Accuracy + Coding Metric Papers

Updated from current HFEPX corpus (Feb 27, 2026). This page tracks 73 papers for Accuracy + Coding Metric 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.

• 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.

• 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

• 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

• 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.

• 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.

• SurGo-R1: Benchmarking and Modeling Contextual Reasoning for Operative Zone in Surgical Video

  Guanyi Qin, Xiaozhen Wang, Zhu Zhuo, Chang Han Low, Yuancan Xiao · Feb 25, 2026

  Automatic Metrics MedicineCoding

  Existing AI systems offer binary safety verification or static detection, ignoring the phase-dependent nature of intraoperative reasoning.

• 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.

• XMorph: Explainable Brain Tumor Analysis Via LLM-Assisted Hybrid Deep Intelligence

  Sepehr Salem Ghahfarokhi, M. Moein Esfahani, Raj Sunderraman, Vince Calhoun, Mohammed Alser · Feb 24, 2026

  Automatic Metrics MedicineCoding

  Deep learning has significantly advanced automated brain tumor diagnosis, yet clinical adoption remains limited by interpretability and computational constraints.

• Diagnosing Causal Reasoning in Vision-Language Models via Structured Relevance Graphs

  Dhita Putri Pratama, Soyeon Caren Han, Yihao Ding · Feb 24, 2026

  Automatic Metrics Coding

  Large Vision-Language Models (LVLMs) achieve strong performance on visual question answering benchmarks, yet often rely on spurious correlations rather than genuine causal reasoning.

• 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.

• 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.

• 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

• 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.

• Continuous Telemonitoring of Heart Failure using Personalised Speech Dynamics

  Yue Pan, Xingyao Wang, Hanyue Zhang, Liwei Liu, Changxin Li · Feb 23, 2026

  Automatic Metrics MedicineCoding

  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.

• 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.

• AgenticRAGTracer: A Hop-Aware Benchmark for Diagnosing Multi-Step Retrieval Reasoning in Agentic RAG

  Qijie You, Wenkai Yu, Wentao Zhang · Feb 22, 2026

  Automatic Metrics MedicineCoding

  With the rapid advancement of agent-based methods in recent years, Agentic RAG has undoubtedly become an important research direction.

• IAPO: Information-Aware Policy Optimization for Token-Efficient Reasoning

  Yinhan He, Yaochen Zhu, Mingjia Shi, Wendy Zheng, Lin Su · Feb 22, 2026

  Automatic Metrics Coding

  Extensive empirical evaluations demonstrate that information-aware advantage shaping is a powerful and general direction for token-efficient post-training.

• Luna-2: Scalable Single-Token Evaluation with Small Language Models

  Vatsal Goel, Rishon Dsouza, Nikhil Ega, Amey Ramesh Rambatla, Rob Friel · Feb 20, 2026

  Automatic Metrics Coding

  Real-time guardrails require evaluation that is accurate, cheap, and fast - yet today's default, LLM-as-a-judge (LLMAJ), is slow, expensive, and operationally non-deterministic due to multi-token generation.

• SPQ: An Ensemble Technique for Large Language Model Compression

  Jiamin Yao, Eren Gultepe · Feb 20, 2026

  Automatic MetricsSimulation Env MathCoding

  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.

• On the Semantic and Syntactic Information Encoded in Proto-Tokens for One-Step Text Reconstruction

  Ivan Bondarenko, Egor Palkin, Fedor Tikunov · Feb 20, 2026

  Automatic Metrics Coding

  Autoregressive large language models (LLMs) generate text token-by-token, requiring n forward passes to produce a sequence of length n.

• Analyzing and Improving Chain-of-Thought Monitorability Through Information Theory

  Usman Anwar, Tim Bakker, Dana Kianfar, Cristina Pinneri, Christos Louizos · Feb 20, 2026

  Automatic MetricsSimulation Env Coding

  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.

