Repository Analysis

lucidrains/x-transformers

A concise but complete full-attention transformer with a set of promising experimental features from various papers

9.6 Low AI signal View on GitHub

Analysis Overview

This report presents the forensic synthetic code analysis of lucidrains/x-transformers, a Python project with 5,932 GitHub stars. SynthScan v2.0 examined 19,108 lines of code across 39 source files, recording 190 pattern matches distributed across 6 syntactic categories. The overall adjusted score of 9.6 places this repository in the Low AI signal band.

The scanner applied 160+ deterministic lexical heuristics, multi-line block detectors, abstract syntax tree depth profilers, and a cross-file Jaccard similarity matrix to construct a statistically normalised synthetic code estimate. All matches are individually weighted by severity coefficient and contextual multiplier before summation, and the resulting headline score is temporally discounted to account for the repository's development history relative to the commercial emergence of large language model coding tooling (November 2022 onward).

9.6
Adjusted Score
9.6
Raw Score
100%
Time Factor
2026-08-02
Last Push
5.9K
Stars
Python
Language
19.1K
Lines of Code
39
Files
190
Pattern Hits
2026-08-02
Scan Date
0.00
HC Hit Rate

What These Metrics Mean

Adjusted Score
Primary synthetic code indicator. Raw score normalised per 1,000 lines of code and multiplied by the temporal discount factor. This is the definitive comparative metric — use it to rank repositories by AI authorship density.
Raw Score
The unmodified sum of all severity-weighted, context-multiplied pattern match scores before temporal discounting. Reflects the absolute signal strength independent of when the repository was last active.
Time Factor
The temporal discount multiplier (0–100%) applied to the raw score. Repositories last updated before ChatGPT's launch (Nov 2022) receive a 5% factor. Full signal is only assigned to repositories active in the post-adoption era (Jan 2024+).
Pattern Hits
Total count of individual pattern matches across all files and categories. A high hit count with a low score may indicate a very large codebase with isolated AI snippets; a low count with a high score indicates dense, concentrated AI signatures.
HC Hit Rate
High+Critical pattern hits per file, averaged across the repository. This orthogonal signal catches repositories where a few files are densely packed with high-severity AI tells — a strong indicator even when the normalised score appears moderate due to codebase size.
Lines of Code / Files
Total lines and files analysed. The scanner examines 94 file extensions. These denominators are used to normalise the score, enabling fair comparison between repositories of vastly different sizes.

Score History

Longitudinal tracking requires multiple scan runs. Once this repository is re-scanned after new commits land, this chart will visualise how the synthetic code signal evolves over time — enabling you to detect whether AI authorship is growing, stabilising, or being actively corrected by human engineers.

No multi-scan history yet — run the scanner again to build trend data.

Severity Breakdown

Classifies detected patterns by their diagnostic confidence and structural impact. CRITICAL patterns (coefficient 10) represent definitive synthetic signatures — hallucinated imports, explicit LLM attribution metadata — virtually never produced by human authors. HIGH (5) indicates strong structural tells such as cross-file repetition or cross-linguistic idioms. MEDIUM (2) covers recognisable conversational padding and AI-specific vocabulary. LOW (1) captures subtle indicators like tautological comments and generic boilerplate that require density to carry independent signal.

CRITICAL 0HIGH 0MEDIUM 1LOW 189

Directory Score Breakdown

This horizontal bar chart decomposes the repository's raw synthetic code score by top-level directory, allowing you to pinpoint precisely which modules or components carry the highest AI authorship density. Directories with disproportionately high scores relative to their size warrant targeted manual review: concentrated AI signatures often trace back to mass-generated configuration layers, auto-ported test suites, LLM-scaffolded boilerplate classes, or entire subsystems authored under heavy copilot assistance. Use this view to prioritise your human code-review effort.

Pattern Findings

The scanner identified 190 distinct pattern matches across 6 syntactic categories. Each entry below represents a discrete location in the source code where the engine recorded a statistically significant AI authorship indicator. Expand any category row to inspect the individual file paths, line numbers, code snippets, and the lexical context (CODE, COMMENT, or STRING) in which each match was detected.

Reading the findings table: The Severity column indicates the diagnostic confidence level (CRITICAL / HIGH / MEDIUM / LOW). The Context column identifies whether the match occurred inside executable code, an inline comment, or a string literal — comment-context matches receive a ×1.5 weight because LLMs systematically over-annotate. The ⚡ bolt icon marks clustered matches: three or more patterns within a 10-line window, each receiving an additional ×1.5 density multiplier as dense clusters constitute far stronger evidence of synthetic authorship than isolated hits.

