Restructure data pipeline: replace IFD analysis with length bias appendix

- Remove Section 2.4 (IFD analysis) from main text
- Delete obsolete IFD figures (ifd_compare_clean, ifd_density_dist,
  ifd_both_vs_lossratio, ifd_loss_ratio_density)
- Update ifd_length_grid.png with new data
- Rewrite Appendix A: IFD definition + quantitative summary +
  representative samples table + length bias analysis
- Update abstract to remove IFD findings
- Fix .gitignore to only track .png in data/
- Clean up stale analysis scripts

.gitignore

@@ -5,4 +5,4 @@
 # Only track .tex files and .gitignore itself
 !.gitignore
 !*.tex
-!data/**
+!data/**.png

main.tex

@@ -31,25 +31,19 @@
 Training billion-parameter language models requires careful co-design of
 data infrastructure, distributed execution, and numerical precision
 management. This paper presents {\sc AstrAI}, an open-source framework
-for end-to-end training of a 1.2B-parameter autoregressive Transformer.
-We describe the full pipeline: JSON-driven preprocessing with BBPE
-tokenization and multi-strategy packing, HDF5 and memory-mapped storage
-backends, and a companion SFT pipeline ({\sc Alembic}) with MinHash-based
-near-duplicate detection and LLM-as-Judge scoring. Using IFD (Instruction
-Fulfillment Difficulty) analysis on 3000 SFT samples, we find that Base
-IFD and Loss Ratio are nearly orthogonal ($r=0.10$), forming a
-complementary two-dimensional screening space, while Instruct IFD is
-redundant with Loss Ratio ($r=0.90$) due to a shared numerator---a
-tautological artifact we identify and warn against. The model is a 24-layer
-decoder-only Transformer with Grouped Query Attention, SwiGLU, RoPE, and
-RMSNorm, trained with AdamW and cosine scheduling via DDP/FSDP.
-A central focus is BF16 numerical stability: through variance propagation
-analysis we show that GPT-2 residual scaling reduces per-block residual
+for end-to-end training of a 1.2B-parameter autoregressive Transformer
+over 15B tokens. We describe the full pipeline: JSON-driven preprocessing
+with BBPE tokenization and multi-strategy packing, HDF5 and memory-mapped
+storage backends, and a companion SFT pipeline ({\sc Alembic}) with
+MinHash-based near-duplicate detection and LLM-as-Judge scoring. The
+model is a 24-layer decoder-only Transformer with Grouped Query Attention,
+SwiGLU, RoPE, and RMSNorm, trained with AdamW and cosine scheduling via
+DDP/FSDP. A central focus is BF16 numerical stability: variance propagation
+analysis shows that GPT-2 residual scaling reduces per-block residual
 variance by a factor of 48, containing post-24-layer variance at 1.34
-compared to 17.5 without scaling. Empirical evaluations over 15B training
-tokens demonstrate that residual scaling consistently outperforms Kaiming
-initialization, with the gap peaking at 0.79 in the mid-training regime.
-The complete framework and model weights are open-source.
+compared to 17.5 without scaling. Empirical evaluations confirm that
+residual scaling consistently outperforms Kaiming initialization, with the
+loss gap peaking at 0.79 in the mid-training regime.
 \end{abstract}
 
 % ======================================================================
@@ -141,157 +135,8 @@ pipeline proceeds as follows:
 An optional LLM-as-Judge scoring module provides multi-dimensional
 quality scores that can be used to filter low-quality samples.
 
