DOT-MoE uses differentiable optimal transport and straight-through estimators to partition FFN layers into capacity-constrained experts, outperforming heuristic baselines in retaining 90% performance at 50% active parameters.
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LASER introduces curvature-weighted SVD from second-order loss approximation and loss-aware rank allocation to compress VLMs, reporting over 2.3x decoding speedup under low-precision settings.
A comprehensive review of self-evolving AI agents that improve themselves over time, organized via a framework of inputs, agent system, environment, and optimizers, with domain-specific and safety discussions.
citing papers explorer
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DOT-MoE: Differentiable Optimal Transport for MoEfication
DOT-MoE uses differentiable optimal transport and straight-through estimators to partition FFN layers into capacity-constrained experts, outperforming heuristic baselines in retaining 90% performance at 50% active parameters.
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LASER: Loss-Aware Singular-value Decomposition and Rank Allocation for Efficient Low-Precision Vision-Language Models
LASER introduces curvature-weighted SVD from second-order loss approximation and loss-aware rank allocation to compress VLMs, reporting over 2.3x decoding speedup under low-precision settings.
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A Comprehensive Survey of Self-Evolving AI Agents: A New Paradigm Bridging Foundation Models and Lifelong Agentic Systems
A comprehensive review of self-evolving AI agents that improve themselves over time, organized via a framework of inputs, agent system, environment, and optimizers, with domain-specific and safety discussions.