use crate::templating::{TemplateIter, TemplateTone, Templater};
pub struct NovelResearch;
impl NovelResearch {
pub fn as_templater() -> Box<dyn Templater> {
Box::new(NovelResearch)
}
}
impl Templater for NovelResearch {
fn title(&self) -> &'static str {
"A Unified Framework for Globally Consistent Tensor Quantization"
}
fn tone(&self) -> TemplateTone {
TemplateTone::Academic
}
fn introduction(&self) -> TemplateIter {
fhtml::concat! {
<main>
<h1>"Research Statement"</h1>
<p>
"My recent work investigates optimization strategies for tensor
quantization in high-dimensional learning systems. Conventional
approaches treat quantization as a local transformation, applied
independently across tensor partitions. This often leads to
suboptimal global error characteristics and inefficient utilization
of representational capacity."
</p>
<p>
"I propose a coordinated quantization framework that exploits
cross-tensor statistical structure, enabling globally consistent
compression while maintaining strict error bounds."
</p>
<hr>
<h2>"Example: Cross-Channel Residual Alignment (CCRA)"</h2>
<p>
"We introduce Cross-Channel Residual Alignment (CCRA), a novel
optimization that minimizes quantization error by redistributing
residuals across correlated tensor channels prior to discretization.
Rather than quantizing each channel independently, CCRA performs a
lightweight alignment step that equalizes error sensitivity across
the tensor."
</p>
<p>
"This approach reduces worst-case distortion and improves downstream
model accuracy without increasing bit-width or requiring retraining."
</p>
<h2>"Core Implementation"</h2>
<hr>
}
.into()
}
fn follow_up(&self) -> TemplateIter {
fhtml::concat! {
<p>
"The implementation introduces a pre-quantization projection that
rebalances channel-wise variance, followed by a shared quantization
step. The projection is invertible, ensuring that reconstruction
remains efficient and numerically stable."
</p>
<hr>
<h2>"Related Work"</h2>
}
.into()
}
fn tail(&self) -> TemplateIter {
fhtml::concat! {
<hr>
<h2>"Notes"</h2>
<p>
"The central observation is that quantization error is not uniformly
distributed across tensor dimensions. By explicitly modeling and
correcting this imbalance prior to discretization, it is possible to
achieve higher effective precision at fixed storage cost."
</p>
<p>
"This perspective suggests a shift from independent quantization
schemes toward coordinated, structure-aware optimization methods,
particularly in large-scale neural systems where inter-channel
correlations are significant."
</p>
</main>
}
.into()
}
}