-\subsection{IFD-Based Instruction Difficulty Analysis}
-
-Instruction Fulfillment Difficulty (IFD)~\cite{li2023ifd} quantifies
-how challenging an instruction is for a model by comparing conditional
-and unconditional per-token losses over a response
-$\mathbf{y} = (y_1,\dots,y_T)$:
-\begin{equation}
-\begin{aligned}
-\mathrm{IFD} &= \frac{L_{\text{cond}}}{L_{\text{uncond}}},\\[2mm]
-L_{\text{cond}} &= -\frac{1}{T}\sum_{t=1}^T \log P(y_t \mid \mathbf{x}, y_{<t}),\\[2mm]
-L_{\text{uncond}} &= -\frac{1}{T}\sum_{t=1}^T \log P(y_t \mid y_{<t}).
-\end{aligned}
-\end{equation}
-An IFD $>1$ indicates the instruction increases the loss relative to
-unconditional generation (the model struggles to follow it), while
-IFD $<1$ means the instruction provides useful guidance.
-
-We compute IFD for $N=3000$ SFT samples drawn from the
-Alpaca-GPT4 dataset~\cite{alpaca} using both the pretrained
-base model (after 15B tokens of pretraining) and a supervised
-fine-tuned checkpoint (after 1K SFT steps).
-Figure~\ref{fig:ifd} shows the distribution.
-
-\begin{figure}[H]
-\centering
-\includegraphics[width=0.80\linewidth]{data/ifd_compare_clean.png}
-\caption{IFD scatter: base model vs.\ trained checkpoint. The
-diagonal line marks $\mathrm{IFD}_{\text{base}} = \mathrm{IFD}_{\text{ckpt}}$.}
-\label{fig:ifd}
-\end{figure}
-
-\begin{figure}[H]
-\centering
-\includegraphics[width=0.80\linewidth]{data/ifd_density_dist.png}
-\caption{IFD density distribution: base model and SFT checkpoint.}
-\label{fig:ifd_density}
-\end{figure}
-
-Figure~\ref{fig:ifd_density} shows the corresponding density
-estimates, confirming the systematic leftward shift after SFT.
-The pretrained base model (15B tokens) has mean IFD $0.9625$;
-$29.8\%$ of samples exceed $1.0$. After 1K SFT steps, mean IFD drops
-to $0.7539$, with only $0.4\%$ of samples above $1.0$. The average
-per-sample IFD reduction is $0.2086$. Conditional loss drops
-$5.3\times$ more than unconditional loss, confirming that SFT teaches
-instruction following rather than merely improving generic language
-modeling. Detailed analysis is provided in Appendix~\ref{app:ifd}.
-
-\subsubsection{IFD vs.\ Loss Ratio}
-
-We further define the \emph{loss ratio}---the fraction of
-conditional loss retained after SFT---as:
-\begin{equation}
-\text{Loss Ratio} = \frac{L_{\text{cond}}^{\text{ckpt}}}{L_{\text{cond}}^{\text{base}}}.
-\end{equation}
-Table~\ref{tab:ifd_lossratio_corr} reports the pairwise correlations.
-
-\begin{table}[H]
-\centering
-\caption{Pairwise correlations among IFD and Loss Ratio.}
-\label{tab:ifd_lossratio_corr}
-\small
-\begin{tabular}{@{}lcc@{}}
-\toprule
-\textbf{Pair} & \textbf{Pearson $r$} & \textbf{Spearman $\rho$} \\
-\midrule
-IFD\textsubscript{base} vs.\ Loss Ratio & $+0.10$ & $+0.05$ \\
-IFD\textsubscript{ckpt} vs.\ Loss Ratio & $+0.90$ & $+0.91$ \\
-IFD\textsubscript{base} vs.\ IFD\textsubscript{ckpt} & $+0.38$ & $+0.49$ \\
-\bottomrule
-\end{tabular}
-\end{table}
-
-The near-perfect correlation between IFD\textsubscript{ckpt} and
-Loss Ratio ($r = 0.90$) reflects a mathematical near-identity:
-both are dominated by $L_{\text{cond}}^{\text{ckpt}}$ in the
-numerator. Consequently, IFD\textsubscript{ckpt} is
