Papers by Guojie Song
From Pseudo-Balancing to True Specialization: Memory-Aware Routing for Mixture-of-Experts (2026.findings-acl)
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| Challenge: | Existing methods to optimize expert-centered load balancing fail to account for pseudo-balance phenomenon . severe knowledge overlap among experts leads to redundant representations and inefficient parameter utilization . |
| Approach: | They propose a method that prioritizes expert utilization over semantic alignment . they use memory-aware routing to ensure expert load balancing is consistent . |
| Outcome: | Experimental results show that MAR improves expert specialization by 35% and accuracy by 2%-25% . MAR matches baseline performance with only half the experts . |
Generative Psycho-Lexical Approach for Constructing Value Systems in Large Language Models (2025.acl-long)
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| Challenge: | Large Language Models (LLMs) have raised concerns regarding their intrinsic values. |
| Approach: | They propose a psychologically grounded five-factor value system for Large Language Models that integrates psychological principles with cutting-edge AI priorities. |
| Outcome: | The proposed value system meets standard psychological criteria, improves LLM safety prediction, and enhances Llm alignment, when compared to the canonical Schwartz’s values. |
FoE: Forest of Errors Makes the First Solution the Best in Large Reasoning Models (2026.acl-long)
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| Challenge: | Recent Large Reasoning Models (LRMs) have demonstrated remarkable success in complex reasoning tasks. |
| Approach: | They propose a self-guided efficient reasoning framework that reduces FoE by pruning subs. |
| Outcome: | The proposed model outperforms eight competitive baselines while reducing token consumption by 37.7% 70.4%. |
NeuReasoner: Towards Explainable, Controllable, and Unified Reasoning via Mixture-of-Neurons (2026.acl-long)
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| Challenge: | Existing Large Reasoning Models (LRMs) lack explainability and controllability . Existing models target isolated levels without unification, while relying on RL . |
| Approach: | They propose an explainable, controllable, and unified reasoning framework driven by MoN. |
| Outcome: | The proposed framework achieves performance gains of 27.0% while reducing token consumption by 19.6% 63.3%. |
ValueBench: Towards Comprehensively Evaluating Value Orientations and Understanding of Large Language Models (2024.acl-long)
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| Challenge: | Large Language Models (LLMs) are transforming diverse fields and gaining increasing influence as human proxies. |
| Approach: | They propose a psychometric evaluation pipeline grounded in realistic human-AI interactions to probe value orientations and novel tasks for evaluating value understanding in an open-ended value space. |
| Outcome: | The proposed evaluation pipeline is grounded in realistic human-AI interactions and performs tasks that approximate expert conclusions in value-related extraction and generation tasks. |
Context-Value-Action Architecture for Value-Driven Large Language Model Agents (2026.findings-acl)
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| Challenge: | Existing LLMs exhibit behavioral rigidity, a flaw often masked by the self-referential bias of current "LLM-as-a-judge" evaluations. |
| Approach: | They propose a Context-Value-Action architecture that decouples action generation from cognitive reasoning via a Value Verifier trained on authentic human data to explicitly model dynamic value activation. |
| Outcome: | The proposed architecture significantly outperforms baseline models on 1.1 million real-world interaction traces on CVABench. |