:: Library Catalog

Copertina

Salvato in:

Dettagli Bibliografici
Autori principali:	Raftopoulos, Athanassios, Müller, Vincent C.
Natura:	Preprint
Pubblicazione:	2025
Soggetti:	Computation and Language
Accesso online:	https://arxiv.org/abs/2503.03495
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

PLAN-TUNING: Post-Training Language Models to Learn Step-by-Step Planning for Complex Problem Solving
di: Parmar, Mihir, et al.
Pubblicazione: (2025)

GeoMathCode: Understanding Interleaved Math-Code Reasoning for Geometry Problem Solving
di: Zhang, Yingji, et al.
Pubblicazione: (2026)

Structure-Grounded Knowledge Retrieval via Code Dependencies for Multi-Step Data Reasoning
di: Huang, Xinyi
Pubblicazione: (2026)

Symbol grounding in computational systems: A paradox of intentions
di: Müller, Vincent C.
Pubblicazione: (2025)

Solving an Open Problem in Theoretical Physics using AI-Assisted Discovery
di: Brenner, Michael P., et al.
Pubblicazione: (2026)

Idea First, Code Later: Disentangling Problem Solving from Code Generation in Evaluating LLMs for Competitive Programming
di: Hadhoud, Sama, et al.
Pubblicazione: (2026)

MapCoder: Multi-Agent Code Generation for Competitive Problem Solving
di: Islam, Md. Ashraful, et al.
Pubblicazione: (2024)

Large Language Models Are Self-Taught Reasoners: Enhancing LLM Applications via Tailored Problem-Solving Demonstrations
di: Ong, Kai Tzu-iunn, et al.
Pubblicazione: (2024)

Isolating Language-Coding from Problem-Solving: Benchmarking LLMs with PseudoEval
di: Wu, Jiarong, et al.
Pubblicazione: (2025)

Surrogate Signals from Format and Length: Reinforcement Learning for Solving Mathematical Problems without Ground Truth Answers
di: Xin, Rihui, et al.
Pubblicazione: (2025)

PhysicsSolutionAgent: Towards Multimodal Explanations for Numerical Physics Problem Solving
di: Thole, Aditya, et al.
Pubblicazione: (2026)

Can Multi-turn Self-refined Single Agent LMs with Retrieval Solve Hard Coding Problems?
di: Hosain, Md Tanzib, et al.
Pubblicazione: (2025)

Code Generation and Algorithmic Problem Solving Using Llama 3.1 405B
di: Deroy, Aniket, et al.
Pubblicazione: (2024)

Step-by-Step Reasoning to Solve Grid Puzzles: Where do LLMs Falter?
di: Tyagi, Nemika, et al.
Pubblicazione: (2024)

Prompt-to-Gesture: Measuring the Capabilities of Image-to-Video Deictic Gesture Generation
di: Ali, Hassan, et al.
Pubblicazione: (2026)

The Vector Grounding Problem
di: Mollo, Dimitri Coelho, et al.
Pubblicazione: (2023)

Using Code Generation to Solve Open Instances of Combinatorial Design Problems
di: Rosin, Christopher D.
Pubblicazione: (2025)

GeoCoder: Solving Geometry Problems by Generating Modular Code through Vision-Language Models
di: Sharma, Aditya, et al.
Pubblicazione: (2024)

Collaborative Problem-Solving in an Optimization Game
di: Jeknic, Isidora, et al.
Pubblicazione: (2025)

ChartREG++: Towards Benchmarking and Improving Chart Referring Expression Grounding under Diverse referring clues and Multi-Target Referring
di: Niu, Tianhao, et al.
Pubblicazione: (2026)

Which symbol grounding problem should we try to solve?
di: Müller, Vincent C.
Pubblicazione: (2025)

Understanding the Language Model to Solve the Symbolic Multi-Step Reasoning Problem from the Perspective of Buffer Mechanism
di: Wang, Zhiwei, et al.
Pubblicazione: (2024)

CoT Referring: Improving Referring Expression Tasks with Grounded Reasoning
di: Dong, Qihua, et al.
Pubblicazione: (2025)

Let's Revise Step-by-Step: A Unified Local Search Framework for Code Generation with LLMs
di: Lyu, Zhiyi, et al.
Pubblicazione: (2025)

From Problem-Solving to Teaching Problem-Solving: Aligning LLMs with Pedagogy using Reinforcement Learning
di: Dinucu-Jianu, David, et al.
Pubblicazione: (2025)

Problem-Solving Guide: Predicting the Algorithm Tags and Difficulty for Competitive Programming Problems
di: Kim, Juntae, et al.
Pubblicazione: (2023)

Can Large Language Models Always Solve Easy Problems if They Can Solve Harder Ones?
di: Yang, Zhe, et al.
Pubblicazione: (2024)

CODESIM: Multi-Agent Code Generation and Problem Solving through Simulation-Driven Planning and Debugging
di: Islam, Md. Ashraful, et al.
Pubblicazione: (2025)

Self-consistent Reasoning For Solving Math Word Problems
di: Xiong, Jing, et al.
Pubblicazione: (2022)

Grounding Language Plans in Demonstrations Through Counterfactual Perturbations
di: Wang, Yanwei, et al.
Pubblicazione: (2024)

OptiVerse: A Comprehensive Benchmark towards Optimization Problem Solving
di: Zhang, Xinyu, et al.
Pubblicazione: (2026)

TEC: A Collection of Human Trial-and-error Trajectories for Problem Solving
di: Zhang, Xinkai, et al.
Pubblicazione: (2026)

Chinese Language Is Not More Efficient Than English in Vibe Coding: A Preliminary Study on Token Cost and Problem-Solving Rate
di: Ren, Simiao, et al.
Pubblicazione: (2026)

A Neural Rewriting System to Solve Algorithmic Problems
di: Petruzzellis, Flavio, et al.
Pubblicazione: (2024)

DeiSAM: Segment Anything with Deictic Prompting
di: Shindo, Hikaru, et al.
Pubblicazione: (2024)

NeuroSync: Intent-Aware Code-Based Problem Solving via Direct LLM Understanding Modification
di: Zhang, Wenshuo, et al.
Pubblicazione: (2025)

SBSC: Step-By-Step Coding for Improving Mathematical Olympiad Performance
di: Singh, Kunal, et al.
Pubblicazione: (2025)

How Diversely Can Language Models Solve Problems? Exploring the Algorithmic Diversity of Model-Generated Code
di: Lee, Seonghyeon, et al.
Pubblicazione: (2025)

Solving the Inverse Alignment Problem for Efficient RLHF
di: Krishna, Shambhavi, et al.
Pubblicazione: (2024)

Demystifying Scientific Problem-Solving in LLMs by Probing Knowledge and Reasoning
di: Li, Alan, et al.
Pubblicazione: (2025)