-redundant---it essentially measures how much the conditional loss
-has dropped after SFT, i.e., the learning speed of each sample. In contrast, IFD\textsubscript{base} and Loss
-Ratio are nearly orthogonal ($r = 0.10$), forming a complementary
-two-dimensional screening space: IFD\textsubscript{base} measures
-``how hard does the base model find this,'' while Loss Ratio
-measures ``how much did SFT improve it.'' Samples with high
-IFD\textsubscript{base} \emph{and} low Loss Ratio are the most
-informative for training.
-
-\begin{figure}[H]
-\centering
-\includegraphics[width=0.80\linewidth]{data/ifd_both_vs_lossratio.png}
-\caption{IFD\textsubscript{base} vs.\ Loss Ratio (left),
-IFD\textsubscript{ckpt} vs.\ Loss Ratio (right).}
-\label{fig:ifd_lossratio}
-\end{figure}
-
-\subsubsection{Loss Ratio Density by IFD Group}
-\label{sec:ifd_loss_ratio_density}
-
-Figure~\ref{fig:ifd_loss_ratio_density} compares the Loss Ratio
-density grouped by base IFD (left) and instruct IFD (right).
-
-\begin{figure}[H]
-\centering
-\includegraphics[width=0.90\linewidth]{data/ifd_loss_ratio_density.png}
-\caption{Loss Ratio density grouped by base IFD (left) and instruct IFD
-(right).}
-\label{fig:ifd_loss_ratio_density}
-\end{figure}
-
-\textbf{Left panel (Base IFD grouping).}
-The four density curves overlap almost completely, all peaking at
-Loss Ratio $0.75$--$0.85$. Whether a sample has base IFD $< 0.85$,
-$0.85$--$0.95$, $0.95$--$1.05$, or $> 1.05$, its Loss Ratio
-distribution is nearly identical. Base IFD cannot distinguish
-which samples learn during SFT and which do not. This
-near-orthogonality ($r = 0.10$, Table~\ref{tab:ifd_lossratio_corr})
-implies that how \emph{hard} an instruction appears to the base
-model carries almost no information about how much the model will
-improve on it. The signal is either dominated by data quality
-variation, or the current training budget is insufficient for
-high-IFD samples to realize their potential.
-
-\textbf{Right panel (Instruct IFD grouping).}
-The four curves separate into near-perfectly stratified layers:
-
-\medskip
-\begin{minipage}{\linewidth}
-\begin{tabular}{@{}lcc@{}}
-\toprule
-\textbf{Instruct IFD} & \textbf{\#Samples} & \textbf{Loss Ratio peak} \\
-\midrule
-$< 0.50$   & 356  & $\sim 0.25$ (75\% drop) \\
-$0.50$--$0.70$ & 702  & $\sim 0.55$ (45\% drop) \\
-$0.70$--$0.85$ & 1056 & $\sim 0.78$ (22\% drop) \\
-$> 0.85$   & 886  & $\sim 0.95$ (5\% drop) \\
-\bottomrule
-\end{tabular}
-\end{minipage}
-\medskip
-
-This separation, however, is a mathematical artifact. Instruct IFD
-and Loss Ratio share the numerator $L_{\text{cond}}^{\text{ckpt}}$,
-producing a tautological correlation ($r = 0.90$, $p \ll 0.001$).
-Grouping by instruct IFD is equivalent to grouping by Loss Ratio
-itself---explaining the outcome with the outcome, not predicting it
-from input features.
-
-The contrast between the two panels is the central finding:
-base IFD and Loss Ratio carry independent information
-($r = 0.10$), forming a two-dimensional screening space.
-Instruct IFD, despite its apparent predictive power, is redundant
-with Loss Ratio and should not be used for data selection.
+An IFD (Instruction Fulfillment Difficulty) analysis is provided in
+Appendix~\ref{app:ifd}.
 
