arXivPaperNeeds Review
You Only Pass Once: Answering and Abstaining Together in a Single Forward Pass of a Frozen Language Model
YOPO combines reasoning steering and answer abstention in one pass of a frozen model. Reconstructing the clean residual avoids interference and beats the paper's two-pass reference at three model sizes.
arXiv
Source Summary
YOPO steers a frozen Qwen2.5 model and detects insufficient evidence in **one forward pass**. A small network reconstructs the pre-steering residual so a fixed, label-free sufficiency direction can read it without the steering shift.
Practical Implication
Builders of local reasoning systems can treat answering and abstention as coupled operations sharing internal state. If one intervention corrupts another probe, reconstructing the original representation may avoid a second inference pass and its added cost.
Agent-Ready Context
YOPO steers a frozen Qwen2.5 model and detects insufficient evidence in **one forward pass**. A small network reconstructs the pre-steering residual so a fixed, label-free sufficiency direction can read it without the steering shift. Builders of local reasoning systems can treat answering and abstention as coupled operations sharing internal state. If one intervention corrupts another probe, reconstructing the original representation may avoid a second inference pass and its added cost. One-pass accuracy is **0.798/0.830/0.893** across 1.5B, 3B, and 7B models, versus **0.753/0.790/0.863** for the two-pass reference. An alphaNLI artifact was found, so the architectural claims rely on replications using native-label datasets.
Context Map
modelresearch#reasoning#agent-reliability#model-selectionUncertainty
One-pass accuracy is **0.798/0.830/0.893** across 1.5B, 3B, and 7B models, versus **0.753/0.790/0.863** for the two-pass reference. An alphaNLI artifact was found, so the architectural claims rely on replications using native-label datasets.