diff --git a/vllm/model_executor/layers/quantization/turboquant/__init__.py b/vllm/model_executor/layers/quantization/turboquant/__init__.py
index 333aef5f825..f9f4384e6e2 100644
--- a/vllm/model_executor/layers/quantization/turboquant/__init__.py
+++ b/vllm/model_executor/layers/quantization/turboquant/__init__.py
@@ -5,15 +5,20 @@
 Hadamard rotation + per-coordinate Lloyd-Max scalar quantization for
 keys, uniform quantization for values.
 
-The technique implemented here consists of the scalar case of the HIGGS
+The core algorithmic pattern implemented for key quantization (Hadamard
+rotation followed by deterministic scalar quantization and
+re-normalization) was originally established in DRIVE (Vargaftik et al.,
+NeurIPS 2021) and EDEN (Vargaftik et al., ICML 2022). This formulation is
+also mathematically equivalent to the scalar case of the HIGGS
 quantization method (Malinovskii et al., "Pushing the Limits of Large
 Language Model Quantization via the Linearity Theorem", NAACL 2025;
-preprint arXiv:2411.17525): rotation + optimized grid + optional
-re-normalization, applied to KV cache compression. A first application
-of this approach to KV-cache compression is in "Cache Me If You Must:
-Adaptive Key-Value Quantization for Large Language Models" (Shutova
-et al., ICML 2025; preprint arXiv:2501.19392). Both these references
-pre-date the TurboQuant paper (Zandieh et al., ICLR 2026).
+preprint arXiv:2411.17525), which subsequently generalized these concepts.
+
+A first application of this approach to KV-cache compression is in "Cache
+Me If You Must: Adaptive Key-Value Quantization for Large Language Models"
+(Shutova et al., ICML 2025; preprint arXiv:2501.19392). All of these
+foundational and application references pre-date the TurboQuant paper
+(Zandieh et al., ICLR 2026).
 """
 
 from vllm.model_executor.layers.quantization.turboquant.config import TurboQuantConfig
diff --git a/vllm/model_executor/layers/quantization/turboquant/config.py b/vllm/model_executor/layers/quantization/turboquant/config.py
index 50beb8d1d9b..84e3940d264 100644
--- a/vllm/model_executor/layers/quantization/turboquant/config.py
+++ b/vllm/model_executor/layers/quantization/turboquant/config.py
@@ -48,19 +48,25 @@ class TurboQuantConfig:
     Applies Hadamard rotation followed by per-coordinate Lloyd-Max scalar
     quantization for keys, and uniform quantization for values.
 
-    Historical note: this is the scalar case of the HIGGS quantization
-    method (Malinovskii et al., "Pushing the Limits of Large Language Model
-    Quantization via the Linearity Theorem", NAACL 2025; preprint
-    arXiv:2411.17525): rotation + optimized grid + optional re-normalization,
-    applied to KV cache compression. A first application of this approach to
-    KV-cache compression is in "Cache Me If You Must: Adaptive Key-Value
-    Quantization for Large Language Models" (Shutova et al., ICML 2025;
-    preprint arXiv:2501.19392). Both these references pre-date the
-    TurboQuant paper.
+    Historical note: the core algorithmic pattern implemented for key
+    quantization (Hadamard rotation followed by deterministic scalar
+    quantization and re-normalization) was originally established in DRIVE
+    (Vargaftik et al., NeurIPS 2021) and EDEN (Vargaftik et al., ICML
+    2022). This formulation is also mathematically equivalent to the
+    scalar case of the HIGGS quantization method (Malinovskii et al.,
+    "Pushing the Limits of Large Language Model Quantization via the
+    Linearity Theorem", NAACL 2025; preprint arXiv:2411.17525), which
+    subsequently generalized these concepts.
 
-    QJL is intentionally omitted — community consensus (5+ independent
-    groups) found it hurts attention quality by amplifying variance through
-    softmax.
+    A first application of this approach to KV-cache compression is in
+    "Cache Me If You Must: Adaptive Key-Value Quantization for Large
+    Language Models" (Shutova et al., ICML 2025; preprint
+    arXiv:2501.19392). All of these foundational and application
+    references pre-date the TurboQuant paper (Zandieh et al., ICLR 2026).
+
+    QJL is intentionally omitted: community consensus (5+ independent
+    groups) found it hurts attention quality by amplifying variance
+    through softmax.
 
     Named presets (use via --kv-cache-dtype):
         turboquant_k8v4:   FP8 keys + 4-bit values, 2.6x, +1.17% PPL