trace

name	trace
description	Trace a single concept's paper lineage in detail. Use when user says "trace [concept]", "where did [concept] come from", "[concept] paper history", "[concept] lineage", or wants deep genealogy of one specific concept.
version	0.1.0

Trace: Concept Lineage Deep Dive

Trace the complete research genealogy of a single concept. Answers "Where did this come from? How did it evolve?"

When to Use

• "Trace [concept]"
• "Where did [concept] come from?"
• "Show me [concept]'s paper history"
• "History of Attention mechanism"
• Understanding one concept's full evolution

When NOT to Use

• Learning multiple concepts → use deep-dive
• General domain exploration → use domain-vocab
• Latest research only → use frontier

Core Value

When you dig deep into one concept, you see the entire field.

Trace provides vertical depth (one concept, complete history) vs. deep-dive's horizontal breadth (many concepts, key papers).

Workflow

Phase 1: Concept Identification

Input: Concept name (optionally with domain context)

Actions:

1. Confirm the concept and its domain
2. Identify potential ambiguity (e.g., "Attention" in NLP vs. psychology)
3. Prime context with domain-vocab tokens (light version)

Output:

concept: "Attention Mechanism"
domain: "NLP / Deep Learning"
disambiguation: "Neural attention for sequence models, not cognitive attention"
search_keywords: ["attention", "neural machine translation", "sequence to sequence"]

Phase 2: Root Paper Discovery

Objective: Find the seminal paper that introduced this concept

Actions:

1. Search Semantic Scholar + arXiv for concept
2. Filter by:
   • High citation count
   • Early publication date
   • Title/abstract directly mentions concept introduction
3. Verify with heuristics:
   • Does the abstract say "we propose/introduce"?
   • Is it widely cited as origin?
4. If multiple candidates, ask user to confirm

Decision Matrix:

Signal	Indicates Root	Score
"We propose/introduce X" in abstract	Strong	+3
Published before all high-citation papers on topic	Strong	+3
1000+ citations	Likely influential	+2
Cited by survey papers as origin	Strong	+2
Author is known pioneer	Supporting	+1

Output:

root_paper:
  id: "semantic_scholar_id"
  title: "Neural Machine Translation by Jointly Learning to Align and Translate"
  authors: ["Bahdanau", "Cho", "Bengio"]
  year: 2014
  venue: "ICLR 2015"
  citations: 45000+
  key_contribution: "Introduced attention mechanism for NMT"
  abstract: "..."
  url: "https://arxiv.org/abs/1409.0473"

Phase 3: Ancestry Extraction (Parents)

Objective: What did the root paper build upon?

Actions:

1. Get references from root paper
2. Identify influential predecessors:
   • Papers cited multiple times in root
   • Papers providing key techniques used
   • Conceptual foundations
3. Categorize ancestors:
   • Direct parents: Immediate building blocks
   • Grandparents: Foundational work
   • Parallel influences: Contemporary related work

Output:

ancestry:
  direct_parents:
    - title: "Sequence to Sequence Learning with Neural Networks"
      authors: ["Sutskever", "Vinyals", "Le"]
      year: 2014
      relationship: "Seq2Seq framework that attention extends"

    - title: "Learning Phrase Representations using RNN Encoder-Decoder"
      authors: ["Cho", "van Merrienboer", "et al"]
      year: 2014
      relationship: "GRU architecture used in attention model"

  grandparents:
    - title: "Long Short-Term Memory"
      authors: ["Hochreiter", "Schmidhuber"]
      year: 1997
      relationship: "Foundational recurrent architecture"

  parallel_influences:
    - title: "Neural Turing Machines"
      authors: ["Graves", "Wayne", "Danihelka"]
      year: 2014
      relationship: "Independent attention-like mechanism"

Phase 4: Descendant Extraction (Children)

Objective: How did the concept evolve after introduction?

