MiniCPM-SALA: Hybridizing Sparse and Linear Attention for Efficient Long-Context Modeling | ScienceToStartup | ScienceToStartup

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References (45)

[1]

Hybrid Linear Attention Done Right: Efficient Distillation and Effective Architectures for Extremely Long Contexts

2026Yingfa Chen, Z. Thai et al.

[2]

AgencyBench: Benchmarking the Frontiers of Autonomous Agents in 1M-Token Real-World Contexts

2026Keyu Li, Junhao Shi et al.

[3]

Falcon-H1R: Pushing the Reasoning Frontiers with a Hybrid Model for Efficient Test-Time Scaling

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"Transform long-context modeling with MiniCPM-SALA, a cost-efficient hybrid attention framework reducing memory and computational demands."

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MiniCPM-SALA: Hybridizing Sparse and Linear Attention for Efficient Long-Context Modeling

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References (45)

Founder's Pitch

"Transform long-context modeling with MiniCPM-SALA, a cost-efficient hybrid attention framework reducing memory and computational demands."

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MiniCPM-SALA: Hybridizing Sparse and Linear Attention for Efficient Long-Context Modeling

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References (45)

Founder's Pitch

"Transform long-context modeling with MiniCPM-SALA, a cost-efficient hybrid attention framework reducing memory and computational demands."

Commercial Viability Breakdown

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