 % ======================================================================
 \section{Model Architecture}
@@ -532,181 +377,104 @@ weights are available at \url{https://github.com/ViperEkura/AstrAI}.
 % ======================================================================
 
 % ======================================================================
-\section{IFD Data Examples}
+\section{IFD Data Analysis}
 \label{app:ifd}
 % ======================================================================
 
-Table~\ref{tab:ifd_examples} lists representative samples from the
-IFD evaluation set, covering high, medium, and low IFD values
-for the base model.
+Instruction Fulfillment Difficulty (IFD)~\cite{li2023ifd} compares
+conditional and unconditional per-token losses:
+\begin{equation}
+\begin{aligned}
+\mathrm{IFD} &= \frac{L_{\text{cond}}}{L_{\text{uncond}}},\\[2mm]
+L_{\text{cond}} &= -\frac{1}{T}\sum_{t=1}^T \log P(y_t \mid \mathbf{x}, y_{<t}),\\[2mm]
+L_{\text{uncond}} &= -\frac{1}{T}\sum_{t=1}^T \log P(y_t \mid y_{<t}).
+\end{aligned}
+\end{equation}
+We compute IFD for $N=3000$ SFT samples (Alpaca-GPT4~\cite{alpaca})
+using the base model (15B tokens) and the 1K-step SFT checkpoint.
+After 1K SFT steps, both losses increase slightly; the mean IFD
+changes from $0.8263$ (base) to $0.8485$ (1K SFT).
+
+\subsection{Quantitative Summary}
+
+Over $N=3000$ SFT samples from Alpaca-GPT4:
+\begin{itemize}[nosep]
+  \item \textbf{Base model}: mean IFD $= 0.8263$,
+        median $= 0.8858$, std $= 0.1699$; $1.9\%$ of samples
+        have IFD $> 1.0$.
+  \item \textbf{1K SFT}: mean IFD $= 0.8485$,
+        median $= 0.9083$, std $= 0.1588$; $3.1\%$ of samples
+        exceed $1.0$.
+  \item \textbf{Stability}: Pearson $r > 0.97$ between base and
+        1K SFT IFD. The slight upward shift ($0.8263 \to 0.8485$)
+        reflects both losses increasing after SFT, consistent with
+        distribution shift during fine-tuning rather than uniform
+        instruction-following improvement.
+\end{itemize}
+
+\subsection{Representative Samples}
+
+Table~\ref{tab:ifd_examples} lists samples spanning the IFD range.
 
 \begin{table}[H]
 \centering
-\caption{Representative IFD samples covering four patterns.}
+\caption{Representative IFD samples.}
 \label{tab:ifd_examples}
 \small
-\begin{tabular}{@{}c c c c c c c p{4.5cm}@{}}
+\begin{tabular}{@{}c c c c c p{4.2cm}@{}}
 \toprule
 \textbf{Idx} &
-  \textbf{Base IFD} &
-  \textbf{Ckpt IFD} &
   \textbf{$L_{\text{cond}}^{\text{base}}$} &
   \textbf{$L_{\text{uncond}}^{\text{base}}$} &
-  \textbf{$L_{\text{cond}}^{\text{ckpt}}$} &
-  \textbf{$L_{\text{uncond}}^{\text{ckpt}}$} &
+  \textbf{$L_{\text{cond}}^{\text{1K}}$} &
+  \textbf{$L_{\text{uncond}}^{\text{1K}}$} &
   \textbf{Instruction} \\
 \midrule
-0 & 4.605 & 1.525 & 12.38 & 2.69 & 3.77 & 2.47 & Complete analogy: loud is to quiet as day is to \\
-1 & 3.741 & 0.702 & 11.75 & 3.14 & 2.17 & 3.09 & Label news article as ``Political'' or ``Entertainment'' \\
-2 & 1.044 & 0.089 & 3.50 & 3.35 & 0.28 & 3.10 & Find the capital of Spain \\
-3 & 1.056 & 0.147 & 4.09 & 3.88 & 0.60 & 4.07 & Edit sentence for correct grammar: ``I were just going to'' \\
-4 & 0.977 & 0.904 & 2.57 & 2.63 & 2.24 & 2.48 & Describe the role of a project manager \\
-5 & 0.370 & 0.249 & 1.37 & 3.70 & 0.85 & 3.42 & Convert the given paragraph to a list \\
-6 & 0.307 & 0.062 & 0.70 & 2.29 & 0.15 & 2.43 & Remove third-person words from sentence \\
+81   & 13.38 & 5.84  & 13.25 & 5.69  & Classify incident as breach of protocol \\
+906  & 13.12 & 9.75  & 13.06 & 9.75  & Convert numbers from words to digits \\
+1076 & 2.53  & 2.46  & 2.53  & 2.53  & Pick best synonym \\
+7    & 2.62  & 2.70  & 2.68  & 2.77  & Write a short story in third person \\
+2427 & 2.59  & 2.84  & 2.69  & 2.90  & Find five most similar sentences \\
+798  & 2.02  & 2.75  & 2.11  & 2.31  & List four social media platforms \\
+223  & 1.34  & 3.16  & 1.36  & 3.27  & Classify text as Fiction or Non-fiction \\
 \bottomrule
 \end{tabular}
 \end{table}
 
