Advancing Reasoning with Off-the-Shelf LLMs: A Semantic Structure Perspective (2025.findings-emnlp)
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| Challenge: | Existing reasoning models suffer from hallucinations and unfaithfulness, whereas general LLMs perform suboptimal on complex tasks. |
| Approach: | They propose a structure analysis method that helps LLMs better understand the question structure and guide the problem-solving process. |
| Outcome: | The proposed method improves zero-shot performance on knowledge-intensive and mathematical tasks while demonstrating strong robustness against corrupted reasoning paths. |
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| Challenge: | Long document question answering requires locating relevant paragraphs within a document to answer a question. |
| Approach: | They propose to exploit the discourse structure commonly found in documents to create a condensed representation of the document, enabling a more comprehensive understanding and analysis of relationships between different parts. |
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Current Advances in LLM Reasoning (2026.acl-tutorials)
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| Challenge: | This tutorial examines comprehensive evaluation strategies to assess the reasoning abilities of large language models (LLMs) advanced inference time methods and post-training methods that aim to make LLMs think more like humans are discussed in this tutorial. |
| Approach: | This tutorial explores comprehensive evaluation strategies to assess the reasoning abilities of large language models (LLMs) and discusses two types of methods to improve models’ reasoning: advanced inference time methods, structured and self-improvement inference methods, and post-training methods, such as RLHF, DPO, and GRPO. |
| Outcome: | This tutorial examines evaluation strategies to assess the reasoning abilities of large language models and discusses two types of methods to improve models’ reasoning. |
DIVKNOWQA: Assessing the Reasoning Ability of LLMs via Open-Domain Question Answering over Knowledge Base and Text (2024.findings-naacl)
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| Challenge: | Retrievalaugmented LLMs have been used to ground LLM in external knowledge . a gap exists in the current landscape regarding the effectiveness of grounding LLM on heterogeneous knowledge sources. |
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Mapping the Minds of LLMs: A Graph-Based Analysis of Reasoning LLMs (2025.emnlp-main)
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| Challenge: | Large Reasoning Models (LRMs) often display unstable behaviors, e.g., hallucinating unsupported premises, overthinking simple tasks, and displaying higher sensitivity to prompt variations. |
| Approach: | They propose a graph-based analytical framework that clusters long, verbose CoT outputs into semantically coherent reasoning steps, then constructs directed reasoning graphs to capture contextual and logical dependencies among these steps. |
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Structure Guided Prompt: Instructing Large Language Model in Multi-Step Reasoning by Exploring Graph Structure of the Text (2024.emnlp-main)
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| Challenge: | Large Language Models (LLMs) excel at straightforward reasoning tasks, but struggle when faced with complex multi-step reasoning. |
| Approach: | They propose a framework that converts unstructured text into a graph and instructs LLMs to navigate this graph using task-specific strategies. |
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Exploring the Role of Reasoning Structures for Constructing Proofs in Multi-Step Natural Language Reasoning with Large Language Models (2024.emnlp-main)
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| Challenge: | Large language models (LLMs) are essential for performing complex multi-step reasoning tasks, such as multi-hop reasoning tasks. |
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Towards Effective and Efficient Multi-Agent Language Model Systems: Foundations, Prospects, and Applications (2026.acl-tutorials)
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| Challenge: | Multi-agent systems powered by large language models still face challenges . tutorial focuses on three core components to build effective and efficient systems . |
| Approach: | This tutorial introduces recent advances in building effective and efficient multi-agent LLM systems . it focuses on three core components: model distillation, dynamic routing, memory- and compute efficient serving . |
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A Survey on LLM-powered Agents for Recommender Systems (2025.findings-emnlp)
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| Challenge: | Large Language Models have demonstrated remarkable capabilities in natural language understanding, reasoning, and generation. |
| Approach: | They present a comprehensive synthesis of large language models and their applications . they dissect a four-module agent architecture and review representative designs . |
| Outcome: | The proposed models address fundamental challenges in traditional recommender systems . they include limited comprehension of complex user intents, insufficient interaction capabilities . |
Rethinking the Bounds of LLM Reasoning: Are Multi-Agent Discussions the Key? (2024.acl-long)
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| Challenge: | Recent progress in LLMs discussion suggests that multi-agent discussion improves the reasoning abilities of LLM. |
| Approach: | They propose a group discussion framework to enrich the set of discussion mechanisms. |
| Outcome: | The proposed framework performs better on a wide range of reasoning tasks and backbone LLMs. |
Structural Reasoning Improves Molecular Understanding of LLM (2025.acl-long)
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| Challenge: | Recent advances in large language models (LLMs) have shown significant performance, approaching human perception levels. |
| Approach: | They propose an approach that sketches molecular structures for reasoning by explicitly incorporating key structural features into the model. |
| Outcome: | The proposed framework improves molecular understanding through extensive experiments. |