Saved in:
| Main Authors: | Zhang, Min, Takumi, Sato, Zhang, Jack, Wang, Jun |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2402.09967 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Optimal Sparsity of Mixture-of-Experts Language Models for Reasoning Tasks
by: Nakamura, Taishi, et al.
Published: (2025)
by: Nakamura, Taishi, et al.
Published: (2025)
Socrates or Smartypants: Testing Logic Reasoning Capabilities of Large Language Models with Logic Programming-based Test Oracles
by: Xu, Zihao, et al.
Published: (2025)
by: Xu, Zihao, et al.
Published: (2025)
Reasoning Capabilities of Large Language Models on Dynamic Tasks
by: Wong, Annie, et al.
Published: (2025)
by: Wong, Annie, et al.
Published: (2025)
WebAggregator: Enhancing Compositional Reasoning Capabilities of Deep Research Agent Foundation Models
by: Wang, Rui, et al.
Published: (2025)
by: Wang, Rui, et al.
Published: (2025)
Evaluating Accounting Reasoning Capabilities of Large Language Models
by: Zhou, Jie, et al.
Published: (2026)
by: Zhou, Jie, et al.
Published: (2026)
Reasoning or Reciting? Exploring the Capabilities and Limitations of Language Models Through Counterfactual Tasks
by: Wu, Zhaofeng, et al.
Published: (2023)
by: Wu, Zhaofeng, et al.
Published: (2023)
CDT: A Comprehensive Capability Framework for Large Language Models Across Cognition, Domain, and Task
by: Mo, Haosi, et al.
Published: (2025)
by: Mo, Haosi, et al.
Published: (2025)
Mathematical Reasoning Enhanced LLM for Formula Derivation: A Case Study on Fiber NLI Modellin
by: Zhang, Yao, et al.
Published: (2026)
by: Zhang, Yao, et al.
Published: (2026)
Exploring the System 1 Thinking Capability of Large Reasoning Models
by: Zhang, Wenyuan, et al.
Published: (2025)
by: Zhang, Wenyuan, et al.
Published: (2025)
CityBench: Evaluating the Capabilities of Large Language Models for Urban Tasks
by: Feng, Jie, et al.
Published: (2024)
by: Feng, Jie, et al.
Published: (2024)
Unlocking General Long Chain-of-Thought Reasoning Capabilities of Large Language Models via Representation Engineering
by: Tang, Xinyu, et al.
Published: (2025)
by: Tang, Xinyu, et al.
Published: (2025)
ChartMuseum: Testing Visual Reasoning Capabilities of Large Vision-Language Models
by: Tang, Liyan, et al.
Published: (2025)
by: Tang, Liyan, et al.
Published: (2025)
Reasoning Vectors: Transferring Chain-of-Thought Capabilities via Task Arithmetic
by: Zbeeb, Mohammad, et al.
Published: (2025)
by: Zbeeb, Mohammad, et al.
Published: (2025)
Leveraging Reasoning Model Answers to Enhance Non-Reasoning Model Capability
by: Wang, Haotian, et al.
Published: (2025)
by: Wang, Haotian, et al.
Published: (2025)
Are Your LLMs Capable of Stable Reasoning?
by: Liu, Junnan, et al.
Published: (2024)
by: Liu, Junnan, et al.
Published: (2024)
Logit Arithmetic Elicits Long Reasoning Capabilities Without Training
by: Zhang, Yunxiang, et al.
Published: (2025)
by: Zhang, Yunxiang, et al.
Published: (2025)
Optimizing Case-Based Reasoning System for Functional Test Script Generation with Large Language Models
by: Guo, Siyuan, et al.
Published: (2025)
by: Guo, Siyuan, et al.
Published: (2025)
Phenomenal Yet Puzzling: Testing Inductive Reasoning Capabilities of Language Models with Hypothesis Refinement
by: Qiu, Linlu, et al.
Published: (2023)
by: Qiu, Linlu, et al.
Published: (2023)
MMLU-SR: A Benchmark for Stress-Testing Reasoning Capability of Large Language Models
by: Wang, Wentian, et al.
Published: (2024)
by: Wang, Wentian, et al.
Published: (2024)
Capability Salience Vector: Fine-grained Alignment of Loss and Capabilities for Downstream Task Scaling Law
by: Ge, Qiming, et al.
Published: (2025)
by: Ge, Qiming, et al.
Published: (2025)
Neural-Symbolic Collaborative Distillation: Advancing Small Language Models for Complex Reasoning Tasks
by: Liao, Huanxuan, et al.
Published: (2024)
by: Liao, Huanxuan, et al.
Published: (2024)
Dynamic Embeddings with Task-Oriented prompting
by: Balloccu, Allmin, et al.
