Papers where the original authors have engaged with our plain-language explanation.
For every paper on this page, at least one of the original authors has seen our plain-language explanation and engaged with it — either confirming it reads accurately or requesting corrections that we then applied. An endorsement does not mean the authors formally approve every sentence, but it does mean the explanation has passed the eyes of the people who wrote the paper.
1343 papers reviewed by authors · 621–630 / 1343
This review article explores the integration of manganese-based materials with GelMA hydrogels to create multifunctional, MRI-guided immunotheranostic platforms for precision oncology, while addressing their design strategies, therapeutic mechanisms, current challenges, and future clinical translation.
This cross-sectional study of manual laborers in Pakistan reveals that those exhibiting signs of scapholunate instability demonstrate significantly reduced grip force, abnormal wrist ulnar deviation, and compensatory muscle hyperactivation during dynamic gripping, with occupational exposure duration and specific biomechanical loading patterns serving as key predictors of ligament compromise.
This paper proposes a "gravitational interpretation" of fine-tuning reversion—where the term "gravitational" serves as a geometric/optimization metaphor for a history-induced bias rather than a literal physical dynamical law—arguing that early training establishes dominant behavioral manifolds that exert a geometric pull, causing models to revert toward pre-alignment behaviors during subsequent updates, a phenomenon characterized by a specific history-defined direction () that can be blocked to prevent safety erosion.
This paper proposes a robust SINDy framework called ILTS-SINDy that combines Iterative Least Trimmed Squares for outlier filtering with Sequentially Thresholded Least Squares for sparse regression, demonstrating superior performance in identifying nonlinear dynamics from datasets with up to 20% corrupted observations.
This paper demonstrates that the Axiom of Choice leaves a measurable geometric signature in neural proof embeddings—characterized by declining anomaly scores and reconstruction losses as proofs move further from the axiom in the dependency graph—which correlates with and predicts the performance disparity between constructive and classical theorem proving in systems like Lean 4.
Building on Erdos's proof of the existence of these numbers for Sidon sets 70 years ago, this paper establishes upper and lower bounds on the asymptotic density of -Golomb rulers (sets where each positive difference occurs at most times), proving that their size is bounded above by a term proportional to and below by a term proportional to .
This paper introduces Trajectory-Invariant Latent Refinement (TILR), a training-free framework that identifies and manipulates stable, low-rank invariant directions within language model latent trajectories to significantly improve reasoning consistency and reduce sensitivity to paraphrasing and perturbations without sacrificing accuracy.
This paper provides a perspective on the theoretical landscape of cuprate high-Tc superconductivity after 40 years, focusing on the growing consensus around repulsive interaction mechanisms, outlining three main theoretical schools, and updating Anderson's "dogmas" in light of recent experimental findings.
This paper introduces a unified framework to independently evaluate and compare data-generating priors for tabular foundation models by training identical architectures on tasks derived from various priors, revealing that different priors significantly influence downstream performance and consistency beyond mere data-level similarity.
This paper identifies and quantifies "intervention bias" in zero-shot LLM educational advisors, which erroneously recommend unnecessary actions compared to an optimal oracle, and demonstrates that supervised policy learning using Decision Transformers or XGBoost effectively eliminates this bias while achieving high accuracy and low-latency decisions suitable for high-stakes deployment.