Accuracy In CS.CL Papers

Updated from current HFEPX corpus (Feb 27, 2026). This page tracks 213 papers for Accuracy In CS.CL Papers. Dominant protocol signals include automatic metrics, simulation environments, human evaluation, with frequent benchmark focus on Retrieval, MATH 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

• AgentDropoutV2: Optimizing Information Flow in Multi-Agent Systems via Test-Time Rectify-or-Reject Pruning

  Yutong Wang, Siyuan Xiong, Xuebo Liu, Wenkang Zhou, Liang Ding · Feb 26, 2026

  Automatic Metrics MathCoding

  While Multi-Agent Systems (MAS) excel in complex reasoning, they suffer from the cascading impact of erroneous information generated by individual participants.

• InnerQ: Hardware-aware Tuning-free Quantization of KV Cache for Large Language Models

  Sayed Mohammadreza Tayaranian Hosseini, Amir Ardakani, Warren J. Gross · Feb 26, 2026

  Automatic Metrics MathCoding

  Our evaluation experiments on Llama models shows that InnerQ maintains a few-shot GSM8K performance comparable to non-quantized KV caches and surpasses prior KV cache quantization methods.

• 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.

• MoDora: Tree-Based Semi-Structured Document Analysis System

  Bangrui Xu, Qihang Yao, Zirui Tang, Xuanhe Zhou, Yeye He · Feb 26, 2026

  Automatic Metrics Coding

  Semi-structured documents integrate diverse interleaved data elements (e.g., tables, charts, hierarchical paragraphs) arranged in various and often irregular layouts.

• Test-Time Scaling with Diffusion Language Models via Reward-Guided Stitching

  Roy Miles, Aysim Toker, Andreea-Maria Oncescu, Songcen Xu, Jiankang Deng · Feb 26, 2026

  Automatic Metrics MathCoding

  This modular pipeline separates exploration (diffusion) from evaluation and solution synthesis, avoiding monolithic unified hybrids while preserving broad search.

• 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.

• Towards Faithful Industrial RAG: A Reinforced Co-adaptation Framework for Advertising QA

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

  Automatic Metrics Law

  We propose a reinforced co-adaptation framework that jointly optimizes retrieval and generation through two components: (1) Graph-aware Retrieval (GraphRAG), which models entity-relation structure over a high-citation knowledge subgraph for

• Strategy Executability in Mathematical Reasoning: Leveraging Human-Model Differences for Effective Guidance

  Weida Liang, Yiyou Sun, Shuyuan Nan, Chuang Li, Dawn Song · Feb 26, 2026

  Automatic Metrics MathMedicine

  Through a controlled analysis of paired human-written and model-generated solutions, we identify a systematic dissociation between usage and executability: human- and model-derived strategies differ in structured, domain-dependent ways, lea

• 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.

• Importance of Prompt Optimisation for Error Detection in Medical Notes Using Language Models

  Craig Myles, Patrick Schrempf, David Harris-Birtill · Feb 25, 2026

  Automatic Metrics MedicineCoding

  We show that automatic prompt optimisation with Genetic-Pareto (GEPA) improves error detection over the baseline accuracy performance from 0.669 to 0.785 with GPT-5 and 0.578 to 0.690 with Qwen3-32B, approaching the performance of medical d

• 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.

• How Do Latent Reasoning Methods Perform Under Weak and Strong Supervision?

  Yingqian Cui, Zhenwei Dai, Bing He, Zhan Shi, Hui Liu · Feb 25, 2026

  Automatic Metrics Coding

  Latent reasoning has been recently proposed as a reasoning paradigm and performs multi-step reasoning through generating steps in the latent space instead of the textual space.

• GUI-Libra: Training Native GUI Agents to Reason and Act with Action-aware Supervision and Partially Verifiable RL

  Rui Yang, Qianhui Wu, Zhaoyang Wang, Hanyang Chen, Ke Yang · Feb 25, 2026

  Automatic Metrics Coding

  Open-source native GUI agents still lag behind closed-source systems on long-horizon navigation tasks.

• DySCO: Dynamic Attention-Scaling Decoding for Long-Context LMs

  Xi Ye, Wuwei Zhang, Fangcong Yin, Howard Yen, Danqi Chen · Feb 25, 2026

  Automatic Metrics Coding

  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

• NoLan: Mitigating Object Hallucinations in Large Vision-Language Models via Dynamic Suppression of Language Priors

  Lingfeng Ren, Weihao Yu, Runpeng Yu, Xinchao Wang · Feb 25, 2026

  Automatic Metrics Coding

  Object hallucination is a critical issue in Large Vision-Language Models (LVLMs), where outputs include objects that do not appear in the input image.

• 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.

• MEDSYN: Benchmarking Multi-EviDence SYNthesis in Complex Clinical Cases for Multimodal Large Language Models

  Boqi Chen, Xudong Liu, Jiachuan Peng, Marianne Frey-Marti, Bang Zheng · Feb 25, 2026

  Automatic Metrics MedicineCoding

  Multimodal large language models (MLLMs) have shown great potential in medical applications, yet existing benchmarks inadequately capture real-world clinical complexity.

• Small Wins Big: Comparing Large Language Models and Domain Fine-Tuned Models for Sarcasm Detection in Code-Mixed Hinglish Text

  Bitan Majumder, Anirban Sen · Feb 25, 2026

  Automatic MetricsSimulation Env CodingMultilingual

  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.

• 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)).

• 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.

• 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.

