SageBwd: A Trainable Low-bit Attention | ScienceToStartup | ScienceToStartup

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References (29)

[1]

SLA2: Sparse-Linear Attention with Learnable Routing and QAT

2026Jintao Zhang, Haoxu Wang et al.

[2]

SpargeAttention2: Trainable Sparse Attention via Hybrid Top-k+Top-p Masking and Distillation Fine-Tuning

2026Jintao Zhang, Kai Jiang et al.

[3]

Geometry-Aware Rotary Position Embedding for Consistent Video World Model

Founder's Pitch

"SageBwd enables efficient training with low-bit attention by reducing quantization errors, providing an alternative to full-precision models."

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SageBwd: A Trainable Low-bit Attention

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References (29)

Founder's Pitch

"SageBwd enables efficient training with low-bit attention by reducing quantization errors, providing an alternative to full-precision models."

Commercial Viability Breakdown

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SageBwd: A Trainable Low-bit Attention

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References (29)

Founder's Pitch

"SageBwd enables efficient training with low-bit attention by reducing quantization errors, providing an alternative to full-precision models."

Commercial Viability Breakdown

Related Papers

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