Tag
The article discusses a scientific paper that formally proves the boundaries of verification in systems, arguing that trust decisions must be explicit and managed beyond a certain point.
The article discusses how market failures lead to underinvestment in critical AI applications like medical research, and proposes state intervention through mechanisms such as demand contracts and public compute to address this imbalance.
Research exposes vulnerabilities in encrypted reasoning traces from LLM APIs, allowing adversaries to extract proprietary model reasoning, personal data, and enable malicious prompt injections.
A report synthesizes field studies to argue that while AI coding agents increase code output, gains shrink in shipping reliable software due to bottlenecks in review, integration, and cost management.
This paper proposes extending the spherical cow approximation to a full multipole expansion to improve modeling of bovine potentials and interactions in physics, addressing limitations in spherical symmetry.
The paper introduces reSolve, a surrogate-guided solve-and-reproduce framework for self-evolving agent skills that achieves 74.9% performance, surpassing human-curated baselines by 14.8 points.
This paper proposes SLM-Conditioned Hierarchical Relation Routing, an architecture that integrates small language models into graph neural networks to enable adaptive message selection in labeled property graphs, enhancing prediction accuracy by leveraging contextual semantic information.
This paper explores the theoretical representation of MAX functions using two-hidden-layer ReLU neural networks, providing detailed coefficient lists and identities for exact representations.
This paper introduces grounded glossary generation for Classical Sanskrit, a task involving recovering Sanskrit phrases and producing translation-grounded meanings from sloka-translation pairs. It constructs a benchmark from Hindu texts and evaluates various AI models, finding that instruction fine-tuning improves performance, with morphological modeling identified as a key challenge.
This paper presents generative semantic scene completion with corrected performance metrics and real-time inference, releasing code, weights, and the PS3 corpus for reproducibility.
This paper analyzes NLP conference papers from 2020 to 2025 to examine the relationship between reported GPU resources and scholarly impact, finding that while resources are associated with higher citations, they explain little of the variance in impact.
The article highlights the paper 'A Gentle Introduction to Matrix Calculus' by Jan Magnus, published in the Journal of Econometrics in 2024, as a clear and valuable resource for fields like econometrics, machine learning, statistics, and optimization.
The paper proposes refactoring introductory calculus to simplify teaching by reducing redundancy and reordering topics, using analogies from code refactoring in computer programming.
This paper establishes new lower and upper bounds on the Grothendieck constant, determining its previously unknown tenths digit through a method that combines human and AI-assisted research.
A classic 1960 paper by Norbert Wiener examining the moral and technical implications of automation, including social responsibility and unintended consequences of machine decision-making.
Yohei Nakajima announces his first SSRN paper, sharing a link to it.
Introduces an SMT-based pipeline for synthesizing maze solution paths from input patterns and constructing planar and 3D maze structures. Extends a conference paper with detailed construction methods and SMT-LIB examples.
This paper examines the economic incentives and dynamics of recursive self-improvement in AI systems, addressing how such processes could scale and their implications for governance and safety.
BlockServe introduces block-grained continuous batching to address convergence heterogeneity in diffusion LLMs, enabling 1.9–10.6x throughput improvement over Fast-dLLM while maintaining generation quality.
COALA is a robust framework for contextual biasing in automatic speech recognition (ASR) that uses a contrastive regularizer and biasing score estimation to improve recognition of domain-specific entities from large biasing lists. Experiments on LibriSpeech show consistent superior performance.