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.
956 papers reviewed by authors · 541–550 / 956
This paper demonstrates that randomly dropping a fixed fraction (specifically 25%) of transitions from PPO rollouts effectively breaks the redundancy of causally chained gradients, thereby stabilizing training dynamics across diverse environments without altering the core algorithm or sacrificing final reward performance.
This paper proposes a unified cybernetic framework that models cognitive processes as feedback-driven dynamical systems, utilizing a combination of internal transformations, interpretative mappings, and semantic equivalence constraints to ensure stable, invariant interpretations through categorical and fixed-point analysis.
This paper presents a deep learning model trained on TESS light curves that achieves 99.8% accuracy in identifying solar-like oscillations in cool dwarfs, successfully narrowing down thousands of candidates to 24 promising stars to extend the detection frontier of asteroseismology for main-sequence and sub-giant stars.
This paper presents an exact analytical solution for heterogeneous chains of overdamped, harmonically coupled particles with momentum-conserving dissipation, revealing that a free end induces a peculiar staircase response where particle interactions are independent of intervening chain properties and that rank-deficient matrices lead to a distinct separation between steady-state and relaxation dynamics.
This study demonstrates that an out-of-plane electric field can induce dominant spin splitting in the Q and Q' valleys of multilayer n-type WSe2 via spin-valley-layer coupling, offering a powerful non-magnetic alternative to magnetic fields for controlling spin states in low-power spintronic and quantum devices.
This paper presents a novel Helicity Softer Dipole Pomeron model based on Regge theory that successfully describes the cross sections and spin observables of vector meson photoproduction by arbitrarily polarized photons across a wide energy range, significantly improving upon previous models and offering predictions for future experiments and cosmic-photon polarimetry.
This paper presents a hybrid Computational Fluid Dynamics and Multi-Objective Multi-Agent Reinforcement Learning framework that successfully coordinates magnetically actuated micro-robotic swarms in dynamic, pulsatile flows by utilizing PCGrad to resolve gradient conflicts, thereby achieving simultaneous optimization of upstream progression, energy efficiency, and motion smoothness through emergent hydrodynamic behaviors.
This paper introduces a unified multi-dimensional behavioral framework that evaluates LLM reasoning across six distinct dimensions—Correctness, Consistency, Robustness, Logical Coherence, Efficiency, and Stability—to reveal critical insights and prevent ranking errors that traditional accuracy-only metrics overlook.
This paper introduces Habermolt, a public platform for AI-delegated deliberation where agents represent humans in collective decision-making, and evaluates its effectiveness through the dimensions of representation, aggregation, and revision to address the novel design and alignment challenges of scalable, trustworthy AI representatives.
This paper introduces the Concept Allocation Zone (CAZ), a framework that redefines concept formation in transformer models as a depth-extended process occurring across a contiguous region of the residual stream rather than at a single "best" layer, utilizing new metrics to identify these zones and revealing that many concepts reside in subtle, multimodal allocation regions that are causally active yet invisible to standard peak detection methods.