dr3s

Neuros Introduction

Developing an artificial reasoning system that operates without explicit symbols requires rethinking how AI perceives and interprets the world. Humans and animals seamlessly combine raw sensory perceptions – sight, sound, touch – to form abstract inferences, all via neural processes rather than discrete logical rules. Emulating this capability in AI promises more flexible and robust intelligence, free from the brittleness of predefined symbolic representations. Traditional symbolic AI systems demand hand-crafted knowledge structures and struggle to connect with raw data streams (e.g. images or audio) without extensive pre-processing. In contrast, connectionist approaches (neural networks) learn directly from data, offering a path to bridge low-level perception and high-level reasoning in one system ([A neural approach to relational reasoning - Google DeepMind](https://deepmind.google/discover/blog/a-neural-approach-to-relational-reasoning/#:~:text=flexibility%20and%20efficiency%20of

Cohen’s Agentic Conjecture: A Dual-Process Neuro-Symbolic Framework for Agentic AI

Abstract

This research introduces Cohen’s Agentic Conjecture (CAC), proposing that an artificial intelligence system integrating fast, neural heuristics (System 1) with slow, symbolic logic (System 2) through a dynamic gating mechanism can exhibit emergent agentic properties. These properties include context-aware decision-making, self-directed learning, robust reasoning, and reflective self-correction. Drawing inspiration from dual-process cognitive theories and neuro-symbolic AI paradigms, this work formalizes CAC, presents a comprehensive Python implementation, and validates the conjecture through empirical experiments. The findings demonstrate that CAC-enhanced systems outperform purely neural or purely symbolic counterparts in terms of accuracy, interpretability, and adaptability. This framework lays the groundwork for developing next-generation AI agents capable of autonomous, reliable, and

DeepSeek R1 Distill: Complete Tutorial for Deployment & Fine-Tuning

This guide shows how to deploy an uncensored DeepSeek R1 Distill model to Google Cloud Run with GPU support and how to perform a basic, functional fine-tuning process. The tutorial is split into:

Environment Setup
FastAPI Inference Server
Docker Configuration
Google Cloud Run Deployment
Fine-Tuning Pipeline (Cold Start, Reasoning RL, Data Collection, Final RL Phase)

	# SPARC Agentic Development Rules

	Core Philosophy

	1. Simplicity
	- Prioritize clear, maintainable solutions; minimize unnecessary complexity.

	2. Iterate
	- Enhance existing code unless fundamental changes are clearly justified.

	# Symbolic Representation of Prompt

	# Initialization: Define Universal State
	Ψ(t) ∈ H # Ψ(t): State vector in Hilbert space H

	# Field Configuration Space
	M = { (g, φ) \| g ∈ G, φ ∈ Φ } # G: Symmetry group, Φ: Field space
	μ : M → ℝ^+ # Measure on configuration space

	# Complexity Operator

	# ---- Base Node ----
	FROM mhart/alpine-node:10 AS base
	# install node
	RUN apk add --no-cache nodejs-current
	# set working directory
	WORKDIR /root/nextApp
	# copy project file
	COPY package.json .
	COPY tsconfig.server.json .
	COPY .npmrc .


	#
	# Slightly tighter CORS config for nginx
	#
	# A modification of https://gist.github.com/1064640/ to include a white-list of URLs
	#
	# Despite the W3C guidance suggesting that a list of origins can be passed as part of
	# Access-Control-Allow-Origin headers, several browsers (well, at least Firefox)
	# don't seem to play nicely with this.
	#