Fix: link real GitHub repo instead of dangling local paths

README.md

@@ -24,8 +24,12 @@ cite both papers if you use this model** (citations below).
 **What Sajal Labs added**: a from-scratch native C++ port of the encoder
 and the WordPiece tokenizer — no PyTorch, no `transformers`, no Python at
 inference time — with rigorous equivalence and benchmark validation against
-the original. See `research/experiments/exp11-real-pretrained-transformer`
-through `exp14-wordpiece-native` in the Sajal Labs repo for full methodology.
+the original. See [Sajal Labs](https://github.com/Sajalmadan09/sajal-labs),
+experiments
+[exp11](https://github.com/Sajalmadan09/sajal-labs/tree/main/research/experiments/exp11-real-pretrained-transformer)
+through
+[exp14](https://github.com/Sajalmadan09/sajal-labs/tree/main/research/experiments/exp14-wordpiece-native),
+for full methodology.
 
 ## Model details (unchanged from the original)
 
@@ -66,12 +70,15 @@ Worth knowing before you read too much into "ONNX Runtime": its own cold-start
 number depends heavily on which tokenizer library it's paired with — using
 `transformers` for convenience costs ~25x more than using the lean, standalone
 `tokenizers` library for the exact same token IDs. Native sidesteps that
-whole dependency-choice question by construction. Full discussion in exp14.
+whole dependency-choice question by construction. Full discussion in
+[exp14](https://github.com/Sajalmadan09/sajal-labs/tree/main/research/experiments/exp14-wordpiece-native).
 
 **Honest scope note on the warm-loop numbers**: native's advantage is
 *not* unconditional the way cold-invocation is — Sajal Labs found it
 depends on model width (`hidden_size`), with a measured crossover around
-`hidden≈250` on this hardware (exp12/exp13). This model's `hidden=128`
+`hidden≈250` on this hardware
+([exp12](https://github.com/Sajalmadan09/sajal-labs/tree/main/research/experiments/exp12-warm-latency-width-depth)/[exp13](https://github.com/Sajalmadan09/sajal-labs/tree/main/research/experiments/exp13-width-threshold)).
+This model's `hidden=128`
 sits comfortably below that, so native keeps a real warm-loop edge too —
 but that's a property of this model's size, not a general claim.
 

benchmark_results.json

@@ -1,6 +1,6 @@
 {
-  "hardware": "Apple M4, 10 cores, 16GB RAM, macOS (see research/environment.md)",
-  "methodology": "research/experiments/exp11-real-pretrained-transformer/results.md, exp12-warm-latency-width-depth/results.md, exp13-width-threshold/results.md, exp14-wordpiece-native/results.md",
+  "hardware": "Apple M4, 10 cores, 16GB RAM, macOS (see https://github.com/Sajalmadan09/sajal-labs/tree/main/research/environment.md)",
+  "methodology": "https://github.com/Sajalmadan09/sajal-labs/tree/main/research/experiments/exp11-real-pretrained-transformer/results.md, https://github.com/Sajalmadan09/sajal-labs/tree/main/research/experiments/exp12-warm-latency-width-depth/results.md, https://github.com/Sajalmadan09/sajal-labs/tree/main/research/experiments/exp13-width-threshold/results.md, https://github.com/Sajalmadan09/sajal-labs/tree/main/research/experiments/exp14-wordpiece-native/results.md",
   "equivalence_native_vs_pytorch": {
     "note": "native encoder + tokenizer vs. HF BertModel + BertTokenizerFast, 10 real sentences (lengths 4-25 tokens)",
     "hidden_state_max_abs_error": 9.5367431640625e-06,