-\subsection{Quantitative Summary}
+Samples with the highest conditional loss (rows~81,~906) are
+short-answer classification tasks ($L_{\text{cond}} \approx 13$).
+Lowest-IFD samples (row~223) are tasks where the instruction constrains
+the output space so tightly that unconditional loss far exceeds
+conditional loss. The four loss values remain nearly unchanged after
+SFT across all samples.
 
-Over $N=3000$ SFT samples:
-\begin{itemize}[nosep]
-  \item \textbf{Pretrained base model (15B tokens)}: mean IFD $= 0.9625$,
-        median $= 0.9773$, std $= 0.1925$; $29.8\%$ of samples have
-        IFD $> 1.0$.
-  \item \textbf{SFT checkpoint (1K steps)}: mean IFD $= 0.7539$,
-        median $= 0.8547$, std $= 0.2352$; only $0.4\%$ of samples
-        exceed $1.0$.
-  \item \textbf{Average IFD reduction}: $0.2086$ per sample.
-  \item \textbf{Loss decomposition}: conditional loss drops by $0.9657$
-        ($3.2424 \rightarrow 2.2767$), while unconditional loss drops by
-        only $0.1838$ ($3.4142 \rightarrow 3.2303$). The $5.3\times$
-        larger conditional reduction confirms the model primarily learns
-        instruction following.
-  \item \textbf{Correlation}: Pearson $r = 0.38$ between base and
-        checkpoint IFD, indicating a moderate tendency for relatively
-        hard instructions to remain relatively hard after training.
-\end{itemize}
-
-\subsection{Observed Patterns}
-
-\paragraph{High-IFD samples (base IFD $> 3$, e.g.,~rows~0,~1).}
-These are tasks requiring task-intent comprehension: analogy completion
-and article labeling. In the base model (15B pretraining), conditional
-loss is extremely high ($L_{\text{cond}} \approx 12$), meaning the
-instruction still acts as noise. After 1K SFT steps, IFD drops
-sharply (e.g., $4.605 \rightarrow 1.525$), demonstrating
-that SFT teaches the model to interpret and follow abstract task
-descriptions.
-
-\paragraph{Low-IFD samples (base IFD $< 0.4$, e.g.,~rows~5,~6).}
-These are formatting or extraction tasks: ``Convert paragraph to list,''
-``Remove third-person words.'' Unconditional
-loss is much higher than conditional loss even in the base model,
-because the instruction naturally constrains the output space. The
-pattern persists after SFT but with lower absolute values.
-
-\paragraph{Mid-range with large drop (e.g.,~rows~2,~3).}
-These are factual QA or grammar correction tasks. Base IFD is
-$\approx 1.05$ (instruction has little effect), but after SFT
-IFD drops to $\approx 0.1$ as the model learns the precise answer
-(e.g., ``Madrid'' for ``capital of Spain''), making conditional loss
-near-zero while unconditional loss remains high.
-
-\paragraph{Mid-range with small drop (e.g.,~row~4).}
-These are open-ended generation tasks (``Describe the role of a
-project manager''). Base IFD $\approx 0.98$; after SFT it drops
-only modestly to $\approx 0.9$, since both conditional and
-unconditional losses decrease proportionally without a memorized
-target.
-
-\paragraph{Cross-model correlation.}
-The moderate Pearson correlation ($r = 0.38$) suggests that while
-training reshapes the model's perception of instruction difficulty,
-a residual signal persists: instructions that require complex reasoning
-tend to remain non-trivially harder than simple rewrite or extraction
-tasks even after SFT.
-
-\subsection{A Note on IFD Bias from Response Length}
+\subsection{IFD Bias from Response Length}
 \label{sec:ifd_bias}
 
