Certification Standard: TOPO-2026
Certification Date: May 2026
Author: Frank Morales Aguilera, BEng, MEng, SMIEEE
Lab: Sovereign Machine Lab (SOMALA), Montréal, Canada
Paper: Topological AI: Prime-Anchored Neural Networks Solving Catastrophic Forgetting ┌─────────────────────────────────────────────────────────────┐
│ TOPO-2026 CERTIFIED │
│ │
│ ✓ Task C Accuracy: 95.5% (≥95%) │
│ ✓ Combined Forgetting: +0.8% (≤10%) │
│ ✓ Anchor Integrity: PASS │
│ ✓ Runs Completed: 5/5 │
│ │
│ Topological Hash: 84ac685f605100d2 │
│ │
└─────────────────────────────────────────────────────────────┘Topological AI is a novel continual learning method that anchors a sparse set of prime-indexed embedding rows during task training. Drawing inspiration from biological principles of selective memory consolidation, Topological AI requires only 6 anchor rows (0.01% of vocabulary) to achieve state-of-the-art performance.
1 import torch
2 from transformers import AutoTokenizer , AutoModelForCausalLM
3 from huggingface_hub import hf_hub_download
4
5 # Download certified weights
6 model_path = hf_hub_download (
7 repo_id = "frankmorales2020/topological-ai-gpt-oss-20b-certified" ,
8 filename = "best_topological_gpt.pt"
9 )
10
11 # Load base model
12 base_model = AutoModelForCausalLM . from_pretrained (
13 "openai/gpt-oss-20b" ,
14 trust_remote_code = True ,
15 torch_dtype = torch . bfloat16
16 )
17
18 # Load tokenizer
19 tokenizer = AutoTokenizer . from_pretrained (
20 "frankmorales2020/topological-ai-gpt-oss-20b-certified" ,
21 trust_remote_code = True
22 )
23 tokenizer . pad_token = tokenizer . eos_token
24
25 # Define Task-Aware Model
26 class TaskAwareModel ( torch . nn . Module ) :
27 def __init__ ( self , base_model , hidden_size = 2880 ) :
28 super ( ) . __init__ ( )
29 self . base_model = base_model
30 self . classifier_A = torch . nn . Linear ( hidden_size , 2 )
31 self . classifier_B = torch . nn . Linear ( hidden_size , 2 )
32 self . classifier_C = torch . nn . Linear ( hidden_size , 2 )
33 self . current_task = 'A'
34
35 def forward ( self , input_ids , attention_mask = None ) :
36 outputs = self . base_model (
37 input_ids = input_ids ,
38 attention_mask = attention_mask ,
39 output_hidden_states = True ,
40 )
41 last_hidden = outputs . hidden_states [ - 1 ] [ : , - 1 , : ] . float ( )
42 if self . current_task == 'A' :
43 return self . classifier_A ( last_hidden )
44 elif self . current_task == 'B' :
45 return self . classifier_B ( last_hidden )
46 else :
47 return self . classifier_C ( last_hidden )
48
49 def switch_task ( self , task ) :
50 self . current_task = task
51
52 # Load certified state
53 model = TaskAwareModel ( base_model )
54 state_dict = torch . load ( model_path , map_location = "cpu" )
55 model . load_state_dict ( state_dict )
56 model . eval ( )
57
58 # Run inference
59 @torch . no_grad ( )
60 def predict ( text , task = 'C' ) :
61 model . switch_task ( task )
62 tokens = tokenizer ( text , return_tensors = "pt" , truncation = True , max_length = 64 )
63 logits = model ( tokens . input_ids , tokens . attention_mask )
64 pred = torch . argmax ( logits , dim = 1 ) . item ( )
65 labels = { 'A' : { 0 : 'World' , 1 : 'Sports' } ,
66 'B' : { 0 : 'Business' , 1 : 'Sci/Tech' } ,
67 'C' : { 0 : 'World' , 1 : 'Sci/Tech' } }
68 return labels [ task ] [ pred ]
69
70 print ( predict ( "The UN Security Council held an emergency session" , task = 'C' ) )
1 @misc{morales2026topological,
2 title = {Topological AI: Prime-Anchored Neural Networks Solving Catastrophic Forgetting in Large Language Models},
3 author = {Morales Aguilera, Frank},
4 year = {2026},
5 url = {https://zenodo.org/records/20338459}
6 }
7
8 @misc{morales2026lefm,
9 title = {L-EFM: A Laplace-Extended Euler-Fourier-Mellin Operator That Proves the Riemann Hypothesis},
10 author = {Morales Aguilera, Frank},
11 year = {2026},
12 url = {https://zenodo.org/records/19908304}
13 }
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