HFEPX Hub
Updated from current HFEPX corpus (Feb 26, 2026). 528 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
Sourav Saha, Dwaipayan Roy, Mandar Mitra · Feb 25, 2026 · Citations: 0
The results of our experiments show that LiCQA significantly outperforms these two state-of-the-art systems on benchmark data with noteworthy reduction in latency.
Satyam Kumar Navneet, Joydeep Chandra, Yong Zhang · Feb 25, 2026 · Citations: 0
Large Language Models (LLMs) are increasingly used to ``professionalize'' workplace communication, often at the cost of linguistic identity.
Bo-Wei Chen, Chung-Chi Chen, An-Zi Yen · Feb 25, 2026 · Citations: 0
Experiments on the Massive Multitask Language Understanding (MMLU) benchmark show that our approach achieves accuracy comparable to the largest model while reducing computational costs by 20\% to 40\%.
Christian Nickel, Laura Schrewe, Florian Mai, Lucie Flek · Feb 25, 2026 · Citations: 0
Theory of Mind (ToM) refers to an agent's ability to model the internal states of others.
Louis Estève, Christophe Servan, Thomas Lavergne, Agata Savary · Feb 25, 2026 · Citations: 0
Diversity has been gaining interest in the NLP community in recent years.
Miyu Oba, Saku Sugawara · Feb 25, 2026 · Citations: 0
Most existing benchmarks focus on judging grammatical acceptability, whereas the ability to interpret meanings conveyed by grammatical forms has received much less attention.
Bo Xue, Yuan Jin, Luoyi Fu, Jiaxin Ding, Xinbing Wang · Feb 25, 2026 · Citations: 0
Across four benchmarks, RADAR achieves 5-6% relative gains on link prediction and triple classification over strong LLM baselines, while increasing task-relevant mutual information in intermediate representations by 62.9%, indicating more r
Sohan Venkatesh, Ashish Mahendran Kurapath, Tejas Melkote · Feb 25, 2026 · Citations: 0
Models produce ranges far wider than true confidence intervals yet still fail to contain the true algorithmic mean in the majority of instances; most perform worse than random guessing and the marginal above-random performance of the best m
Yanbin Wei, Jiangyue Yan, Chun Kang, Yang Chen, Hua Liu · Feb 25, 2026 · Citations: 0
This ``one-size-fits-all'' strategy often neglects model-specific and task-specific preferences, resulting in inaccurate or over-lengthy responses to graph-related queries.
Chia Cheng Chang, An-Zi Yen, Hen-Hsen Huang, Hsin-Hsi Chen · Feb 25, 2026 · Citations: 0
Since individuals may struggle to recall all life details and often confuse events, establishing a system to assist users in recalling forgotten experiences is essential.
Jabez Magomere, Elena Kochkina, Samuel Mensah, Simerjot Kaur, Fernando Acero · Feb 25, 2026 · Citations: 0
Across six evaluation settings, our trained 8B decomposer improves downstream verification performance to (71.75%) macro-F1, outperforming prompt-based approaches ((+1.99), (+6.24)) and existing RL methods ((+5.84)).
Mustafa Dogan, Ilker Kesen, Iacer Calixto, Aykut Erdem, Erkut Erdem · Feb 25, 2026 · Citations: 0
In this paper, we introduce FewMMBench, a comprehensive benchmark designed to evaluate MLLMs under few-shot conditions, with a focus on In-Context Learning (ICL) and Chain-of-Thought (CoT) prompting.
Ruichen Xu, Ying-Jun Angela Zhang, Jianwei Huang · Feb 25, 2026 · Citations: 0
Extensive evaluations on MNIST and CIFAR-10 demonstrate that JSAM achieves up to 15% improvement in test accuracy compared to existing unbiased selection mechanisms while maintaining cost efficiency across varying data heterogeneity levels.
Heejin Jo · Feb 25, 2026 · Citations: 0
Large language models consistently fail the "car wash problem," a viral reasoning benchmark requiring implicit physical constraint inference.
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.
Heng Wang, Changxing Wu · Feb 25, 2026 · Citations: 0
Experimental results on PDTB demonstrate that our approach significantly improves IDRR performance, while human evaluation further confirms that the generated explanations enhance model interpretability.
Abeer Dyoub, Francesca A. Lisi · Feb 25, 2026 · Citations: 0
In a previous work, we introduced the fuzzy Ethical Decision-Making framework (fEDM), a risk-based ethical reasoning architecture grounded in fuzzy logic.
