arXiv (All)
open access
PULSE: Identifying Demonstration-Utility Features with Sparse Autoencoders
In-context learning is highly sensitive to demonstration choice, yet most methods select demonstrations using external query-demonstration similarity. Such criteria can miss model-specific signals: Similar demonstrations may activate different internal features and downstream behaviors. We introduce
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
- When Structured Sparse Autoencoders Learn Consistent Concepts Across Modalities#353053
- Generalizable VLA Finetuning via Representation Anchoring and Language-Action Alignment#1128623
- Message Passing Does More with Less for In-Context Learning on Graphs#1132258
- Rethinking Demonstration Unlearning in Imitation Learning for Robotics#659333
- Rethinking Demonstration Unlearning in Imitation Learning for Robotics#657197
Record · ID 1116800
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