• Thinking by Subtraction: Confidence-Driven Contrastive Decoding for LLM Reasoning

  Lexiang Tang, Weihao Gao, Bingchen Zhao, Lu Ma, Qiao jin · Feb 20, 2026

  Automatic Metrics MathCoding

  Experiments show that CCD significantly improves accuracy across mathematical reasoning benchmarks while substantially reducing output length, with minimal KV-cache overhead.

• Improving Sampling for Masked Diffusion Models via Information Gain

  Kaisen Yang, Jayden Teoh, Kaicheng Yang, Yitong Zhang, Alex Lamb · Feb 20, 2026

  Automatic Metrics Coding

  Extensive evaluations across diverse architectures and tasks (reasoning, coding, creative writing, and image generation) demonstrate that Info-Gain Sampler consistently outperforms existing samplers for MDMs.

• Analyzing LLM Instruction Optimization for Tabular Fact Verification

  Xiaotang Du, Giwon Hong, Wai-Chung Kwan, Rohit Saxena, Ivan Titov · Feb 20, 2026

  Automatic Metrics Coding

  We study three optimizers from the DSPy framework -- COPRO, MiPROv2, and SIMBA -- across four benchmarks and three model families.

• Mechanistic Interpretability of Cognitive Complexity in LLMs via Linear Probing using Bloom's Taxonomy

  Bianca Raimondi, Maurizio Gabbrielli · Feb 19, 2026

  Automatic Metrics Coding

  The black-box nature of Large Language Models necessitates novel evaluation frameworks that transcend surface-level performance metrics.

• RFEval: Benchmarking Reasoning Faithfulness under Counterfactual Reasoning Intervention in Large Reasoning Models

  Yunseok Han, Yejoon Lee, Jaeyoung Do · Feb 19, 2026

  Automatic Metrics MathCoding

  To operationalize this, we present RFEval, a benchmark of 7,186 instances across seven tasks that probes faithfulness via controlled, output-level counterfactual interventions.

• Quecto-V1: Empirical Analysis of 8-bit Quantized Small Language Models for On-Device Legal Retrieval

  Subrit Dikshit · Feb 18, 2026

  Automatic MetricsSimulation Env LawCoding

  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

• Anatomy of Capability Emergence: Scale-Invariant Representation Collapse and Top-Down Reorganization in Neural Networks

  Jayadev Billa · Feb 17, 2026

  Automatic Metrics Coding

  Capability emergence during neural network training remains mechanistically opaque.

• Recursive Concept Evolution for Compositional Reasoning in Large Language Models

  Sarim Chaudhry · Feb 17, 2026

  Automatic Metrics MathCoding

  Large language models achieve strong performance on many complex reasoning tasks, yet their accuracy degrades sharply on benchmarks that require compositional reasoning, including ARC-AGI-2, GPQA, MATH, BBH, and HLE.

• Extracting Consumer Insight from Text: A Large Language Model Approach to Emotion and Evaluation Measurement

  Stephan Ludwig, Peter J. Danaher, Xiaohao Yang, Yu-Ting Lin, Ehsan Abedin · Feb 17, 2026

  Automatic Metrics Coding

  Accurately measuring consumer emotions and evaluations from unstructured text remains a core challenge for marketing research and practice.

• Seeing to Generalize: How Visual Data Corrects Binding Shortcuts

  Nicolas Buzeta, Felipe del Rio, Cristian Hinostroza, Denis Parra, Hans Lobel · Feb 16, 2026

  Automatic Metrics CodingMultilingual

  Vision Language Models (VLMs) are designed to extend Large Language Models (LLMs) with visual capabilities, yet in this work we observe a surprising phenomenon: VLMs can outperform their underlying LLMs on purely text-only tasks, particular

• Breaking Data Efficiency Dilemma: A Federated and Augmented Learning Framework For Alzheimer's Disease Detection via Speech

  Xiao Wei, Bin Wen, Yuqin Lin, Kai Li, Mingyang gu · Feb 16, 2026

  Automatic Metrics MedicineCoding

  Early diagnosis of Alzheimer's Disease (AD) is crucial for delaying its progression.

