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.
1356 papers reviewed by authors · 891–900 / 1356
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.
This paper introduces a unified Physics-Informed Deep Learning framework that enforces both differential equation residuals and information-theoretic bounds to accurately predict entropy across thermodynamic and financial systems, achieving zero Second Law violations, superior data efficiency, and the ability to identify phase instabilities through geometric analysis.
This paper demonstrates that low-resource safety failures stem from a misalignment in decision calibration rather than a lack of harmfulness representations, and proposes a method to fix this by recalibrating existing high-resource safety gates using only a few target-language examples.
This paper establishes, for the first time, the convergence of practical Inexact Proximal Point and Tseng Algorithms for solving monotone inclusions in Hilbert spaces under nonsummable errors by leveraging Tikhonov regularization, contraction properties, and R-continuity theory.
The paper introduces DECK, a novel 2x2 taxonomy that classifies LLM hallucinations based on their detectability signatures across inter-sample consistency and token-level confidence, revealing that specific error types (Drift, Entrenched, Confabulation, Knotted) require distinct detection methods and exposing a universal blind spot where confident, repeatable fabrications on knowledge-gap inputs remain undetectable by current output-level uncertainty quantification approaches.
This paper introduces ChartArena, a comprehensive bilingual benchmark featuring eight chart families across digital, printed, and hand-drawn scenarios with a format-agnostic evaluation protocol, to systematically assess and reveal the current capabilities and limitations of 26 leading multimodal large language models in parsing diverse chart types.
This paper introduces Fair Fine-tuning (FFt), a method that mitigates distribution inference attacks by fine-tuning models on complementary data under Equalized Odds constraints, theoretically proving that adversarial advantage is bounded by fairness disparity and empirically demonstrating significant reductions in attack success across diverse datasets.
JenBridge is a novel, modular framework that leverages a Transformer-based generative model and an LLM-driven director agent to produce high-fidelity, long-form video soundtracks with natural, narrative-coherent transitions across scene changes, validated by the newly proposed LVS Benchmark.