Accuracy In CS.LG Papers

Updated from current HFEPX corpus (Feb 27, 2026). This page tracks 82 papers for Accuracy In CS.LG Papers. Dominant protocol signals include automatic metrics, simulation environments, human evaluation, with frequent benchmark focus on MATH, Retrieval 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

• 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.

• 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.

• 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.

• 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.

• 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.

• 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.

• 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.

• Overconfident Errors Need Stronger Correction: Asymmetric Confidence Penalties for Reinforcement Learning

  Yuanda Xu, Hejian Sang, Zhengze Zhou, Ran He, Zhipeng Wang · Feb 24, 2026

  Automatic Metrics Math

  Evaluated on MATH-500 and AIME 2025, ACE composes seamlessly with existing methods and consistently improves the full Pass@k spectrum across all three model families and benchmarks.

• 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.

• 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.

• 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

• 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.

• 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.

• 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.

• 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.

• Why Agent Caching Fails and How to Fix It: Structured Intent Canonicalization with Few-Shot Learning

  Abhinaba Basu · Feb 21, 2026

  Automatic Metrics Multilingual

  Personal AI agents incur substantial cost via repeated LLM calls.

• 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.

• 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.

• Agentic Adversarial QA for Improving Domain-Specific LLMs

  Vincent Grari, Ciprian Tomoiaga, Sylvain Lamprier, Tatsunori Hashimoto, Marcin Detyniecki · Feb 20, 2026

  Automatic Metrics Law

  Evaluation on specialized subsets of the LegalBench corpus demonstrates that our method achieves greater accuracy with substantially fewer synthetic samples.

• Gradient Regularization Prevents Reward Hacking in Reinforcement Learning from Human Feedback and Verifiable Rewards

  Johannes Ackermann, Michael Noukhovitch, Takashi Ishida, Masashi Sugiyama · Feb 20, 2026

  Automatic Metrics Math

  Reinforcement Learning from Human Feedback (RLHF) or Verifiable Rewards (RLVR) are two key steps in the post-training of modern Language Models (LMs).

• 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.

• Training Large Reasoning Models Efficiently via Progressive Thought Encoding

  Zeliang Zhang, Xiaodong Liu, Hao Cheng, Hao Sun, Chenliang Xu · Feb 18, 2026

  Automatic Metrics Math

  Experiments on three models, including Qwen2.5-3B-Instruct, Qwen2.5-7B-Instruct, and DeepSeek-R1-Distill-Llama-8B, on six widely used challenging mathematical benchmarks show consistent gains: our method achieves +19.3% improvement over LoR

• 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.

• From Growing to Looping: A Unified View of Iterative Computation in LLMs

  Ferdinand Kapl, Emmanouil Angelis, Kaitlin Maile, Johannes von Oswald, Stefan Bauer · Feb 18, 2026

  Automatic Metrics Math

  Looping, reusing a block of layers across depth, and depth growing, training shallow-to-deep models by duplicating middle layers, have both been linked to stronger reasoning, but their relationship remains unclear.

• 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.

• 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.

• Prescriptive Scaling Reveals the Evolution of Language Model Capabilities

  Hanlin Zhang, Jikai Jin, Vasilis Syrgkanis, Sham Kakade · Feb 17, 2026

  Automatic Metrics MathLaw

  Using large scale observational evaluations with 5k observational and 2k newly sampled data on model performance, we estimate capability boundaries, high conditional quantiles of benchmark scores as a function of log pre training FLOPs, via

• Weight space Detection of Backdoors in LoRA Adapters

  David Puertolas Merenciano, Ekaterina Vasyagina, Raghav Dixit, Kevin Zhu, Ruizhe Li · Feb 16, 2026

  Automatic Metrics Math

  We evaluate the method on 500 LoRA adapters -- 400 clean, and 100 poisoned for Llama-3.2-3B on instruction and reasoning datasets: Alpaca, Dolly, GSM8K, ARC-Challenge, SQuADv2, NaturalQuestions, HumanEval, and GLUE dataset.

• 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

• LLMStructBench: Benchmarking Large Language Model Structured Data Extraction

  Sönke Tenckhoff, Mario Koddenbrock, Erik Rodner · Feb 16, 2026

  Automatic Metrics General

  We present LLMStructBench, a novel benchmark for evaluating Large Language Models (LLMs) on extracting structured data and generating valid JavaScript Object Notation (JSON) outputs from natural-language text.

• Unleashing Low-Bit Inference on Ascend NPUs: A Comprehensive Evaluation of HiFloat Formats

  Pengxiang Zhao, Hui-Ling Zhen, Xing Li, Han Bao, Weizhe Lin · Feb 13, 2026

  Automatic Metrics General

  As LLMs scale, low-bit floating-point formats like MXFP and NVFP4 offer new opportunities for precision and efficiency.

• Think like a Scientist: Physics-guided LLM Agent for Equation Discovery

  Jianke Yang, Ohm Venkatachalam, Mohammad Kianezhad, Sharvaree Vadgama, Rose Yu · Feb 12, 2026

  Automatic Metrics General

  We introduce KeplerAgent, an agentic framework that explicitly follows this scientific reasoning process.

• 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.

• 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

• Conflict-Aware Fusion: Resolving Logic Inertia in Large Language Models via Structured Cognitive Priors

  Qiming Bao, Xiaoxuan Fu, Michael Witbrock · Dec 6, 2025

  Automatic Metrics Law

  We present a controlled evaluation framework consisting of four stress tests: (1) rule deletion (redundant vs.

• 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.

• Intelligence per Watt: Measuring Intelligence Efficiency of Local AI

  Jon Saad-Falcon, Avanika Narayan, Hakki Orhun Akengin, J. Wes Griffin, Herumb Shandilya · Nov 11, 2025

  Automatic Metrics General

  Large language model (LLM) queries are predominantly processed by frontier models in centralized cloud infrastructure.

• 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.

• MNO: Multiscale Neural Operator for 3D Computational Fluid Dynamics

  Qinxuan Wang, Chuang Wang, Mingyu Zhang, Jingwei Sun, Peipei Yang · Oct 17, 2025

  Automatic Metrics General

  We evaluate MNO on diverse benchmarks, covering steady-state and unsteady flow scenarios with up to 300k points.

• 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.

• The Speech-LLM Takes It All: A Truly Fully End-to-End Spoken Dialogue State Tracking Approach

  Nizar El Ghazal, Antoine Caubrière, Valentin Vielzeuf · Oct 10, 2025

  Automatic Metrics General

  This paper presents a comparative study of context management strategies for end-to-end Spoken Dialog State Tracking using Speech-LLMs.

• Lossless Vocabulary Reduction for Auto-Regressive Language Models

  Daiki Chijiwa, Taku Hasegawa, Kyosuke Nishida, Shin'ya Yamaguchi, Tomoya Ohba · Oct 9, 2025

  Automatic Metrics General

  Tokenization -- the process of decomposing a given text into a sequence of subwords called tokens -- is one of the key components in the development of language models.

• 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.

• PeruMedQA: Benchmarking Large Language Models (LLMs) on Peruvian Medical Exams -- Dataset Construction and Evaluation

  Rodrigo M. Carrillo-Larco, Jesus Lovón Melgarejo, Manuel Castillo-Cara, Gusseppe Bravo-Rocca · Sep 15, 2025

  Automatic Metrics Medicine

  BACKGROUND: Medical large language models (LLMs) have demonstrated remarkable performance in answering medical examinations.

• 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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