# A Bertology View Of LLM Orchestrations Token And

> 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 ...

- Skill: `adu2021/a-bertology-view-of-llm-orchestrations-token-and` (Agent Skill)
- Install (CLI): `npx skillmds@latest add adu2021/a-bertology-view-of-llm-orchestrations-token-and`
- Raw SKILL.md: https://api.skillmd.com/api/skills/adu2021/a-bertology-view-of-llm-orchestrations-token-and/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- License: MIT
- Author: adu2021 (https://skillmd.com/u/adu2021)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/adu2021/a-bertology-view-of-llm-orchestrations-token-and

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## 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.