HFEPX Hub
Updated from current HFEPX corpus (Feb 26, 2026). 281 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
Cole Simmons, Richard Diehl Martinez, Dan Jurafsky · Feb 25, 2026 · Citations: 0
Sumerian transliteration is a conventional system for representing a scholar's interpretation of a tablet in the Latin script.
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.
Xi Ye, Wuwei Zhang, Fangcong Yin, Howard Yen, Danqi Chen · Feb 25, 2026 · Citations: 0
Across multiple instruction-tuned and reasoning models, DySCO consistently improves performance on challenging long-context reasoning benchmarks, yielding relative gains of up to 25% on MRCR and LongBenchV2 at 128K context length with modes
Lingfeng Ren, Weihao Yu, Runpeng Yu, Xinchao Wang · Feb 25, 2026 · Citations: 0
Object hallucination is a critical issue in Large Vision-Language Models (LVLMs), where outputs include objects that do not appear in the input image.
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.
Walter Hernandez Cruz, Peter Devine, Nikhil Vadgama, Paolo Tasca, Jiahua Xu · Feb 25, 2026 · Citations: 0
We introduce DLT-Corpus, the largest domain-specific text collection for Distributed Ledger Technology (DLT) research to date: 2.98 billion tokens from 22.12 million documents spanning scientific literature (37,440 publications), United Sta
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.
Boqi Chen, Xudong Liu, Jiachuan Peng, Marianne Frey-Marti, Bang Zheng · Feb 25, 2026 · Citations: 0
Multimodal large language models (MLLMs) have shown great potential in medical applications, yet existing benchmarks inadequately capture real-world clinical complexity.
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.
Lin Zhu, Lei You · Feb 25, 2026 · Citations: 0
Counterfactual explanation (CE) is an important domain within post-hoc explainability.
Marcel Lamott, Saifullah Saifullah, Nauman Riaz, Yves-Noel Weweler, Tobias Alt-Veit · Feb 25, 2026 · Citations: 0
We evaluate across eleven benchmarks spanning key information extraction, question answering, document classification, and document layout analysis.
MD. Sagor Chowdhury, Adiba Fairooz Chowdhury · Feb 25, 2026 · Citations: 0
We describe our end-to-end system for Bengali long-form speech recognition (ASR) and speaker diarization submitted to the DL Sprint 4.0 competition on Kaggle.
Xu Yang, Chenhui Lin, Xiang Ma, Dong Liu, Ran Zheng · Feb 25, 2026 · Citations: 0
Considering the operational scenarios and requirements in real-world ADNs, in this paper, we propose a hybrid knowledge-data-driven approach that leverages dynamic collaboration between a large language model (LLM) agent and a reinforcement
Guanyi Qin, Xiaozhen Wang, Zhu Zhuo, Chang Han Low, Yuancan Xiao · Feb 25, 2026 · Citations: 0
Existing AI systems offer binary safety verification or static detection, ignoring the phase-dependent nature of intraoperative reasoning.
Shaoxuan Wu, Jingkun Chen, Chong Ma, Cong Shen, Xiao Zhang · Feb 25, 2026 · Citations: 0
Human-AI collaboration seeks to enhance the reliability of diagnostic models by integrating the behaviors of controllable radiologists.
Yexing Du, Youcheng Pan, Zekun Wang, Zheng Chu, Yichong Huang · Feb 25, 2026 · Citations: 0
Experimental results demonstrate that our framework surpasses all existing methods on the Multi30K multimodal machine translation benchmark, achieving new state-of-the-art results.
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.
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.
Changli Tang, Shurui Li, Junliang Wang, Qinfan Xiao, Zhonghao Zhai · Feb 25, 2026 · Citations: 0
Extensive evaluations demonstrate that NOBEL serves as a robust generalist across standard single-modal tasks.
Jesse He, Helen Jenne, Max Vargas, Davis Brown, Gal Mishne · Feb 24, 2026 · Citations: 0
The recent field of neural algorithmic reasoning (NAR) studies the ability of graph neural networks (GNNs) to emulate classical algorithms like Bellman-Ford, a phenomenon known as algorithmic alignment.
Alina Devkota, Jacob Thrasher, Donald Adjeroh, Binod Bhattarai, Prashnna K. Gyawali · Feb 24, 2026 · Citations: 0
Extensive experiments on both natural and medical image benchmarking datasets, across diverse model architectures, demonstrate that FedVG consistently improves performance, particularly in highly heterogeneous settings.