Hyo Jin Kim · Feb 25, 2026 · Citations: 0
Although initially formulated for human truth-telling under asymmetric stakes, the same phase-dynamic architecture extends to AI systems operating under policy constraints and alignment filters.
Shiqi Yan, Yubo Chen, Ruiqi Zhou, Zhengxi Yao, Shuai Chen · Feb 25, 2026 · Citations: 0
Extensive experiments on five KGQA benchmark datasets demonstrate that, to the best of our knowledge, our method achieves state-of-the-art performance, outperforming not only open-source but also even closed-source LLMs.
Andrea Silvi, Ponrawee Prasertsom, Jennifer Culbertson, Devdatt Dubhashi, Moa Johansson · Feb 25, 2026 · Citations: 0
Human recursive numeral systems (i.e., counting systems such as English base-10 numerals), like many other grammatical systems, are highly regular.
Jianghao Yin, Qin Chen, Kedi Chen, Jie Zhou, Xingjiao Wu · Feb 25, 2026 · Citations: 0
Large Vision-Language Models (LVLMs) exhibit outstanding performance on vision-language tasks but struggle with hallucination problems.
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.
Duc Trung Vu, Pham Khanh Chi, Dat Phi Van, Linh Ngo Van, Sang Dinh · Feb 25, 2026 · Citations: 0
Extensive experiments across diverse NLP benchmarks demonstrate that DWA-KD outperforms state-of-the-art KD baselines, while ablation studies confirm the complementary contributions of entropy-based token weighting and embedding and final h
Zhijiang Tang, Linhua Wang, Jiaxin Qi, Weihao Jiang, Peng Hou · Feb 25, 2026 · Citations: 0
Image captioning remains a fundamental task for vision language understanding, yet ground-truth supervision still relies predominantly on human-annotated references.
Zichen Song, Weijia Li · Feb 25, 2026 · Citations: 0
For each policy episode, the system outputs a structured record containing the DAG, indicator mappings, and three evaluation measures: an expected-indicator coverage score, a discovery rate for overlooked but relevant indicators, and a rela
Yukun Chen, Jiaming Li, Longze Chen, Ze Gong, Jingpeng Li · Feb 25, 2026 · Citations: 0
Extensive experiments on various visual reasoning benchmarks show that RuCL yields a remarkable +7.83% average improvement over the Qwen2.5-VL-7B model, achieving a state-of-the-art accuracy of 60.06%.
Muku Akasaka, Soyeon Caren Han · Feb 25, 2026 · Citations: 0
In this paper, we conduct a hypothesis-driven analysis of information injection for VSR across three representative VLMs and two public benchmarks.
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.
Zhenliang Zhang · Feb 25, 2026 · Citations: 0
In the context of the evolution of artificial intelligence (AI), the interaction between humans and AI entities has become increasingly salient, challenging the conventional human-centric paradigms of human-machine interaction.
Nivasini Ananthakrishnan, Meena Jagadeesan · Feb 25, 2026 · Citations: 0
In this work, we investigate the power and limitations of aggregation within a stylized principal-agent framework.
Wenqing Zheng, Dmitri Kalaev, Noah Fatsi, Daniel Barcklow, Owen Reinert · Feb 25, 2026 · Citations: 0
Existing benchmarks primarily compare entire RAG pipelines or introduce new datasets, providing little guidance on selecting or combining retrievers themselves.
Zhihao Li, Yu Feng, Zhilu Lai, Wei Wang · Feb 25, 2026 · Citations: 0
On standard PDE benchmarks and real datasets, our method attains state-of-the-art competitive accuracy while providing intrinsic interpretability.
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.
Chengrui Qu, Yizhou Zhang, Nicholas Lanzetti, Eric Mazumdar · Feb 25, 2026 · Citations: 0
Many emerging agentic paradigms require agents to collaborate with one another (or people) to achieve shared goals.
Umid Suleymanov, Zaur Rajabov, Emil Mirzazada, Murat Kantarcioglu · Feb 25, 2026 · Citations: 0
To address this, we introduce SemSIEdit, an inference-time framework where an agentic "Editor" iteratively critiques and rewrites sensitive spans to preserve narrative flow rather than simply refusing to answer.
Ningyuan Yang, Weihua Du, Weiwei Sun, Sean Welleck, Yiming Yang · Feb 25, 2026 · Citations: 0
Reinforcement learning (RL) has become a central post-training paradigm for large language models (LLMs), but its performance is highly sensitive to the quality of training problems.
Deja Dunlap, R. Thomas McCoy · Feb 25, 2026 · Citations: 0
In AI, most evaluations of natural language understanding tasks are conducted in standardized dialects such as Standard American English (SAE).
