Accuracy + General Metric Papers

Updated from current HFEPX corpus (Feb 27, 2026). This page tracks 117 papers for Accuracy + General Metric Papers. Dominant protocol signals include automatic metrics, simulation environments, human evaluation, with frequent benchmark focus on Retrieval, BrowseComp and metric focus on accuracy, cost. Use the grounded sections below to prioritize reproducible protocol choices, benchmark-matched comparisons, and judge-vs-human evaluation checks.

Top Papers Reporting This Metric

• Assessing Deanonymization Risks with Stylometry-Assisted LLM Agent

  Boyang Zhang, Yang Zhang · Feb 26, 2026

  Automatic Metrics General

  In this work, we introduce an LLM agent designed to evaluate and mitigate such risks through a structured, interpretable pipeline.

• Quantity Convergence, Quality Divergence: Disentangling Fluency and Accuracy in L2 Mandarin Prosody

  Yuqi Shi, Hao Yang, Xiyao Lu, Jinsong Zhang · Feb 26, 2026

  Automatic Metrics General

  While second language (L2) learners may acquire target syntactic word order, mapping this syntax onto appropriate prosodic structures remains a persistent challenge.

• Make It Hard to Hear, Easy to Learn: Long-Form Bengali ASR and Speaker Diarization via Extreme Augmentation and Perfect Alignment

  Sanjid Hasan, Risalat Labib, A H M Fuad, Bayazid Hasan · Feb 26, 2026

  Automatic Metrics General

  Ultimately, this work outlines a highly optimized dual pipeline achieving a $\sim$0.019 Real-Time Factor (RTF), establishing a practical, empirically backed benchmark for low-resource, long-form speech processing.

• Replacing Multi-Step Assembly of Data Preparation Pipelines with One-Step LLM Pipeline Generation for Table QA

  Fengyu Li, Junhao Zhu, Kaishi Song, Lu Chen, Zhongming Yao · Feb 26, 2026

  Automatic Metrics General

  Experiments on two benchmark datasets show that, with the same LLM backbone, Operation-R1 achieves average absolute accuracy gains of 9.55 and 6.08 percentage points over multi-step preparation baselines, with 79\% table compression and a 2

• Search More, Think Less: Rethinking Long-Horizon Agentic Search for Efficiency and Generalization

  Qianben Chen, Tianrui Qin, King Zhu, Qiexiang Wang, Chengjun Yu · Feb 26, 2026

  Automatic Metrics General

  Recent deep research agents primarily improve performance by scaling reasoning depth, but this leads to high inference cost and latency in search-intensive scenarios.

• ContextRL: Enhancing MLLM's Knowledge Discovery Efficiency with Context-Augmented RL

  Xingyu Lu, Jinpeng Wang, YiFan Zhang, Shijie Ma, Xiao Hu · Feb 26, 2026

  Automatic Metrics General

  Experimental results on 11 perception and reasoning benchmarks show that ContextRL significantly improves knowledge discovery efficiency.

• pQuant: Towards Effective Low-Bit Language Models via Decoupled Linear Quantization-Aware Training

  Wenzheng Zhang, Bingzheng Liu, Yang Hu, Xiaoying Bai, Wentao Zhang · Feb 26, 2026

  Automatic Metrics General

  Quantization-Aware Training from scratch has emerged as a promising approach for building efficient large language models (LLMs) with extremely low-bit weights (sub 2-bit), which can offer substantial advantages for edge deployment.

• Search-P1: Path-Centric Reward Shaping for Stable and Efficient Agentic RAG Training

  Tianle Xia, Ming Xu, Lingxiang Hu, Yiding Sun, Wenwei Li · Feb 26, 2026

  Automatic Metrics General

  Agentic RAG addresses this by enabling LLMs to dynamically decide when and what to retrieve, but current RL-based training methods suffer from sparse outcome rewards that discard intermediate signals and low sample efficiency where failed s

• Stable Adaptive Thinking via Advantage Shaping and Length-Aware Gradient Regulation

  Zihang Xu, Haozhi Xie, Ziqi Miao, Wuxuan Gong, Chen Qian · Feb 26, 2026

  Automatic Metrics General

  Large reasoning models (LRMs) achieve strong performance through extended reasoning traces, but they often exhibit overthinking behavior for low-complexity queries.

