arXiv (All)
open access
Compute Has Shape: Self-Morphing the Internal Computation of Frozen LLMs
Test-time scaling improves large language models through additional computation, but how to adapt the topology of internal computation to individual inputs remains underexplored. Existing approaches largely fix recurrent or parallel architectures during training, or reconfigure frozen models along a
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Refinement Buys Intelligibility, Search Buys Identity: What Test-Time Compute Buys in Masked-Diffusion TTS#1187998
- When Does Tool Use Increase the Expressive Power of Finite-Precision Recurrent Models?#1413724
- From Rollouts to Recipes: Self-Contained Post-Training for LLMs#821357
- The Language-Energy Divide: Measuring Energy Costs of Multilingual LLM Inference#1084260
- Automatic Model-Hardware Co-Adaptation for Heterogeneous AI Accelerators#820550
Record · ID 1489174
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive