Papers with reasoning stage
LogRules: Enhancing Log Analysis Capability of Large Language Models through Rules (2025.findings-naacl)
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| Challenge: | Existing large language models (LLMs) exhibit hallucinations when analyzing logs due to the implicit knowledge and rules in logs that LLMs cannot capture. |
| Approach: | They propose a lightweight log analysis framework that generates and utilizes rules through LLMs. |
| Outcome: | The proposed framework outperforms LLM-based methods in log parsing and anomaly detection tasks and achieves better performance compared to case-based approaches. |
DeepPlanner: Scaling Planning Capability for Deep Research Agents via Advantage Shaping (2026.findings-acl)
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| Challenge: | Existing approaches to planning involve implicit planning or introduce explicit planners without systematically optimizing the planning stage. |
| Approach: | They propose an end-to-end RL framework that enhances the planning capabilities of deep research agents. |
| Outcome: | Experiments show that DeepPlanner improves planning quality and achieves state-of-the-art results under a lower training budget. |
Dr.ECI: Infusing Large Language Models with Causal Knowledge for Decomposed Reasoning in Event Causality Identification (2025.coling-main)
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| Challenge: | Existing solutions lack generalizability to unseen domains, underscoring the urgent need for generalization capabilities in the field of ECI. |
| Approach: | They propose a multi-agent Decomposed reasoning framework for Event Causality Identification that incorporates specialized agents such as Causal Explorer and Mediator Detector. |
| Outcome: | The proposed framework improves the state-of-the-art performance of LLMs for event causality identification (ECI) tasks compared with baselines based on LLM and supervised training. |
RSVP: Reasoning Segmentation via Visual Prompting and Multi-modal Chain-of-Thought (2025.acl-long)
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Yi Lu, Jiawang Cao, Yongliang Wu, Bozheng Li, Licheng Tang, Yangguang Ji, Chong Wu, Jay Wu, Wenbo Zhu
| Challenge: | Recent advances in multi-modal learning have enhanced MLLMs' ability to reason about visual content. |
| Approach: | They propose a framework that unifies multi-step multimodal reasoning with grounded visual understanding. |
| Outcome: | The proposed framework surpasses state-of-the-art methods by +6.5 gIoU and +9.2 cIou on ReasonSeg and achieves 49.7 mAP on SegInW under zero-shot settings. |