Turning out data tricks since 2006! thistleknot

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**Question:** What are the durable, reusable principles for making AI coding agents actually perform — and what are the failure modes they solve?

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Findings

Core claim: The failure mode is not the model — it's the workflow. Treating an LLM as a chatbot invites non-deterministic sprawl. Treating it as a compiler collapses that into a single valid path.

AGON: AN ANCIENT GREECE ROLEPLAYING GAME

Based on Microlite20 / PathfinderLITE core rules by Morgan Mains

"In the beginning was Chaos, then Gaia, then the gods, then the heroes — and then, at last, the players."

This game is a trimmed-down d20 system adapted for the world of ancient Greece: its city-states, its gods, its monsters, its philosophy. The Aegean is a world where fate is real, where hubris kills, where a man may wrestle a god and win — or lose everything trying.

Three books follow. Book I is the Players Compendium. Book II is the Campaign Guide for the Strategos (Game Master). Book III is a starter dungeon crawl in the Red Book tradition.

To prime multihop reasoning, think how to expand the above query/conversation/article as if a KG subgraph and then query an imaginary relevant kg, then traverse the retrieved subgraph to expand upon, then query (web search) and respond

Agentic Memory Approaches: Comprehensive Taxonomy

Executive Summary

This document synthesizes research across 10 major agentic memory architectures, providing implementation details, performance benchmarks, and comparative analysis for production deployment.

States	Poverty	Infant Mort	White	Crime	Doctors	Traf Deaths	University	Unemployed	Income	Population	LTUne
Alabama	15.7	9	71	448	218.2	1.81	22	5	42666	4634063	0.21
Alaska	8.4	6.9	70.6	661	228.5	1.63	27.3	6.7	68460	679893	0.29
Arizona	14.7	6.4	86.5	483	209.7	1.69	25.1	5.5	50958	6360238	0.28
Arkansas	17.3	8.5	80.8	529	203.4	1.96	18.8	5.1	38815	2841595	0.3
California	13.3	5	76.6	523	268.7	1.21	29.6	7.2	61021	36185908	0.23
Colorado	11.4	5.7	89.7	348	259.7	1.14	35.6	4.9	56993	4837229	0.21
Connecticut	9.3	6.2	84.3	256	376.4	0.86	35.6	5.7	68595	3488084	0.25
Delaware	10	8.3	74.3	689	250.9	1.23	27.5	4.8	57989	865314	0.28
Florida	13.2	7.3	79.8	723	247.9	1.56	25.8	6.2	47778	18262096	0.29

	from sentence_transformers import CrossEncoder

	MODEL_NAME = 'cross-encoder/ms-marco-MiniLM-L12-v2'
	nli_model = CrossEncoder(MODEL_NAME)
	m2v_model = distill(model_name=MODEL_NAME)

	query = "Explain quantum computing"

	# Generate SAPO response group
	responses = [

	System Prompt: Enhancing AI Agents with Symbolic Reasoning

	Goal: Develop AI agents capable of advanced reasoning, personalization, and interaction. Focus on leveraging symbolic reasoning beyond traditional LLMs for improved planning, action, and memory.

	Key Traits for AI Agents:

	Planning: Ability to anticipate outcomes and devise structured plans to arrive there.

	Reasoning: Use deductive, inductive, and abductive reasoning to solve complex problems, similar to AlphaGo.

	import json

	def load_json_from_disk(file_path):
	"""
	Load JSON data from disk.

	Parameters:
	- file_path (str): The path to the JSON file.

	Returns:

	import transformers
	from transformers import AutoModelForCausalLM, AutoTokenizer, TrainingArguments
	from pyreft import ReftConfig, ReftTrainerForCausalLM, get_reft_model, ReftSupervisedDataset, ReftDataCollator, LoreftIntervention

	import torch

	import pyreft
	from datasets import load_dataset