AAAC learns two 64-byte codebooks per layer for 4-bit LLM weights and lets each group pick the one minimizing activation-weighted reconstruction error, storing the choice at zero extra cost.
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6 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 6representative citing papers
BWLA is the first post-training quantization method for LLMs that achieves 1-bit weights paired with low-bit activations such as 6 bits, using OKT to reshape weights and suppress activation tails plus PSP for low-rank refinement.
Pruning MoE LLMs according to the stationary distribution of a Markov chain over (layer, expert) routing transitions retains more task performance than local importance heuristics, with up to ~3.5% relative gains over the strongest baseline at 50% compression.
XPERT extracts and reuses cross-domain expert knowledge from pre-trained MoE LLMs via inference analysis and tensor decomposition to improve performance and convergence in downstream language model training.
ExecuTorch is a unified PyTorch-native deployment framework that enables seamless on-device execution of AI models across heterogeneous hardware while preserving original PyTorch semantics.
Hy-MT2 presents three new multilingual translation models that claim to outperform listed open-source and commercial systems on diverse tasks while enabling low-storage on-device use.
citing papers explorer
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AAAC: Activation-Aware Adaptive Codebooks for 4-bit LLM Weight Quantization
AAAC learns two 64-byte codebooks per layer for 4-bit LLM weights and lets each group pick the one minimizing activation-weighted reconstruction error, storing the choice at zero extra cost.
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BWLA: Breaking the Barrier of W1AX Post-Training Quantization for LLMs
BWLA is the first post-training quantization method for LLMs that achieves 1-bit weights paired with low-bit activations such as 6 bits, using OKT to reshape weights and suppress activation tails plus PSP for low-rank refinement.
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It Takes a MAESTRO To Prune Bad Experts
Pruning MoE LLMs according to the stationary distribution of a Markov chain over (layer, expert) routing transitions retains more task performance than local importance heuristics, with up to ~3.5% relative gains over the strongest baseline at 50% compression.
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XPERT: Expert Knowledge Transfer for Effective Training of Language Models
XPERT extracts and reuses cross-domain expert knowledge from pre-trained MoE LLMs via inference analysis and tensor decomposition to improve performance and convergence in downstream language model training.
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ExecuTorch -- A Unified PyTorch Solution to Run AI Models On-Device
ExecuTorch is a unified PyTorch-native deployment framework that enables seamless on-device execution of AI models across heterogeneous hardware while preserving original PyTorch semantics.
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Hy-MT2: A Family of Fast, Efficient and Powerful Multilingual Translation Models in the Wild
Hy-MT2 presents three new multilingual translation models that claim to outperform listed open-source and commercial systems on diverse tasks while enabling low-storage on-device use.