Gsm8k Benchmark Papers

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Top Papers On This Benchmark

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

  Charafeddine Mouzouni · Feb 24, 2026

  Automatic Metrics

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

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

  Arindam Khaled · Feb 23, 2026

  Automatic Metrics

  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.

• Think$^{2}$: Grounded Metacognitive Reasoning in Large Language Models

  Abraham Paul Elenjical, Vivek Hruday Kavuri, Vasudeva Varma · Feb 21, 2026

  Human Eval

  We introduce a psychologically grounded metacognitive framework that operationalizes Ann Brown's regulatory cycle (Planning, Monitoring, and Evaluation) as a structured prompting architecture, and study its integration within a lightweight

• SPQ: An Ensemble Technique for Large Language Model Compression

  Jiamin Yao, Eren Gultepe · Feb 20, 2026

  Automatic MetricsSimulation Env

  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.

• TFL: Targeted Bit-Flip Attack on Large Language Model

  Jingkai Guo, Chaitali Chakrabarti, Deliang Fan · Feb 19, 2026

  Automatic Metrics

  Large language models (LLMs) are increasingly deployed in safety and security critical applications, raising concerns about their robustness to model parameter fault injection attacks.

• Weight space Detection of Backdoors in LoRA Adapters

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

  Automatic Metrics

  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.

• Scaling Beyond Masked Diffusion Language Models

  Subham Sekhar Sahoo, Jean-Marie Lemercier, Zhihan Yang, Justin Deschenaux, Jingyu Liu · Feb 16, 2026

  Automatic Metrics

  Among discrete diffusion approaches, Masked diffusion currently dominates, largely driven by strong perplexity on language modeling benchmarks.

• Search or Accelerate: Confidence-Switched Position Beam Search for Diffusion Language Models

  Mingyu Cao, Alvaro H. C. Correia, Christos Louizos, Shiwei Liu, Lu Yin · Feb 11, 2026

  Automatic Metrics

  Across mathematical reasoning and code generation benchmarks (GSM8K, MBPP, HumanEval) on Dream-7B and LLaDA-8B, SOAR improves generation quality while maintaining competitive inference speed, offering a practical way to balance quality and

• Slm-mux: Orchestrating small language models for reasoning

  Chenyu Wang, Zishen Wan, Hao Kang, Emma Chen, Zhiqiang Xie · Oct 6, 2025

  Automatic Metrics

  Additional experiments show that the core principle of SLM-MUX extends to open-ended generation tasks (e.g., HumanEval) and benefits other model classes, including frontier LLMs and domain-specific fine-tuned SLMs.

• Diffusion Language Models Know the Answer Before Decoding

  Pengxiang Li, Yefan Zhou, Dilxat Muhtar, Lu Yin, Shilin Yan · Aug 27, 2025

  Automatic Metrics

  Empirical evaluations of LLaDA-8B and Dream-7B across multiple tasks show that Prophet reduces the number of decoding steps by up to 3.4x while preserving high generation quality.

• Not All Errors Are Created Equal: ASCoT Addresses Late-Stage Fragility in Efficient LLM Reasoning

  Dongxu Zhang, Ning Yang, Yiding Sun, Jihua Zhu, Jinnan Yang · Aug 7, 2025

  Automatic Metrics

  While Chain-of-Thought (CoT) prompting empowers Large Language Models (LLMs), ensuring reasoning reliability remains an open challenge.

• MathFimer: Enhancing Mathematical Reasoning by Expanding Reasoning Steps through Fill-in-the-Middle Task

  Yuchen Yan, Yongliang Shen, Yang Liu, Jin Jiang, Xin Xu · Feb 17, 2025

  Automatic Metrics

  Through comprehensive experiments on multiple mathematical reasoning datasets, including MathInstruct, MetaMathQA and etc., we demonstrate that models trained on MathFimer-expanded data consistently outperform their counterparts trained on

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