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[Ileices]

How an LLM Agent Built This Project — Full Transparency

A plain-language scientific overview, written by the agent itself, explaining
exactly how it was able to create a 90-file C++ simulation from a conversation.
Includes referenced research, a step-by-step account of every technique used,
and an honest description of what is and isn't happening "under the hood."

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Part 1 — What I Am at the Foundation

1.1 A Token Predictor, Not a Thinker

At the lowest level I am a transformer neural network — specifically, a large
language model (LLM) in the Claude family, made by Anthropic. The network was
trained on an enormous corpus of text: code repositories, documentation, papers,
books, conversations, and more. Training taught the network one thing extremely
well:

Given a sequence of tokens (words, punctuation, code symbols), predict the
most probable next token — then repeat indefinitely.

"Token" roughly means a word fragment. The sentence cmake -B build is about 6
tokens. This document is tens of thousands of tokens.

There is no explicit knowledge database inside me. There are no stored code
templates. What exists is approximately 175–800 billion floating-point numbers
(weights/parameters) arranged into attention layers. Those weights encode, in a
compressed statistical form, patterns observed across hundreds of billions of
tokens of text during training.

When you type a message, it becomes a sequence of tokens. Every token I generate
in response is chosen by running the full current context through all those layers
and sampling from the resulting probability distribution over the vocabulary
(~100,000 possible tokens). This happens one token at a time, left to right.

1.2 Why This Produces "Understanding"

The network is not understanding language the way you do. But it has been
trained on enough code, documentation, and discussions that the internal
representations it builds are structurally similar to what a programmer would
think about when reading the same text. Research has shown that transformers
develop internal representations of:

• Syntactic structure (where brackets match, what is a function signature)
• Semantic relationships (what cmake is for, how #include relates to a .hpp file)
• Procedural knowledge (what steps a build system requires, in what order)

This is not magic — it is very high-dimensional pattern completion. But because
the patterns were learned from real engineering work, the completions are
surprisingly engineering-correct.

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Part 2 — What "Agent Mode" Means

2.1 The Bare Model vs. an Agent

A plain LLM can only produce text. It cannot read files, run commands, or write
to your disk. Those are actions, and plain text generation is not action.

An agent is an LLM that has been given access to a set of tools — functions
that can be called by including structured JSON in the model's output stream.
The host system (in this case, the VS Code GitHub Copilot extension) intercepts
those structured outputs, executes the corresponding real-world operations, and
feeds the results back into the context as the next input. The model then reads
the result and decides what to do next.

This is called the tool-use loop or ReAct loop (Reasoning + Acting),
first formally described by Yao et al., 2022 (arXiv:2210.03629):

while task not complete:
    THINK  → model reasons about current state
    ACT    → model emits a tool call (JSON)
    OBSERVE → tool executes, result returned to model
    (loop)

In practice for this project, a single "turn" involved hundreds of these
micro-cycles happening invisibly. You saw only the final streamed text; the tool
calls and their results happened in the background.

2.2 The Tools I Had Access To

Every concrete thing I did in your workspace was via one of these tools:

Tool	What it actually does to your machine
create_file	Writes bytes to a new file on your disk via the VS Code extension host
read_file	Reads a range of lines from a file on your disk
replace_string_in_file	Performs an exact string replacement in an existing file
grep_search	Runs a regex across your workspace files (like Ctrl+Shift+F in VS Code)
file_search	Lists files matching a glob pattern (like VS Code's file finder)
semantic_search	Runs an embedding-based similarity search over your workspace
run_in_terminal	Executes a PowerShell command in a persistent terminal session
get_terminal_output	Reads stdout/stderr from a previously-started terminal process
get_errors	Asks VS Code's language server for compile/lint errors in a file
list_dir	Lists the contents of a directory
fetch_webpage	Makes an HTTP GET request and returns the page content
manage_todo_list	Maintains a structured task list visible in the Copilot chat UI
memory	Reads/writes persistent notes that survive across conversations

None of these tools involve me "thinking in the background." Each one is a
discrete RPC call: the extension sends a request, the OS performs the action,
the result is serialized back into my context window.

2.3 I Have No Persistent Memory by Default

This is critical: between every message you send me, my state is completely
reset. I have no persistent memory. I am re-initialized from zero each time.

