Papers with dynamic routing
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 . |
| Outcome: | This tutorial introduces state-of-the-art techniques for building efficient and efficient multi-agent LLM systems . it covers coordination and communication among agents, crucial for collective performance . |
AgentMaster: A Multi-Agent Conversational Framework Using A2A and MCP Protocols for Multimodal Information Retrieval and Analysis (2025.emnlp-demos)
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| Challenge: | Recent advances in AI focus on multi-agent systems (MAS) that can be integrated with Large Language Models (LLMs) but current systems still face challenges of inter-agency communication, coordination, and interaction with heterogeneous tools and resources. |
| Approach: | They propose a modular multi-protocol MAS framework with self-implemented A2A and MCP . the framework supports natural language interaction without prior technical expertise . |
| Outcome: | The proposed framework supports natural language interaction without prior technical expertise and responds to multimodal queries for tasks including information retrieval, question answering, and image analysis. |
Towards Linear Time Neural Machine Translation with Capsule Networks (D19-1)
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| Challenge: | Neural Machine Translation (NMT) is an endto-end learning approach to machine translation. |
| Approach: | They propose a capsule network with dynamic routing for linear time Neural Machine Translation . they map the source sentence into a matrix with pre-determined size and apply a deep LSTM network to decode the target sequence from the source representation. |
| Outcome: | The proposed network achieves comparable results with the Transformer system on English-German and English-French tasks. |
Tailoring Memory Granularity for Multi-Hop Reasoning over Long Contexts (2026.findings-eacl)
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| Challenge: | Extensive experiments on long-context multi-hop question answering benchmarks show TAG achieves state-of-the-art performance. |
| Approach: | They propose a framework that prestructures memory into diverse granularities and employs a reward-guided navigator to adaptively compose hybrid memory tailored to each query. |
| Outcome: | Experiments on long-context multi-hop question answering show that the framework achieves state-of-the-art performance. |
Beyond Spurious Signals: Debiasing Multimodal Large Language Models via Counterfactual Inference and Adaptive Expert Routing (2025.findings-emnlp)
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| Challenge: | Multimodal Large Language Models (MLLMs) often rely on spurious correlations, undermining their robustness and generalization. |
| Approach: | They propose a causal mediation-based debiasing framework to address correlation bias in MLLMs . they distinguish core semantics from spurious textual and visual contexts using counterfactual examples . |
| Outcome: | The proposed framework surpasses existing state-of-the-art models on sarcasm detection and sentiment analysis tasks. |
Information Aggregation via Dynamic Routing for Sequence Encoding (C18-1)
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| Challenge: | Currently, little attention is paid to how to aggregate text sequences into fixed-size vectors. |
| Approach: | They propose an aggregation mechanism to obtain a fixed-size encoding with a dynamic routing policy. |
| Outcome: | The proposed method outperforms other aggregating methods on five text classification tasks. |
Investigating Capsule Networks with Dynamic Routing for Text Classification (D18-1)
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| Challenge: | Earlier efforts in text modeling have achieved limited success on word meanings . convolutional neural networks (CNNs) are used to model higher level concepts and facts in texts . |
| Approach: | They propose three strategies to stabilize dynamic routing process to alleviate disturbance of noise capsules. |
| Outcome: | The proposed methods achieve state-of-the-art on 4 out of 6 datasets . they show that capsule networks exhibit significant improvement over baseline methods . |
RouteMoA: Dynamic Routing without Pre-Inference Boosts Efficient Mixture-of-Agents (2026.acl-long)
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Jize Wang, Han Wu, Zhiyuan You, Yiming Song, Yijun Wang, Zifei Shan, Yining Li, Songyang Zhang, Xinyi Le, Cailian Chen, Xinping Guan, Dacheng Tao
| Challenge: | Existing methods for mixing-of-agents (MoA) lack model selection criteria and struggle with large model pools. |
| Approach: | They propose a mixture-of-agents framework with dynamic routing that uses a lightweight scorer to perform initial screening and refines the model scores through self- and cross-assessment. |
| Outcome: | The proposed framework outperforms existing methods for large model pools and tasks . it reduces cost by 89.8% and latency by 63.6% in the large-scale model pool. |
Multimodal Robustness for Neural Machine Translation (2022.emnlp-main)
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| Challenge: | Existing approaches to deal with noisy multimodal inputs are not robust enough to deal effectively with noisy data. |
| Approach: | They propose a method that composes domain adapters to deal with noisy inputs . they combine these adapters at runtime via dynamic routing or when source of noise is unknown . |
| Outcome: | The proposed model is flexible and state-of-the-art to deal with noisy multimodal inputs. |
Seeing Through VisualBERT: A Causal Adventure on Memetic Landscapes (2024.findings-emnlp)
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| Challenge: | Existing models for detecting offensive memes lack transparency and are often unreliability in safety-critical applications. |
| Approach: | They propose a framework that uses a Structural Causal Model to predict the class of an input meme based on meme input and causal concepts, allowing for transparent interpretation. |
| Outcome: | The proposed framework is able to predict class of an input meme based on meme input and causal concepts, allowing for transparent interpretation. |
LLM-empowered Dynamic Prompt Routing for Vision-Language Models Tuning under Long-Tailed Distributions (2025.findings-emnlp)
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| Challenge: | Pre-trained vision-language models (VLMs) often suffer from bias in class-imbalanced scenes. |
| Approach: | They propose a multi-dimensional dynamic prompt routing framework that integrates a knowledge base for classes spanning multiple visual-semantic dimensions. |
| Outcome: | The proposed framework achieves comparable results with current SOTA methods on long-tailed benchmarks, including CIFAR-LT, ImageNet-LT and Places-LT. |