Published: (2024)
by: Balloccu, Allmin, et al.
Published: (2024)
Make Some Noise: Unlocking Language Model Parallel Inference Capability through Noisy Training
by: Wang, Yixuan, et al.
Published: (2024)
by: Wang, Yixuan, et al.
Published: (2024)
Logit Arithmetic Elicits Long Reasoning Capabilities Without Training
by: Zhang, Yunxiang, et al.
Published: (2025)
by: Zhang, Yunxiang, et al.
Published: (2025)
ScanReason: Empowering 3D Visual Grounding with Reasoning Capabilities
by: Zhu, Chenming, et al.
Published: (2024)
by: Zhu, Chenming, et al.
Published: (2024)
Reasoning Capabilities and Invariability of Large Language Models
by: Raganato, Alessandro, et al.
Published: (2025)
by: Raganato, Alessandro, et al.
Published: (2025)
STEP: Staged Parameter-Efficient Pre-training for Large Language Models
by: Yano, Kazuki, et al.
Published: (2025)
by: Yano, Kazuki, et al.
Published: (2025)
Adaptive Detoxification: Safeguarding General Capabilities of LLMs through Toxicity-Aware Knowledge Editing
by: Lu, Yifan, et al.
Published: (2025)
by: Lu, Yifan, et al.
Published: (2025)
Let's Reason Formally: Natural-Formal Hybrid Reasoning Enhances LLM's Math Capability
by: Wang, Ruida, et al.
Published: (2025)
by: Wang, Ruida, et al.
Published: (2025)
NaturalThoughts: Selecting and Distilling Reasoning Traces for General Reasoning Tasks
by: Li, Yang, et al.
Published: (2025)
by: Li, Yang, et al.
Published: (2025)
Unleashing LLM Reasoning Capability via Scalable Question Synthesis from Scratch
by: Ding, Yuyang, et al.
Published: (2024)
by: Ding, Yuyang, et al.
Published: (2024)
Stop Before You Fail: Operational Capability Boundaries for Mitigating Unproductive Reasoning in Large Reasoning Models
by: Zhang, Qingjie, et al.
Published: (2025)
by: Zhang, Qingjie, et al.
Published: (2025)
Re-TASK: Revisiting LLM Tasks from Capability, Skill, and Knowledge Perspectives
by: Wang, Zhihu, et al.
Published: (2024)
by: Wang, Zhihu, et al.
Published: (2024)
LLM for Complex Reasoning Task: An Exploratory Study in Fermi Problems
by: Liu, Zishuo, et al.
Published: (2025)
by: Liu, Zishuo, et al.
Published: (2025)
LLaMA Beyond English: An Empirical Study on Language Capability Transfer
by: Zhao, Jun, et al.
Published: (2024)
by: Zhao, Jun, et al.
Published: (2024)
Your Reasoning Benchmark May Not Test Reasoning: Revealing Perception Bottleneck in Abstract Reasoning Benchmarks
by: Wang, Xinhe, et al.
Published: (2025)
by: Wang, Xinhe, et al.
Published: (2025)
Distilling Mathematical Reasoning Capabilities into Small Language Models
by: Zhu, Xunyu, et al.
Published: (2024)
by: Zhu, Xunyu, et al.
Published: (2024)
TimeMachine-bench: A Benchmark for Evaluating Model Capabilities in Repository-Level Migration Tasks
by: Fujii, Ryo, et al.
Published: (2026)
by: Fujii, Ryo, et al.
Published: (2026)
ORPP: Self-Optimizing Role-playing Prompts to Enhance Language Model Capabilities
by: Duan, Yifan, et al.
Published: (2025)
by: Duan, Yifan, et al.
Published: (2025)
What Are the Odds? Language Models Are Capable of Probabilistic Reasoning
by: Paruchuri, Akshay, et al.
Published: (2024)
by: Paruchuri, Akshay, et al.
Published: (2024)
Similar Items
-
Optimal Sparsity of Mixture-of-Experts Language Models for Reasoning Tasks
by: Nakamura, Taishi, et al.
Published: (2025) -
Socrates or Smartypants: Testing Logic Reasoning Capabilities of Large Language Models with Logic Programming-based Test Oracles
by: Xu, Zihao, et al.
Published: (2025) -
Reasoning Capabilities of Large Language Models on Dynamic Tasks
by: Wong, Annie, et al.
Published: (2025) -
WebAggregator: Enhancing Compositional Reasoning Capabilities of Deep Research Agent Foundation Models
by: Wang, Rui, et al.
Published: (2025) -
Evaluating Accounting Reasoning Capabilities of Large Language Models
by: Zhou, Jie, et al.
Published: (2026)