paper-with-me

홈 › Papers

Thoughts on Architecture

2023-06-23 · Paul S. Rosenbloom

The term architecture has evolved considerably from its original Greek roots and its application to buildings and computers to its more recent manifestation for minds. This article considers lessons from this history, in terms of a set of relevant distinctions introduced at each of these stages and a definition of architecture that spans all three, and a reconsideration of three key issues from cognitive architectures for architectures in general and cognitive architectures more particularly.

📄 PDF Abstract BibTeX arXiv:2306.13572

Code (0)

등록된 구현이 없습니다.

Similar Papers 제목 키워드 기반

Team of Thoughts: Efficient Test-time Scaling of Agentic Systems through Orchestrated Tool Calling

2026-02-18 · Jeffrey T. H. Wong, Zixi Zhang, Junyi Liu, Yiren Zhao arxiv

Existing Multi-Agent Systems (MAS) typically rely on homogeneous model configurations, failing to exploit the diverse expertise inherent in different post-trained architectures. We propose Team-of-Thoughts, a heterogeneo…

Mathematical ReasoningCode Generation

How Well Can Reasoning Models Identify and Recover from Unhelpful Thoughts?

2025-06-12 · Sohee Yang, Sang-Woo Lee, Nora Kassner, Daniela Gottesman 외

Recent reasoning models show the ability to reflect, backtrack, and self-validate their reasoning, which is crucial in spotting mistakes and arriving at accurate solutions. A natural question that arises is how effective…

Rapidly Deploying a Neural Search Engine for the COVID-19 Open Research Dataset: Preliminary Thoughts and Lessons Learned

2020-04-10 · Edwin Zhang, Nikhil Gupta, Rodrigo Nogueira, Kyunghyun Cho 외

We present the Neural Covidex, a search engine that exploits the latest neural ranking architectures to provide information access to the COVID-19 Open Research Dataset curated by the Allen Institute for AI. This web app…

Decision Making

MeTHanol: Modularized Thinking Language Models with Intermediate Layer Thinking, Decoding and Bootstrapping Reasoning

2024-09-18 · Ningyuan Xi, Xiaoyu Wang, Yetao Wu, Teng Chen 외

Large Language Model can reasonably understand and generate human expressions but may lack of thorough thinking and reasoning mechanisms. Recently there have been several studies which enhance the thinking ability of lan…

Language ModelingLanguage ModellingLarge Language Model

Latent Thinking Optimization: Your Latent Reasoning Language Model Secretly Encodes Reward Signals in Its Latent Thoughts

2025-09-30 · Hanwen Du, Yuxin Dong, Xia Ning arxiv

Large Language Models (LLMs) excel at problem solving by generating chain of thoughts in natural language, but such verbal thinking is computationally costly and prone to overthinking. A recent work instead proposes a la…