Daniel Tamayo, Iñaki Lacunza, Paula Rivera-Hidalgo, Severino Da Dalt, Javier Aula-Blasco · Feb 24, 2026 · Citations: 0
We introduce MrBERT, a family of 150M-300M parameter encoders built on the ModernBERT architecture and pre-trained on 35 languages and code.
Sameer Ambekar, Reza Nasirigerdeh, Peter J. Schuffler, Lina Felsner, Daniel M. Lang · Feb 24, 2026 · Citations: 0
We extensively evaluate our method with state-of-the-art baselines using two backbones across nine medical and natural-domain generalization image classification datasets, showing consistent gains across standard evaluation and challenging
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.
Evan Kim, Hyunwoo Ryu, Thomas W. Mitchel, Vincent Sitzmann · Feb 24, 2026 · Citations: 0
Across several compute levels, we demonstrate that our encoder-decoder architecture, which we call the Scalable View Synthesis Model (SVSM), scales as effectively as decoder-only models, achieves a superior performance-compute Pareto fronti
Hanxiang Qin, Alexander Martin, Rohan Jha, Chunsheng Zuo, Reno Kriz · Feb 24, 2026 · Citations: 0
We study efficient multi-vector retrieval for late interaction in any modality.
Anas Barakat, Souradip Chakraborty, Khushbu Pahwa, Amrit Singh Bedi · Feb 24, 2026 · Citations: 0
Pass@k is a widely used performance metric for verifiable large language model tasks, including mathematical reasoning, code generation, and short-answer reasoning.
Sepehr Salem Ghahfarokhi, M. Moein Esfahani, Raj Sunderraman, Vince Calhoun, Mohammed Alser · Feb 24, 2026 · Citations: 0
Deep learning has significantly advanced automated brain tumor diagnosis, yet clinical adoption remains limited by interpretability and computational constraints.
Samah Fodeh, Linhai Ma, Yan Wang, Srivani Talakokkul, Ganesh Puthiaraju · Feb 24, 2026 · Citations: 0
Patient-generated text such as secure messages, surveys, and interviews contains rich expressions of the patient voice (PV), reflecting communicative behaviors and social determinants of health (SDoH).
Ziwei Niu, Hao Sun, Shujun Bian, Xihong Yang, Lanfen Lin · Feb 24, 2026 · Citations: 0
Accurate interpretation of electrocardiogram (ECG) signals is crucial for diagnosing cardiovascular diseases.
Debjit Paul, Daniel Murphy, Milan Gritta, Ronald Cardenas, Victor Prokhorov · Feb 24, 2026 · Citations: 0
Large language model (LLM)-based agents are increasingly used to solve complex tasks involving tool use, such as web browsing, code execution, and data analysis.
David Anugraha, Vishakh Padmakumar, Diyi Yang · Feb 24, 2026 · Citations: 0
Based on this formulation, we introduce SparkMe, a multi-agent LLM interviewer that performs deliberative planning via simulated conversation rollouts to select questions with high expected utility.
Yanrui Wu, Lingling Zhang, Xinyu Zhang, Jiayu Chang, Pengyu Li · Feb 24, 2026 · Citations: 0
Evaluations of large language models (LLMs) primarily emphasize convergent logical reasoning, where success is defined by producing a single correct proof.
Tianhao Fu, Yucheng Chen · Feb 24, 2026 · Citations: 0
MIPCandy provides a complete, modular pipeline spanning data loading, training, inference, and evaluation, allowing researchers to obtain a fully functional process workflow by implementing a single method, $\texttt{build_network}$, while r
Kun Yuan, Junyu Bi, Daixuan Cheng, Changfa Wu, Shuwen Xiao · Feb 24, 2026 · Citations: 0
Modern recommender systems leverage ultra-long user behavior sequences to capture dynamic preferences, but end-to-end modeling is infeasible in production due to latency and memory constraints.
Yang Zhang, Danyang Li, Yuxuan Li, Xin Zhang, Tianyu Xie · Feb 24, 2026 · Citations: 0
Extensive experiments on perception-intensive benchmarks demonstrate that CrystaL consistently outperforms state-of-the-art baselines, achieving substantial gains in fine-grained visual understanding while maintaining robust reasoning capab
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.
Jaehyun Park, Minyoung Ahn, Minkyu Kim, Jonghyun Lee, Jae-Gil Lee · Feb 24, 2026 · Citations: 0
Previous artifact-aware methodologies depend on human-labeled artifact datasets, which are costly and difficult to scale, underscoring the need for an automated approach to reliably acquire artifact-annotated datasets.