-Both $L_{\text{cond}}$ and $L_{\text{uncond}}$ are reported as per-token
-average losses. For a response of length $T$, the unconditional loss is
-$L_{\text{uncond}} = \frac{1}{T} \sum_{t=1}^T \log P(x_t)$.
-Since the variance of this average scales as $1/T$, shorter responses
-exhibit much larger fluctuations in $L_{\text{uncond}}$---a mathematical
-necessity, not a signal of instruction difficulty. Consequently, IFD,
-being a ratio of two such averages, inherits a systematic length bias:
-short responses inflate IFD variance.
-
-Figure~\ref{fig:length_bias} confirms this artifact across a 9-panel
-grid. The top row shows conditional loss, middle row unconditional
-loss, and bottom row IFD---each plotted against response length and
-loss magnitude. Short responses ($<20$ tokens, e.g., ``Paris,'' ``42'')
-produce wildly scattered $L_{\text{uncond}}$ values, which in turn
-generate spurious high or low IFD scores in the bottom panels.
-Longer responses ($>50$ tokens) converge toward the model's intrinsic
-mean loss, yielding stable IFD estimates across both base and
-checkpoint models.
+Both losses are per-token averages. The variance of
+$L_{\text{uncond}} = \frac{1}{T} \sum_{t=1}^T \log P(x_t)$
+scales as $1/T$, so shorter responses produce noisier estimates.
+Figure~\ref{fig:length_bias} plots the three metrics against response
+length for the base model; samples with $<20$ tokens ($21.9\%$ of
+the dataset) exhibit substantially higher scatter.
 
 \begin{figure}[H]
 \centering
-\includegraphics[width=0.80\linewidth]{data/ifd_length_grid.png}
-\caption{Response length vs.\ conditional loss, unconditional loss,
-and IFD. Short responses produce high-variance $L_{\text{uncond}}$
-estimates, inflating IFD noise.}
+\includegraphics[width=0.95\linewidth]{data/ifd_length_grid.png}
+\caption{Response length vs.\ $L_{\text{cond}}$, $L_{\text{uncond}}$,
+and IFD (base model, log scale on $x$-axis).}
 \label{fig:length_bias}
 \end{figure}
 
