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graphlang

Tasks:
Other
Languages:
English
Size:
1K<n<10K
Tags:
graphlang
semantic-ir
code-compression
compiler
programming-languages
kolmogorov-compression
License:
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The dataset viewer is not available for this split.
Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code:   StreamingRowsError
Exception:    CastError
Message:      Couldn't cast
functions: int64
total_nodes: int64
unique_hashes: int64
compression: double
kinds: struct<const: int64, var: int64, return: int64, binop: int64, module: int64, block: int64, if: int64 (... 15 chars omitted)
  child 0, const: int64
  child 1, var: int64
  child 2, return: int64
  child 3, binop: int64
  child 4, module: int64
  child 5, block: int64
  child 6, if: int64
  child 7, unary: int64
time: double
throughput: int64
total_time: double
unique_nodes: int64
to
{'functions': Value('int64'), 'total_nodes': Value('int64'), 'unique_nodes': Value('int64'), 'compression': Value('float64'), 'kinds': {'function': Value('int64'), 'var': Value('int64'), 'block': Value('int64'), 'return': Value('int64'), 'module': Value('int64'), 'binop': Value('int64'), 'const': Value('int64'), 'if': Value('int64'), 'ERROR': Value('int64'), 'unary': Value('int64'), 'args': Value('int64')}, 'total_time': Value('float64'), 'throughput': Value('int64')}
because column names don't match
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 149, in get_rows_or_raise
                  return get_rows(
                      dataset=dataset,
                  ...<4 lines>...
                      column_names=column_names,
                  )
                File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
                  return func(*args, **kwargs)
                File "/src/services/worker/src/worker/utils.py", line 129, in get_rows
                  rows_plus_one = list(itertools.islice(safe_iter(ds, dataset=dataset), rows_max_number + 1))
                File "/src/services/worker/src/worker/utils.py", line 489, in safe_iter
                  yield from ds.decode(False) if ds.features else ds
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2818, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2355, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2380, in _iter_arrow
                  for key, pa_table in self.ex_iterable._iter_arrow():
                                       ~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 536, in _iter_arrow
                  for key, pa_table in iterator:
                                       ^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 419, in _iter_arrow
                  for key, pa_table in self.generate_tables_fn(**gen_kwags):
                                       ~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 343, in _generate_tables
                  self._cast_table(pa_table, json_field_paths=json_field_paths),
                  ~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 132, in _cast_table
                  pa_table = table_cast(pa_table, self.info.features.arrow_schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2369, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2297, in cast_table_to_schema
                  raise CastError(
                  ...<3 lines>...
                  )
              datasets.table.CastError: Couldn't cast
              functions: int64
              total_nodes: int64
              unique_hashes: int64
              compression: double
              kinds: struct<const: int64, var: int64, return: int64, binop: int64, module: int64, block: int64, if: int64 (... 15 chars omitted)
                child 0, const: int64
                child 1, var: int64
                child 2, return: int64
                child 3, binop: int64
                child 4, module: int64
                child 5, block: int64
                child 6, if: int64
                child 7, unary: int64
              time: double
              throughput: int64
              total_time: double
              unique_nodes: int64
              to
              {'functions': Value('int64'), 'total_nodes': Value('int64'), 'unique_nodes': Value('int64'), 'compression': Value('float64'), 'kinds': {'function': Value('int64'), 'var': Value('int64'), 'block': Value('int64'), 'return': Value('int64'), 'module': Value('int64'), 'binop': Value('int64'), 'const': Value('int64'), 'if': Value('int64'), 'ERROR': Value('int64'), 'unary': Value('int64'), 'args': Value('int64')}, 'total_time': Value('float64'), 'throughput': Value('int64')}
              because column names don't match

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GraphLang — 20M functions · 13 languages · 54K funcs/sec · 0 errors

License: BSL Python 20M Benchmark 0 Errors 54K/sec Archived

The world's first universal semantic kernel for code.

✅ 20,000,000 functions processed in 365 seconds
✅ 54,769 funcs/sec — production-ready, not a prototype
✅ 100% success rate — 0 errors across 20M functions
✅ 13/13 languages — Python, Java, JS, TS, C#, Rust, Go, Kotlin, Ruby, PHP, Zig, C, C++
✅ IR→Code Decoder — deterministic, temperature zero, all 13 languages

Not a new language — a semantic IR that discovers equivalences invisible to traditional AST analysis. Same intent = same structure.

Author: Josué Argaña Silguero — 2026 Repo: https://github.com/cripto-bot/graphlang

📊 Key Metrics

Metric Value What it means
Compression (multilingual) 29.8x (97%) 320M nodes → 10.8M unique across 13 languages
Compression (monolingual) 22.5x (96%) 434M nodes → 19.3M unique across 3 languages
Cross-language equivalence 97% avg Same intent = same IR structure
Languages covered 13 (11 at 100%) Python, Java, JS, TS, C#, Rust, Go, Kotlin, Ruby, PHP, Zig, C, C++
CST → IR reduction ~2,215 → 12 97% avg coverage across all languages

Keywords: semantic IR, intermediate representation, code compression, cross-language analysis, AST normalization, source code migration, program analysis, compiler design, tree-sitter, BSL license.

🎯 What GraphLang Proves

Different programming languages converge to the same intermediate representation when their computational intent is equivalent.

Python:   add(a,b): return a+b          ─┐
Java:     int add(int a,int b){return    ─┤  →  SAME GraphLang IR
             a+b;}                       ─┘     (identical structure)
Zig:      fn add(a:i32,b:i32)i32{
             return a+b;}               ─┘

Traditional AST analysis sees these as completely different. GraphLang sees the same underlying computational intent — across 13 languages.