• A Fusion of context-aware based BanglaBERT and Two-Layer Stacked LSTM Framework for Multi-Label Cyberbullying Detection

  Mirza Raquib, Asif Pervez Polok, Kedar Nath Biswas, Rahat Uddin Azad, Saydul Akbar Murad · Feb 25, 2026

  Automatic Metrics General

  Evaluation uses multiple metrics, including accuracy, precision, recall, F1-score, Hamming loss, Cohens kappa, and AUC-ROC.

• Confidence-Driven Multi-Scale Model Selection for Cost-Efficient Inference

  Bo-Wei Chen, Chung-Chi Chen, An-Zi Yen · Feb 25, 2026

  Automatic Metrics General

  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\%.

• Understanding Artificial Theory of Mind: Perturbed Tasks and Reasoning in Large Language Models

  Christian Nickel, Laura Schrewe, Florian Mai, Lucie Flek · Feb 25, 2026

  Automatic Metrics General

  Theory of Mind (ToM) refers to an agent's ability to model the internal states of others.

• DynamicGTR: Leveraging Graph Topology Representation Preferences to Boost VLM Capabilities on Graph QAs

  Yanbin Wei, Jiangyue Yan, Chun Kang, Yang Chen, Hua Liu · Feb 25, 2026

  Automatic Metrics General

  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.

• Distill and Align Decomposition for Enhanced Claim Verification

  Jabez Magomere, Elena Kochkina, Samuel Mensah, Simerjot Kaur, Fernando Acero · Feb 25, 2026

  Human EvalAutomatic Metrics General

  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)).

• JSAM: Privacy Straggler-Resilient Joint Client Selection and Incentive Mechanism Design in Differentially Private Federated Learning

  Ruichen Xu, Ying-Jun Angela Zhang, Jianwei Huang · Feb 25, 2026

  Automatic Metrics General

  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.

• Prompt Architecture Determines Reasoning Quality: A Variable Isolation Study on the Car Wash Problem

  Heejin Jo · Feb 25, 2026

  Automatic Metrics General

  Large language models consistently fail the "car wash problem," a viral reasoning benchmark requiring implicit physical constraint inference.

• D-COT: Disciplined Chain-of-Thought Learning for Efficient Reasoning in Small Language Models

  Shunsuke Ubukata · Feb 25, 2026

  Automatic Metrics General

  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.

• Hierarchical LLM-Based Multi-Agent Framework with Prompt Optimization for Multi-Robot Task Planning

  Tomoya Kawabe, Rin Takano · Feb 25, 2026

  Automatic Metrics General

  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.

• RuCL: Stratified Rubric-Based Curriculum Learning for Multimodal Large Language Model Reasoning

  Yukun Chen, Jiaming Li, Longze Chen, Ze Gong, Jingpeng Li · Feb 25, 2026

  Automatic Metrics General

  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%.

• When More Is Less: A Systematic Analysis of Spatial and Commonsense Information for Visual Spatial Reasoning

  Muku Akasaka, Soyeon Caren Han · Feb 25, 2026

  Automatic Metrics General

  In this paper, we conduct a hypothesis-driven analysis of information injection for VSR across three representative VLMs and two public benchmarks.

• From Basis to Basis: Gaussian Particle Representation for Interpretable PDE Operators

  Zhihao Li, Yu Feng, Zhilu Lai, Wei Wang · Feb 25, 2026

  Automatic Metrics General

  On standard PDE benchmarks and real datasets, our method attains state-of-the-art competitive accuracy while providing intrinsic interpretability.

• GradAlign: Gradient-Aligned Data Selection for LLM Reinforcement Learning

  Ningyuan Yang, Weihua Du, Weiwei Sun, Sean Welleck, Yiming Yang · Feb 25, 2026

  Automatic Metrics General

  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.

• Attention-Based SINR Estimation in User-Centric Non-Terrestrial Networks

  Bruno De Filippo, Alessandro Guidotti, Alessandro Vanelli-Coralli · Feb 24, 2026

  Automatic Metrics General

  These results enable the integration of DMHSA-based estimators into scheduling procedures, allowing the evaluation of multiple candidate user groups and the selection of those offering the highest average SINR and capacity.

• HELP: HyperNode Expansion and Logical Path-Guided Evidence Localization for Accurate and Efficient GraphRAG

  Yuqi Huang, Ning Liao, Kai Yang, Anning Hu, Shengchao Hu · Feb 24, 2026

  Automatic Metrics General

  Extensive experiments demonstrate that HELP achieves competitive performance across multiple simple and multi-hop QA benchmarks and up to a 28.8$\times$ speedup over leading Graph-based RAG baselines.

