Challenge: Large Language Models (LLMs) struggle to efficiently narrow down the search space . external engineered systems may not fully utilize the inherent problem-solving capabilities of LLMs .
Approach: They propose to implicitly guide Large Language Models to enhance their interactive feature collection abilities within a single prompt.
Outcome: The proposed approach improves the performance of large language models in real-world scenarios.

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Enhancing Function-Calling Capabilities in LLMs: Strategies for Prompt Formats, Data Integration, and Multilingual Translation (2025.naacl-industry)

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Challenge: Large language models (LLMs) have significantly advanced autonomous agents, particularly in zero-shot tool usage, also known as function calling.
Approach: They propose to integrate function descriptions into prompt formats and introduce a new Decision Token for conditional prompts.
Outcome: The proposed decision token improves function-calling accuracy and relevance detection and a translation pipeline overcomes multilingual limitations.
From Implicit Exploration to Structured Reasoning: Guideline and Refinement for LLMs (2025.findings-emnlp)

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Challenge: Existing models rely on implicit exploration, which leads to unstable reasoning paths and lack of error correction.
Approach: They propose a framework that shifts from implicit exploration to structured reasoning through guideline and refinement.
Outcome: The proposed model outperforms strong baselines on the Big-Bench Hard benchmark.
Making Large Language Models Better Reasoners with Orchestrated Streaming Experiences (2024.emnlp-main)

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Challenge: Recent studies show that large language models can perform complex reasoning tasks without labeled data and unlabeled data.
Approach: They propose a framework for solving reasoning tasks that store answers in a streaming experience pool and orchestrate helpful questions from the pool to assist itself in answering new questions.
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Reasoning While Asking: Transforming Reasoning Large Language Models from Passive Solvers to Proactive Inquirers (2026.acl-long)

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Challenge: Existing reasoning-oriented LLMs lack a blind self-thinking paradigm . current models fail to recognize when their reasoning is underinformed or based on ambiguous user instructions .
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Improving Large Language Models Function Calling and Interpretability via Guided-Structured Templates (2025.emnlp-main)

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Challenge: Large language models (LLMs) have strong reasoning and tool-use capabilities, yet fail in real-world tool-interactions due to incorrect parameterization, poor tool selection, or misinterpretation of user intent.
Approach: They propose a curriculum-inspired framework that leverages structured reasoning templates to guide LLMs through more deliberate step-by-step instructions for generating function calls.
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AMAS: Adaptively Determining Communication Topology for LLM-based Multi-agent System (2025.emnlp-industry)

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Challenge: Large language models (LLMs) have revolutionized natural language processing, but their practical implementation as autonomous multi-agent systems remains fraught with unresolved challenges.
Approach: They propose a dynamic graph selector that redefines LLM-based MAS by exploiting the intrinsic properties of individual inputs to intelligently direct query trajectories.
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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.
Approach: They propose a model that uses symbolic language to generate symbolic queries . they use a dataset that is generated using predefined reasoning chains and human annotation .
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Enhancing Text-to-SQL Capabilities of Large Language Models: A Study on Prompt Design Strategies (2023.findings-emnlp)

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Challenge: In-context learning (ICL) is a new approach to natural language processing tasks that rely on large language models to make predictions based on context . recent studies have shown that neural symbolic design is the preferred choice for question answering systems because of its limited working memory and unreliable long-term memory.
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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.
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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 .

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