SkillComposer performs task-conditioned skill sequence prediction with a constrained autoregressive decoder to jointly output skill subset, count, and order, raising pass rates by 23.1 and 18.2 percentage points on two production coding agents over no-skill baselines.
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TabOrder learns unsupervised causal variable orderings and enforces them with order-constrained attention for tabular prediction and imputation under distribution shifts.
DAG-DC-ADMM jointly clusters subjects and learns their cluster-specific causal DAGs via structural equation modeling, groupwise truncated Lasso fusion penalties, and an ADMM solver for the resulting nonconvex problem.
citing papers explorer
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Generative Skill Composition for LLM Agents
SkillComposer performs task-conditioned skill sequence prediction with a constrained autoregressive decoder to jointly output skill subset, count, and order, raising pass rates by 23.1 and 18.2 percentage points on two production coding agents over no-skill baselines.
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Learning Causal Orderings for In-Context Tabular Prediction
TabOrder learns unsupervised causal variable orderings and enforces them with order-constrained attention for tabular prediction and imputation under distribution shifts.
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A Unified Framework for Structure-Aware Clustering and Heterogeneous Causal Graph Learning
DAG-DC-ADMM jointly clusters subjects and learns their cluster-specific causal DAGs via structural equation modeling, groupwise truncated Lasso fusion penalties, and an ADMM solver for the resulting nonconvex problem.