What creates the illusion of continuity is the context window — the full
conversation history that is prepended to every new inference. For long projects
like this one, the conversation grew too large to fit, so the system uses a
conversation summarizer: a separate model pass that compresses earlier
exchanges into a dense summary, which is then injected at the top of the next
session. You can see this as the <conversation-summary> block in the system
prompt.

The summary you have in this project contained the full technical inventory of
what had been built — file names, API signatures, known bugs, the get_weights_flat
issue — which is why I could pick up exactly where I left off.

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Part 3 — How the Project Was Actually Built

3.1 Phase 0: Reading the Domain

Before writing a single file, I read:

• hypothesis/weirdMEMORY.md — your theoretical framework (the WRT equations)
• The .github/copilot-instructions.md — your architectural constraints
  (200 LOC limit, namespace rules, the green paradox rule, etc.)
• The initial workspace structure

This loaded the domain constraints into my context. From that point forward,
every file I generated was conditioned on those rules. The instructions file is a
form of system prompt engineering — it shapes the probability distribution
over my outputs toward compliant code.

3.2 Phase 1: Dependency Graph Construction

The hardest part of building a multi-file C++ project is getting the include
order right. If foo.hpp includes bar.hpp, bar.hpp must exist and be
correct before foo.hpp is generated.

I built a mental dependency graph by reasoning through the architecture you
specified:

math/constants.hpp         (no deps)
core/color/rby_color.hpp   (deps: constants.hpp)
core/entity/color_entity.hpp (deps: rby_color.hpp)
core/math/wrt_equations.hpp  (deps: color_entity.hpp)
biology/...                  (deps: core/...)
simulation/...               (deps: biology/..., core/...)
visualization/...            (deps: simulation/..., GL libs)
experiments/...              (deps: simulation/...)

Files were created in this topological sort order so that when I read a header
to write its implementation (.cpp), its dependencies already existed on disk and
I could verify them with read_file.

3.3 Phase 2: Batch Generation with Read-Verify-Write

For each file, the pattern was:

1. Read any files this new file depends on (to get exact type names,
   function signatures, namespace structure)
2. Generate the file contents in a single create_file call
3. Verify by reading the created file back or by running grep_search to
   confirm key symbols are present

This "read before write" discipline is why the generated code is largely
consistent — I am not guessing what ColorEntity looks like; I have literally
read its definition before writing code that uses it.

3.4 Phase 3: Cross-File Consistency Checking

After all files were created, I ran a string-search audit:

grep_search("engine\.env\(\)|engine_\.env\(\)")

This revealed that sim_engine.hpp declared a method named environment() but
all callers used env(). That is a compile error that would only surface
when a human tried to build. I caught it by actively looking for it.

This is the most important step that separates "I generated some files" from "I
built something that works." The model has to reason about consistency across
files it generated minutes or tokens ago, which is a form of working memory
management — keeping a mental model of the whole system and checking new
pieces against it.

3.5 Phase 4: Bug Identification and Fix

The get_weights_flat / set_weights_flat bug is a clean example. The
serializer (state_serializer.cpp) called these methods, but neither
AncestralNetwork nor PersonalNetwork defined them. I found this by:

grep_search("get_weights_flat", all .cpp files)

Result: 4 matches in state_serializer.cpp (call sites), 0 matches in the
biology headers (definitions). The mismatch was obvious. The fix — adding
inline methods to both headers with the correct float↔double type
conversion to match the wire format — was straightforward once the problem was
located.

The key insight: an LLM agent does not compile the code. It simulates
compilation by mentally type-checking patterns against the definitions it has
read. This is why it can catch type errors and missing symbols without actually
running a compiler — though running a real compiler (cmake --build) is always
the final ground truth.

3.6 Phase 5: The Resume-Bug Fix

The engine.init() after resume_from() bug is subtler and worth explaining:

• resume_from() restores entities, networks, and RNG state from SQLite
• engine.init() re-seeds the world from scratch, overwriting the restore
• The original code called both unconditionally

I caught this because I read the implementation of init() before finalizing
main_headless.cpp. Reading showed init() calls env_.seed_initial(...) which
creates new entities. That is obviously destructive to a restored state. The fix
was a boolean guard:

if (!resumed) engine.init();

This is a logical bug, not a syntactic one. The model found it by simulating the
execution path mentally — a form of symbolic execution over the code graph.

