AMATA: Adaptive Multi-Agent Trajectory Alignment for Knowledge-Intensive Question Answering (2026.acl-long)
Copied to clipboard
Taolin Zhang, Dongyang Li, Chen Chen, Qizhou Chen, Jiuheng Wan, Xiaofeng He, Chengyu Wang, Richang Hong
| Challenge: | Empirical results show that AMATA outperforms baseline approaches, knowledge-augmented frameworks, and LLMs on knowledge-intensive QA benchmarks. |
| Approach: | They propose an Adaptive Multi-Agent Trajectory Alignment framework that integrates external knowledge to improve response interpretability and factual grounding. |
| Outcome: | The proposed framework outperforms baseline approaches, knowledge-augmented frameworks, and LLM-based trajectory systems on five established knowledge-intensive QA benchmarks. |
Similar Papers
AMA: Adaptive Memory via Multi-Agent Collaboration (2026.findings-acl)
Copied to clipboard
Weiquan Huang, Zixuan Wang, Hehai Lin, Sudong Wang, Bo Xu, Qian Li, Beier Zhu, Linyi Yang, Chengwei Qin
| Challenge: | Existing approaches to longterm memory rely on rigid retrieval granularity, accumulation-heavy maintenance strategies, and coarse-grained update mechanisms. |
| Approach: | They propose a framework that leverages coordinated agents to manage memory across multiple granularities. |
| Outcome: | The proposed framework outperforms state-of-the-art benchmarks while reducing token consumption by approximately 80%. |
AgentRouter: A Knowledge-Graph-Guided LLM Router for Collaborative Multi-Agent Question Answering (2026.acl-long)
Copied to clipboard
Zheyuan Zhang, Kaiwen Shi, Zhengqing Yuan, Zehong Wang, Tianyi Ma, Keerthiram Murugesan, Vincent Galassi, Chuxu Zhang, Yanfang Ye
| Challenge: | Existing approaches to agent routing emphasize cost efficiency while overlooking the fine-grained contextual and relational structure inherent in QA tasks. |
| Approach: | They propose a framework that formulates multi-agent QA as a knowledge-graph-guided routing problem supervised by empirical performance signals. |
| Outcome: | The proposed framework outperforms single-agent and ensemble baselines while generalizing across benchmarks and LLM backbones. |
MATA: Multi-Agent Framework for Reliable and Flexible Table Question Answering (2026.findings-acl)
Copied to clipboard
| Challenge: | Recent advances in Large Language Models have significantly improved table understanding tasks . practical deployment of TableQA systems presents several persistent challenges . |
| Approach: | They propose a multi-agent TableQA framework that leverages multiple reasoning paths and tools built with small language models. |
| Outcome: | The proposed framework achieves state-of-the-art accuracy and efficient reasoning while avoiding excessive LLM inference. |
Efficient Multi-Agent Collaboration with Tool Use for Online Planning in Complex Table Question Answering (2025.findings-naacl)
Copied to clipboard
| Challenge: | Recent studies on complex table question answering focus more and more on complex instances, as they are ubiquitous in table data analysis. |
| Approach: | They propose a framework that requires neither fine-tuning nor closed-source models to solve complex table question answering (TQA) their framework outperforms previous SoTA systems on three out of four benchmarks and performs comparably to the larger and more expensive closed-sourced model GPT-4 on two benchmarks. |
| Outcome: | The proposed framework outperforms closed-source models and closed-sourced models on three out of four benchmarks and performs comparable to the larger and more expensive closed-Source model GPT-4 on two benchmarks. |
PrefIx: Understand and Adapt to User Preference in Human-Agent Interaction (2026.findings-acl)
Copied to clipboard
| Challenge: | Current benchmarks evaluate task accuracy but overlook how agents interact . Preference-aware agents show 7.6% average UX improvement and 18.5% gain in preference alignment. |
| Approach: | They propose a configurable environment that evaluates both what agents accomplish and how they interact. |
| Outcome: | The proposed model improves performance and improves user experience by 7.6% and 18.5% respectively. |
Internalizing Multi-Agent Reasoning for Accurate and Efficient LLM-based Recommendation (2026.findings-acl)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) are reshaping recommender systems by leveraging extensive world knowledge and semantic reasoning to interpret user intent. |
| Approach: | They propose a single-agent Trajectory-Aligned Recommender to integrate reasoning capabilities into a model by a multi-agend teacher system. |
| Outcome: | The proposed model surpasses its teacher by 8.7% to 39.5% while eliminating iterative latency. |
TraveLER: A Modular Multi-LMM Agent Framework for Video Question-Answering (2024.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods that can find relevant information, extract it, and answer video questions in a single pass are not able to adapt if insufficient or incorrect information is collected. |
| Approach: | They propose a modular multi-LMM agent framework that can find relevant information, extract it, and answer the question simultaneously. |
| Outcome: | The proposed model improves performance on several VideoQA benchmarks without fine-tuning on specific datasets. |
DARA: Decomposition-Alignment-Reasoning Autonomous Language Agent for Question Answering over Knowledge Graphs (2024.findings-acl)
Copied to clipboard
| Challenge: | Existing approaches to answer questions over Knowledge Graphs (KGQA) are not available for KGQA. |
| Approach: | They propose a framework to improve the neural-symbolic reasoning capabilities of language agents powered by Large Language Models (LLMs) they show that DARA can be efficiently trained with a small number of high-quality reasoning trajectories. |
| Outcome: | The proposed framework outperforms in-context learning-based agents with GPT-4 and alternative fine-tuned agents across different benchmarks. |
Aligning Language Models to Explicitly Handle Ambiguity (2024.emnlp-main)
Copied to clipboard
Hyuhng Joon Kim, Youna Kim, Cheonbok Park, Junyeob Kim, Choonghyun Park, Kang Min Yoo, Sang-goo Lee, Taeuk Kim
| Challenge: | Large language models (LLMs) are not specifically trained to deal with ambiguous utterances . ambiguity can lead to varying interpretations of the same input based on different assumptions or background knowledge . |
| Approach: | They propose a pipeline that aligns large language models to manage ambiguous queries . they propose to use their own assessment of perceived ambiguity to detect and manage queries a . |
| Outcome: | Experimental results show that APA empowers LLMs to detect and manage ambiguous queries while retaining the ability to answer clear questions. |
Knowledgeable Preference Alignment for LLMs in Domain-specific Question Answering (2024.findings-acl)
Copied to clipboard
| Challenge: | Domain-specific question answering (QA) requires a comprehensive understanding of a specific domain to answer specialized questions. |
| Approach: | They propose a new alignment objective to align the LLM preference with different human preferences uniformly to optimize LLM performance in real-world, domain-specific QA settings. |
| Outcome: | The proposed pipeline is superior for real-scenario domain-specific question answering with LLMs. |