Papers by Guibo Luo
SeLaR: Selective Latent Reasoning in Large Language Models (2026.acl-long)
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| Challenge: | Recent latent reasoning approaches replace discrete tokens with soft embeddings or hidden states, but they often suffer from two issues: (1) global activation injects perturbations into high-confidence steps, impairing reasoning stability; and (2) soft embeds quickly collapse toward the highest-probability token, limiting exploration of alternative trajectories. |
| Approach: | They propose a lightweight and training-free framework that replaces discrete tokens with soft embeddings or hidden states to address these challenges. |
| Outcome: | Experiments on five reasoning benchmarks show that SeLaR outperforms standard CoT and state-of-the-art training-free methods. |
Unraveling the Mystery: Defending Against Jailbreak Attacks Via Unearthing Real Intention (2025.coling-main)
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| Challenge: | Large Language Models (LLMs) are increasingly vulnerable to elusive and implicit intentions, causing security risks and compromising user experience. |
| Approach: | They propose a method to detect and mitigate implicit jailbreak attacks using LLMs by unearthing real intentions and a greedy gradient-based algorithm to remove the least important parts of a sentence. |
| Outcome: | The proposed method reduces attacks success rate and Harmful Score while maintaining overall model performance. |
LEASH: Adaptive Length Penalty and Reward Shaping for Efficient Large Reasoning Model (2026.acl-long)
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| Challenge: | Existing approaches to long reasoning traces are hard to tune and fail to adapt to evolving LLMs. |
| Approach: | They propose a reinforcement learning framework that optimizes the length of reasoning traces by a Lagrangian primal–dual method. |
| Outcome: | The proposed framework reduces the average reasoning length by 60% across diverse tasks while maintaining competitive performance. |