The paper introduces Risk-Set Transported Synthetic Control with Difference-in-Differences Adjustment (RT‑SC‑DiD), a method for staggered treatment‑adoption studies that keeps the donor pool fixed by reallocating weights from exiting donors to similar surviving donors while applying a DiD baseline correction. It analyzes distortion from horizon‑by‑horizon re‑optimization, derives bounds on error propagation, and proposes diagnostics and a donor‑only placebo for tuning the transport penalty. Empirical simulations show that intermediate transport regularization reduces average RMSE compared to independent horizon‑specific estimation and strong anchoring, supporting the method’s bias‑variance trade‑off.
"whyItMatters":"The method offers a principled way to stabilize synthetic‑control weights over time in staggered designs, potentially improving causal inference when donor support contracts as treatments roll out."
By Mojtaba Eslami
arXiv:2609.23937v1 Announce Type: cross
Abstract: Robust linear fits can resist response contamination yet remain too dense or unstable for useful global explanations. We propose penalized distillati...
By Wooyoung Shin, Seunghwan Park
arXiv:2608. 12489v1 Announce Type: new Abstract: Organizations decide whom to treat under a budget and want to know what a targeting rule would have earned before deploying it.
By Binshuang Li
arXiv:2606. 14636v2 Announce Type: replace Abstract: Control-function instrumental-variable estimators pass an estimated first-stage residual to an outcome model.
By Rui Wu, Zongyuan Chen, Hong Xie, Defu Lian, Enhong Chen
arXiv:2606.14636v3 Announce Type: replace
Abstract: Many two-stage estimators assess the first-stage learner by prediction error, even when the next stage uses its residual. In control-function instr...
By Rui Wu, Zongyuan Chen, Hong Xie, Defu Lian, Enhong Chen
arXiv:2608. 02829v1 Announce Type: new Abstract: Model families train every size from scratch.
By Ravi Satya Durga Prasad Yenugula
The paper introduces a schema‑adaptive action‑conditioned Joint‑Embedding Predictive Architecture (SAAC‑JEPA) for cross‑machine CNC transfer when only a subset of sensors overlap between source and target machines. Experiments show that pretraining does not improve source‑only forecasting, but a carefully selected action‑conditioned JEPA model achieves a zero‑shot RMSE of 0.546 on the target, outperforming persistence but falling short of certain baseline models. Ablation studies reveal that adding RevIN improves RMSE but harms calibration, and limited post‑lock adaptation can further reduce error.
By Ayoub Louaye Bouaziz, Matthieu Ostertag, Anton Demasles
The paper introduces “ℝD_{CF5}”, a probe‑based estimator that predicts the region‑wise gain of a dynamic ensemble over the best static blend in regression tasks under distribution shift. Across 12 benchmark dataset‑shift pairs, the estimator achieves a Spearman correlation of +0.98 with actual test gains, outperforming alternative diagnostics. The authors also present a Probe‑Validated Ensemble Selector that chooses between a static affine stacker and dynamic realizers, demonstrating risk reductions of up to 16% in prospective deployments.
By Tianxin Zhou, Ruixi Lin
arXiv:2607. 23711v1 Announce Type: new Abstract: LoRA fine-tuning can create intruder dimensions: new leading singular vectors of the updated weight matrix $W+BA$ that are nearly orthogonal to all pretrained singular vectors and that drive catastrophic forgetting.
By Peng Xie
arXiv:2608. 13096v1 Announce Type: new Abstract: Limit order book (LOB) simulators are most useful to practitioners when they combine realistic market dynamics, computationally efficient sampling, controllable scenario generation, and the ability to generalize beyond the instruments seen during training---properties that existing agent-based and deep generative simulators provide only partially.
By Zhuohan Wang, Andreea Bacalum, Ollie Olby, Carmine Ventre, Namid Stillman
The paper evaluates additive activation steering in chat and agent contexts, showing that the commonly used gain ratio (Δ_agent/Δ_chat) fails to reliably indicate potency and efficacy across multiple models and dose-response cells. By replacing the gain with a location metric, dEC50 (difference in EC50 between agent and chat), the authors demonstrate a more robust, two‑sided measure that consistently captures cross‑context shifts. The study also reports several refuted and unanswered claims, emphasizing that a single operating point cannot distinguish between displacement and gain effects.
By Lucas Pinto
arXiv:2607. 27680v1 Announce Type: new Abstract: Low-Rank Adaptation (LoRA) has become the standard mechanism for fine-tuning large pretrained models, yet its statistical properties remain only partially understood.
By Arunan J