MATH Benchmark Papers (Last 60 Days)

Updated from current HFEPX corpus (Feb 27, 2026). This page tracks 13 papers for MATH Benchmark Papers (Last 60 Days). Dominant protocol signals include automatic metrics, simulation environments, with frequent benchmark focus on MATH, AIME 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 On This Benchmark

• 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

  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.

• 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

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

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

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

  Automatic Metrics

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

• Utility-Preserving De-Identification for Math Tutoring: Investigating Numeric Ambiguity in the MathEd-PII Benchmark Dataset

  Zhuqian Zhou, Kirk Vanacore, Bakhtawar Ahtisham, Jinsook Lee, Doug Pietrzak · Feb 18, 2026

  Automatic Metrics

  To address this challenge, we investigate the "numeric ambiguity" problem and introduce MathEd-PII, the first benchmark dataset for PII detection in math tutoring dialogues, created through a human-in-the-loop LLM workflow that audits upstr

• 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

  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.

• Learning to Learn from Language Feedback with Social Meta-Learning

  Jonathan Cook, Diego Antognini, Martin Klissarov, Claudiu Musat, Edward Grefenstette · Feb 18, 2026

  Automatic Metrics

  They are rarely proactive in soliciting this feedback, even when faced with ambiguity, which can make their dialogues feel static, one-sided, and lacking the adaptive qualities of human conversation.

• Recursive Concept Evolution for Compositional Reasoning in Large Language Models

  Sarim Chaudhry · Feb 17, 2026

  Automatic Metrics

  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

  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

• Cognitive networks reconstruct mindsets about STEM subjects and educational contexts in almost 1000 high-schoolers, University students and LLM-based digital twins

  Francesco Gariboldi, Emma Franchino, Edith Haim, Gianluca Lattanzi, Alessandro Grecucci · Feb 16, 2026

  Automatic Metrics

  Human networks show greater overlapping between mathematics and anxiety than GPT-oss.

• Step 3.5 Flash: Open Frontier-Level Intelligence with 11B Active Parameters

  Ailin Huang, Ang Li, Aobo Kong, Bin Wang, Binxing Jiao · Feb 11, 2026

  Simulation Env Tool Use

  We introduce Step 3.5 Flash, a sparse Mixture-of-Experts (MoE) model that bridges frontier-level agentic intelligence and computational efficiency.

• LLMs Know More About Numbers than They Can Say

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

  Automatic Metrics

  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

• Proof-RM: A Scalable and Generalizable Reward Model for Math Proof

  Haotong Yang, Zitong Wang, Shijia Kang, Siqi Yang, Wenkai Yu · Feb 2, 2026

  Automatic Metrics

  In this work, we design a *scalable* data-construction pipeline that, with minimal human effort, leverages LLMs to generate a large quantity of high-quality ``**question-proof-check**'' triplet data.

• Orthogonalized Policy Optimization:Policy Optimization as Orthogonal Projection in Hilbert Space

  Wang Zixian · Jan 18, 2026

  Automatic Metrics Long Horizon

  Experiments on MATH benchmarks show that the Hilbert projection formulation prevents gradient saturation typical of KL-constrained methods.

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