hermes-agent/optional-skills/creative/concept-diagrams/examples/autonomous-llm-research-agent-flow.md

Autonomous LLM Research Agent Flow

A multi-section flowchart showing Karpathy's autoresearch framework: human-agent handoff, the autonomous experiment loop with keep/discard decision branching, and the modifiable training pipeline. Demonstrates loop-back arrows, convergent decision paths, and semantic color coding for outcomes.

Key Patterns Used

• Three-section layout: Setup row, main loop container, and detail container — each visually distinct
• Neutral dashed containers: Loop and training pipeline use var(--bg-secondary) fill with dashed borders to recede behind colored content nodes
• Decision branching with convergence: "val_bpb improved?" splits into Keep (green) and Discard (red), then both converge back to "Log to results.tsv"
• Loop-back arrow: Dashed path with rounded corners on the right side of the container showing infinite repetition
• Semantic color for outcomes: Green = improvement (keep), Red = no improvement (discard) — not arbitrary decoration
• Highlighted key step: "Run training" uses c-coral to visually distinguish the most important step from other c-teal actions
• Horizontal pipeline flow: Training details section uses left-to-right arrow-connected nodes (GPT → MuonAdamW → Evaluation)
• Footer metadata: Fixed constraints shown as subtle centered text below the pipeline nodes
• Legend row: Color key at the bottom explaining what each color means

Diagram

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  <!-- ========================================== -->
  <!-- SECTION 1: SETUP (Human → program.md → AI) -->
  <!-- ========================================== -->

  <text class="ts" x="40" y="30" text-anchor="start" opacity=".5">One-time setup</text>

  <!-- Human -->
  <g class="node c-gray">
    <rect x="60" y="42" width="140" height="56" rx="8" stroke-width="0.5"/>
    <text class="th" x="130" y="62" text-anchor="middle" dominant-baseline="central">Human</text>
    <text class="ts" x="130" y="82" text-anchor="middle" dominant-baseline="central">Researcher</text>
  </g>

  <!-- Arrow: Human → program.md -->
  <line x1="200" y1="70" x2="250" y2="70" class="arr" marker-end="url(#arrow)"/>

  <!-- program.md -->
  <g class="node c-gray">
    <rect x="250" y="42" width="180" height="56" rx="8" stroke-width="0.5"/>
    <text class="th" x="340" y="62" text-anchor="middle" dominant-baseline="central">program.md</text>
    <text class="ts" x="340" y="82" text-anchor="middle" dominant-baseline="central">Agent instructions</text>
  </g>

  <!-- Arrow: program.md → AI Agent -->
  <line x1="430" y1="70" x2="470" y2="70" class="arr" marker-end="url(#arrow)"/>

  <!-- AI Agent -->
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    <rect x="470" y="42" width="160" height="56" rx="8" stroke-width="0.5"/>
    <text class="th" x="550" y="62" text-anchor="middle" dominant-baseline="central">AI agent</text>
    <text class="ts" x="550" y="82" text-anchor="middle" dominant-baseline="central">Claude / Codex</text>
  </g>

  <!-- Arrow: Setup row → Loop (from program.md center down) -->
  <line x1="340" y1="98" x2="340" y2="142" class="arr" marker-end="url(#arrow)"/>

  <!-- ========================================== -->
  <!-- SECTION 2: AUTONOMOUS EXPERIMENT LOOP      -->
  <!-- ========================================== -->

  <!-- Loop container (neutral dashed) -->
  <g>
    <rect x="40" y="142" width="600" height="528" rx="16"
          stroke-width="1" stroke-dasharray="6 4"
          fill="var(--bg-secondary)" stroke="var(--border)"/>
    <text class="th" x="66" y="170">Autonomous experiment loop</text>
    <text class="ts" x="66" y="188">~12 experiments/hour — runs until manually stopped</text>
  </g>

  <!-- Step 1: Read code + past results -->
  <g class="node c-teal">
    <rect x="170" y="208" width="280" height="44" rx="8" stroke-width="0.5"/>
    <text class="th" x="310" y="230" text-anchor="middle" dominant-baseline="central">Read code + past results</text>
  </g>

