Aaron Mueller: Time- and Context-aware Interpretability (2025-11-05)

Aaron Mueller: Time- and Context-aware Interpretability (2025-11-05)

by Aaron Mueller

Formal & Physical Sciences AI — News & Applications UYComputer scienceUYQArtificial intelligence (AI)
🎙 Aaron Mueller 👥 845 📅 August 25, 2026 ⏱ 54 min 👁 0 📄 expert opinion 🧭 2026-08-25
Available in: English (current) Français

Keywords

circuit discoveryposition-awaretemporal sparse autoencodersgarden-path sentencescausal mediation analysis

Summary

Aaron Mueller presents three research efforts aimed at incorporating temporal and positional dynamics into language model interpretability. The first part introduces a method for discovering position-aware circuits, which are task-specific subgraphs of the computation graph. Traditional circuit discovery aggregates importance scores across token positions, assuming stationarity, which leads to low precision and recall. The proposed method uses semantic spans to align positions across examples, generated via language models with binary attribution masks, and defines cross-positional edges based on attention head interactions. Experiments on tasks like greater-than and indirect object identification show that position-aware circuits achieve higher faithfulness at smaller sizes compared to non-positional circuits, and LM-generated schemas perform as well as human-crafted ones. The second part introduces Temporal Sparse Autoencoders, which relax the assumption of feature stationarity, allowing features to evolve over time and trace how concepts increase in complexity with context. The third part is a case study on garden-path sentences, where a time-aware perspective helps explain incremental sentence processing and how models revise interpretations. The talk concludes that interpretability must capture not just what models represent, but also how they evolve through time, arguing for a more predictive science of language model behavior.

195 words

Critical Evaluation

Value of the Information & Strength of the Argument

The talk provides substantial value by addressing a critical gap in interpretability research: the assumption of static mechanisms. The argumentation is solid, grounded in experimental evidence and comparisons against baselines. The speaker clearly explains the motivation, methodology, and results, making a compelling case for time-aware interpretability. The use of faithfulness metrics and validation against human-generated schemas strengthens the claims. However, the talk is a summary of ongoing research, and some details are glossed over, but the overall argument is coherent and well-supported.

Scientific Rigor, Source Quality, Title Accuracy

The talk demonstrates scientific rigor by referencing foundational work (e.g., Elman 1998) and presenting original research with methodological details. The quality of sources is high, as the speaker is a recognized expert and the work has been published in top venues (ACL, TMLR). The title accurately reflects the content, focusing on time- and context-aware interpretability. The talk does not include external sources beyond the speaker’s own work, but the internal consistency and experimental validation are strong. The adéquation titre/contenu is excellent, with no significant discrepancies.

182 words

Title / Content Match

The title accurately reflects the content, focusing on time- and context-aware interpretability methods.

Quality & Reliability

8/10

The talk presents original research by a recognized expert, with methodological details and validation against human baselines. Claims are supported by experiments, but the presentation is a summary without full peer-reviewed context.

Key Moments

Markers derived by PSI from the transcript: the creator did not define chapters.

Cited Sources

  • Elman (1998) - Finding Structure in Time — Cited as foundational work on temporal structure in language.
  • ACL paper on position-aware circuits — Mentioned as presented at ACL, but no specific URL provided.

Concurring Sources

  • Elman (1998) - Finding Structure in Time — Supports the temporal nature of language.

Contribution & Novelties

The talk contributes original methods for incorporating temporal and positional dynamics into interpretability, specifically position-aware circuit discovery and Temporal Sparse Autoencoders. These methods address limitations of static interpretability approaches and provide more faithful and concise explanations. The case study on garden-path sentences demonstrates practical applications.

Pour aller plus loin :

  • Causal mediation analysis — Relevant to the circuit discovery methodology.
  • Sparse autoencoder — Background for Temporal Sparse Autoencoders.
  • Garden-path sentence — Context for the case study.

76 words

Radar Profile

The radar profile shows high scores across all dimensions, indicating a well-rounded and technically rigorous presentation. The talk excels in information quantity and quality, with a strong technical level and high reliability, reflecting the speaker's expertise and the experimental validation.

Reliability 8/10