CAST is a successor-local operator for causal forecasting of simplex-valued time series that retrieves empirical successors from causal context, stabilizes them with a persistence anchor, and applies bounded local stochastic transport while preserving the simplex by construction.
Bach, and Jure Leskovec
6 Pith papers cite this work, alongside 666 external citations. Polarity classification is still indexing.
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UNVERDICTED 6representative citing papers
CoCoMagic applies constrained cooperative co-evolution to metamorphic and differential testing to find up to 287% more distinct behavioral divergences in an end-to-end ADS than baseline search methods.
Humans chatting with an unreliable LLM assistant outperform both the model alone and unaided humans on MMLU and time-limited QuALITY tasks.
Human-grounded evaluation finds no significant performance improvement from adding SHAP explanations to model confidence scores in alert processing.
CaB predicts Before/Hit/After boundary tokens to produce auditable switching decisions and boundary-stable control in VLA agents on a Minecraft benchmark under single global calibration.
A behavioral engagement scoring method predicts patient response propensity in care management using real-world data and supplies interpretable insights via prototypical patients without performance loss.
citing papers explorer
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CAST: Causal Anchored Simplex Transport for Distribution-Valued Time Series
CAST is a successor-local operator for causal forecasting of simplex-valued time series that retrieves empirical successors from causal context, stabilizes them with a persistence anchor, and applies bounded local stochastic transport while preserving the simplex by construction.
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Constrained Co-evolutionary Metamorphic Differential Testing for Autonomous Systems with an Interpretability Approach
CoCoMagic applies constrained cooperative co-evolution to metamorphic and differential testing to find up to 287% more distinct behavioral divergences in an end-to-end ADS than baseline search methods.
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Measuring Progress on Scalable Oversight for Large Language Models
Humans chatting with an unreliable LLM assistant outperform both the model alone and unaided humans on MMLU and time-limited QuALITY tasks.
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A Human-Grounded Evaluation of SHAP for Alert Processing
Human-grounded evaluation finds no significant performance improvement from adding SHAP explanations to model confidence scores in alert processing.
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Completion at the Boundary (CaB): Deployable Switching with Completion-Aware Control under Limited Calibration
CaB predicts Before/Hit/After boundary tokens to produce auditable switching decisions and boundary-stable control in VLA agents on a Minecraft benchmark under single global calibration.
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Learning Patient Engagement in Care Management: Performance vs. Interpretability
A behavioral engagement scoring method predicts patient response propensity in care management using real-world data and supplies interpretable insights via prototypical patients without performance loss.