Philosophy and History of Science

Últimos artículos

• Deontic Argumentation

  Guido Governatori, Antonino Rotolo · 14 de mayo de 2026

  We address the issue of defining a semantics for deontic argumentation that supports weak permission. Some recent results show that grounded semantics do not support weak permission when there is a conflict between two obligations. We provide a definition of Deontic Argumentation Theory that account…

• The Existential Theory of Research: Why Discovery Is Hard

  Angshul Majumdar · 24 de abril de 2026

  Can scientific discovery be made arbitrarily easy by choosing the right representation, collecting enough data, and deploying sufficiently powerful algorithms? This paper argues that the answer is fundamentally negative. We introduce the Existential Theory of Research (ETR), a formal framework that …

• Selection, Reflection and Self-Refinement: Revisit Reasoning Tasks via a Causal Lens

  Yunlong Deng, Boyang Sun, Yan Li, Lingjing Kong, Zeyu Tang, Kun Zhang, Guangyi Chen · 30 de marzo de 2026

  Due to their inherent complexity, reasoning tasks have long been regarded as rigorous benchmarks for assessing the capabilities of machine learning models, especially large language models (LLMs). Although humans can solve these tasks with ease, existing models, even after extensive pre-training and…

• Towards Unifying Perceptual Reasoning and Logical Reasoning

  Hiroyuki Kido · 24 de febrero de 2026

  An increasing number of scientific experiments support the view of perception as Bayesian inference, which is rooted in Helmholtz's view of perception as unconscious inference. Recent study of logic presents a view of logical reasoning as Bayesian inference. In this paper, we give a simple probabili…

• Stable but Wrong: When More Data Degrades Scientific Conclusions

  Zhipeng Zhang, Kai Li · 6 de febrero de 2026

  Modern science increasingly relies on ever-growing observational datasets and automated inference pipelines, under the implicit belief that accumulating more data makes scientific conclusions more reliable. Here we show that this belief can fail in a fundamental and irreversible way. We identify a s…

• Epistemic Control and the Normativity of Machine Learning-Based Science

  Emanuele Ratti · 19 de enero de 2026

  The past few years have witnessed an increasing use of machine learning (ML) systems in science. Paul Humphreys has argued that, because of specific characteristics of ML systems, human scientists are pushed out of the loop of science. In this chapter, I investigate to what extent this is true. Firs…

• CSQL: Mapping Documents into Causal Databases

  Sridhar Mahadevan · 14 de enero de 2026

  We describe a novel system, CSQL, which automatically converts a collection of unstructured text documents into an SQL-queryable causal database (CDB). A CDB differs from a traditional DB: it is designed to answer "why'' questions via causal interventions and structured causal queries. CSQL builds o…

• Simulated Reasoning is Reasoning

  Hendrik Kempt, Alon Lavie · 6 de enero de 2026

  Reasoning has long been understood as a pathway between stages of understanding. Proper reasoning leads to understanding of a given subject. This reasoning was conceptualized as a process of understanding in a particular way, i.e., "symbolic reasoning". Foundational Models (FM) demonstrate that this…

• Deep Actor-Critics with Tight Risk Certificates

  Bahareh Tasdighi, Manuel Haussmann, Yi-Shan Wu, Andres R. Masegosa, Melih Kandemir · 27 de noviembre de 2025

  Deep actor-critic algorithms have reached a level where they influence everyday life. They are a driving force behind continual improvement of large language models through user feedback. However, their deployment in physical systems is not yet widely adopted, mainly because no validation scheme ful…

• Causality Without Causal Models

  Joseph Y. Halpern (Cornell University), Rafael Pass (Cornell University) · 27 de noviembre de 2025

  Perhaps the most prominent current definition of (actual) causality is due to Halpern and Pearl. It is defined using causal models (also known as structural equations models). We abstract the definition, extracting its key features, so that it can be applied to any other model where counterfactual…

• Causal Sufficiency and Necessity Improves Chain-of-Thought Reasoning

  Xiangning Yu, Zhuohan Wang, Linyi Yang, Haoxuan Li, Anjie Liu, Xiao Xue, Jun Wang, Mengyue Yang · 28 de octubre de 2025

  Chain-of-Thought (CoT) prompting plays an indispensable role in endowing large language models (LLMs) with complex reasoning capabilities. However, CoT currently faces two fundamental challenges: (1) Sufficiency, which ensures that the generated intermediate inference steps comprehensively cover and…

• Two Causally Related Needles in a Video Haystack

  Miaoyu Li, Qin Chao, Boyang Li · 27 de octubre de 2025