Actions:

1. Get papers citing root paper
2. Filter for high-impact descendants (citations > threshold)
3. Identify evolution branches:
   • Direct extensions: Improve original mechanism
   • Applications: Apply to new domains
   • Paradigm shifts: Fundamental reimagining

Output:

descendants:
  direct_extensions:
    - title: "Effective Approaches to Attention-based NMT"
      authors: ["Luong", "Pham", "Manning"]
      year: 2015
      contribution: "Local vs global attention variants"
      citations: 12000+

  paradigm_shifts:
    - title: "Attention Is All You Need"
      authors: ["Vaswani", "et al"]
      year: 2017
      contribution: "Self-attention, eliminated RNNs entirely"
      citations: 100000+
      spawned: ["BERT", "GPT", "Vision Transformer"]

  applications:
    - title: "Show, Attend and Tell"
      authors: ["Xu", "et al"]
      year: 2015
      contribution: "Attention for image captioning"
      domain: "Computer Vision"

Phase 5: Timeline Construction

Objective: Visualize the complete evolution

Output:

timeline
    title Attention Mechanism Evolution
    1997 : LSTM (Hochreiter)
         : Foundation for sequence modeling
    2014 : Seq2Seq (Sutskever)
         : Encoder-decoder framework
    2014 : Bahdanau Attention ⭐
         : ROOT - Attention mechanism introduced
    2015 : Luong Attention
         : Local/global variants
    2015 : Show, Attend, Tell
         : Attention for vision
    2017 : Transformer ⭐⭐
         : Self-attention revolution
    2018 : BERT, GPT
         : Pretrained transformers
    2020 : Vision Transformer
         : Attention conquers CV
    2023 : Modern LLMs
         : Attention at scale

Phase 6: Insight Synthesis

Objective: Extract learnings from the lineage

Output:

## Key Insights from Tracing "Attention Mechanism"

### Origin Story
Attention emerged from a practical problem: RNN encoder-decoder models
struggled with long sequences. Bahdanau's insight was to let the decoder
"look back" at relevant parts of the input.

### Evolution Pattern
1. **Problem → Solution**: Long-range dependency problem → Attention
2. **Generalization**: NMT-specific → General sequence mechanism
3. **Paradigm Shift**: Auxiliary mechanism → Primary architecture (Transformer)
4. **Cross-Domain Transfer**: NLP → Vision → Multimodal

### Branching Points
- 2015: Luong's local attention (efficiency branch)
- 2017: Transformer (self-attention revolution)
- 2020: Vision Transformer (modality transfer)

### Key Researchers
- Dzmitry Bahdanau: Original attention
- Ashish Vaswani: Transformer architecture
- Alexey Dosovitskiy: Vision Transformer

### If You Learn This
Understanding attention's evolution helps you:
- See why Transformers replaced RNNs
- Understand architectural decisions in modern LLMs
- Predict where attention might go next

Output Formats

Format A: Lineage Report (Default)

Complete markdown report with all phases.

Format B: Visual Tree

                    [LSTM 1997]
                         │
                    [Seq2Seq 2014]
                         │
              ┌──────────┴──────────┐
              │                     │
    [Bahdanau Attention 2014] ⭐    [Neural Turing Machines]
              │
    ┌─────────┼─────────┐
    │         │         │
[Luong   [Show,Attend] [Pointer
 2015]    Tell 2015]    Networks]
    │
    └─────────────────┐
                      │
            [Transformer 2017] ⭐⭐
                      │
         ┌────────────┼────────────┐
         │            │            │
      [BERT]       [GPT]      [ViT 2020]

Format C: Obsidian Canvas

Visual representation with clickable paper nodes.

Format D: Timeline Only

Compact timeline view for quick reference.

Example Session

Input: "Trace Transformer architecture"

Output Summary:

# Transformer Lineage

## Root Paper
"Attention Is All You Need" (Vaswani et al., 2017)
- Google Brain / Google Research
- 100,000+ citations
- Introduced: Self-attention, multi-head attention, positional encoding

## Ancestry
├── Bahdanau Attention (2014) - Attention mechanism
├── Seq2Seq (2014) - Encoder-decoder framework
├── Layer Normalization (2016) - Training stability
└── Residual Connections (2015) - Deep network training

## Key Descendants
├── BERT (2018) - Bidirectional pretraining
├── GPT (2018) - Autoregressive pretraining
├── GPT-2/3/4 (2019-2023) - Scale revolution
├── Vision Transformer (2020) - CV application
└── Multimodal Models (2021+) - Cross-modal attention

## Timeline: 7 years of dominance
2017 → 2024: From NMT improvement to foundation of modern AI

Depth Levels

Level	Ancestry Depth	Descendant Depth	Papers
Quick	Parents only	Top 5 children	~10
Standard	Grandparents	2 generations	~25
Exhaustive	3 generations	3 generations	~50+

Default: Standard

Error Handling

Situation	Recovery
Multiple possible roots	Present options, ask user
Concept too recent (< 2 years)	Note as "emerging", limited lineage
Concept from practice (not academia)	Note origin, trace related academic work
Too many descendants (1000+)	Filter by citations, ask for sub-branch focus