CITE certifies that a prespecified answer is the unique mode of an LLM response distribution with anytime-valid error control under arbitrary data-driven stopping and without prior knowledge of the answer set.
Beyond Benchmarks: MathArena as an Evaluation Platform for Mathematics with LLMs
5 Pith papers cite this work. Polarity classification is still indexing.
5
Pith papers citing it
abstract
Large language models (LLMs) are becoming increasingly capable mathematical collaborators, but static benchmarks are no longer sufficient for evaluating progress: they are often narrow in scope, quickly saturated, and rarely updated. This makes it hard to compare models reliably and track progress over time. Instead, we need evaluation platforms: continuously maintained systems that run, aggregate, and analyze evaluations across many benchmarks to give a comprehensive picture of model performance within a broad domain. In this work, we build on the original MathArena benchmark by substantially broadening its scope from final-answer olympiad problems to a continuously maintained evaluation platform for mathematical reasoning with LLMs. MathArena now covers a much wider range of tasks, including proof-based competitions, research-level arXiv problems, and formal proof generation in Lean. Additionally, we maintain a clear evaluation protocol for all models and regularly design new benchmarks as model capabilities improve to ensure that MathArena remains challenging. Notably, the strongest model, GPT-5.5, now reaches 98% on the 2026 USA Math Olympiad and 74% on research-level questions, showing that frontier models can now comfortably solve extremely challenging mathematical problems. This highlights the importance of continuously maintained evaluation platforms like MathArena to track the rapid progress of LLMs in mathematical reasoning.
years
2026 5verdicts
UNVERDICTED 5representative citing papers
Segment-level supervision extracts coherent proof segments to train policy models that achieve 61-66% success on miniF2F, outperforming step-level and whole-proof methods while also improving existing provers.
Anti-Self-Distillation reverses self-distillation signals via PMI to fix overconfidence on structural tokens, matching GRPO baseline accuracy 2-10x faster with up to 11.5 point gains across 4B-30B models.
A training recipe for tool-integrated reasoning models achieves state-of-the-art open-source results on math benchmarks such as 96.7% and 99.2% on AIME 2025 at 4B and 30B scales by balancing tool-use trajectories and optimizing for pass@k during SFT before stable RLVR.
BFLA is a two-stage block-filtered sparse prefill attention mechanism that constructs an input-dependent block mask and applies tile-level rescues to skip unimportant KV tiles while preserving exact attention inside retained tiles, delivering speedups on models like Llama 3.1 with minimal accuracy 0
citing papers explorer
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CITE: Anytime-Valid Statistical Inference in LLM Self-Consistency
CITE certifies that a prespecified answer is the unique mode of an LLM response distribution with anytime-valid error control under arbitrary data-driven stopping and without prior knowledge of the answer set.
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Rethinking Supervision Granularity: Segment-Level Learning for LLM-Based Theorem Proving
Segment-level supervision extracts coherent proof segments to train policy models that achieve 61-66% success on miniF2F, outperforming step-level and whole-proof methods while also improving existing provers.
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Anti-Self-Distillation for Reasoning RL via Pointwise Mutual Information
Anti-Self-Distillation reverses self-distillation signals via PMI to fix overconfidence on structural tokens, matching GRPO baseline accuracy 2-10x faster with up to 11.5 point gains across 4B-30B models.
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Teaching Thinking Models to Reason with Tools: A Full-Pipeline Recipe for Tool-Integrated Reasoning
A training recipe for tool-integrated reasoning models achieves state-of-the-art open-source results on math benchmarks such as 96.7% and 99.2% on AIME 2025 at 4B and 30B scales by balancing tool-use trajectories and optimizing for pass@k during SFT before stable RLVR.
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BFLA: Block-Filtered Long-Context Attention Mechanism
BFLA is a two-stage block-filtered sparse prefill attention mechanism that constructs an input-dependent block mask and applies tile-level rescues to skip unimportant KV tiles while preserving exact attention inside retained tiles, delivering speedups on models like Llama 3.1 with minimal accuracy 0