• Index Light, Reason Deep: Deferred Visual Ingestion for Visual-Dense Document Question Answering

  Tao Xu · Feb 15, 2026

  Automatic Metrics Coding

  16.1\% (+14.5pp); on CircuitVQA, a public benchmark (9,315 questions), retrieval ImgR@3 achieves 31.2\% vs.

• When Audio-LLMs Don't Listen: A Cross-Linguistic Study of Modality Arbitration

  Jayadev Billa · Feb 12, 2026

  Automatic Metrics Coding

  Using ALME, a benchmark of 57,602 controlled audio-text conflict stimuli across 8 languages, we find that Gemini 2.0 Flash exhibits 16.6% text dominance under audio-text conflict versus 1.6% under text-text conflict with identical reliabili

• LLMs Know More About Numbers than They Can Say

  Fengting Yuchi, Li Du, Jason Eisner · Feb 8, 2026

  Automatic Metrics MathCoding

  Although state-of-the-art LLMs can solve math problems, we find that they make errors on numerical comparisons with mixed notation: "Which is larger, $5.7 \times 10^2$ or $580$?" This raises a fundamental question: Do LLMs even know how big

• KBVQ-MoE: KLT-guided SVD with Bias-Corrected Vector Quantization for MoE Large Language Models

  Zukang Xu, Zhixiong Zhao, Xing Hu, Zhixuan Chen, Dawei Yang · Jan 30, 2026

  Automatic MetricsSimulation Env Coding

  Mixture of Experts (MoE) models have achieved great success by significantly improving performance while maintaining computational efficiency through sparse expert activation.

• FROST: Filtering Reasoning Outliers with Attention for Efficient Reasoning

  Haozheng Luo, Zhuolin Jiang, Md Zahid Hasan, Yan Chen, Soumalya Sarkar · Jan 26, 2026

  Automatic Metrics Coding

  Empirically, we validate FROST on four benchmarks using two strong reasoning models (Phi-4-Reasoning and GPT-OSS-20B), outperforming state-of-the-art methods such as TALE and ThinkLess.

• CricBench: A Multilingual Benchmark for Evaluating LLMs in Cricket Analytics

  Vaibhav Devraj, Dhruv Kumar, Jagat Sesh Challa, Parth Agarwal, Navya Kommuri · Dec 26, 2025

  Automatic Metrics CodingMultilingual

  To investigate this potential capability gap, we present CricBench, a comprehensive benchmark suite for evaluating LLMs on specialized cricket data.

• In-Context Algebra

  Eric Todd, Jannik Brinkmann, Rohit Gandikota, David Bau · Dec 18, 2025

  Automatic Metrics Coding

  We investigate the mechanisms that arise when transformers are trained to solve arithmetic on sequences where tokens are variables whose meaning is determined only through their interactions in-context.

• KD-OCT: Efficient Knowledge Distillation for Clinical-Grade Retinal OCT Classification

  Erfan Nourbakhsh, Nasrin Sanjari, Ali Nourbakhsh · Dec 9, 2025

  Automatic Metrics MedicineCoding

  Age-related macular degeneration (AMD) and choroidal neovascularization (CNV)-related conditions are leading causes of vision loss worldwide, with optical coherence tomography (OCT) serving as a cornerstone for early detection and managemen

• CDLM: Consistency Diffusion Language Models For Faster Sampling

  Minseo Kim, Chenfeng Xu, Coleman Hooper, Harman Singh, Ben Athiwaratkun · Nov 24, 2025

  Automatic Metrics MathCoding

  The full training and evaluation code is available at https://github.com/SqueezeAILab/CDLM.

