arXiv (OAI Expanded)
open access
One Size Does Not Fit All: Setting Inference Depth from the Questions a Deployment Actually Asks
A transformer language model is trained to respond to any prompt, but each deployment asks only a narrow range of questions: a support assistant sees delivery complaints, a coding tool sees Python. Every deployment nonetheless pays the same computation per token. This paper measures how much of that
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
- Trace Grafting: Cross-Model Reasoning in Large Language Model Code Generation#1092484
- Toollery: Scaling LLM Agents to Thousands of Skills and Tools#1036901
- Unified Deployment-Aware Evaluation of Open Reasoning Language Models#927280
- A Survey on Large Language Models for Code Generation#1135850
- Harness Engineering in LLM Tool Use via Agent-Native Reusable Tool Primitives#822166
Record · ID 1083613
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