Copertina

NeuroProlog: Multi-Task Fine-Tuning for Neurosymbolic Mathematical Reasoning via the Cocktail Effect

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Zunjare, Pratibha, Hsiao, Michael
Natura:	Preprint
Pubblicazione:	2026
Soggetti:	Artificial Intelligence
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

A Hybrid Multi-Agent Prompting Approach for Simplifying Complex Sentences
di: Zunjare, Pratibha, et al.
Pubblicazione: (2025)

Mixing It Up: The Cocktail Effect of Multi-Task Fine-Tuning on LLM Performance -- A Case Study in Finance
di: Brief, Meni, et al.
Pubblicazione: (2024)

$π^2$: Structure-Originated Reasoning Data Improves Long-Context Reasoning Ability of Large Language Models
di: Do, Quyet V., et al.
Pubblicazione: (2026)

SealQA: Raising the Bar for Reasoning in Search-Augmented Language Models
di: Pham, Thinh, et al.
Pubblicazione: (2025)

Arithmetic Reasoning with LLM: Prolog Generation & Permutation
di: Yang, Xiaocheng, et al.
Pubblicazione: (2024)

Unveiling Reasoning Thresholds in Language Models: Scaling, Fine-Tuning, and Interpretability through Attention Maps
di: Hsiao, Yen-Che, et al.
Pubblicazione: (2025)

Cross-Domain Demo-to-Code via Neurosymbolic Counterfactual Reasoning
di: Kim, Jooyoung, et al.
Pubblicazione: (2026)

Reasoning in Neurosymbolic AI
di: Tran, Son, et al.
Pubblicazione: (2025)

Learning to Solve Abstract Reasoning Problems with Neurosymbolic Program Synthesis and Task Generation
di: Bednarek, Jakub, et al.
Pubblicazione: (2024)

Neurosymbolic Language Reasoning as Satisfiability Modulo Theory
di: Oh, Hyunseok, et al.
Pubblicazione: (2026)

Towards Unified Neurosymbolic Reasoning on Knowledge Graphs
di: Lin, Qika, et al.
Pubblicazione: (2025)

Large Language Models Are Neurosymbolic Reasoners
di: Fang, Meng, et al.
Pubblicazione: (2024)

Constraint-Based Analysis of Reasoning Shortcuts in Neurosymbolic Learning
di: Takemura, Akihiro, et al.
Pubblicazione: (2026)

NeuroStrata: Harnessing Neurosymbolic Paradigms for Improved Design, Testability, and Verifiability of Autonomous CPS
di: Zheng, Xi, et al.
Pubblicazione: (2025)

System-2 Mathematical Reasoning via Enriched Instruction Tuning
di: Cai, Huanqia, et al.
Pubblicazione: (2024)

Advanced Weakly-Supervised Formula Exploration for Neuro-Symbolic Mathematical Reasoning
di: Wu, Yuxuan, et al.
Pubblicazione: (2025)

MathSE: Improving Multimodal Mathematical Reasoning via Self-Evolving Iterative Reflection and Reward-Guided Fine-Tuning
di: Chen, Jinhao, et al.
Pubblicazione: (2025)

OpenRFT: Adapting Reasoning Foundation Model for Domain-specific Tasks with Reinforcement Fine-Tuning
di: Zhang, Yuxiang, et al.
Pubblicazione: (2024)

VeriTrans: Fine-Tuned LLM-Assisted NL-to-PL Translation via a Deterministic Neuro-Symbolic Pipeline
di: Liu, Xuan, et al.
Pubblicazione: (2026)

Coarse-to-Fine Process Reward Modeling for Mathematical Reasoning
di: Hu, Yulan, et al.
Pubblicazione: (2025)

Wireless Federated Multi-Task LLM Fine-Tuning via Sparse-and-Orthogonal LoRA
di: Yang, Nuocheng, et al.
Pubblicazione: (2026)

NeSyPr: Neurosymbolic Proceduralization For Efficient Embodied Reasoning
di: Choi, Wonje, et al.
Pubblicazione: (2025)

Rethinking Fine-Tuning when Scaling Test-Time Compute: Limiting Confidence Improves Mathematical Reasoning
di: Chen, Feng, et al.
Pubblicazione: (2025)

On-Policy Supervised Fine-Tuning for Efficient Reasoning
di: Zhao, Anhao, et al.
Pubblicazione: (2026)

Generalization-Enhanced Code Vulnerability Detection via Multi-Task Instruction Fine-Tuning
di: Du, Xiaohu, et al.
Pubblicazione: (2024)

LANTERN: LLM-Augmented Neurosymbolic Transfer with Experience-Gated Reasoning Networks
di: Alinejad, Mahyar, et al.
Pubblicazione: (2026)

Intermediate Languages Matter: Formal Languages and LLMs affect Neurosymbolic Reasoning
di: Beiser, Alexander, et al.
Pubblicazione: (2025)

AnyTaskTune: Advanced Domain-Specific Solutions through Task-Fine-Tuning
di: Cui, Jiaxi, et al.
Pubblicazione: (2024)

LogicGraph : Benchmarking Multi-Path Logical Reasoning via Neuro-Symbolic Generation and Verification
di: Wu, Yanrui, et al.
Pubblicazione: (2026)

On the Promise for Assurance of Differentiable Neurosymbolic Reasoning Paradigms
di: Richards, Luke E., et al.
Pubblicazione: (2025)

Compositional Neuro-Symbolic Reasoning
di: Das, Anugyan, et al.
Pubblicazione: (2026)

NeuroNL2LTL: A Neurosymbolic Framework for Natural Language Translation of Linear Temporal Logic
di: Quansah, Paapa Kwesi, et al.
Pubblicazione: (2026)

Reliable Classroom AI via Neuro-Symbolic Multimodal Reasoning
di: Nezhad, Sina Bagheri
Pubblicazione: (2026)

Improving Rule-based Reasoning in LLMs using Neurosymbolic Representations
di: Dhanraj, Varun, et al.
Pubblicazione: (2025)

Towards a Neurosymbolic Reasoning System Grounded in Schematic Representations
di: Olivier, François, et al.
Pubblicazione: (2025)

NeuroAda: Activating Each Neuron's Potential for Parameter-Efficient Fine-Tuning
di: Zhang, Zhi, et al.
Pubblicazione: (2025)

A Comparative Study of Neurosymbolic AI Approaches to Interpretable Logical Reasoning
di: Chen, Michael K.
Pubblicazione: (2025)

Enhancing SQL Query Generation with Neurosymbolic Reasoning
di: Princis, Henrijs, et al.
Pubblicazione: (2024)

Low-Resource Fine-Tuning for Multi-Task Structured Information Extraction with a Billion-Parameter Instruction-Tuned Model
di: Chih, Yu Cheng, et al.
Pubblicazione: (2025)

Agentic Neurosymbolic Collaboration for Mathematical Discovery: A Case Study in Combinatorial Design
di: Xia, Hai, et al.
Pubblicazione: (2026)