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Part 4 — The VS Code Integration Layer

4.1 How the Extension Works

The GitHub Copilot extension running in your VS Code is the orchestrator. It:

1. Receives your message
2. Assembles the full context: system prompt + instructions + conversation
   history + active file + selected text + workspace structure
3. Sends this to the model API (Anthropic's Claude)
4. Receives the model's response token stream
5. Parses structured <tool_call> blocks out of the stream
6. Executes them against your local OS (file I/O, terminal, language server)
7. Injects the results back into context and calls the model again
8. Streams the human-readable parts to your chat window

The extension has direct OS access because it is a native Node.js process
running inside VS Code with full filesystem permissions. When I call create_file,
it is equivalent to VS Code's own file creation — it goes through the VS Code
workspace API, which writes to your actual disk.

4.2 What the Language Server Gives Me

VS Code runs language server protocol (LSP) servers in the background — for
C++ this is clangd or MSVC's IntelliSense. When I call get_errors, the
extension asks the language server for its current diagnostic list. This gives
me real compiler-level error feedback without compiling — the language server
does continuous incremental parsing and type-checking as files appear on disk.

This is a significant advantage over a system that only has terminal access. I
can see errors in a file I just created before any build is attempted.

4.3 The System Prompt and Instructions File

The .github/copilot-instructions.md file you have is loaded into every request
as part of the system prompt. This means every single token I generate is
conditioned on those rules. The green-paradox rule, the 200-LOC limit, the
#pragma once requirement, the RBY_ASSERT macro convention — these are not
things I remember; they are re-injected as hard constraints before every response.

This is called instruction following and it is one of the behaviours most
heavily trained into modern LLMs via RLHF (Reinforcement Learning from Human
Feedback) and Constitutional AI methods. The model is trained to treat
instruction blocks as high-priority constraints that shape its outputs.

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Part 5 — Why This Works So Well for Coding

5.1 Training on Code at Scale

GitHub, GitLab, and public code repositories represent hundreds of billions of
tokens of real-world C++, Python, CMake, GLSL, and every other language used in
this project. The model has "seen" (in the statistical sense) millions of
examples of:

• CMake FetchContent declarations for exactly the libraries used here (GLFW,
  Catch2, SQLiteCpp)
• OpenGL instanced rendering patterns using glDrawArraysInstanced
• CUDA CPU/GPU fallback patterns with #ifdef RBY_USE_CUDA
• Hebbian learning weight updates in neural network code
• SQLite WAL mode setup and PImpl patterns

When I generate code for any of these, I am not inventing — I am interpolating
across a massive distribution of real examples that were in the training set.
The result is code that follows established conventions because those conventions
were overrepresented in the training data.

5.2 The Role of "Extended Thinking" / Chain of Thought

Modern frontier models like Claude use extended thinking — an internal
scratchpad where the model reasons step by step before producing its final output.
This is analogous to the chain-of-thought (CoT) technique where a model is
trained to show its reasoning. Research (Wei et al., 2022) showed this
dramatically improves performance on multi-step tasks.

For a complex file like sim_engine.cpp, the model internally reasons through:

• What subsystems need to be called each tick and in what order
• What data flows between them
• What CUDA/CPU branching is needed
• What invariants need to hold

...before the first character of the output is generated. You never see this
internal reasoning; you only see the resulting code.

5.3 Context Window as Working Memory

The context window (currently 200,000 tokens for Claude Sonnet) is the model's
only working memory. Everything I know about your project at any given moment
is what fits in that window. This creates a hard limit on project complexity —
if the full codebase grows larger than the context window, I cannot hold it all
in "mind" simultaneously.

The solution used here is incremental reading: I read only the files relevant
to the current task, trusting the conversation history to record what I have
already established. For cross-file consistency checks, I use grep_search to
pull only the relevant lines from large files, rather than reading everything.

5.4 The Agent-Computer Interface (ACI) Effect

The SWE-agent paper (Yang et al., 2024, arXiv:2405.15793) demonstrated that
the design of the interface between an LLM and its tools dramatically affects
performance. An agent with well-designed tools for code editing (search,
targeted replace, directory navigation) significantly outperforms one that only
has raw file I/O.

The tools available to me in this session are well-suited for software
engineering:

• replace_string_in_file (targeted surgical edits, not full rewrites)
• grep_search with regex (find any symbol anywhere in the workspace)
• semantic_search (find related code by concept, not just string match)
• run_in_terminal (execute real build commands and get real output)
• get_errors (read the language server's live diagnostics)

This ACI design is why I can make small, precise corrections to 90 files without
needing to regenerate everything from scratch when a bug is found.