• Predicting Sentence Acceptability Judgments in Multimodal Contexts

  Hyewon Jang, Nikolai Ilinykh, Sharid Loáiciga, Jey Han Lau, Shalom Lappin · Feb 24, 2026

  Automatic Metrics General

  Previous work has examined the capacity of deep neural networks (DNNs), particularly transformers, to predict human sentence acceptability judgments, both independently of context, and in document contexts.

• Overton Pluralistic Reinforcement Learning for Large Language Models

  Yu Fu, Seongho Son, Ilija Bogunovic · Feb 24, 2026

  Llm As JudgeAutomatic Metrics General

  Existing alignment paradigms remain limited in capturing the pluralistic nature of human values.

• Voices of the Mountains: Deep Learning-Based Vocal Error Detection System for Kurdish Maqams

  Darvan Shvan Khairaldeen, Hossein Hassani · Feb 24, 2026

  Automatic Metrics General

  On the full 50-song evaluation at a 0.750 threshold, recall was 39.4% and precision 25.8% .

• CAMEL: Confidence-Gated Reflection for Reward Modeling

  Zirui Zhu, Hailun Xu, Yang Luo, Yong Liu, Kanchan Sarkar · Feb 24, 2026

  Automatic Metrics General

  Reward models play a fundamental role in aligning large language models with human preferences.

• CARE: An Explainable Computational Framework for Assessing Client-Perceived Therapeutic Alliance Using Large Language Models

  Anqi Li, Chenxiao Wang, Yu Lu, Renjun Xu, Lizhi Ma · Feb 24, 2026

  Human EvalAutomatic Metrics General

  Experiments show that CARE outperforms leading LLMs and substantially reduces the gap between counselor evaluations and client-perceived alliance, achieving over 70% higher Pearson correlation with client ratings.

• Enhancing Hate Speech Detection on Social Media: A Comparative Analysis of Machine Learning Models and Text Transformation Approaches

  Saurabh Mishra, Shivani Thakur, Radhika Mamidi · Feb 24, 2026

  Automatic Metrics General

  The proliferation of hate speech on social media platforms has necessitated the development of effective detection and moderation tools.

• Protein Language Models Diverge from Natural Language: Comparative Analysis and Improved Inference

  Anna Hart, Chi Han, Jeonghwan Kim, Huimin Zhao, Heng Ji · Feb 24, 2026

  Automatic Metrics General

  Modern Protein Language Models (PLMs) apply transformer-based model architectures from natural language processing to biological sequences, predicting a variety of protein functions and properties.

• Natural Language Processing Models for Robust Document Categorization

  Radoslaw Roszczyk, Pawel Tecza, Maciej Stodolski, Krzysztof Siwek · Feb 23, 2026

  Automatic Metrics General

  This article presents an evaluation of several machine learning methods applied to automated text classification, alongside the design of a demonstrative system for unbalanced document categorization and distribution.

• When Pretty Isn't Useful: Investigating Why Modern Text-to-Image Models Fail as Reliable Training Data Generators

  Krzysztof Adamkiewicz, Brian Moser, Stanislav Frolov, Tobias Christian Nauen, Federico Raue · Feb 23, 2026

  Automatic Metrics General

  Recent text-to-image (T2I) diffusion models produce visually stunning images and demonstrate excellent prompt following.

• Janus-Q: End-to-End Event-Driven Trading via Hierarchical-Gated Reward Modeling

  Xiang Li, Zikai Wei, Yiyan Qi, Wanyun Zhou, Xiang Liu · Feb 23, 2026

  Automatic Metrics General

  Financial market movements are often driven by discrete financial events conveyed through news, whose impacts are heterogeneous, abrupt, and difficult to capture under purely numerical prediction objectives.

• NILE: Formalizing Natural-Language Descriptions of Formal Languages

  Tristan Kneisel, Marko Schmellenkamp, Fabian Vehlken, Thomas Zeume · Feb 23, 2026

  Automatic Metrics General

  This is motivated from educational scenarios where learners describe a formal language (presented, e.g., by a finite state automaton, regular expression, pushdown automaton, context-free grammar or in set notation) in natural language, and

• KGHaluBench: A Knowledge Graph-Based Hallucination Benchmark for Evaluating the Breadth and Depth of LLM Knowledge

  Alex Robertson, Huizhi Liang, Mahbub Gani, Rohit Kumar, Srijith Rajamohan · Feb 23, 2026

  Automatic Metrics General

  Existing benchmarks are limited by static and narrow questions, leading to limited coverage and misleading evaluations.

