ADu2021/a-bertology-view-of-llm-orchestrations-token-and
skills/skillxiv-v0.0.2-claude-opus-4.6/a-bertology-view-of-llm-orchestrations-token-and/SKILL.md
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 ...
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skillsauthnpx skillsauth add ADu2021/skillXiv a-bertology-view-of-llm-orchestrations-token-andInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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SKILL.md
- 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.
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