divide." State-of-the-art legal intelligence systems typically rely on massive parameter
Zexue He, Yu Wang, Churan Zhi, Yuanzhe Hu, Tzu-Ping Chen · Feb 18, 2026 · Citations: 0
Existing evaluations of agents with memory typically assess memorization and action in isolation.
Adnan El Assadi, Isaac Chung, Chenghao Xiao, Roman Solomatin, Animesh Jha · Feb 17, 2026 · Citations: 0
We introduce the Massive Audio Embedding Benchmark (MAEB), a large-scale benchmark covering 30 tasks across speech, music, environmental sounds, and cross-modal audio-text reasoning in 100+ languages.
Zhouzhou Shen, Xueyu Hu, Xiyun Li, Tianqing Fang, Juncheng Li · Feb 17, 2026 · Citations: 0
Web agents based on large language models have demonstrated promising capability in automating web tasks.
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.
Mason Nakamura, Abhinav Kumar, Saswat Das, Sahar Abdelnabi, Saaduddin Mahmud · Feb 16, 2026 · Citations: 0
Multi-agent systems, where LLM agents communicate through free-form language, enable sophisticated coordination for solving complex cooperative tasks.
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.
Jaione Bengoetxea, Itziar Gonzalez-Dios, Rodrigo Agerri · Feb 16, 2026 · Citations: 0
Physical commonsense reasoning represents a fundamental capability of human intelligence, enabling individuals to understand their environment, predict future events, and navigate physical spaces.
Ming Li, Xirui Li, Tianyi Zhou · Feb 15, 2026 · Citations: 0
As large language model agents increasingly populate networked environments, a fundamental question arises: do artificial intelligence (AI) agent societies undergo convergence dynamics similar to human social systems?
Yiming Zhu, Gareth Tyson, Pan Hui · Feb 14, 2026 · Citations: 0
Recent advances in agent-mediated systems have enabled a new paradigm of social network simulation, where AI agents interact with human-like autonomy.
Sha Li, Stefano Petrangeli, Yu Shen, Xiang Chen · Feb 14, 2026 · Citations: 0
We introduce LaySPA, a reinforcement learning framework that equips large language models (LLMs) with explicit and interpretable spatial reasoning for content-aware graphic layout design.
Qi Liu, Ruochen Hao, Can Li, Wanjing Ma · Feb 14, 2026 · Citations: 0
We present OR-Agent, a configurable multi-agent research framework designed for automated exploration in rich experimental environments.
Kais Allkivi · Feb 13, 2026 · Citations: 0
Additional evaluation on an earlier exam sample revealed that the writings have become more complex over a 7-10-year period, while accuracy still reached 0.8 with some feature sets.
Huanyao Zhang, Jiepeng Zhou, Bo Li, Bowen Zhou, Yanzhe Shan · Feb 13, 2026 · Citations: 0
Multimodal large language models (MLLMs), equipped with increasingly advanced planning and tool-use capabilities, are evolving into autonomous agents capable of performing multimodal web browsing and deep search in open-world environments.
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.
Ailin Huang, Ang Li, Aobo Kong, Bin Wang, Binxing Jiao · Feb 11, 2026 · Citations: 0
We introduce Step 3.5 Flash, a sparse Mixture-of-Experts (MoE) model that bridges frontier-level agentic intelligence and computational efficiency.
Steven Liu, Jane Luo, Xin Zhang, Aofan Liu, Hao Liu · Feb 11, 2026 · Citations: 0
Current evaluations systematically overlook the third goal.
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.