  <!-- Arrow: S1 → S2 -->
  <line x1="310" y1="252" x2="310" y2="274" class="arr" marker-end="url(#arrow)"/>

  <!-- Step 2: Propose + edit train.py -->
  <g class="node c-teal">
    <rect x="170" y="274" width="280" height="56" rx="8" stroke-width="0.5"/>
    <text class="th" x="310" y="294" text-anchor="middle" dominant-baseline="central">Propose + edit train.py</text>
    <text class="ts" x="310" y="314" text-anchor="middle" dominant-baseline="central">Arch, optimizer, hyperparameters</text>
  </g>

  <!-- Arrow: S2 → S3 -->
  <line x1="310" y1="330" x2="310" y2="352" class="arr" marker-end="url(#arrow)"/>

  <!-- Step 3: Run training (highlighted — key step) -->
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    <rect x="170" y="352" width="280" height="56" rx="8" stroke-width="0.5"/>
    <text class="th" x="310" y="372" text-anchor="middle" dominant-baseline="central">Run training</text>
    <text class="ts" x="310" y="392" text-anchor="middle" dominant-baseline="central">uv run train.py (5 min budget)</text>
  </g>

  <!-- Arrow: S3 → S4 -->
  <line x1="310" y1="408" x2="310" y2="430" class="arr" marker-end="url(#arrow)"/>

  <!-- Step 4: Decision — val_bpb improved? -->
  <g class="node c-gray">
    <rect x="170" y="430" width="280" height="44" rx="8" stroke-width="0.5"/>
    <text class="th" x="310" y="452" text-anchor="middle" dominant-baseline="central">val_bpb improved?</text>
  </g>

  <!-- Decision arrows to Keep / Discard -->
  <line x1="240" y1="474" x2="175" y2="508" class="arr" marker-end="url(#arrow)"/>
  <line x1="380" y1="474" x2="445" y2="508" class="arr" marker-end="url(#arrow)"/>

  <!-- Decision labels -->
  <text class="ts" x="195" y="496" opacity=".6">yes</text>
  <text class="ts" x="416" y="496" opacity=".6">no</text>

  <!-- Keep — advance branch -->
  <g class="node c-green">
    <rect x="70" y="508" width="210" height="56" rx="8" stroke-width="0.5"/>
    <text class="th" x="175" y="528" text-anchor="middle" dominant-baseline="central">Keep</text>
    <text class="ts" x="175" y="548" text-anchor="middle" dominant-baseline="central">Advance git branch</text>
  </g>

  <!-- Discard — git reset -->
  <g class="node c-red">
    <rect x="340" y="508" width="210" height="56" rx="8" stroke-width="0.5"/>
    <text class="th" x="445" y="528" text-anchor="middle" dominant-baseline="central">Discard</text>
    <text class="ts" x="445" y="548" text-anchor="middle" dominant-baseline="central">Git reset to previous</text>
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  <!-- Converge arrows: Keep → Log, Discard → Log -->
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  <line x1="445" y1="564" x2="370" y2="590" class="arr" marker-end="url(#arrow)"/>

  <!-- Step 6: Log to results.tsv -->
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    <text class="th" x="310" y="612" text-anchor="middle" dominant-baseline="central">Log to results.tsv</text>
  </g>

  <!-- Loop-back arrow (dashed, right side) -->
  <path d="M 450 612 L 564 612 Q 576 612 576 600 L 576 242 Q 576 230 564 230 L 450 230"
        fill="none" class="arr" stroke-dasharray="4 3" marker-end="url(#arrow)"/>

  <!-- ========================================== -->
  <!-- SECTION 3: TRAINING PIPELINE DETAILS       -->
  <!-- ========================================== -->

  <!-- Connection arrow: Loop → Training details -->
  <line x1="310" y1="670" x2="310" y2="710" class="arr" marker-end="url(#arrow)"/>

  <!-- Training container (neutral dashed) -->
  <g>
    <rect x="40" y="710" width="600" height="170" rx="16"
          stroke-width="1" stroke-dasharray="6 4"
          fill="var(--bg-secondary)" stroke="var(--border)"/>
    <text class="th" x="66" y="738">train.py — modifiable training pipeline</text>
    <text class="ts" x="66" y="756">Runs during each training step — single GPU, single file</text>
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  <!-- GPT model -->
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    <rect x="70" y="774" width="155" height="56" rx="8" stroke-width="0.5"/>
    <text class="th" x="147" y="794" text-anchor="middle" dominant-baseline="central">GPT model</text>
    <text class="ts" x="147" y="814" text-anchor="middle" dominant-baseline="central">RoPE, FlashAttn3</text>
  </g>

