Research map / Architectures and efficiency / Attention and transformers
The Devil is in the Spectrum: Mitigating Representation Collapse in LLMs via Topologically Regularized Side-Path
Read the paperCheck your idea against 108,967 papers
AI-extracted summary. Written by AI from the paper's title only (no summary yet). Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.
Closest papers by meaning
- UltraLLaDA: Scaling the Context Length to 128K for Diffusion Large Language Models (ICLR 2026 · similarity 0.87)
- Dynamics of the Transformer Residual Stream: Coupling Spectral Geometry to Network Topology (NeurIPS 2026 · similarity 0.87)
- Tracing the Representation Geometry of Language Models from Pretraining to Post-training (NeurIPS 2025 · similarity 0.87)
- Hierarchical Context Merging: Better Long Context Understanding for Pre-trained LLMs (ICLR 2024 · similarity 0.87)
- DuoAttention: Efficient Long-Context LLM Inference with Retrieval and Streaming Heads (ICLR 2025 · similarity 0.87)
- Rope to Nope and Back Again: A New Hybrid Attention Strategy (NeurIPS 2025 · similarity 0.87)
Writing a paper in this area? scime draft is a free LaTeX editor with AI included, real-time co-authors and a check of your references and venue rules before you submit. It also imports OpenAI Prism projects.
