Production LLM systems often rely on separate models for safety and other classification-heavy steps, increasing latency, VRAM footprint, and operational complexity. We instead reuse computation already paid for by the serving LLM: we train lightweight probes on its hidden states and predict labels in the same forward pass used for generation. We frame classification as representation selection over the full token-layer hidden-state tensor, rather than committing to a fixed token or fixed lay...
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---
name: a-bertology-view-of-llm-orchestrations-token-and
title: "A BERTology View of LLM Orchestrations: Token- and Layer-Selective Prompting"
version: 0.0.2
engine: skillxiv-v0.0.2-claude-opus-4.6
license: MIT
url: "https://arxiv.org/abs/2601.13288"
keywords: [Agents, Benchmarking]
description: "Production LLM systems often rely on separate models for safety and other classification-heavy steps, increasing latency, VRAM footprint, and operational complexity. We instead reuse computation already paid for by the serving LLM: we train lightweight probes on its hidden states and predict labels in the same forward pass used for generation. We frame classification as representation selection over the full token-layer hidden-state tensor, rather than committing to a fixed token or fixed layer ..."
---
## Overview
This skill covers research on a bertology view of llm orchestrations: token- and layer-selective prompting. It addresses important challenges in agent development and evaluation.
## Key Insights
The paper provides:
- Novel approaches or frameworks for agent systems
- Empirical evaluation results and benchmarks
- Generalizable principles for practitioners
## When to Use
Use this skill when working on:
- Agent-based systems and applications
- Autonomous reasoning and planning
- Agent performance evaluation and improvement
## When NOT to Use
- For non-agent-related tasks
- When seeking implementation code (consult the paper)
## Resources
- ArXiv Abstract: https://arxiv.org/abs/2601.13288
- Full PDF: https://arxiv.org/pdf/2601.13288
- HTML: https://arxiv.org/html/2601.13288
Refer to the original paper for complete technical details, methodology, and experimental protocols.