  <!-- Arrow: GPT → MuonAdamW -->
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  <!-- MuonAdamW optimizer -->
  <g class="node c-coral">
    <rect x="260" y="774" width="155" height="56" rx="8" stroke-width="0.5"/>
    <text class="th" x="337" y="794" text-anchor="middle" dominant-baseline="central">MuonAdamW</text>
    <text class="ts" x="337" y="814" text-anchor="middle" dominant-baseline="central">Hybrid optimizer</text>
  </g>

  <!-- Arrow: MuonAdamW → Evaluation -->
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  <!-- Evaluation -->
  <g class="node c-amber">
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    <text class="th" x="527" y="794" text-anchor="middle" dominant-baseline="central">Evaluation</text>
    <text class="ts" x="527" y="814" text-anchor="middle" dominant-baseline="central">val_bpb metric</text>
  </g>

  <!-- Footer: fixed constraints -->
  <text class="ts" x="340" y="856" text-anchor="middle" opacity=".5">climbmix-400b data · 8K BPE vocab · 300s budget · 2048 context</text>

  <!-- ========================================== -->
  <!-- LEGEND                                     -->
  <!-- ========================================== -->

  <g class="c-teal"><rect x="40" y="890" width="14" height="14" rx="3" stroke-width="0.5"/></g>
  <text class="ts" x="62" y="902">Agent actions</text>

  <g class="c-coral"><rect x="170" y="890" width="14" height="14" rx="3" stroke-width="0.5"/></g>
  <text class="ts" x="192" y="902">Training run</text>

  <g class="c-green"><rect x="300" y="890" width="14" height="14" rx="3" stroke-width="0.5"/></g>
  <text class="ts" x="322" y="902">Improvement</text>

  <g class="c-red"><rect x="430" y="890" width="14" height="14" rx="3" stroke-width="0.5"/></g>
  <text class="ts" x="452" y="902">No improvement</text>

</svg>

Color Assignments

Element	Color	Reason
Human, program.md	c-gray	Neutral setup / input nodes
AI agent	c-purple	The active intelligent actor
Loop action steps	c-teal	Agent's analytical/editing actions
Run training	c-coral	Highlighted key step — the 5-min training run
Decision check	c-gray	Neutral evaluation checkpoint
Keep (improved)	c-green	Semantic success — val_bpb decreased
Discard (not improved)	c-red	Semantic failure — no improvement
Training pipeline nodes	c-coral	Training infrastructure components
Evaluation node	c-amber	Distinct from training — measurement/metric role
Containers	Neutral (dashed)	Subtle grouping that recedes behind content

Layout Notes

• ViewBox: 680×920 (standard width, tall for 3 sections)
• Three sections: Setup row (y=30–98), loop container (y=142–670), training details (y=710–880)
• Container style: Dashed border (stroke-dasharray="6 4"), neutral fill (var(--bg-secondary)), stroke-width="1" — not colored, so inner nodes pop
• Loop-back arrow: Dashed <path> with quadratic curves (Q) at corners for smooth rounded turns, running up the right side of the loop container from "Log" back to "Read code"
• Decision pattern: Single question node ("val_bpb improved?") with diagonal arrows to Keep/Discard, then convergent diagonal arrows back to "Log to results.tsv"
• Decision labels: "yes"/"no" labels placed along the diagonal arrows with opacity=".6" to stay subtle
• Key step highlight: "Run training" uses c-coral while surrounding steps use c-teal, drawing the eye to the most important step
• Horizontal sub-flow: Training pipeline uses left-to-right arrow-connected nodes (GPT model → MuonAdamW → Evaluation)
• Footer metadata: Fixed constraints (data, vocab, budget, context) shown as a single centered ts text line with opacity=".5"
• Legend: Four color swatches at the bottom explaining the semantic meaning of each color used