DeconDTN-Toolkit simulates provenance shifts to expose ERM vulnerabilities and provides tools plus a robust OOD indicator for mitigating confounding by data provenance.
Towards causal representation learning
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Robots discover causal tool features through VLM suggestions and physics-based counterfactual perturbations in simulation, then transfer manipulation skills via conditioned keypoint matching.
Causal mediation analysis shows harmful LLM outputs arise in late layers from MLP failures and gating neurons, with early layers handling harm context detection and signal propagation.
Integrates partial ODE physics into SDE-based causal discovery via drift-diffusion separation, with sparsity-inducing quasi-likelihood estimation, recovery guarantees for stable/unstable systems, and robustness analysis to model misspecification.
citing papers explorer
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DeconDTN-Toolkit: A Library for Evaluation and Enhancement of Robustness to Provenance Shift
DeconDTN-Toolkit simulates provenance shifts to expose ERM vulnerabilities and provides tools plus a robust OOD indicator for mitigating confounding by data provenance.
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Creative Robot Tool Use by Counterfactual Reasoning
Robots discover causal tool features through VLM suggestions and physics-based counterfactual perturbations in simulation, then transfer manipulation skills via conditioned keypoint matching.
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Why Do Large Language Models Generate Harmful Content?
Causal mediation analysis shows harmful LLM outputs arise in late layers from MLP failures and gating neurons, with early layers handling harm context detection and signal propagation.
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Causal Discovery from Heteroscedastic Stochastic Dynamical Systems under Imperfect Physical Models
Integrates partial ODE physics into SDE-based causal discovery via drift-diffusion separation, with sparsity-inducing quasi-likelihood estimation, recovery guarantees for stable/unstable systems, and robustness analysis to model misspecification.