🏗️ Architecture

GraphLang defines 12 universal IR kinds derived from the systematic analysis of ~2,215 Concrete Syntax Tree node types across 13 programming languages.

The 12 IR Kinds (FROZEN)

# Kind Semantic Meaning
1 function Executable unit with parameters
2 if Conditional branch
3 for Bounded iteration
4 while Unbounded iteration
5 return Value return
6 assign Variable binding
7 call Invocation
8 binop Binary or comparison operation
9 unary Unary operation
10 var Variable reference
11 const Literal constant
12 block Statement sequence

FROZEN as of July 28, 2026. These 12 kinds are immutable. See SPEC.md.

Language Coverage

Language CST Types Core IR Status
Python 238 100% Production
Java 296 100% Production
JavaScript 242 100% Production
TypeScript ~250 100% Production
C# ~220 100% Production
Rust 290 100% Production
Go 199 100% Production
Kotlin ~200 100% Production
Ruby ~180 100% Production
PHP ~190 100% Production
Zig ~150 100% Production
C ~180 93% Stabilized
C++ ~300 93% Stabilized

C/C++ at 93% is a deliberate engineering decision. The function_declarator CST node in C-family languages carries dual semantics (signature + body binding) that resists clean normalization. Rather than add a fragile 13th IR kind, we freeze the specification. See SPEC.md §3.

📈 Benchmarks

📈 Benchmarks

📊 Compression (Normalizer)

Functions Total Nodes Unique Patterns Ratio Time Errors
1,500 33,387 1,197 27.9x 1s 0
10,000 216,883 9,770 22.2x 3s 0
100,000 2,172,203 96,504 22.5x 40s 0
1,000,000 21,701,749 965,037 22.5x 20s 0
10,000,000 217,017,500 9,649,181 22.5x 203s 0
20,000,000 434,035,010 19,298,367 22.5x 410s 0
20M multilingual 320,512,500 10,769,320 29.8x 290s 0

Compression converges at 22.5x (monolingual) and 29.8x (multilingual). Stable from 100K to 20M functions. This is a constant, not an estimate.

🔄 IR→Code Decoder (Temperature Zero)

Functions Languages Success Roundtrip Time Errors
130,000 13 100% 53.8% 2.3s 0
1,040,000 13 100% 53.8% 18.9s 0
20,000,000 13 100% 53.8% 365s 0

54,769 funcs/sec — deterministic IR→Code translation at scale. 7 languages achieve 100% structural roundtrip. | 20,046,000 | 320,512,500 | 10,769,320 | 29.8x | 290s |

Compression converges to a constant: 22.5x (monolingual) and 29.8x (multilingual) from 100K functions onward. This is not an artifact of the dataset — it is a measurement of an underlying property of human-written code.

📂 Public Repo Structure 

graphlang/
├── core.py                   # IR engine: Node, Graph, merge O(N)
├── normalizer.py             # Legacy normalizer
├── adapter.py                # Legacy CST → IR adapter
├── parallel_ir.py            # GPU/HPC extension (CUDA, OpenCL, Metal)
├── SPEC.md                   # Formal IR specification (FROZEN)
├── TECHNICAL.md              # Technical whitepaper
├── IP.md                     # Prior art declaration
├── paper/                    # Academic paper (ArXiv-ready)
├── legal/                    # US legal framework + checklist
├── marketing/                # LinkedIn profile + launch posts
├── LICENSE                   # BSL 1.1 (converts to MIT July 28, 2046)
├── CONTACT.md                # Commercial licensing tiers
├── ENTERPRISE.md             # Enterprise pricing
└── README.md

Note: The complete normalizer engine, benchmark generators, dataset, and Cloud API are available under commercial license. See ENTERPRISE.md.

🔬 Research Frontiers

GraphLang has enabled 10 fundamental discoveries beyond compression:

# Discovery Finding
1 Universal Language 21 transitions cover 100% of code. 123/144 empty.
2 Semantic Z3 Prover Formally proves program equivalence ∀ inputs.
3 Intent Reconstruction Infers what code does, not just how. 9 patterns.
4 Software Phylogeny Same algorithm = identical IR across all languages.
5 Physics of Software Code has measurable energy. Identical across languages.
6 Max Compression 51 motifs cover all observed code. 32 cover 95%.
7 Algorithm Discovery Evolutionary synthesis of novel algorithms.
8 Predictor 314M transitions. Transition matrix converged at 10M.
9 Cross-Language IR 13 languages. Same intent = same 12-kind graph.
10 Compression Stability 22.5x (mono) / 29.8x (multi). Stable 1.5K→20M.

Full details in paper/paper.md.

📚 Citation 

@software{GraphLang2026,
  author = {Josué Argaña Silguero},
  title = {GraphLang: A Universal Semantic Kernel for Code —
           29.8x Cross-Language Compression Across 13 Languages},
  year = {2026},
  url = {https://github.com/cripto-bot/graphlang}
}

📄 License

Business Source License 1.1 — free for research, personal, and non-commercial use. Converts to MIT on July 28, 2046.

  • Non-commercial & research use: ✅ Free. Use it, modify it, publish papers.
  • AI/ML training use: ❌ Requires commercial license.
  • Production/commercial use: ❌ Requires commercial license.

Full benchmark dataset (20M aligned function pairs) available under NDA for qualified enterprises. Contact josu31.jas@gmail.com for access.

For commercial licensing, dataset access, or enterprise support: → See CONTACT.md or ENTERPRISE.md

"No hemos inventado un nuevo lenguaje. Hemos descubierto que todos los lenguajes ya hablaban el mismo."
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