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References (20)

[1]
From Entropy to Epiplexity: Rethinking Information for Computationally Bounded Intelligence
2026Marc Finzi, Shikai Qiu et al.
[2]
How and Why LLMs Generalize: A Fine-Grained Analysis of LLM Reasoning from Cognitive Behaviors to Low-Level Patterns
2025Haoyue Bai, Yiyou Sun et al.
[3]
LLLMs: A Data-Driven Survey of Evolving Research on Limitations of Large Language Models
2025Aida Kostikova, Zhipin Wang et al.
[4]
Evaluating the Logical Reasoning Abilities of Large Reasoning Models
2025Hanmeng Liu, Yiran Ding et al.
[5]
Beyond Accuracy: Evaluating and Explaining the Capability Boundaries of Large Language Models in Syntax-Preserving Code Translation
2025Yaxin Zhao, Qi Han et al.
[6]
Knowledge Boundary of Large Language Models: A Survey
2024Moxin Li, Yong Zhao et al.
[7]
GSM-Symbolic: Understanding the Limitations of Mathematical Reasoning in Large Language Models
2024Iman Mirzadeh, Keivan Alizadeh-Vahid et al.
[8]
LiveBench: A Challenging, Contamination-Limited LLM Benchmark
2024Colin White, Samuel Dooley et al.
[9]
Achieving >97% on GSM8K: deeply understanding the problems makes LLMs better solvers for math word problems
2024Qihuang Zhong, Kang Wang et al.
[10]
MACM: Utilizing a Multi-Agent System for Condition Mining in Solving Complex Mathematical Problems
2024Bin Lei
[11]
OlympiadBench: A Challenging Benchmark for Promoting AGI with Olympiad-Level Bilingual Multimodal Scientific Problems
2024Chaoqun He, Renjie Luo et al.
[12]
NPHardEval: Dynamic Benchmark on Reasoning Ability of Large Language Models via Complexity Classes
2023Lizhou Fan, Wenyue Hua et al.
[13]
Qwen Technical Report
2023Jinze Bai, Shuai Bai et al.
[14]
Faith and Fate: Limits of Transformers on Compositionality
2023Nouha Dziri, Ximing Lu et al.
[15]
Impact of Pretraining Term Frequencies on Few-Shot Reasoning
2022Yasaman Razeghi, Robert L Logan IV et al.
[16]
Deduplicating Training Data Mitigates Privacy Risks in Language Models
2022Nikhil Kandpal, Eric Wallace et al.
[17]
Training Verifiers to Solve Math Word Problems
2021K. Cobbe, Vineet Kosaraju et al.
[18]
Language Models are Few-Shot Learners
2020Tom B. Brown, Benjamin Mann et al.
[19]
Z3: An Efficient SMT Solver
2008L. D. Moura, Nikolaj S. Bjørner
[20]
Think*
1979Richard A. Shade

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