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This work presents Fairness Pruning, a lightweight structural intervention method designed for the management and fut...
Sparse mixture-of-experts (MoE) language models route each token to multiple experts, suggesting a geometric account ...
On-policy self-distillation (OPSD) is a promising approach to improve reasoning language models, but it remains britt...
Existing token compression methods for omnimodal large language models typically rely on one modality to determine wh...
All-in-one image restoration aims to handle diverse degradations within a unified framework. Existing methods commonl...
Large language model-based multi-agent systems improve complex problem solving through task decomposition, agent spec...
Large language models can write, patch, and search code, but oncall root cause analysis (RCA) demands something diffe...
Predicting the 3D structures of atomic systems is fundamental to advancing material science and drug discovery. While...
Deploying autonomous computer-use agents (CUAs) locally is increasingly important for privacy, cost efficiency, and p...
We study the computational complexity of winner determination problems in approval-based committee elections under Th...
Methods that make a language model plan, criticise and rewrite its own answer, reflect on mistakes, pick the best of ...
While Multimodal Retrieval-Augmented Generation (MM-RAG) has shown promising results, it still struggles with complex...