• MixSarc: A Bangla-English Code-Mixed Corpus for Implicit Meaning Identification

  Kazi Samin Yasar Alam, Md Tanbir Chowdhury, Tamim Ahmed, Ajwad Abrar, Md Rafid Haque · Feb 25, 2026

  Human EvalAutomatic Metrics Coding

  We benchmark transformer-based models and evaluate zero-shot large language models under structured prompting.

• Duel-Evolve: Reward-Free Test-Time Scaling via LLM Self-Preferences

  Sweta Karlekar, Carolina Zheng, Magnus Saebo, Nicolas Beltran-Velez, Shuyang Yu · Feb 25, 2026

  Automatic Metrics MathCoding

  Building on this observation, we introduce Duel-Evolve, an evolutionary optimization algorithm that replaces external scalar rewards with pairwise preferences elicited from the same LLM used to generate candidates.

• One Brain, Omni Modalities: Towards Unified Non-Invasive Brain Decoding with Large Language Models

  Changli Tang, Shurui Li, Junliang Wang, Qinfan Xiao, Zhonghao Zhai · Feb 25, 2026

  Automatic Metrics Coding

  Extensive evaluations demonstrate that NOBEL serves as a robust generalist across standard single-modal tasks.

• 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.

• Black-Box Reliability Certification for AI Agents via Self-Consistency Sampling and Conformal Calibration

  Charafeddine Mouzouni · Feb 24, 2026

  Automatic Metrics Math

  We validate across five benchmarks, five models from three families, and both synthetic and real data.

• 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.

• Counterfactual Simulation Training for Chain-of-Thought Faithfulness

  Peter Hase, Christopher Potts · Feb 24, 2026

  Automatic MetricsSimulation Env Coding

  Inspecting Chain-of-Thought reasoning is among the most common means of understanding why an LLM produced its output.

• 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.

• SpecMind: Cognitively Inspired, Interactive Multi-Turn Framework for Postcondition Inference

  Cuong Chi Le, Minh V. T Pham, Tung Vu Duy, Cuong Duc Van, Huy N. Phan · Feb 24, 2026

  Automatic Metrics Coding

  Our empirical evaluation shows that SpecMind significantly outperforms state-of-the-art approaches in both accuracy and completeness of generated postconditions.

• GATES: Self-Distillation under Privileged Context with Consensus Gating

  Alex Stein, Furong Huang, Tom Goldstein · Feb 24, 2026

  Automatic Metrics Math

  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\%.

• 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.

• MedCLIPSeg: Probabilistic Vision-Language Adaptation for Data-Efficient and Generalizable Medical Image Segmentation

  Taha Koleilat, Hojat Asgariandehkordi, Omid Nejati Manzari, Berardino Barile, Yiming Xiao · Feb 23, 2026

  Automatic Metrics Medicine

  Medical image segmentation remains challenging due to limited annotations for training, ambiguous anatomical features, and domain shifts.

• 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.

• To Reason or Not to: Selective Chain-of-Thought in Medical Question Answering

  Zaifu Zhan, Min Zeng, Shuang Zhou, Yiran Song, Xiaoyi Chen · Feb 23, 2026

  Automatic Metrics Medicine

  Two open-source LLMs (Llama-3.1-8B and Qwen-2.5-7B) were evaluated on four biomedical QA benchmarks-HeadQA, MedQA-USMLE, MedMCQA, and PubMedQA.

• NanoKnow: How to Know What Your Language Model Knows

  Lingwei Gu, Nour Jedidi, Jimmy Lin · Feb 23, 2026

  Automatic Metrics Coding

  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

• Multilingual Large Language Models do not comprehend all natural languages to equal degrees

  Natalia Moskvina, Raquel Montero, Masaya Yoshida, Ferdy Hubers, Paolo Morosi · Feb 23, 2026

  Automatic Metrics Multilingual

  Large Language Models (LLMs) play a critical role in how humans access information.

• Cross-lingual Matryoshka Representation Learning across Speech and Text

  Yaya Sy, Dioula Doucouré, Christophe Cerisara, Irina Illina · Feb 23, 2026

  Automatic Metrics Multilingual

  We introduce large-scale data curation pipelines and new benchmarks, compare modeling strategies, and show that modality fusion within a frozen text Matryoshka model performs best.

• 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.

• DSDR: Dual-Scale Diversity Regularization for Exploration in LLM Reasoning

  Zhongwei Wan, Yun Shen, Zhihao Dou, Donghao Zhou, Yu Zhang · Feb 23, 2026

  Automatic Metrics Coding

  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.

• SAMAS: A Spectrum-Guided Multi-Agent System for Achieving Style Fidelity in Literary Translation

  Jingzhuo Wu, Jiajun Zhang, Keyan Jin, Dehua Ma, Junbo Wang · Feb 23, 2026

  Automatic Metrics Multilingual

  This limitation stems from the inability of current single-model and static multi-agent systems to perceive and adapt to stylistic variations.

• 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.

• Classroom Final Exam: An Instructor-Tested Reasoning Benchmark

  Chongyang Gao, Diji Yang, Shuyan Zhou, Xichen Yan, Luchuan Song · Feb 23, 2026

  Automatic Metrics Coding

  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.

• Pyramid MoA: A Probabilistic Framework for Cost-Optimized Anytime Inference

  Arindam Khaled · Feb 23, 2026

  Automatic Metrics Math

  In this work, we propose "Pyramid MoA", a hierarchical Mixture-of-Agents architecture that uses a lightweight Router to dynamically escalate queries only when necessary.

• 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.

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