Unused Imports96 hits · 92 pts
SeverityFileLineSnippetContext
LOWtrain_with_muon.py17CODE
LOWtrain_enwik8.py20CODE
LOWtrain_path_star.py24CODE
LOWtrain_self_masked_repr.py12CODE
LOWtrain_self_masked_repr.py26CODE
LOWtrain_entropy_tokenizer.py22CODE
LOWtrain_xl_enwik8.py20CODE
LOWtrain_parity.py3CODE
LOWtrain_gpt_vae.py12CODE
LOWtrain_length_extrapolate.py18CODE
LOWtrain_free.py18CODE
LOWtrain_free.py19CODE
LOWtrain_belief_state.py2CODE
LOWtrain_belief_state.py10CODE
LOWx_transformers/xl_autoregressive_wrapper.py1CODE
LOWx_transformers/xl_autoregressive_wrapper.py2CODE
LOWx_transformers/xl_autoregressive_wrapper.py13CODE
LOWx_transformers/xl_autoregressive_wrapper.py15CODE
LOWx_transformers/belief_state_wrapper.py7CODE
LOWx_transformers/belief_state_wrapper.py12CODE
LOWx_transformers/belief_state_wrapper.py13CODE
LOWx_transformers/belief_state_wrapper.py21CODE
LOWx_transformers/gpt_lejepa.py6CODE
LOWx_transformers/gpt_lejepa.py18CODE
LOWx_transformers/gpt_lejepa.py19CODE
LOWx_transformers/neo_mlp.py1CODE
LOWx_transformers/neo_mlp.py4CODE
LOWx_transformers/neo_mlp.py4CODE
LOWx_transformers/neo_mlp.py5CODE
LOWx_transformers/neo_mlp.py6CODE
LOWx_transformers/autoregressive_wrapper.py1CODE
LOWx_transformers/autoregressive_wrapper.py7CODE
LOWx_transformers/x_transformers.py1CODE
LOWx_transformers/x_transformers.py9CODE
LOWx_transformers/x_transformers.py12CODE
LOWx_transformers/multi_input.py1CODE
LOWx_transformers/multi_input.py6CODE
LOWx_transformers/__init__.py1CODE
LOWx_transformers/__init__.py1CODE
LOWx_transformers/__init__.py1CODE
LOWx_transformers/__init__.py1CODE
LOWx_transformers/__init__.py1CODE
LOWx_transformers/__init__.py1CODE
LOWx_transformers/__init__.py1CODE
LOWx_transformers/__init__.py1CODE
LOWx_transformers/__init__.py1CODE
LOWx_transformers/__init__.py1CODE
LOWx_transformers/__init__.py1CODE
LOWx_transformers/__init__.py1CODE
LOWx_transformers/__init__.py1CODE
LOWx_transformers/__init__.py1CODE
LOWx_transformers/__init__.py18CODE
LOWx_transformers/__init__.py19CODE
LOWx_transformers/__init__.py20CODE
LOWx_transformers/__init__.py21CODE
LOWx_transformers/__init__.py23CODE
LOWx_transformers/__init__.py23CODE
LOWx_transformers/__init__.py28CODE
LOWx_transformers/__init__.py30CODE
LOWx_transformers/__init__.py30CODE
36 more matches not shown…
AI Structural Patterns39 hits · 39 pts
SeverityFileLineSnippetContext
LOWtrain_enwik8_lejepa_style.py37CODE
LOWtrain_enwik8.py45CODE
LOWtrain_path_star.py113CODE
LOWtrain_self_masked_repr.py424CODE
LOWtrain_self_masked_repr.py72CODE
LOWtrain_self_masked_repr.py223CODE
LOWtrain_entropy_tokenizer.py53CODE
LOWtrain_xl_enwik8.py48CODE
LOWtrain_enwik8_xm.py100CODE
LOWx_transformers/xl_autoregressive_wrapper.py224CODE
LOWx_transformers/belief_state_wrapper.py84CODE
LOWx_transformers/gpt_lejepa.py69CODE
LOWx_transformers/autoregressive_wrapper.py159CODE
LOWx_transformers/autoregressive_wrapper.py200CODE
LOWx_transformers/autoregressive_wrapper.py367CODE
LOWx_transformers/x_transformers.py1526CODE
LOWx_transformers/x_transformers.py1633CODE
LOWx_transformers/x_transformers.py1719CODE
LOWx_transformers/x_transformers.py2126CODE
LOWx_transformers/x_transformers.py2580CODE
LOWx_transformers/x_transformers.py3119CODE
LOWx_transformers/x_transformers.py3569CODE
LOWx_transformers/x_transformers.py3720CODE
LOWx_transformers/x_transformers.py3960CODE
LOWx_transformers/x_transformers.py4502CODE
LOWx_transformers/multi_input.py35CODE
LOWx_transformers/multi_input.py108CODE
LOWx_transformers/next_latent_wrapper.py154CODE
LOWx_transformers/attend.py176CODE
LOWx_transformers/up_wrapper.py146CODE
LOWx_transformers/gpt_vae.py33CODE
LOWx_transformers/continuous.py63CODE
LOWx_transformers/continuous.py138CODE
LOWx_transformers/nonautoregressive_wrapper.py105CODE
LOWx_transformers/xm_induced_latent_decoder.py45CODE
LOWx_transformers/xval.py49CODE
LOWx_transformers/xval.py109CODE
LOWx_transformers/continuous_autoencoder.py80CODE
LOWx_transformers/free_transformer.py133CODE
Hyper-Verbose Identifiers36 hits · 32 pts