-\paragraph{Distribution summary.}
-Over the full 3000-sample set, the base model's conditional loss
-has median $2.56$, unconditional loss median $2.80$, and IFD median
-$0.95$, concentrated in the $0.6$--$1.1$ range with a slight left
-skew (cond $<$ uncond for most samples).
-
-\paragraph{Correlation analysis.}
-Table~\ref{tab:corr_bias} reports Pearson $r$ and Spearman $\rho$
-between key dimensions and the three IFD components.
-
-Three patterns stand out:
-
-\begin{enumerate}[nosep]
-  \item \textbf{Instruction length is nearly independent}
-        ($r \approx 0$ for all three targets). The length of the
-        instruction text itself has no meaningful correlation with
-        either loss or IFD. The slight negative IFD correlation
-        ($r = -0.24$, $\rho = -0.35$) is an indirect artifact driven
-        by response length (longer instructions tend to elicit shorter
-        answers in our Alpaca distribution).
-
-  \item \textbf{Response length is the dominant confound.}
-        $L_{\text{uncond}}$ shows a strong negative monotonic trend
-        ($\rho = -0.70$), a direct consequence of the per-token
-        average variance scaling as $1/T$ (Section~\ref{sec:ifd_bias}).
-        $L_{\text{cond}}$ has a weaker negative correlation
-        ($r = -0.38$), because conditional generation already
-        constrains the output distribution regardless of length.
-        The net effect on IFD is a moderate positive bias
-        ($r = +0.31$, $\rho = +0.47$): long responses produce
-        higher IFD not because they are harder, but because
-        $L_{\text{uncond}}$ drops faster with length than
-        $L_{\text{cond}}$.
-
-  \item \textbf{The ratio (resp/inst) is collinear with response
-        length} and provides no independent information.
-        All three columns mirror those of response length with
-        slightly attenuated magnitudes. Filtering by response
-        length alone suffices.
-\end{enumerate}
-
-The consistently larger $\rho$ than $r$ across all rows confirms
-that the relationships are monotonic but nonlinear---steep at
-the short end and flat for long sequences, consistent with the
-$1/T$ variance decay predicted in Section~\ref{sec:ifd_bias}.
+Table~\ref{tab:corr_bias} reports the correlations. Response length
+is the dominant confound: $L_{\text{uncond}}$ shows a strong negative
+monotonic trend ($\rho = -0.79$), while $L_{\text{cond}}$ is less
+affected ($\rho = -0.48$). The net effect on IFD is a positive
+correlation ($\rho = +0.72$).
 
 \begin{table}[H]
 \centering
-\caption{Pearson $r$ and Spearman $\rho$ between sample dimensions and IFD components.}
+\caption{Pearson $r$ and Spearman $\rho$ between sample dimensions and IFD components (base model).}
 \label{tab:corr_bias}
 \small
 \begin{tabular}{@{}lcccccc@{}}
@@ -717,21 +485,12 @@ $1/T$ variance decay predicted in Section~\ref{sec:ifd_bias}.
 \cmidrule(lr){2-3} \cmidrule(lr){4-5} \cmidrule(lr){6-7}
 \textbf{Dimension} & $r$ & $\rho$ & $r$ & $\rho$ & $r$ & $\rho$ \\
 \midrule
-Instruction length & $-0.01$ & $+0.04$ & $+0.11$ & $+0.22$ & $-0.24$ & $-0.35$ \\
-Response length    & $-0.38$ & $-0.46$ & $-0.52$ & $-0.70$ & $+0.31$ & $+0.47$ \\
-Ratio (resp/inst)  & $-0.32$ & $-0.41$ & $-0.46$ & $-0.67$ & $+0.30$ & $+0.52$ \\
+Instruction length & $+0.07$ & $+0.06$ & $+0.15$ & $+0.24$ & $-0.25$ & $-0.34$ \\
+Response length    & $-0.36$ & $-0.48$ & $-0.56$ & $-0.79$ & $+0.58$ & $+0.72$ \\
 \bottomrule
 \end{tabular}
 \end{table}
 
-\paragraph{Practical recommendation.}
-Filter samples with response length $<20$ or $>300$ tokens before
-computing IFD. This retains the middle interval where per-token
-loss averages are stable and IFD rankings are most reliable.
-In our Alpaca-style dataset, this removes approximately
-$5$--$8\%$ of samples and substantially reduces false positives
-in the high-IFD tail.
-
 % ======================================================================
 \section{Weight Distribution by Component}
 \label{app:weight_dist}