Rework targeted manuscript result tables

manuscript/targeted/main.tex

@@ -182,6 +182,9 @@ second, and (v) one OR-pooled aggregate-visible history.  These conditions
 never expose future activity, but their speech labels make them privileged
 diagnostics, not deployable features or upper bounds.
 
+\input{tables_compact}
+% Historical full-precision table layouts retained as source audit only.
+\iffalse
 \begin{table*}[t]
 \caption{Measured EasyCom screens (\%). Panel A: deployable models; Panel B:
 three-seed privileged past-activity audit, which is not deployable. Mean is
@@ -270,6 +273,7 @@ Completed blocks + observed tail & 70.85 & 59.17 & -- \\
 \end{tabular}
 \end{table*}
 
+\fi
 \begin{table}[t]
 \caption{Pending paired $\G\rightarrow\G+\A$ transfer (unmeasured).
 EC is EasyCom; MiniCPM first needs readout/refresh/history audits.}
@@ -467,7 +471,7 @@ shared GRU, while head capacity and training batch differ; raw boundary
 metrics and repeat seeds are unavailable. Thus the +0.66/+0.43-point
 difference does not isolate visual motion or floor behavior.
 
-Table~\ref{tab:main}, panel B, locates the missing information.  Aggregate visible
+Table~\ref{tab:oracle} locates the missing information. Aggregate visible
 activity improves over the audio baseline by 4.826 points at 0.2~s and 1.775
 mean-mAP points.  Visibility alone changes mean mAP by only +0.040, and
 one-second-delayed activity by +0.164.  Thus availability or extra parameters
@@ -495,16 +499,31 @@ behavioral improvement is asserted by this placeholder.}
 
 \subsection{Chunk Timing and Cross-Dataset Limits}
 
-Table~\ref{tab:stream_context}, panel B, reports a separate global-only
+Table~\ref{tab:chunk} reports a separate global-only
 chunk-construction screen.  Non-native 200-ms commits exceed native 2-s hold
 at 0.2~s (67.05 versus 59.62\% mAP), but use 95,453 rather than 9,539
 processor calls over 12 sessions.  Completed blocks plus observed tail reach
 70.85\%; that older bank is not extraction-matched.  Freshness and chunk
 construction vary together, so this is motivation for, not evidence of, an
-isolated PTR benefit or real-time throughput.  Panel A reserves the matched
-tail $\times$ audio and context-cap tests.  Tables~\ref{tab:backbone}--
+isolated PTR benefit or real-time throughput. Table~\ref{tab:stream_context}
+reserves the matched tail $\times$ audio and context-cap tests.
+Tables~\ref{tab:backbone}--
 \ref{tab:efficiency} reserve backbone, hard-slice, and cost analyses; the
 cutoff-marked H/Q case in Fig.~\ref{fig:hq_cases} remains unmeasured.
+\textbf{Historical LoRA diagnostic.}
+A single-run EasyCom 8-s/2-FPS screen used sparse training anchors (1~Hz,
+plus 5~Hz near transitions) and dense 5-Hz evaluation.  Within that legacy
+pipeline, strict mean mAP was 58.17\% for a frozen dense-cache control,
+57.21\% for frozen sparse training, and 56.21\% for a separate three-epoch
+Thinker-LoRA run initialized from a trained frozen head.  An epoch-1 LoRA
+weights-only continuation fell to 54.21\% after optimizer/scheduler reset.
+Sparse frozen training is 0.96 points below the dense control; the separate
+LoRA run is another 1.01 below sparse frozen, but the epoch budgets differ.
+Sparse-train/dense-test mismatch and the optimizer reset are possible, not
+isolated, explanations.
+Missing remote raw metrics, matched seeds, and feature equivalence make this
+a negative recipe diagnostic, not a Table~\ref{tab:main} $\G+\A$ comparison
+or proof that LoRA is inherently harmful.
 Ego4D has no archived test metrics in this checkout, so transfer cannot yet
 be claimed.  The oracle uses privileged labels, provided boxes omit online
 detection, and aggregate others cannot establish addressee or semantic floor
@@ -512,16 +531,13 @@ ownership.
 
 \subsection{Decision Rules and Reproducibility}
 
-Comparisons require identical physical cutoffs and future targets.  New runs
-must archive source timestamps, common valid-pair counts, seeds, per-session
-AP, trainable counts, revisions, checkpoints, and predictions; correlated
-frames are not independent replicates.  The matched matrix keeps a fixed
-four-epoch recipe, with selection rules frozen on training-side evidence.
-Sessions~1--3 have already been inspected, so repeat seeds remain exploratory.
-Raw endpoint AP is not one-event-one-vote ENTRY/YIELD: response windows must
-be censored at the next other onset and yield windows at the next other
-offset.  Until that evaluator passes a real-timeline audit, we report dense
-forecasting and endpoint anticipation only.
+Matched comparisons require identical cutoffs and targets; archive source
+timestamps, valid-pair counts, seeds, per-session AP, trainable counts, and
+checkpoint/processor revisions.  The fixed recipe and selection rules use
+training-side evidence only.  Already inspected test sessions make repeat
+seeds exploratory.  Raw endpoint AP is not one-event-one-vote ENTRY/YIELD:
+response windows must end at the next other onset, and yield windows at the
+next other offset.  Behavioral claims await a real-timeline evaluator audit.
 
 \section{Conclusion}