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Retrieval-of-Thought: Efficient Reasoning via Reusing Thoughts
Ammar Ahmed, Azal Ahmad Khan, Ayaan Ahmad +3
Large reasoning models improve accuracy by producing long reasoning traces, but this inflates latency and cost, motivating inference-time efficiency. We propose Retrieval-of-Though…
Accelerating LLM Reasoning via Early Rejection with Partial Reward Modeling
Seyyed Saeid Cheshmi, Azal Ahmad Khan, Xinran Wang +2
Large Language Models (LLMs) are increasingly relied upon for solving complex reasoning tasks in domains such as mathematics, logic, and multi-step question answering. A growing li…
Systematic Evaluation of Optimization Techniques for Long-Context Language Models
Ammar Ahmed, Sheng Di, Franck Cappello +3
Large language models (LLMs) excel across diverse natural language processing tasks but face resource demands and limited context windows. Although techniques like pruning, quantiz…
Sem-DPO: Mitigating Semantic Inconsistency in Preference Optimization for Prompt Engineering
Anas Mohamed, Azal Ahmad Khan, Xinran Wang +5
Generative AI can now synthesize strikingly realistic images from text, yet output quality remains highly sensitive to how prompts are phrased. Direct Preference Optimization (DPO)…
Safety Aware Task Planning via Large Language Models in Robotics
Azal Ahmad Khan, Michael Andrev, Muhammad Ali Murtaza +5
The integration of large language models (LLMs) into robotic task planning has unlocked better reasoning capabilities for complex, long-horizon workflows. However, ensuring safety…
Probe Pruning: Accelerating LLMs through Dynamic Pruning via Model-Probing
Qi Le, Enmao Diao, Ziyan Wang +4
We introduce Probe Pruning (PP), a novel framework for online, dynamic, structured pruning of Large Language Models (LLMs) applied in a batch-wise manner. PP leverages the insight…