Cover Image

ReportLogic: Evaluating Logical Quality in Deep Research Reports

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Zhao, Jujia, Huan, Zhaoxin, Wang, Zihan, Zhang, Xiaolu, Zhou, Jun, Verberne, Suzan, Ren, Zhaochun
Format:	Preprint
Published:	2026
Subjects:	Computation and Language Artificial Intelligence
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Undesirable Memorization in Large Language Models: A Survey
by: Satvaty, Ali, et al.
Published: (2024)

Generative Retrieval with Few-shot Indexing
by: Askari, Arian, et al.
Published: (2024)

LLMs Enable Bag-of-Texts Representations for Short-Text Clustering
by: Lin, I-Fan, et al.
Published: (2025)

Improving RAG for Personalization with Author Features and Contrastive Examples
by: Yazan, Mert, et al.
Published: (2025)

The Impact of Quantization on Retrieval-Augmented Generation: An Analysis of Small LLMs
by: Yazan, Mert, et al.
Published: (2024)

LogicPoison: Logical Attacks on Graph Retrieval-Augmented Generation
by: Xiao, Yilin, et al.
Published: (2026)

Unifying Search and Recommendation in LLMs via Gradient Multi-Subspace Tuning
by: Zhao, Jujia, et al.
Published: (2026)

Dr. Bench: A Multidimensional Evaluation for Deep Research Agents, from Answers to Reports
by: Yao, Yang, et al.
Published: (2025)

Logic Jailbreak: Efficiently Unlocking LLM Safety Restrictions Through Formal Logical Expression
by: Peng, Jingyu, et al.
Published: (2025)

Logic Agent: Enhancing Validity with Logic Rule Invocation
by: Liu, Hanmeng, et al.
Published: (2024)

Are Large Language Models Really Good Logical Reasoners? A Comprehensive Evaluation and Beyond
by: Xu, Fangzhi, et al.
Published: (2023)

LogicBench: Towards Systematic Evaluation of Logical Reasoning Ability of Large Language Models
by: Parmar, Mihir, et al.
Published: (2024)

Lost in the Logic: An Evaluation of Large Language Models' Reasoning Capabilities on LSAT Logic Games
by: Malik, Saumya
Published: (2024)

Enhancing RAG Efficiency with Adaptive Context Compression
by: Guo, Shuyu, et al.
Published: (2025)

LogicPrpBank: A Corpus for Logical Implication and Equivalence
by: Liu, Zhexiong, et al.
Published: (2024)

Do Large Language Models Understand Logic or Just Mimick Context?
by: Yan, Junbing, et al.
Published: (2024)

MACPO: Weak-to-Strong Alignment via Multi-Agent Contrastive Preference Optimization
by: Lyu, Yougang, et al.
Published: (2024)

SynLogic: Synthesizing Verifiable Reasoning Data at Scale for Learning Logical Reasoning and Beyond
by: Liu, Junteng, et al.
Published: (2025)

GLoRE: Evaluating Logical Reasoning of Large Language Models
by: liu, Hanmeng, et al.
Published: (2023)

P-FOLIO: Evaluating and Improving Logical Reasoning with Abundant Human-Written Reasoning Chains
by: Han, Simeng, et al.
Published: (2024)

MultiZebraLogic: A Multilingual Logical Reasoning Benchmark
by: Bruun, Sofie Helene, et al.
Published: (2025)

ReportBench: Evaluating Deep Research Agents via Academic Survey Tasks
by: Li, Minghao, et al.
Published: (2025)

A Logical Pattern Memory Pre-trained Model for Entailment Tree Generation
by: Yuan, Li, et al.
Published: (2024)

Aligning with Logic: Measuring, Evaluating and Improving Logical Preference Consistency in Large Language Models
by: Liu, Yinhong, et al.
Published: (2024)

Logical Phase Transitions: Understanding Collapse in LLM Logical Reasoning
by: Zhang, Xinglang, et al.
Published: (2026)

Bridging Logic and Learning: Decoding Temporal Logic Embeddings via Transformers
by: Candussio, Sara, et al.
Published: (2025)

Logic Augmented Generation
by: Gangemi, Aldo, et al.
Published: (2024)

LogicAsker: Evaluating and Improving the Logical Reasoning Ability of Large Language Models
by: Wan, Yuxuan, et al.
Published: (2024)

Abstract Meaning Representation-Based Logic-Driven Data Augmentation for Logical Reasoning
by: Bao, Qiming, et al.
Published: (2023)

Towards Verifiable Multimodal Deep Research: A Multi-Agent Harness for Interleaved Report Generation
by: Zhang, Chenghao, et al.
Published: (2026)

Logic-RL: Unleashing LLM Reasoning with Rule-Based Reinforcement Learning
by: Xie, Tian, et al.
Published: (2025)

Reason from Fallacy: Enhancing Large Language Models' Logical Reasoning through Logical Fallacy Understanding
by: Li, Yanda, et al.
Published: (2024)

LAG: Logic-Augmented Generation from a Cartesian Perspective
by: Xiao, Yilin, et al.
Published: (2025)

Logic-Regularized Verifier Elicits Reasoning from LLMs
by: Wang, Xinyu, et al.
Published: (2026)

DivLogicEval: A Framework for Benchmarking Logical Reasoning Evaluation in Large Language Models
by: Chung, Tsz Ting, et al.
Published: (2025)

LINC: A Neurosymbolic Approach for Logical Reasoning by Combining Language Models with First-Order Logic Provers
by: Olausson, Theo X., et al.
Published: (2023)

Multi-Step Reasoning with Large Language Models, a Survey
by: Plaat, Aske, et al.
Published: (2024)

Deep Research: A Systematic Survey
by: Shi, Zhengliang, et al.
Published: (2025)

Dissecting Logical Reasoning in LLMs: A Fine-Grained Evaluation and Supervision Study
by: Zhou, Yujun, et al.
Published: (2025)

Beyond Single-shot Writing: Deep Research Agents are Unreliable at Multi-turn Report Revision
by: Chen, Bingsen, et al.
Published: (2026)