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Tag: Math

Math evaluation papers that call for domain expertise or specialist review (123 papers).

Papers in tag: 123

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Research Utility Snapshot

Evaluation Modes

  • Automatic Metrics (12)
  • Human Eval (1)

Human Feedback Types

  • Pairwise Preference (6)
  • Critique Edit (4)
  • Expert Verification (2)

Required Expertise

  • Math (20)
  • Coding (7)
  • Law (3)
TRIMS: Trajectory-Ranked Instruction Masked Supervision for Diffusion Language Models

Lingjie Chen, Ruizhong Qiu, Yuyu Fan, Yanjun Zhao, Hanghang Tong · Apr 1, 2026 · Citations: 0

Automatic Metrics MathCoding
  • Experiments on LLaDA and Dream across math and coding benchmarks show that TRIMS significantly improves the accuracy-parallelism trade-off over both standard MDLM training and train-free acceleration baselines, while achieving competitive…
Agent Q-Mix: Selecting the Right Action for LLM Multi-Agent Systems through Reinforcement Learning

Eric Hanchen Jiang, Levina Li, Rui Sun, Xiao Liang, Yubei Li, Yuchen Wu · Apr 1, 2026 · Citations: 0

Automatic Metrics MathLaw
  • In this paper, we propose Agent Q-Mix, a reinforcement learning framework that reformulates topology selection as a cooperative Multi-Agent Reinforcement Learning (MARL) problem.
  • Across seven core benchmarks in coding, reasoning, and mathematics, Agent Q-Mix achieves the highest average accuracy compared to existing methods while demonstrating superior token efficiency and robustness against agent failure.
Learning to Predict Future-Aligned Research Proposals with Language Models

Heng Wang, Pengcheng Jiang, Jiashuo Sun, Zhiyi Shi, Haofei Yu, Jiawei Han · Mar 28, 2026 · Citations: 0

Human EvalAutomatic Metrics MathCoding
  • Across Llama-3.1 and Qwen2.5 models, future-aligned tuning improves future alignment over unaligned baselines (up to +10.6% overall FAS), and domain-expert human evaluation corroborates improved proposal quality.
  • Finally, we demonstrate practical impact by implementing two model-generated proposals with a code agent, obtaining 4.17% accuracy gain on MATH from a new prompting strategy and consistent improvements for a novel model-merging method.
RASPRef: Retrieval-Augmented Self-Supervised Prompt Refinement for Large Reasoning Models

Rahul Soni · Mar 27, 2026 · Citations: 0

Critique Edit Math
  • Recent reasoning-focused language models such as DeepSeek R1 and OpenAI o1 have demonstrated strong performance on structured reasoning benchmarks including GSM8K, MATH, and multi-hop question answering tasks.
  • To address this limitation, we introduce Retrieval-Augmented Self-Supervised Prompt Refinement (RASPRef), a framework that improves prompts without requiring human annotations or task-specific supervision.
Towards Reward Modeling for AI Tutors in Math Mistake Remediation

Kseniia Petukhova, Ekaterina Kochmar · Mar 25, 2026 · Citations: 0

Pairwise Preference Automatic Metrics Math
  • We develop and release Bradley-Terry preference models trained on weighted-sum rankings that we automatically create from MRBench, synthetic pairs, and data combinations.
  • Using only synthetic data, our best model reaches 0.69 pairwise accuracy on a human preference test, and combining weighted-sum data with targeted synthetic groups improves accuracy to 0.74, outperforming larger general-purpose reward…
Generating and Evaluating Sustainable Procurement Criteria for the Swiss Public Sector using In-Context Prompting with Large Language Models

Yingqiang Gao, Veton Matoshi, Luca Rolshoven, Tilia Ellendorff, Judith Binder, Jeremy Austin Jann · Mar 23, 2026 · Citations: 0