SeverityFileLineSnippetContext
LOWx_transformers/belief_state_wrapper.py177 def generate_with_suffix_cond(CODE
LOWx_transformers/x_transformers.py154def resolve_repeat_blocks_to_layer_execute_order(CODE
LOWx_transformers/x_transformers.py445 def _relative_position_bucket(relative_position, causal = True, num_buckets = 32, max_distance = 128):CODE
LOWx_transformers/entropy_based_tokenizer.py29def get_accumulated_threshold_mask(CODE
LOWx_transformers/attend.py76def log_prob_from_hard_attend(intermeds: Intermediates):CODE
LOWx_transformers/up_wrapper.py206 def get_rand_sequences_from_buffer(self, size = None):CODE
LOWx_transformers/continuous.py35def sample_from_mean_variance(CODE
LOWx_transformers/xm_induced_latent_decoder.py102 def generate_with_candidate_latents(CODE
LOWx_transformers/dpo.py20def log_prob_from_model_and_seq(model, seq):CODE
LOWtests/test_x_transformers.py141def test_attn_softclamp_logits():CODE
LOWtests/test_x_transformers.py158def test_multiple_input_embeds():CODE
LOWtests/test_x_transformers.py231def test_squeeze_logit_dim_one():CODE
LOWtests/test_x_transformers.py371def test_forgetting_transformer(CODE
LOWtests/test_x_transformers.py428def test_custom_rotary_pos_emb(rotary_xpos):CODE
LOWtests/test_x_transformers.py452def test_custom_alibi_across_heads(flash: bool):CODE
LOWtests/test_x_transformers.py700def test_caching_when_inputs_not_include_past():CODE
LOWtests/test_x_transformers.py732def test_caching_when_inputs_not_include_past_continuous():CODE
LOWtests/test_x_transformers.py765def test_multi_latent_attention():CODE
LOWtests/test_x_transformers.py812def test_belief_state_wrapper(CODE
LOWtests/test_x_transformers.py884def test_entropy_based_tokenizer(CODE
LOWtests/test_x_transformers.py914def test_entropy_based_tokenizer_max_token_len():CODE
LOWtests/test_x_transformers.py942def test_custom_ff_activation():CODE
LOWtests/test_x_transformers.py1029def test_autoregressive_wrapper(CODE
LOWtests/test_x_transformers.py1198def test_prompts_given_as_list_tensor():CODE
LOWtests/test_x_transformers.py1252def test_learned_head_attn_sink():CODE
LOWtests/test_x_transformers.py1269def test_accept_layer_intermediates():CODE
LOWtests/test_x_transformers.py1403def test_attn_negative_weights(CODE
LOWtests/test_x_transformers.py1542def test_seq_start_pos_parity():CODE
LOWtests/test_x_transformers.py1627def test_continuous_transformer_external_projects():CODE
LOWtests/test_x_transformers.py1652def test_attn_aggregated_residuals(CODE
LOWtests/test_x_transformers.py1699def test_repeat_blocks_multiple():CODE
LOWtests/test_x_transformers.py1848def test_continuous_autoencoder(CODE
LOWtests/test_x_transformers.py2012def test_ttt_source_target_mapping():CODE
LOWtests/test_x_transformers.py2065def test_ttt_custom_loss_optimization():CODE
LOWtests/test_x_transformers.py2258def test_relative_proj_positional_bias():CODE
LOWtests/test_x_transformers.py2282def test_xm_induced_latent_decoder():CODE
Deep Nesting17 hits · 17 pts
SeverityFileLineSnippetContext
LOWtrain_enwik8_lejepa_style.py37CODE
LOWtrain_enwik8.py45CODE
LOWtrain_path_star.py113CODE
LOWtrain_self_masked_repr.py424CODE
LOWtrain_xl_enwik8.py48CODE
LOWtrain_enwik8_xm.py100CODE
LOWx_transformers/xl_autoregressive_wrapper.py534CODE
LOWx_transformers/belief_state_wrapper.py287CODE
LOWx_transformers/gpt_lejepa.py164CODE
LOWx_transformers/autoregressive_wrapper.py367CODE
LOWx_transformers/x_transformers.py1633CODE
LOWx_transformers/x_transformers.py2126CODE
LOWx_transformers/x_transformers.py2580CODE
LOWx_transformers/x_transformers.py3119CODE
LOWx_transformers/x_transformers.py3960CODE
LOWx_transformers/next_latent_wrapper.py154CODE
LOWx_transformers/attend.py176CODE
AI Slop Vocabulary1 hit · 3 pts
SeverityFileLineSnippetContext
MEDIUMx_transformers/autoregressive_wrapper.py178 # paper shows masking (MLM) in conjunction with autoregressive decoder-only training leads to big improvements hCOMMENT
Over-Commented Block1 hit · 1 pts
SeverityFileLineSnippetContext
LOWtrain_path_star.py1# /// scriptCOMMENT