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.
951 papers reviewed by authors · 481–490 / 951
This paper establishes a microscopic framework demonstrating that rotational symmetry restoration in relativistic heavy-ion collisions acts as a geometric low-pass filter that exponentially suppresses effective deformation modes, thereby reconciling the use of classically deformed geometries with the rotationally invariant quantum ground states of even-even nuclei.
Site4Drug is an AI agent that predicts ranked, modality-aware drug-binding target sites on proteins by integrating diverse biological evidence to overcome the ambiguity and failure rates associated with selecting actionable intervention regions, particularly for membrane proteins.
This paper presents a deep learning-based system utilizing a multiscale network and supervised contrastive learning to achieve real-time facial emotion recognition by modeling continuous expression changes, demonstrating satisfactory performance on standard datasets for applications such as psychological counseling.
This paper proposes a class of Individual Weight (IW) shrinkage estimators for micropanel data that prioritize individual-level accuracy over aggregate performance by leveraging personal history rather than cross-sectional information, thereby overcoming the "tyranny of the majority" inherent in conventional methods like James-Stein and Empirical Bayes.
This paper proposes a communication-centric hierarchical architecture integrating edge-assisted Large Language Models with 6G semantic communication for Tactical Autonomous Defense Vehicle Networks, demonstrating via simulation that this approach significantly outperforms conventional 5G-based AI baselines by reducing latency by 75.2%, increasing mission success rates by 68.7 percentage points, and cutting communication overhead by 88.6% at a 30-vehicle scale.
The paper introduces AXIOM, a trust-first neuro-symbolic architecture that leverages language models solely to canonicalize natural language problems into a deterministic Computer-Algebra-System pipeline, achieving 94.36% correctness with 100% trust (zero confident errors) on mathematical benchmarks while ensuring that system improvements never regress previously verified results.
This paper proposes the "stability distillation hypothesis," arguing that the origin of life is an inevitable, unified process driven by selective enrichment via stability differences, which logically necessitates the spontaneous emergence of information, RNA selection, compartmentalization, and the co-origin of cells and viruses without relying on improbable chance events.
This paper reveals that the Longest Increasing Subsequence problem, despite being solvable in polynomial time, exhibits glassy dynamics and thermodynamic sparsity at low temperatures, where local search algorithms become trapped in metastable states due to a lack of accessible configurations rather than energetic barriers.
This paper demonstrates that the curation and ordering of external information feeds can systematically steer LLM agents toward adversarial decisions, particularly when they are uncertain, revealing that safety evaluations must audit the upstream recommender layer rather than testing the model in isolation.
This paper demonstrates that minimizing irreversible work in weakly driven systems under physical constraints on the protocol derivative yields a global optimal solution of constant driving speed and a linear protocol, a result derived from a shifted eigenvalue equation and confirmed by numerical genetic programming.