• CLARITY: Contextual Linguistic Adaptation and Accent Retrieval for Dual-Bias Mitigation in Text-to-Speech Generation

  Crystal Min Hui Poon, Pai Chet Ng, Xiaoxiao Miao, Immanuel Jun Kai Loh, Bowen Zhang · Nov 14, 2025

  Automatic Metrics Coding

  Instruction-guided text-to-speech (TTS) research has reached a maturity level where excellent speech generation quality is possible on demand, yet two coupled biases persist in reducing perceived quality: accent bias, where models default t

• Graph Representation-based Model Poisoning on the Heterogeneous Internet of Agents

  Hanlin Cai, Houtianfu Wang, Haofan Dong, Kai Li, Sai Zou · Nov 10, 2025

  Automatic Metrics Coding

  Internet of Agents (IoA) envisions a unified, agent-centric paradigm where heterogeneous large language model (LLM) agents can interconnect and collaborate at scale.

• OckBench: Measuring the Efficiency of LLM Reasoning

  Zheng Du, Hao Kang, Song Han, Tushar Krishna, Ligeng Zhu · Nov 7, 2025

  Automatic Metrics Coding

  Yet current benchmarks emphasize accuracy and output quality, neglecting a critical dimension: efficiency of token usage.

• Beyond Understanding: Evaluating the Pragmatic Gap in LLMs' Cultural Processing of Figurative Language

  Mena Attia, Aashiq Muhamed, Mai Alkhamissi, Thamar Solorio, Mona Diab · Oct 27, 2025

  Human EvalAutomatic Metrics Coding

  We present a comprehensive evaluation of the ability of large language models (LLMs) to process culturally grounded language, specifically to understand and pragmatically use figurative expressions that encode local knowledge and cultural n

• Towards Scalable Oversight via Partitioned Human Supervision

  Ren Yin, Takashi Ishida, Masashi Sugiyama · Oct 26, 2025

  Automatic Metrics Coding

  As artificial intelligence (AI) systems approach and surpass expert human performance across a broad range of tasks, obtaining high-quality human supervision for evaluation and training becomes increasingly challenging.

• Precise Attribute Intensity Control in Large Language Models via Targeted Representation Editing

  Rongzhi Zhang, Liqin Ye, Yuzhao Heng, Xiang Chen, Tong Yu · Oct 14, 2025

  Automatic Metrics Coding

  Finally, we demonstrate efficiency enhancements across three downstream tasks: preference data synthesis, Pareto frontier approximation and optimization, and distillation of aligned behaviors for intervention-free inference.

• RHYTHM: Reasoning with Hierarchical Temporal Tokenization for Human Mobility

  Haoyu He, Haozheng Luo, Yan Chen, Qi R. Wang · Sep 27, 2025

  Automatic Metrics Coding

  Predicting human mobility is inherently challenging due to complex long-range dependencies and multi-scale periodic behaviors.

• HEART: Emotionally-Driven Test-Time Scaling of Language Models

  Gabriela Pinto, Palash Goyal, Mihir Parmar, Yiwen Song, Souradip Chakraborty · Sep 26, 2025

  Automatic Metrics Coding

  We introduce HEART, a framework that uses emotional cues to guide the model's focus, much like how feelings contribute to human decision-making.

• ATTS: Asynchronous Test-Time Scaling via Conformal Prediction

  Jing Xiong, Qiujiang Chen, Fanghua Ye, Zhongwei Wan, Chuanyang Zheng · Sep 18, 2025

  Automatic Metrics MathCoding

  Large language models (LLMs) benefit from test-time scaling but are often hampered by high inference latency.

• MedicalPatchNet: A Patch-Based Self-Explainable AI Architecture for Chest X-ray Classification

  Patrick Wienholt, Christiane Kuhl, Jakob Nikolas Kather, Sven Nebelung, Daniel Truhn · Sep 9, 2025

  Automatic Metrics MedicineCoding

  Deep neural networks excel in radiological image classification but frequently suffer from poor interpretability, limiting clinical acceptance.

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