This is an automated rejection. No LLM generated, heavily assisted/co-written, or otherwise reliant work.
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Most AI uncertainty metrics give a single, average score. They tell you the model is unsure, but not where or why it's unsure in a way that matters for deployment. What if we had a probe that was specifically sensitive in the exact regions where models are most likely to fail?
My recent work, "Geometric Safety Features for AI Boundary Detection", tries to build exactly that.
The Core Idea: Geometry as a Probe for Borderline Cases Instead of a monolithic uncertainty score, I extracted seven simple geometric features from a model's embedding space using k-NN—things like the standard deviation of distances to neighbors (knn_std_distance). The hypothesis: the "shape" of the local neighborhood reveals how precarious a model's prediction is.
To test this properly, I had to move beyond average-case metrics. I introduced a "boundary-stratified evaluation" framework, splitting test cases into "Safe," "Borderline," and "Unsafe" zones based on the model's own confidence. The critical question: do these geometric features provide disproportionate signal exactly where the model is teetering on the edge?
The Results: Specificity Over Broad Improvement The findings were more targeted and interesting than a blanket accuracy boost:
· 4.8x Larger Improvement on Borderlines: The geometric features improved explanatory power (R²) by +3.8% on Borderline cases, versus only +0.8% on Safe cases. They provide the most lift precisely where standard uncertainty measures are least reliable. · Predicting Behavioral "Flips": In a separate experiment where we paraphrased inputs to try and flip the model's decision, a classifier using these geometric features achieved an AUC of 0.707, outperforming a baseline using only boundary distance by 23%. · A Clear Signal Emerges: The top-performing feature was consistently knn_std_distance—the spread of a point's nearest neighbors. Its correlation with uncertainty was amplified in borderline regions (r=+0.399) compared to the overall average (r=+0.286).
Why This Might Matter for AI Safety This isn't just another incremental improvement on a benchmark. It's a methodological pivot with safety in mind:
1. Failure-Mode Targeting: It directly addresses the "boundary problem" where unpredictable failures concentrate. A safety monitor that's extra-sensitive here is more useful than one with slightly better average performance. 2. Interpretability via Geometry: The features are conceptually simple—they measure "crowdedness" and "shape." This could offer a more intuitive window into model uncertainty than a black-box confidence score. 3. A New Evaluation Standard: The "boundary-stratified evaluation" itself might be useful. Shouldn't we judge safety tools specifically by their performance in the most precarious regimes?
As the author, my central question for the community is about the kind of progress this represents. I'm less interested in minor methodological nitpicks (though I welcome them!) and more in your thoughts on the strategic direction:
· Failure Mode Detection: Is local embedding geometry a robust foundation for building failure-mode-specific detectors? Where will this intuition break down? · Evaluation Philosophy: Does the boundary-stratified framework resonate as a better way to evaluate safety-critical metrics? What are its blind spots? · Path to Impact: For those working on robustness, monitoring, or interpretability: what would it take for tools like this to be practically useful? What's the next critical experiment?
Links & Reproducibility
· Full Code & Implementation: GitHub Repository · Technical Report (13.5k words): Included in /docs/ in the repo. · Archived Version: Zenodo DOI: 10.5281/zenodo.18290279
I've built this to be fully reproducible. The core mirrorfield.geometry package is production-ready, and the scripts will regenerate all figures and results.
I'm looking forward to your criticism, connections to other work, and ideas about where this targeted, geometric approach could be most valuable—or where it's likely to hit a wall.
Most AI uncertainty metrics give a single, average score. They tell you the model is unsure, but not where or why it's unsure in a way that matters for deployment. What if we had a probe that was specifically sensitive in the exact regions where models are most likely to fail?
My recent work, "Geometric Safety Features for AI Boundary Detection", tries to build exactly that.
The Core Idea: Geometry as a Probe for Borderline Cases
Instead of a monolithic uncertainty score, I extracted seven simple geometric features from a model's embedding space using k-NN—things like the standard deviation of distances to neighbors (knn_std_distance). The hypothesis: the "shape" of the local neighborhood reveals how precarious a model's prediction is.
To test this properly, I had to move beyond average-case metrics. I introduced a "boundary-stratified evaluation" framework, splitting test cases into "Safe," "Borderline," and "Unsafe" zones based on the model's own confidence. The critical question: do these geometric features provide disproportionate signal exactly where the model is teetering on the edge?
The Results: Specificity Over Broad Improvement
The findings were more targeted and interesting than a blanket accuracy boost:
· 4.8x Larger Improvement on Borderlines: The geometric features improved explanatory power (R²) by +3.8% on Borderline cases, versus only +0.8% on Safe cases. They provide the most lift precisely where standard uncertainty measures are least reliable.
· Predicting Behavioral "Flips": In a separate experiment where we paraphrased inputs to try and flip the model's decision, a classifier using these geometric features achieved an AUC of 0.707, outperforming a baseline using only boundary distance by 23%.
· A Clear Signal Emerges: The top-performing feature was consistently knn_std_distance—the spread of a point's nearest neighbors. Its correlation with uncertainty was amplified in borderline regions (r=+0.399) compared to the overall average (r=+0.286).
Why This Might Matter for AI Safety
This isn't just another incremental improvement on a benchmark. It's a methodological pivot with safety in mind:
1. Failure-Mode Targeting: It directly addresses the "boundary problem" where unpredictable failures concentrate. A safety monitor that's extra-sensitive here is more useful than one with slightly better average performance.
2. Interpretability via Geometry: The features are conceptually simple—they measure "crowdedness" and "shape." This could offer a more intuitive window into model uncertainty than a black-box confidence score.
3. A New Evaluation Standard: The "boundary-stratified evaluation" itself might be useful. Shouldn't we judge safety tools specifically by their performance in the most precarious regimes?
As the author, my central question for the community is about the kind of progress this represents. I'm less interested in minor methodological nitpicks (though I welcome them!) and more in your thoughts on the strategic direction:
· Failure Mode Detection: Is local embedding geometry a robust foundation for building failure-mode-specific detectors? Where will this intuition break down?
· Evaluation Philosophy: Does the boundary-stratified framework resonate as a better way to evaluate safety-critical metrics? What are its blind spots?
· Path to Impact: For those working on robustness, monitoring, or interpretability: what would it take for tools like this to be practically useful? What's the next critical experiment?
Links & Reproducibility
· Full Code & Implementation: GitHub Repository
· Technical Report (13.5k words): Included in /docs/ in the repo.
· Archived Version: Zenodo DOI: 10.5281/zenodo.18290279
I've built this to be fully reproducible. The core mirrorfield.geometry package is production-ready, and the scripts will regenerate all figures and results.
I'm looking forward to your criticism, connections to other work, and ideas about where this targeted, geometric approach could be most valuable—or where it's likely to hit a wall.