ReflCtrl: Controlling LLM Reflection Efficiently via Representation Engineering
Quick summary
arXiv:2512.13979v2 Announce Type: replace Abstract: Large reasoning models achieve strong performance on diverse tasks by producing extended chains of thought. Self-reflection, the ability to review and revise prior reasoning steps, is widely regarded as a key contributor to this performance. However, self-reflection also incurs substantial inference cost, and its governing mechanism remains underexplored. In this work, we study self-reflection through the lens of representation engineering. First, we identify a reflection direction in the model's latent space that separates reflection steps f
Key takeaways
- arXiv:2512.13979v2 Announce Type: replace Abstract: Large reasoning models achieve strong performance on diverse tasks by producing extended chains of thought.
- Self-reflection, the ability to review and revise prior reasoning steps, is widely regarded as a key contributor to this performance.
- However, self-reflection also incurs substantial inference cost, and its governing mechanism remains underexplored.
Why it matters
“ReflCtrl: Controlling LLM Reflection Efficiently via Representation Engineering” highlights the need for repeatable measurement rather than a single impressive demonstration. Independent validation across datasets and clearly stated limitations determine whether a result can guide product decisions.
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