MorphoBench: A Benchmark with Difficulty Adaptive to Model Reasoning (2026.findings-acl)
Copied to clipboard
Xukai Wang, Xuanbo Liu, Mingrui Chen, Haitian Zhong, Xuanlin Yang, Bohan Zeng, Jinbo Hu, Hao Liang, Junbo Niu, Xuchen Li, Ruitao Wu, Ruichuan An, Yang Shi, Liu Liu, Qiang Liu, Zhouchen Lin, Xu-Yao Zhang, Wentao Zhang, Bin Dong
| Challenge: | Existing benchmarks designed to evaluate the reasoning capabilities of large models are limited in scope and lack flexibility to adapt difficulty according to evolving reasoning capacities of models. |
| Approach: | They propose a benchmark that incorporates multidisciplinary questions to evaluate the reasoning capabilities of large models and can adjust and update question difficulty based on the reasoning abilities of advanced models. |
| Outcome: | The proposed benchmark incorporates multidisciplinary questions to evaluate the reasoning capabilities of large models and can adjust and update question difficulty based on the reasoning abilities of advanced models. |
Similar Papers
AlgBench: To What Extent Do Large Reasoning Models Understand Algorithms? (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing benchmarks for algorithmic reasoning fail to answer a critical question: do LRMs master algorithmic thinking? Empirical evaluations on leading LRM models reveal substantial performance heterogeneity, while models perform well on non-optimized tasks, accuracy drops sharply to around 49% on globally optimized algorithms. |
| Approach: | They propose an algorithm-centric benchmark that evaluates large reasoning models under an algorithmic paradigm. |
| Outcome: | Empirical evaluations on leading LRMs reveal substantial performance heterogeneity . models perform well on non-optimized tasks, accuracy drops sharply to around 49% . |
ARISE: An Adaptive Resolution-Aware Metric for Test-Time Scaling Evaluation in Large Reasoning Models (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing evaluation methods for test-time scaling are limited. |
| Approach: | They propose an adaptive resolution-aware scaling evaluation metric specifically designed to assess the test-time scaling effectiveness of large reasoning models. |
| Outcome: | The proposed metric provides a reliable and fine-grained measurement of test-time scaling capabilities, revealing significant variations in scaling efficiency across models. |
NPHardEval: Dynamic Benchmark on Reasoning Ability of Large Language Models via Complexity Classes (2024.acl-long)
Copied to clipboard
| Challenge: | Complex reasoning ability is one of the most important features of Large Language Models. |
| Approach: | They propose a new benchmark that measures the reasoning ability of Large Language Models . it contains 900 algorithmic questions belonging to the NP-Hard complexity class . |
| Outcome: | The proposed benchmark contains 900 questions belonging to the NP-Hard complexity class and is updated on a monthly basis. |
Chinese Morpheme-informed Evaluation of Large Language Models (2024.lrec-main)
Copied to clipboard
| Challenge: | Existing evaluations of large language models focused on the perspective of various tasks or abilities. |
| Approach: | They propose to evaluate large language models from a linguistic perspective and use morpheme to measure morphology and syntax. |
| Outcome: | The proposed model outperforms ChatGPT in Chinese scenarios with a morpheme-informed benchmark and human exam questions. |
How Reliable are Confidence Estimators for Large Reasoning Models? A Systematic Benchmark on High-Stakes Domains (2026.eacl-long)
Copied to clipboard
Reza Khanmohammadi, Erfan Miahi, Simerjot Kaur, Charese Smiley, Ivan Brugere, Kundan S Thind, Mohammad M. Ghassemi
| Challenge: | Large Reasoning Models often struggle with confidence calibration, authors say . authors: accurate confidence scores are essential to build trustworthy systems . |
| Approach: | They propose a Reasoning Model Confidence estimation benchmark to assess LRM confidence . the benchmark is constructed from 347,496 reasoning traces from six popular LRMs . |
| Outcome: | The proposed benchmark compares ten different representation-based methods on a wide range of architectures. |
LogicBench: Towards Systematic Evaluation of Logical Reasoning Ability of Large Language Models (2024.acl-long)
Copied to clipboard
Mihir Parmar, Nisarg Patel, Neeraj Varshney, Mutsumi Nakamura, Man Luo, Santosh Mashetty, Arindam Mitra, Chitta Baral
| Challenge: | Existing work investigating the logical reasoning ability of large language models has focused only on a couple of inference rules of propositional and first-order logics. |
| Approach: | They propose to use a natural language question-answering dataset to evaluate the logical reasoning ability of large language models. |
| Outcome: | The proposed model performs poorly on a range of natural language questions using chain-of-thought prompting. |
LogicAsker: Evaluating and Improving the Logical Reasoning Ability of Large Language Models (2024.emnlp-main)
Copied to clipboard
Yuxuan Wan, Wenxuan Wang, Yiliu Yang, Youliang Yuan, Jen-tse Huang, Pinjia He, Wenxiang Jiao, Michael Lyu
| Challenge: | LogicAsker examines and improves the reasoning abilities of large language models such as ChatGPT and GPT-4. |
| Approach: | They propose a set of atomic reasoning skills grounded in propositional and predicate logic to examine and improve the reasoning abilities of large language models such as ChatGPT and GPT-4. |
| Outcome: | The proposed approach improves reasoning abilities in large language models such as ChatGPT and GPT-4 by up to 5%. |
MetaScale: Test-Time Scaling with Evolving Meta-Thoughts (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing approaches impose fixed cognitive structures that enhance performance in specific tasks but lack adaptability across diverse scenarios. |
| Approach: | They propose a test-time scaling framework based on meta-thoughts to improve performance . meta-thinkts are adaptive thinking strategies tailored to a given task . |
| Outcome: | Experimental results show that MetaScale outperforms standard inference approaches . it can scale more effectively with increasing sampling budgets and produces more structured responses . |
Adaption-of-Thought: Learning Question Difficulty Improves Large Language Models for Reasoning (2024.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods do not differentiate question difficulty when designing prompting methods for them. |
| Approach: | They propose an adaptive method to improve large language models for reasoning problems by measuring question difficulty and tailoring demonstration set construction and difficulty-adapted retrieval strategies. |
| Outcome: | The proposed method shows an absolute improvement of up to 5.5% on arithmetic reasoning, 7.4% on symbolic reasoning, and 2.3% on commonsense reasoning. |
AdapThink: Adaptive Thinking Preferences for Reasoning Language Models (2026.findings-acl)
Copied to clipboard
| Challenge: | Recent research has highlighted a significant inefficiency associated with the slow thinking paradigm . models often overthink simple tasks while underthinking complex challenges . |
| Approach: | They propose a framework for adaptive reasoning preference control that dynamically adjusts reflection preferences based on group-level distributional statistics of reasoning length and reflection intensity. |
| Outcome: | The proposed framework reduces average response length by 17.1%-21.4% while improving performance by 6.12-6.59 points under 32K token budgets. |