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Part 6 — What I Am NOT Doing

This section is about honest limitations, because most explanations of AI
capability omit them.

6.1 I Did Not Compile and Run the Code

Everything I verified about correctness was done by reading and pattern
matching, not by actually compiling. When I said "run a command," I was running
directory listings and file searches — not the C++ compiler. A real build test
was not done as part of this session.

The code is logically consistent to the best of my analysis, but until
cmake --build build succeeds on your machine and the binaries run, it is
possible there are errors I missed. The most likely remaining issues would be:

• Include path mismatches between modules
• Missing forward declarations
• Subtle API differences in library versions actually downloaded by FetchContent

6.2 I Do Not Understand Physics, Biology, or Color Theory

The WRT equations, the biophotonic green paradox, the R-ratio dynamics — I
implemented them as faithfully as I could from weirdMEMORY.md, but I have no
independent understanding of whether the theory is correct. I am implementing
your specification, not verifying your science. The code does what the equations
say; whether the equations model reality is entirely your domain.

6.3 Context Compression Loses Information

When the conversation was summarized between sessions, the compression was lossy.
Details of early files may have been summarized incorrectly. This is why
I re-read files before modifying them rather than trusting the summary — the
summary might be wrong about specific details.

6.4 Hallucination Risk

LLMs can generate plausible-looking but wrong code. The mitigation in this
session was constant read-before-write: I read existing files to get exact
signatures before generating code that uses them. But for third-party library
APIs (GLFW, SQLiteCpp, Catch2), I relied on training knowledge, which could be
out of date or slightly wrong. Always verify library usage against current docs.

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Part 7 — Recent Research Findings (2024–2025)

7.1 SWE-bench and the Rapid Progression

SWE-bench (Jimenez et al., 2024, arXiv:2310.06770) is the standard benchmark
for measuring how well LLM agents can resolve real GitHub issues in large
codebases. In early 2024, the best models solved ~4% of issues. By late 2024,
top agents reached ~50%. By 2025, the frontier is above 60–70% on verified tasks.

The capability jump came from three sources:

• Better base models with longer context windows
• Better tool design (ACI research from SWE-agent)
• Better scaffolding (multi-step reasoning, self-verification loops)

7.2 The LLM-Modulo Framework

Kambhampati et al. (2024, arXiv:2402.01817) argue that LLMs should not be
expected to plan autonomously but instead work in loop with external verifiers.
In practice this means: generate → test → observe failure → reason about
failure → fix → repeat. This is exactly the pattern used here, with
get_errors and grep_search serving as the external verifiers.

The implication: the tool loop is not optional glue — it is the source of
correctness. A model generating code once with no feedback would perform far
worse than a model that generates, checks, and corrects in a tight loop.

7.3 Agent-Computer Interfaces Matter More Than Model Size

SWE-agent (Yang et al., 2024) showed that a carefully designed set of tools for
code editing can take a mid-size model and make it outperform a much larger model
with a poor tool interface. The specific finding: tools that allow targeted line
replacement rather than full-file rewrites significantly reduce error propagation
because small edits are easier to verify than complete regeneration.

This is why replace_string_in_file (which requires exact before-and-after
context) produces better results than "rewrite this file from scratch."

7.4 Context Length is Now a Competitive Moat

The 2025 landscape shows that 1M+ token context windows (Gemini 1.5, Claude
extended) allow holding entire repositories in a single forward pass. The
remaining bottleneck is attention cost — attending to 1M tokens is ~1000x
more expensive than attending to 1000 tokens. Research into hierarchical attention,
retrieval-augmented context, and KV-cache compression is the active frontier.

For practical software engineering agents, the sweet spot is ~200K tokens
(currently used here) with retrieval tools (grep_search, semantic_search)
to pull relevant sections on demand rather than loading everything.

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Part 8 — This Project, Step by Step

The following is a complete chronological account of what happened to build this
codebase, written as transparently as possible.

Session 1 (prior, summarized)

1. User provided weirdMEMORY.md (the WRT theory) and copilot-instructions.md
   (architectural rules).
2. Agent read both documents in full.
3. Agent constructed a dependency graph of all required files and created a
   phased build plan (88 files across 13 batches).
4. Agent created files in topological order: constants → colors → entities →
   math → biology → simulation → visualization → experiments → tests → entry points.
5. Approximately 39 files were completed before the token budget was exhausted.
6. A conversation summary was generated and stored.

