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.
948 papers reviewed by authors · 81–90 / 948
This paper demonstrates that for coding agents editing code, the essential context is strictly limited to the specific files being modified, as natural language summaries and surrounding file content contribute negligibly to issue resolution compared to the source code itself, while also revealing a significant noise floor in benchmark results caused by non-deterministic API inference.
This paper proposes Learned Predictive Ambiguity Sets (LPAS), a deep contextual framework that adaptively learns state-dependent Wasserstein radii and nominal distributions to enhance Distributionally Robust Optimization, demonstrating superior performance and reduced conservatism in portfolio optimization compared to traditional fixed-radius baselines.
By modeling proto-neutron star evolution from hot, neutrino-trapped birth states to cold, neutrino-transparent final states using a color-superconducting equation of state, the study identifies four distinct core evolution scenarios and concludes that a stable color-superconducting phase can only persist in the final cold neutron star within a narrow, high-mass region.
This paper proposes a computationally efficient, MCMC-free Bayesian framework for uncertainty quantification in nonlinear PDE-based imaging inverse problems like QPAT and EIT, which utilizes a two-stage pushforward methodology to derive rigorous posterior contraction rates and accurate reconstructions at a lower computational cost.
This paper investigates the phenomenon of semantic drift in reasoning-class large language models through a longitudinal experiment, proposing a mathematical model and a new "operator control stability coefficient" to ensure goal-targeting stability in hybrid human-machine decision support systems.
This paper presents an Autonomous Scientific Knowledge Generation Framework that transforms unstructured scientific literature into a unified, AI-ready knowledge base through an integrated workflow of ontology-guided acquisition, hybrid extraction, and semantic harmonization, successfully demonstrated on electro-optic materials to enable scalable, closed-loop scientific discovery.
This paper establishes rigorous, computer-assisted continuum certificates for passive multiresonator quantum-memory interfaces by deriving fundamental Bode–Fano reflection bounds and proving exact stability and performance guarantees through polynomial positivity and Sturm root counting, thereby replacing sampled efficiency metrics with a mathematically verifiable worst-case write efficiency bound.
This paper proposes a fairness-aware retail pricing framework that utilizes log-log ARDL models with CPI-anchored constraints and Simulated Annealing optimization to prevent consumer exploitation by addressing inflation-driven positive price elasticities while balancing sales targets with consumer welfare.
This paper introduces Motion-Enhanced Gravity (MEG), a metric-affine vector–tensor extension of General Relativity that couples spacetime curvature to matter four-momentum flux, offering a mathematically consistent framework that potentially explains flat galactic rotation curves and late-time cosmic acceleration without dark matter while predicting observable deviations in gravitational waves and black hole shadows.
Motivated by experiments on twisted , this paper proposes that the high-resistivity metal observed near the Fractional Quantum Anomalous Hall state is a Orthogonal Metal characterized by sharp charge- fermionic quasiparticles coupled to a discrete gauge field, which naturally explains large resistivities and connects to an ordinary superconductor via pairing.