Directional Reasoning Trajectory Change (DRTC): Identifying Critical Trace Segments in Reasoning Models
arXiv:2602.15332v1 Announce Type: new Abstract: Understanding how language models carry out long-horizon reasoning remains an open challenge. Existing interpretability methods often highlight tokens or spans correlated with an answer, but they rarely reveal where the model makes consequential reasoning turns, which earlier context causally triggers those turns, or whether the highlighted text actually steers the reasoning process. We introduce Directional Reasoning Trajectory Change (DRTC), a process-causal framework for interpreting long-form reasoning from a single on-policy rollout. DRTC detects pivot decision points using uncertainty and distribution-shift signals, then applies receiver-side interventions that preserve the realized rollout without resampling the continuation while blocking information flow from selected earlier chunks only at a pivot. It measures whether each intervention redirects the direction of the model's log-probability trajectory relative to the realized ro
arXiv:2602.15332v1 Announce Type: new Abstract: Understanding how language models carry out long-horizon reasoning remains an open challenge. Existing interpretability methods often highlight tokens or spans correlated with an answer, but they rarely reveal where the model makes consequential reasoning turns, which earlier context causally triggers those turns, or whether the highlighted text actually steers the reasoning process. We introduce Directional Reasoning Trajectory Change (DRTC), a process-causal framework for interpreting long-form reasoning from a single on-policy rollout. DRTC detects pivot decision points using uncertainty and distribution-shift signals, then applies receiver-side interventions that preserve the realized rollout without resampling the continuation while blocking information flow from selected earlier chunks only at a pivot. It measures whether each intervention redirects the direction of the model's log-probability trajectory relative to the realized rollout direction, producing a signed per-chunk attribution score. We also compute turning-angle curvature changes on raw logits as a complementary diagnostic and introduce curvature signatures to summarize shared intervention-response geometry. Empirically, directional influence is sharply concentrated across four reasoning models (per-example |DRTC| shares yield Gini 0.50 to 0.58 and top-5 percent mass 0.23 to 0.28), and learned pivots induce stronger intervention magnitudes than matched random spans. In a scaling study on 500 MATH problems with R1-Distill-Qwen-1.5B, learned spans outperform matched random spans (median delta = 0.409, 355 of 500 positive; sign test p = 2.3e-21). Overall, DRTC provides a causally grounded, trajectory-level view of how specific context elements steer reasoning under on-policy dynamics.
Executive Summary
This article introduces Directional Reasoning Trajectory Change (DRTC), a process-causal framework for interpreting long-form reasoning from a single on-policy rollout. DRTC detects pivot decision points using uncertainty and distribution-shift signals, then applies receiver-side interventions to measure the directional influence of specific context elements on the model's reasoning process. Empirical results demonstrate the effectiveness of DRTC in identifying critical trace segments and learning pivots that induce stronger intervention magnitudes. The study contributes to our understanding of how language models carry out long-horizon reasoning and has implications for model interpretability and improvement. While promising, the approach requires further validation and exploration of its limitations and applications.
Key Points
- ▸ DRTC provides a causally grounded, trajectory-level view of how specific context elements steer reasoning under on-policy dynamics.
- ▸ The approach detects pivot decision points using uncertainty and distribution-shift signals.
- ▸ DRTC measures the directional influence of specific context elements on the model's reasoning process using receiver-side interventions.
Merits
Strength in Interpretability
DRTC offers a novel and effective way to interpret long-form reasoning from language models, providing insights into the causal relationships between context elements and model decisions.
Improved Model Performance
The study demonstrates that DRTC can be used to identify critical trace segments and learn pivots that induce stronger intervention magnitudes, leading to improved model performance.
Demerits
Limited Generalizability
The study's results are specific to the models and datasets used, and further validation is required to ensure that DRTC generalizes to other architectures and tasks.
Computational Complexity
DRTC requires significant computational resources, particularly for large models and datasets, which may limit its practical applications.
Expert Commentary
The introduction of DRTC marks an important step forward in the field of model interpretability, offering a novel and effective approach to understanding long-form reasoning from language models. While the study's results are promising, further validation and exploration of the approach's limitations and applications are necessary to fully realize its potential. The study's findings highlight the importance of causal relationships in understanding model behavior and have significant implications for the development of more robust and reliable AI systems.
Recommendations
- ✓ Future research should focus on validating DRTC across a broader range of models and tasks, as well as exploring its applications in high-stakes domains such as healthcare and finance.
- ✓ Developers should consider incorporating DRTC into their model development pipelines to improve the interpretability and transparency of language models.
