Challenge: Existing context window extension methods obstruct scaling external knowledge input.
Approach: They develop a multi-agent framework to overcome two core bottlenecks in existing agent orchestration designs.
Outcome: The proposed framework overcomes two core bottlenecks and improves inference-time knowledge integration without longer-context training.

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A Joint Optimization Framework for Enhancing Efficiency of Tool Utilization in LLM Agents (2025.findings-acl)

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Challenge: Existing efforts for tool utilization involve an LLM agent that contains instructions on using the description of the available tools to determine and call the tools required to solve the problem.
Approach: They propose to optimize the context of LLM agents by combining the instructions provided in agent prompts and tool descriptions to enhance their interaction.
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Small LLMs Are Weak Tool Learners: A Multi-LLM Agent (2024.emnlp-main)

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Challenge: Large Language Models (LLMs) have revolutionized natural language processing with impressive capabilities, but they lack domain specificity, real-time information and face challenges in solving specialized problems.
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Beyond the Context Window: Scaling Agentic RL via End-to-end Optimized Context Compression (2026.acl-long)

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Challenge: Existing reinforcement learning pipelines suffer from degraded instruction following, excessive rollout costs, and strict context limits.
Approach: They propose a reinforcement learning (RL) fine-tuning of large language model (LLM) agents for long-horizon multi-turn tool use where context length quickly becomes a bottleneck.
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Agentic Reasoning: A Streamlined Framework for Enhancing LLM Reasoning with Agentic Tools (2025.acl-long)

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Challenge: Existing reasoning methods excel in structured domains like math and code, but they are not all effective in knowledge-intensive tasks.
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Outcome: The proposed framework achieves state-of-the-art among public models and delivers comparable performance to OpenAI Deep Research.
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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What are the Essential Factors in Crafting Effective Long Context Multi-Hop Instruction Datasets? Insights and Best Practices (2025.acl-long)

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Challenge: Existing methods to generate long-context instruction-tuning data are limited by poor quality and fewer than 35% of samples are multi-hop .
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AgentGym: Evaluating and Training Large Language Model-based Agents across Diverse Environments (2025.acl-long)

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Challenge: Large language models (LLMs) are promising foundations to build generally-capable agents . however, the community lacks a unified interactive framework that covers diverse environments for comprehensive evaluation of agents.
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Context-Aware Assistant Selection for Improved Inference Acceleration with Large Language Models (2024.emnlp-main)

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Challenge: Large language models (LLMs) are prohibitive to use under resource constraints due to their high latency and high latex.
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Enhancing LLM Capabilities Beyond Scaling Up (2024.emnlp-tutorials)

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Challenge: general-purpose large language models (LLMs) are expanding in scale and access to unpublic training data.
Approach: This tutorial aims to examine the capabilities of general-purpose large language models . authors discuss adaptation of LLMs to address conflicts, defense against attacks .
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Beyond Frameworks: Unpacking Collaboration Strategies in Multi-Agent Systems (2025.acl-long)

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Challenge: Existing frameworks prioritize structural architectures and role assignments but neglect granular mechanics of agent collaboration.
Approach: They propose to use centralized governance, instructor-led participation, ordered interaction patterns to optimize task accuracy and computational efficiency.
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