Research Reports

schema.org/Report — structured research notes with citations, knowledge graph annotations, and provider metadata.

Research is a first-class workflow in the knowledge layer. When the agent investigates a topic — comparing tools, evaluating architectures, gathering competitive intelligence — the output is a Report entity with a consistent internal structure.

Example

---
type: schema.org/Report
id: e5f6a7b8-9012-34ab-cdef-567890123def
permalink: research/agent-memory/act-r-decay-models
title: "ACT-R Memory Decay in Agent Architectures"
about: applying ACT-R cognitive decay models to AI agent memory systems
mode: adaptive
status: completed
providers:
  exa:
    tool_calls: 3
    model: exa-research-fast
tags:
  - research
  - memory
  - act-r
  - cognitive-science
---

# Instructions

## Context

We're designing a decay model for Attaché's memory system and need
to understand how ACT-R's base-level activation applies to agent
memory retrieval — particularly the power law of forgetting and
how frequency/recency interact.

## Questions

1. How does ACT-R's base-level activation formula work?
2. What optimizations exist for computing activation at scale?
3. How have other agent systems adapted ACT-R for memory decay?

# Report

## Base-Level Activation

ACT-R models declarative memory using base-level activation, where
each memory chunk's accessibility depends on how often and how
recently it's been accessed. The formula combines a logarithmic
sum of recency-weighted access events with a decay parameter,
producing a power law of forgetting that matches human recall data.

Anderson & Lebiere's work at Carnegie Mellon established that both
frequency and recency matter — a note accessed 50 times decays
much slower than one accessed once, even at the same age...

## Buffer Decay Extensions

Thomson, Bennati & Lebiere (2014) extended ACT-R's short-term
memory model with buffer decay, showing how spreading activation
provides implicit contextual information...

# Annotations

## Observations

- [finding] ACT-R base-level activation combines frequency and recency via power law #act-r #memory
- [finding] Optimized O(1) approximation avoids iterating over access history #performance
- [recommendation] Apply metabolic rate multipliers for different memory layers #architecture

## Relations

- informs [[projects/attache]]
- related_to [[research/agent-memory/ori-mnemos-review]]

## References

- [Anderson & Lebiere — The effect of memory decay on predictions from changing categories](http://act-r.psy.cmu.edu/?p=13636&post_type=publications)
- [Thomson et al. — Extending the Influence of Contextual Information in ACT-R using Buffer Decay](http://act-r.psy.cmu.edu/wordpress/wp-content/uploads/2015/09/CogSci-2014-Final-Extending-the-Influence-of-Contextual-Information-in-ACT-R-using-Buffer-Decay.pdf)
- [Ori-Mnemos — Agent memory with ACT-R and graph-aware extensions](https://github.com/aayoawoyemi/Ori-Mnemos)

Fields

Field	Type	Description
title	string	Report title (required)
about	string	Subject matter (Schema.org field)
mode	enum	deep_research, adaptive, or external
status	enum	draft, pending, completed, or failed
providers	Record	Provider metadata: tool calls, model, cost
tags	string[]	Classification tags

Document Structure

Research notes follow a consistent internal structure:

1. Instructions — context, scope, and specific questions (written during draft phase)
2. Report — the actual research output (written during execution)
3. Annotations — knowledge graph metadata:
   • Observations — structured annotations extracted from findings
   • Relations — links to related entities via [[wiki-links]]
   • References — source citations with URLs

Research Modes

Mode	How it works	Example providers
deep_research	Autonomous report generation — provider executes independently and returns a complete report	Exa Deep Research, Perplexity
adaptive	Agent-driven multi-tool exploration — the agent calls search, scrape, and documentation tools in sequence	Firecrawl, Exa search, GitHub CLI, Apollo
external	Import from external AI tools — user conducts research elsewhere and imports the results	Claude Deep Research, ChatGPT share links

Draft-First Workflow

Research follows a draft-first pattern where the plan is written to the knowledge base before execution:

1. Scope — Agent asks clarifying questions to refine the topic
2. Draft — Agent writes a note with status: draft containing instructions and questions
3. Review — User reviews, adds inline feedback
4. Execute — User selects provider/model, research runs with finalized instructions
5. Persist — Report, annotations, and references are written to the note

Provider Selection Guide

Query type	Recommended provider	Why
Semantic/conceptual questions	Exa Deep Search	Neural understanding, query expansion
Exact keywords, error codes	Firecrawl	Search operators, keyword matching
Known URL extraction	Firecrawl scrape	Best content extraction
People and organizations	Apollo	Structured business data
Library/API documentation	Ref	Optimized for docs
GitHub code and issues	GitHub CLI	Direct access

Folder Structure

knowledge/research/
├── ai-agents/
│   ├── coding-agent-taxonomy.md
│   └── agent-memory-patterns.md
├── mcp-tools/
│   └── search-quality-comparison.md
└── infrastructure/
    └── colima-vs-docker-desktop.md

Zod schema

export const ReportSchema = z.object({
  type: z.literal("schema.org/Report"),
  title: z.string(),
  about: z.string().optional(),
  mode: z.enum(["deep_research", "adaptive", "external"]).optional(),
  status: z.enum(["draft", "pending", "completed", "failed"]).optional(),
  providers: z.record(z.object({
    tool_calls: z.number(),
    model: z.string().optional(),
    cost: z.number().optional(),
  })).default({}),
  tags: z.array(z.string()).default([]),
});