Papers with heterogeneous models
How Diversely Can Language Models Solve Problems? Exploring the Algorithmic Diversity of Model-Generated Code (2025.findings-emnlp)
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| Challenge: | Language models (LMs) have exhibited impressive abilities in generating code from natural language requirements. |
| Approach: | They propose to introduce various metrics with inter-code similarity to evaluate the diversity of generated code by comparing model-generated solutions with human-written ones. |
| Outcome: | The proposed method leverages LMs’ capabilities in code understanding and reasoning, resulting in a set of metrics that represent the number of algorithms in model-generated solutions. |
Unleashing the Potentials of Likelihood Composition for Multi-modal Language Models (2024.findings-emnlp)
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| Challenge: | Existing multi-modal language models with different architectures, parameter sizes, training datasets, and pipelines exhibit varying strengths across different tasks. |
| Approach: | They propose a framework for fusing heterogeneous models off-the-shell, which they call likelihood composition, and introduce basic operations to compose multiple models’ likelihood distribution when doing a multi-choice visual-question-answering task. |
| Outcome: | The proposed framework can be used to fusing heterogeneous models off-the-shell. |
Enabling Agents to Communicate Entirely in Latent Space (2026.acl-long)
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Zhuoyun Du, Runze Wang, Huiyu Bai, Zouying Cao, Xiaoyong Zhu, Yu Cheng, Bo Zheng, Wei Chen, Haochao Ying
| Challenge: | Natural language is the de facto communication medium for LLM-based agents, but it presents a fundamental constraint . natural language downsampling limits the depth and nuance of information that can be transmitted . et al.: inter-agent latent space communication is a promising paradigm for solving complex tasks . |
| Approach: | They propose a paradigm that leverages the last hidden states of an LLM as a representation of its thought for direct communication. |
| Outcome: | The proposed paradigm outperforms fine-tuned chain-of-thought prompting and single-agent baselines even across heterogeneous models. |
Building LLMs Like LEGO: Two-dimensional Architecture Reassembly of Large Language Models (2026.acl-long)
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| Challenge: | Existing approaches to LLM reuse treat LLMs as monolithic artifacts. |
| Approach: | They propose to recompose pretrained large language models as modular building blocks . they propose a chromosome-based architectural encoding and evolutionary optimization . |
| Outcome: | The proposed model can be recomposed as modular building blocks without training data. |