Tracing the Traces: Latent Temporal Signals for Efficient and Accurate
  Reasoning
Reasoning models improve their problem-solving ability through inference-time
scaling, allocating more compute via longer token budgets. Identifying which
reasoning traces are likely to succeed remains a key opportunity: reliably
predicting productive paths can substantially reduce wasted computation and
improve overall efficiency. We introduce Latent-Trajectory signals that
characterize the temporal evolution of a model's internal representations
during the generation of intermediate reasoning tokens. By measuring the
overall change in latent representations between the start and end of
reasoning, the change accumulated across intermediate steps, and the extent to
which these changes advance toward the final state, we show that these signals
predict solution accuracy more reliably than both cross-layer metrics and
output-based confidence measures. When used to guide answer selection across
multiple sampled generations, Latent-Trajectory signals make test-time scaling
more effective and efficient than majority voting, reducing token usage by up
to 70% while preserving and even improving accuracy by 2.6% on average.
Moreover, these predictive signals often emerge early in the reasoning trace,
enabling early selection and allocation of compute to the most promising
candidates. Our findings contribute not only practical strategies for
inference-time efficiency, but also a deeper interpretability perspective on
how reasoning processes are represented and differentiated in latent space.
68f11b81df97aa3a74268614
Martina G. Vilas
claimed_verified
2025-10-17T04:12:25.182Z
62cec94de1d5ee194e604177
/avatars/85551a83b430a43af9b12ce09814b16f.svg
Martina Vilas
user
martinagvilas
68f11b81df97aa3a74268615
Safoora Yousefi
68f11b81df97aa3a74268616
Besmira Nushi
68f11b81df97aa3a74268617
Eric Horvitz
68f11b81df97aa3a74268618
Vidhisha Balachandran
Tracing the Traces: Latent Temporal Signals for Efficient and Accurate
  Reasoning
Reasoning models improve their problem-solving ability through inference-time
scaling, allocating more compute via longer token budgets. Identifying which
reasoning traces are likely to succeed remains a key opportunity: reliably
predicting productive paths can substantially reduce wasted computation and
improve overall efficiency. We introduce Latent-Trajectory signals that
characterize the temporal evolution of a model's internal representations
during the generation of intermediate reasoning tokens. By measuring the
overall change in latent representations between the start and end of
reasoning, the change accumulated across intermediate steps, and the extent to
which these changes advance toward the final state, we show that these signals
predict solution accuracy more reliably than both cross-layer metrics and
output-based confidence measures. When used to guide answer selection across
multiple sampled generations, Latent-Trajectory signals make test-time scaling
more effective and efficient than majority voting, reducing token usage by up
to 70% while preserving and even improving accuracy by 2.6% on average.
Moreover, these predictive signals often emerge early in the reasoning trace,
enabling early selection and allocation of compute to the most promising
candidates. Our findings contribute not only practical strategies for
inference-time efficiency, but also a deeper interpretability perspective on
how reasoning processes are represented and differentiated in latent space.
Latent-Trajectory signals improve inference-time efficiency by predicting productive reasoning paths, reducing token usage and enhancing accuracy.
Latent-Trajectory signals internal representations reasoning tokens cross-layer metrics output-based confidence measures test-time scaling majority voting inference-time efficiency latent space