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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 ...

adu2021 Updated
AI & ML

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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

Refer to the original paper for complete technical details, methodology, and experimental protocols.

adu2021/skillxiv/tree/main/skills/skillxiv-v0.0.2-claude-opus-4.6/a-bertology-view-of-llm-orchestrations-token-and commit 08ab595814

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