Apple researchers are presenting new work at ICLR 2026, focusing on advancements in recurrent neural networks (RNNs) and state space models (SSMs). Their paper "ParaRNN" introduces a parallelized training framework that enables large-scale RNNs to achieve performance competitive with transformers, releasing the codebase as open-source. Another paper, "To Infinity and Beyond," demonstrates that while SSMs offer efficiency, their performance degrades on long-form generation tasks due to bounded memory, a limitation that can be overcome with external tool access. AI
影响 Open-source release of ParaRNN could accelerate research into efficient sequence modeling and LLM development, especially for resource-constrained environments.
排序理由 Apple researchers are presenting new papers and open-source code at the ICLR 2026 conference.
在 Apple Machine Learning Research 阅读 →
- Apple
- ICLR 2026
- Mamba
- ParaRNN
- Sharp Monocular View Synthesis
- Transformers
- MLX
- Recurrent Neural Networks
- State Space Models
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