Expert Verification MathLaw
  • Swiss law requires the integration of ecological, social, and economic sustainability requirements into tender evaluations in the format of criteria that have to be fulfilled by a bidder.
  • We evaluate the system through a combination of automated quality checks, including an LLM-based evaluation component, and expert comparison against a manually curated gold standard.
Adaptive Robust Estimator for Multi-Agent Reinforcement Learning

Zhongyi Li, Wan Tian, Jingyu Chen, Kangyao Huang, Huiming Zhang, Hui Yang · Mar 23, 2026 · Citations: 0

Critique Edit Math
  • Multi-agent collaboration has emerged as a powerful paradigm for enhancing the reasoning capabilities of large language models, yet it suffers from interaction-level ambiguity that blurs generation, critique, and revision, making credit…
  • To address both issues, we propose a robust multi-agent reinforcement learning framework for collaborative reasoning, consisting of two components: Dual-Agent Answer-Critique-Rewrite (DACR) and an Adaptive Robust Estimator (ARE).
PAVE: Premise-Aware Validation and Editing for Retrieval-Augmented LLMs

Tianyi Huang, Caden Yang, Emily Yin, Eric Wang, Michael Zhang · Mar 21, 2026 · Citations: 0

Critique Edit Automatic Metrics Math
  • In controlled ablations with a fixed retriever and backbone, PAVE outperforms simpler post-retrieval baselines in two evidence-grounded QA settings, with the largest gain reaching 32.7 accuracy points on a span-grounded benchmark.
Mi:dm K 2.5 Pro

KT Tech innovation Group · Mar 19, 2026 · Citations: 0

Automatic Metrics MathCoding
  • The evolving LLM landscape requires capabilities beyond simple text generation, prioritizing multi-step reasoning, long-context understanding, and agentic workflows.
  • The evaluations show that Mi:dm K 2.5 Pro achieves competitive performance against leading global and domestic models.
TARo: Token-level Adaptive Routing for LLM Test-time Alignment

Arushi Rai, Qiang Zhang, Hanqing Zeng, Yunkai Zhang, Dipesh Tamboli, Xiangjun Fan · Mar 19, 2026 · Citations: 0

Pairwise Preference MathMedicine
  • Recent test-time alignment methods offer a lightweight alternative, but have been explored mainly for preference alignment rather than reasoning.
  • Furthermore, TARo also generalizes from small to large backbones without retraining, extending test-time alignment from preference optimization to robust, cross-domain reasoning.
Truth as a Compression Artifact in Language Model Training

Konstantin Krestnikov · Mar 12, 2026 · Citations: 0

Pairwise Preference Automatic Metrics MathCoding
  • In the random-error setting, models strongly prefer correct completions in paired evaluation: 83.1% accuracy at balanced data and 67.0% even when correct rules appear in only 10% of the corpus.
  • Replacing random errors with a coherent but mathematically incorrect rule system largely eliminates the preference (near-chance accuracy).
Maximizing Mutual Information Between Prompt and Response Improves LLM Performance With No Additional Data

Hyunji Nam, Haoran Li, Natasha Jaques · Mar 10, 2026 · Citations: 0

Pairwise Preference Math
  • We propose **Mutual Information Preference Optimization (MIPO)**, a contrastive data augmentation method that constructs preference pairs by generating a positive response conditioning on the correct prompt, and a negative response by…
  • We show that using Direct Preference Optimization to learn from this paired data maximizes pointwise mutual information *under the base LLM* between prompts and model responses.
You Didn't Have to Say It like That: Subliminal Learning from Faithful Paraphrases

Isaia Gisler, Zhonghao He, Tianyi Qiu · Mar 10, 2026 · Citations: 0

Pairwise Preference MathCoding
  • We investigate whether transmission occurs through natural language paraphrases with fixed semantic content, and whether content explicitly contradicting the teacher's preference can block it.
  • We find that training on paraphrases from a teacher system-prompted to love a particular animal increases a student's preference for that animal by up to 19 percentage points.