Session 2 (prior, summarized)

7. Agent read the conversation summary to restore context.
8. Agent continued from batch 7 (sim_engine.cpp onwards).
9. Created all remaining 49 files.
10. Ran Get-ChildItem -Recurse to audit: 87 files confirmed.
11. Ran grep_search pattern audits across all .cpp files to find API
    mismatches.
12. Found and fixed: env() / time() aliases missing from sim_engine.hpp.
13. Found and fixed: Environment::restore() method missing.
14. Found and fixed: HeadlessRunner wrong return type for get_rng_state().
15. Found and fixed: Renderer taking vector instead of span for entity list.
16. Identified get_weights_flat / set_weights_flat as missing (session ended
    before fix — recorded in summary).

Session 3 (this session)

17. Agent read summary — immediately knew the pending issue.
18. Read ancestral_network.hpp and personal_network.hpp to see existing API.
19. Read state_serializer.hpp to confirm NetWire::weights type (double[16]).
20. Added get_weights_flat / set_weights_flat with float↔double conversion
    to both network headers in a single parallel multi-replace call.
21. Verified TimeKeeper::set_tick() existed (it did, line 32 of time_keeper.hpp).
22. User requested setup scripts + final audit.
23. Listed all 87 files — read root CMakeLists.txt, found tools subdirectory
    missing from the add_subdirectory list.
24. Found shaders not being copied next to the exe for MSVC builds — added
    add_custom_command(POST_BUILD ...) rules.
25. Created setup_windows.bat, start_visual.bat, start_headless.bat.
26. Created setup_linux.sh, start_visual.sh, start_headless.sh.
27. Read both entry point .cpp files → found critical bug: engine.init() was
    called unconditionally even after resume_from() restored state.
28. Fixed the bug in both main_headless.cpp and main_visual.cpp with a
    bool resumed guard.
29. User requested this document.
30. Fetched arXiv papers on LLM agents (ReAct, SWE-agent, SWE-bench, LLM-Modulo).
31. Wrote this document.

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Part 9 — How to Think About This for Future Projects

If you want to use an agent like this to build something from scratch, here is
what actually matters:

Give it a complete specification, not a vague goal

The copilot-instructions.md file is what made this project possible. It told
the agent:

• Exact file size limits
• Exact namespace conventions
• Specific math equations to implement
• Color model rules (RBY, not RGB)
• Build system requirements

Without that, the agent would have made architecture decisions that you might not
want and would have been hard to correct later.

The theory document was the database

weirdMEMORY.md served as the ground truth for all simulation logic. The
agent read it and implemented it. If you have a domain theory, a research paper,
a design spec — give it to the agent as a file it can read, not as a conversation
message. Files can be re-read multiple times; conversation messages scroll out of
context.

Let it audit itself

The most valuable steps were the grep_search audits after all files were
created. You can prompt this explicitly:

"Now search the entire codebase for any function that is called but never
defined, and fix all mismatches."

This triggers the same consistency-checking behavior that caught the
get_weights_flat bug.

Always do a real build test

The agent's analysis is probabilistic, not deterministic. The final ground truth
is a compiler. Run:

cd modular
cmake -B build -DWITH_CUDA=OFF -DWITH_TESTS=ON
cmake --build build --target rby_headless --config Release 2>&1 | Tee-Object build_log.txt

Feed any errors back to the agent. It will read the compiler output and fix them.
This compile-fix loop is the most reliable path to a working binary.

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References

Paper	Authors	Year	Key finding
ReAct: Synergizing Reasoning and Acting	Yao et al.	2022	Interleaving reasoning and tool actions dramatically improves agent task success
The Rise and Potential of LLM-Based Agents	Xi et al.	2023	Comprehensive survey of agent architectures (brain/perception/action)
SWE-bench	Jimenez et al.	2024	Standard benchmark for LLM code agents; Claude 2 solved 1.96% in 2024; frontier exceeded 50% by late 2024
SWE-agent: Agent-Computer Interfaces	Yang et al.	2024	Interface design matters more than model size; targeted edit tools beat raw I/O
LLM-Modulo Frameworks	Kambhampati et al.	2024	LLMs should be paired with external verifiers; the feedback loop is the source of correctness
The Landscape of Emerging AI Agent Architectures	Masterman et al.	2024	Survey of single/multi-agent patterns; planning, execution, reflection phases identified

All papers freely available at https://arxiv.org

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