• Personalized Prediction of Perceived Message Effectiveness Using Large Language Model Based Digital Twins

  Jasmin Han, Janardan Devkota, Joseph Waring, Amanda Luken, Felix Naughton · Feb 23, 2026

  Automatic Metrics General

  Perceived message effectiveness (PME) by potential intervention end-users is important for selecting and optimizing personalized smoking cessation intervention messages for mobile health (mHealth) platform delivery.

• Adaptive Data Augmentation with Multi-armed Bandit: Sample-Efficient Embedding Calibration for Implicit Pattern Recognition

  Minxue Tang, Yangyang Yu, Aolin Ding, Maziyar Baran Pouyan, Taha Belkhouja Yujia Bao · Feb 22, 2026

  Automatic Metrics General

  Recognizing implicit visual and textual patterns is essential in many real-world applications of modern AI.

• Anatomy of Agentic Memory: Taxonomy and Empirical Analysis of Evaluation and System Limitations

  Dongming Jiang, Yi Li, Songtao Wei, Jinxin Yang, Ayushi Kishore · Feb 22, 2026

  Automatic Metrics General

  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.

• VIGiA: Instructional Video Guidance via Dialogue Reasoning and Retrieval

  Diogo Glória-Silva, David Semedo, João Maglhães · Feb 22, 2026

  Automatic Metrics General

  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.

• ArabicNumBench: Evaluating Arabic Number Reading in Large Language Models

  Anas Alhumud, Abdulaziz Alhammadi, Muhammad Badruddin Khan · Feb 21, 2026

  Automatic Metrics General

  We present ArabicNumBench, a comprehensive benchmark for evaluating large language models on Arabic number reading tasks across Eastern Arabic-Indic numerals (0-9 in Arabic script) and Western Arabic numerals (0-9).

• Context-Aware Mapping of 2D Drawing Annotations to 3D CAD Features Using LLM-Assisted Reasoning for Manufacturing Automation

  Muhammad Tayyab Khan, Lequn Chen, Wenhe Feng, Seung Ki Moon · Feb 20, 2026

  Automatic MetricsSimulation Env General

  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.

• FENCE: A Financial and Multimodal Jailbreak Detection Dataset

  Mirae Kim, Seonghun Jeong, Youngjun Kwak · Feb 20, 2026

  Automatic Metrics General

  A baseline detector trained on FENCE achieves 99 percent in-distribution accuracy and maintains strong performance on external benchmarks, underscoring the dataset's robustness for training reliable detection models.

• The Cascade Equivalence Hypothesis: When Do Speech LLMs Behave Like ASR$\rightarrow$LLM Pipelines?

  Jayadev Billa · Feb 19, 2026

  Automatic Metrics General

  Current speech LLMs largely perform implicit ASR: on tasks solvable from a transcript, they are behaviorally and mechanistically equivalent to simple Whisper$\to$LLM cascades.

• Modeling Distinct Human Interaction in Web Agents

  Faria Huq, Zora Zhiruo Wang, Zhanqiu Guo, Venu Arvind Arangarajan, Tianyue Ou · Feb 19, 2026

  Automatic Metrics General

  Despite rapid progress in autonomous web agents, human involvement remains essential for shaping preferences and correcting agent behavior as tasks unfold.

• Evaluating Chain-of-Thought Reasoning through Reusability and Verifiability

  Shashank Aggarwal, Ram Vikas Mishra, Amit Awekar · Feb 19, 2026

  Automatic Metrics General

  In multi-agent IR pipelines for tasks such as search and ranking, LLM-based agents exchange intermediate reasoning in terms of Chain-of-Thought (CoT) with each other.

• What Do LLMs Associate with Your Name? A Human-Centered Black-Box Audit of Personal Data

  Dimitri Staufer, Kirsten Morehouse · Feb 19, 2026

  Automatic Metrics General

  Large language models (LLMs), and conversational agents based on them, are exposed to personal data (PD) during pre-training and during user interactions.

• ABCD: All Biases Come Disguised

  Mateusz Nowak, Xavier Cadet, Peter Chin · Feb 19, 2026

  Automatic Metrics General

  Multiple-choice question (MCQ) benchmarks have been a standard evaluation practice for measuring LLMs' ability to reason and answer knowledge-based questions.