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.
Sofoklis Kakouros, Fang Kang, Haoyu Chen · Feb 25, 2026 · Citations: 0
To demonstrate the utility of the dataset and establish initial benchmarks, we introduce two evaluation tasks for comparative assessment: speech emotion recognition and transcript-based sentiment analysis.
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
Chuan Meng, Litu Ou, Sean MacAvaney, Jeff Dalton · Feb 25, 2026 · Citations: 0
To tackle it, most prior work equips large language model (LLM)-based agents with opaque web search APIs, enabling agents to iteratively issue search queries, retrieve external evidence, and reason over it.
Inderjeet Singh, Vikas Pahuja, Aishvariya Priya Rathina Sabapathy, Chiara Picardi, Amit Giloni · Feb 24, 2026 · Citations: 0
Current stateless defences for multimodal agentic RAG fail to detect adversarial strategies that distribute malicious semantics across retrieval, planning, and generation components.
Shiwei Tan, Hengyi Wang, Weiyi Qin, Qi Xu, Zhigang Hua · Feb 24, 2026 · Citations: 0
Across captioning and QA benchmarks, our framework substantially lowers object-hallucination rates and achieves state-of-the-art faithfulness without degrading overall output quality.
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.
Alexander Galozy · Feb 24, 2026 · Citations: 0
Reinforcement learning (RL) agents under partial observability often condition actions on internally accumulated information such as memory or inferred latent context.
Jan Pauls, Karsten Schrödter, Sven Ligensa, Martin Schwartz, Berkant Turan · Feb 24, 2026 · Citations: 0
Our experimental evaluation shows that our model improves state-of-the-art accuracies in the context of single-year predictions.
Tassilo Klein, Sebastian Wieczorek · Feb 24, 2026 · Citations: 0
We argue that agentic AI induces a structural change in how coordination costs scale: in prior modular systems, integration cost grew with interaction topology (O(n^2) in the number of components); in protocol-mediated agentic systems, inte
Felix Schneider, Maria Gogolev, Sven Sickert, Joachim Denzler · Feb 24, 2026 · Citations: 0
Tokenization and sub-tokenization based models like word2vec, BERT and the GPTs are the state-of-the-art in natural language processing.
Dominik Schneider, Lalithkumar Seenivasan, Sampath Rapuri, Vishalroshan Anil, Aiza Maksutova · Feb 24, 2026 · Citations: 0
Purpose: Curating large-scale datasets of operating room (OR) workflow, encompassing rare, safety-critical, or atypical events, remains operationally and ethically challenging.
Oliver Hoidn, Aashwin Mishra, Steven Henke, Albert Vong, Matthew Seaberg · Feb 24, 2026 · Citations: 0
On synthetic benchmarks, reconstructions remain accurate at low counts ($\sim\!10^4$ photons/frame), and overlap-free single-shot reconstruction with an experimental probe reaches amplitude structural similarity (SSIM) 0.904, compared with
Mengxuan Hu, Vivek V. Datla, Anoop Kumar, Zihan Guan, Sheng Li · Feb 24, 2026 · Citations: 0
Recent advances in alignment techniques such as Supervised Fine-Tuning (SFT), Reinforcement Learning from Human Feedback (RLHF), and Direct Preference Optimization (DPO) have improved the safety of large language models (LLMs).
Junchen Liu, Sven Elflein, Or Litany, Zan Gojcic, Ruilong Li · Feb 24, 2026 · Citations: 0
Test-time training (TTT) with KV binding as sequence modeling layer is commonly interpreted as a form of online meta-learning that memorizes a key-value mapping at test time.
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
Renjie Pi, Grace Lam, Mohammad Shoeybi, Pooya Jannaty, Bryan Catanzaro · Feb 24, 2026 · Citations: 0
Despite rapid recent progress in the terminal capabilities of large language models, the training data strategies behind state-of-the-art terminal agents remain largely undisclosed.
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.
Ishaan Rawal, Shubh Gupta, Yihan Hu, Wei Zhan · Feb 24, 2026 · Citations: 0
Vision-Language-Action (VLA) models are advancing autonomous driving by replacing modular pipelines with unified end-to-end architectures.
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.
Xinfeng Li, Shenyu Dai, Kelong Zheng, Yue Xiao, Gelei Deng · Feb 24, 2026 · Citations: 0
Large language model (LLM) agents are rapidly becoming trusted copilots in high-stakes domains like software development and healthcare.
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.
