Papers by Guanghui Song
TRACE: Two-Phase RL for Causal Graph Exploration and Deeper Psychological Intervention in Dynamic Counseling Scenarios (2026.findings-acl)
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| Challenge: | Existing models lack the ability to actively explore the underlying causes of psychological distress. |
| Approach: | They propose a two-phase reinforcement learning framework that implements a causal-graph-driven reward scheme across two phases: an exploration phase that rewards the causal graph reconstruction following a surface-to-deep path, and an intervention phase that supports targeted restructuring of irrational beliefs. |
| Outcome: | Extensive experiments show that TRACE outperforms existing models, enabling causal-chain-aware psychological intervention beyond surface-level empathy. |
Mixture of Weight-shared Heterogeneous Group Attention Experts for Dynamic Token-wise KV Optimization (2025.emnlp-main)
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| Challenge: | Existing methods for token-level KV optimization and grouping of tokens are inefficient and strain compute and storage resources. |
| Approach: | They propose a mixture-of-expert approach that dynamically optimizes token-wise computation and memory allocation by a token-based expert-choice routing mechanism guided by learned importance scores. |
| Outcome: | The proposed approach retains all tokens while adaptively routing them to specialized experts with varying KV group sizes, balancing granularity and efficiency. |