• The Emergence of Lab-Driven Alignment Signatures: A Psychometric Framework for Auditing Latent Bias and Compounding Risk in Generative AI

  Dusan Bosnjakovic · Feb 19, 2026

  Automatic Metrics General

  As Large Language Models (LLMs) transition from standalone chat interfaces to foundational reasoning layers in multi-agent systems and recursive evaluation loops (LLM-as-a-judge), the detection of durable, provider-level behavioral signatur

• Exploring LLMs for User Story Extraction from Mockups

  Diego Firmenich, Leandro Antonelli, Bruno Pazos, Fabricio Lozada, Leonardo Morales · Feb 19, 2026

  Automatic Metrics General

  User stories are one of the most widely used artifacts in the software industry to define functional requirements.

• Meenz bleibt Meenz, but Large Language Models Do Not Speak Its Dialect

  Minh Duc Bui, Manuel Mager, Peter Herbert Kann, Katharina von der Wense · Feb 18, 2026

  Automatic Metrics General

  We introduce a digital dictionary-an NLP-ready dataset derived from an existing resource (Schramm, 1966)-to support researchers in modeling and benchmarking the language.

• Claim Automation using Large Language Model

  Zhengda Mo, Zhiyu Quan, Eli O'Donohue, Kaiwen Zhong · Feb 18, 2026

  Human EvalAutomatic Metrics General

  We assess this module using a multi-dimensional evaluation framework that combines automated semantic similarity metrics with human evaluation, enabling a rigorous examination of both practical utility and predictive accuracy.

• References Improve LLM Alignment in Non-Verifiable Domains

  Kejian Shi, Yixin Liu, Peifeng Wang, Alexander R. Fabbri, Shafiq Joty · Feb 18, 2026

  Automatic Metrics General

  First, we design evaluation protocols that enhance LLM-based evaluators for LLM alignment using reference outputs.

• The Validity of Coreference-based Evaluations of Natural Language Understanding

  Ian Porada · Feb 18, 2026

  Automatic Metrics General

  In this thesis, I refine our understanding as to what conclusions we can reach from coreference-based evaluations by expanding existing evaluation practices and considering the extent to which evaluation results are either converging or con

• Emotion Collider: Dual Hyperbolic Mirror Manifolds for Sentiment Recovery via Anti Emotion Reflection

  Rong Fu, Ziming Wang, Shuo Yin, Wenxin Zhang, Haiyun Wei · Feb 18, 2026

  Automatic Metrics General

  Emotional expression underpins natural communication and effective human-computer interaction.

• Balancing Faithfulness and Performance in Reasoning via Multi-Listener Soft Execution

  Nithin Sivakumaran, Shoubin Yu, Hyunji Lee, Yue Zhang, Ali Payani · Feb 18, 2026

  Automatic Metrics General

  On multiple reasoning benchmarks (BIG-Bench Extra Hard, MuSR, ZebraLogicBench, and FOLIO), REMUL consistently and substantially improves three measures of faithfulness -- hint attribution, early answering area over the curve (AOC), and mist

• ViTaB-A: Evaluating Multimodal Large Language Models on Visual Table Attribution

  Yahia Alqurnawi, Preetom Biswas, Anmol Rao, Tejas Anvekar, Chitta Baral · Feb 17, 2026

  Automatic Metrics General

  Multimodal Large Language Models (mLLMs) are often used to answer questions in structured data such as tables in Markdown, JSON, and images.

• Revisiting Northrop Frye's Four Myths Theory with Large Language Models

  Edirlei Soares de Lima, Marco A. Casanova, Antonio L. Furtado · Feb 17, 2026

  Automatic Metrics General

  Northrop Frye's theory of four fundamental narrative genres (comedy, romance, tragedy, satire) has profoundly influenced literary criticism, yet computational approaches to his framework have focused primarily on narrative patterns rather t

• Fine-Refine: Iterative Fine-grained Refinement for Mitigating Dialogue Hallucination

  Xiangyan Chen, Yujian Gan, Matthew Purver · Feb 17, 2026

  Automatic Metrics General

  The tendency for hallucination in current large language models (LLMs) negatively impacts dialogue systems.

• Making Large Language Models Speak Tulu: Structured Prompting for an Extremely Low-Resource Language

  Prathamesh Devadiga, Paras Chopra · Feb 17, 2026

  Automatic Metrics General

  Can large language models converse in languages virtually absent from their training data?

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