Alagappan Ramanathan, Eunju Kang, Dongsu Han, Sangeetha Abdu Jyothi · Feb 24, 2026 · Citations: 0
We present Airavat, the first agentic framework for Internet measurement workflow generation with systematic verification and validation.
Dhita Putri Pratama, Soyeon Caren Han, Yihao Ding · Feb 24, 2026 · Citations: 0
Large Vision-Language Models (LVLMs) achieve strong performance on visual question answering benchmarks, yet often rely on spurious correlations rather than genuine causal reasoning.
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.
Sayantan Dasgupta, Trevor Cohn, Timothy Baldwin · Feb 24, 2026 · Citations: 0
The core learning signal used in language model distillation is the standard Kullback-Leibler (KL) divergence between the student and teacher distributions.
Yun-Jui Tsai, Wei-Yu Chen, Yan-Ru Ju, Yu-Hung Chang, Ti-Rong Wu · Feb 24, 2026 · Citations: 0
Reinforcement learning (RL) agents achieve remarkable performance but remain far less learning-efficient than humans.
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.
Chao Fei, Guozhong Li, Chenxi Liu, Panos Kalnis · Feb 24, 2026 · Citations: 0
Extensive evaluations demonstrate that CHESS surpasses Full-KV quality using only \textbf{1\%} of the KV cache, delivers low-latency stable inference with up to \textbf{4.56$\times$} higher throughput, and consistently outperforms other str
Aram Davtyan, Yusuf Sahin, Yasaman Haghighi, Sebastian Stapf, Pablo Acuaviva · Feb 24, 2026 · Citations: 0
Inspired by the incremental and compositional nature of human communication, we introduce COMmunication inspired Tokenization (COMiT), a framework for learning structured discrete visual token sequences.
Xindian Ma, Rundong Kong, Peng Zhang, Ruoxiang Huang, Yongyu Jiang · Feb 24, 2026 · Citations: 0
We evaluate ID-LoRA on five diverse benchmarks: Mathematical Reasoning, Code Generation, MMLU, CommonsenseQA, and Safety Alignment.
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.
Cuong Chi Le, Minh V. T Pham, Tung Vu Duy, Cuong Duc Van, Huy N. Phan · Feb 24, 2026 · Citations: 0
Our empirical evaluation shows that SpecMind significantly outperforms state-of-the-art approaches in both accuracy and completeness of generated postconditions.
Rakshit Trivedi, Kartik Sharma, David C Parkes · Feb 24, 2026 · Citations: 0
Effective human-AI coordination requires artificial agents capable of exhibiting and responding to human-like behaviors while adapting to changing contexts.
Zhenyu Wang, Xiaofen Xing, Yirong Chen, Xiangmin Xu · Feb 24, 2026 · Citations: 0
Multimodal Role-Playing Agents (MRPAs) are attracting increasing attention due to their ability to deliver more immersive multimodal emotional interactions.
Mukul Chhabra, Luigi Medrano, Arush Verma · Feb 23, 2026 · Citations: 0
Enterprise Retrieval-Augmented Generation (RAG) assistants operate in multi-turn, case-based workflows such as technical support and IT operations, where evaluation must reflect operational constraints, structured identifiers (e.g., error c
Lingwei Gu, Nour Jedidi, Jimmy Lin · Feb 23, 2026 · Citations: 0
Towards the goal of understanding how knowledge is encoded by LLMs, we release NanoKnow, a benchmark dataset that partitions questions from Natural Questions and SQuAD into splits based on whether their answers are present in nanochat's pre
Wanyong Feng, Andrew Lan · Feb 23, 2026 · Citations: 0
We train the model in a two-step process, first via Supervised Fine-Tuning and then via preference optimization for knowledge-response alignment.
Zhongwei Wan, Yun Shen, Zhihao Dou, Donghao Zhou, Yu Zhang · Feb 23, 2026 · Citations: 0
Experiments on multiple reasoning benchmarks demonstrate consistent improvements in accuracy and pass@k, highlighting the importance of dual-scale diversity for deep exploration in RLVR.
Daham Mustafa, Diego Collarana, Yixin Peng, Rafiqul Haque, Christoph Lange-Bever · Feb 23, 2026 · Citations: 0
For these operands, a single scalar constraint admits one interpretation per axis, making policy evaluation non-deterministic.
Wall Kim, Chaeyoung Song, Hanul Kim · Feb 23, 2026 · Citations: 0
Recently, TabPFN has gained attention as a foundation model for tabular data.
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.
