Streamline targeted methods and experiment presentation

manuscript/targeted/main.tex

@@ -211,49 +211,28 @@ pending, so PTR is a causal access rule rather than a demonstrated gain.
 
 \subsection{Short-Window Feature Extraction}
 
-We define every local observation by its prediction cutoff, before applying
-any encoder-specific preprocessing. We denote the default Qwen-audio branch
-by $\A$: it encodes the
-mixed waveform in $[t_n-1\,\mathrm{s},t_n)$ with the frozen Qwen2.5-Omni
-audio encoder and averages valid output tokens. We denote by $\A_{\rm E}$
-the wav2vec~2.0 convolutional representation used by EgoSpeak
-\cite{kim2025egospeak}. This encoder-choice control must use the same
-one-second input interval and
-forecasting head before differences can be attributed to the encoder. It is
-not the source of the reported $\G+\A$ result.
+Each local branch observes only media preceding the prediction cutoff
+$t_n$; unavailable observations retain explicit validity masks rather
+than borrowing later samples. All encoders are frozen. The notation below
+distinguishes encoder choice from fusion choice: $\G$ is the global Thinker
+state, and local branches are appended to the same forecasting head.
 
-We denote three visual branches by \emph{Pool}, \emph{SVA}, and \emph{TSVA}.
-Pool averages frozen Qwen vision tokens
-within provided boxes, across visible people, and over valid 2-FPS frames
-in the preceding second. These pooled features summarize appearance but
-discard person identity and explicit motion. Boxes are dataset-provided,
-not predictions from an evaluated online detector. Two no-box full-frame
-alternatives retain the post-merger spatial-token grid. Spatial visual
-attention (SVA) lets the global--audio query attend to the latest frame
-strictly before $t_n$; temporal spatial visual attention (TSVA) first encodes up
-to two observations at each matching grid location in chronological order
-with a shared GRU, then applies the same query to the resulting grid. TSVA
-links image coordinates, not people or tracks. Its difference from SVA
-confounds additional history and temporal capacity, so it cannot alone
-establish a motion or speaker-grounding mechanism. Empty or unavailable
-local evidence is indicated by the validity masks, never filled with a
-future observation.
+The default audio feature $\A$ encodes the mixed waveform in
+$[t_n-1\,\mathrm{s},t_n)$ with the Qwen2.5-Omni audio encoder and averages
+valid tokens. $\A_{\rm E}$ denotes EgoSpeak's wav2vec~2.0 convolutional
+representation \cite{kim2025egospeak}; comparing $\G+\A$ with
+$\G+\A_{\rm E}$ requires the same window and head. Released EgoSpeak A+V
+remains an external baseline, not this encoder substitution.
 
-The shorthand $\G$, $\G+\A$, and $\G+\A+$SVA identifies the three Omni
-rows in the cross-dataset table; $\G+\A+$Pool, $\G+\A+$TSVA, and
-$\G+\A_{\rm E}$ isolate visual or audio choices. Released EgoSpeak A+V
-is an external baseline, not an encoder choice within our frozen-Omni head.
-An unmeasured label specifies a comparison, not a result.
-
-As a distinct, privileged short-window feature, we also derive visible
-non-wearer speech activity from transcript annotations \emph{before} each
-cutoff. Each visible participant contributes a two-second binary history
-on the 5-Hz grid. We test visibility-only, per-person, identity-shuffled,
-one-second-delayed, and OR-pooled aggregate histories to ask which timely
-activity information a learned visual branch would need to recover.
-Although strictly past-only, these annotations are not available from raw
-media at inference; this is an information audit, not a deployable feature
-or a performance upper bound.
+For vision, Pool averages Qwen tokens within provided person boxes and
+across valid 2-FPS frames in the preceding second; the boxes are not
+outputs of an evaluated online detector. Spatial visual attention (SVA)
+instead lets $\G+\A$ query the unpooled spatial tokens of the latest
+strictly pre-cutoff full frame. Temporal spatial visual attention (TSVA)
+first applies a shared GRU to up to two causal tokens at each matching
+grid position, then uses the same query. TSVA links image coordinates,
+not person tracks. We denote these variants by $\G+\A+$Pool,
+$\G+\A+$SVA, and $\G+\A+$TSVA.
 
 \input{tables_compact}
 % Historical full-precision table layouts retained as source audit only.
@@ -405,23 +384,19 @@ Full path & -- & -- & -- \\
 \section{Experiments}
 \label{sec:experiments}
 
-\textbf{Data and protocol.}
-We use EasyCom with the EgoSpeak official split: Sessions~4--12 for training
-and Sessions~1--3 for testing.  Inputs and targets share a 5-Hz grid.  Heads
-are trained for four fixed epochs on all training sessions without test-time
-checkpoint selection.  The deployable feature matrix is an archived
-single-seed screen; the information audit uses seeds 0, 1, and 2 with an
-identical fixed recipe.  We therefore report standard deviations only for the
-latter and treat small differences in the former descriptively.
-
-\textbf{Ego4D transfer protocol (pending results).}
-Ego4D audiovisual clips \cite{grauman2022ego4d} use the existing EgoSpeak
-manifest and targets, with 346 training and 87 test clips.  The same 5-Hz
-cutoffs and local 1-s encoder windows define $\G$, $\G+\A$, $\G+\V$, and
-$\G+\A+\V$ comparisons; visual validity is retained rather than silently
-dropping no-box rows.  The repository contains the extraction and training
-workflow but no archived Ego4D score or matched PTR control.  Hence this
-protocol is specified, not reported as an observed cross-dataset gain.
+\textbf{Data Preparation.}
+We use the EgoSpeak EasyCom split (Sessions~4--12 for training and
+Sessions~1--3 for testing) and its Ego4D audiovisual manifest
+\cite{donley2021easycom,grauman2022ego4d}, which contains 346 training and
+87 test clips. Both datasets use 5-Hz causal cutoffs and targets, the same
+one-second local-audio window, and visual samples strictly preceding each
+cutoff; unavailable vision is masked rather than silently dropping rows.
+EasyCom heads use four fixed training epochs without test-set checkpoint
+selection. Its deployable feature matrix is an archived single-seed screen,
+whereas the privileged information audit uses three seeds under one recipe;
+small single-seed differences are descriptive. Ego4D extraction and training
+workflows exist, but test scores and a matched PTR comparison have not yet
+been archived, so cross-dataset gains are not claimed.
 
 \textbf{Metrics.}
 At each horizon we compute AP for the three activity channels over common
@@ -433,22 +408,6 @@ speech and scores future silence with $1-p(\mathrm{wearer})$.  These
 threshold-free quantities average ten horizons.  They are not one-event-one-
 vote detection, semantic ENTRY/YIELD, or evidence of addressee understanding.
 
-\textbf{Comparisons.}
-The cross-dataset main matrix contains released EgoSpeak A+V and the three
-Omni variants defined in Sec.~\ref{sec:method}. Missing SVA and Ego4D
-cells remain blank until measured. EasyCom-only encoder choices serve as
-diagnostic ablations rather than extra cross-dataset main rows. A three-seed
-current-frame person-set attention
-screen used Session~12 model selection whereas the historical pooled row did
-not; their difference is not a matched causal attribution.
-
-\textbf{Pre-specified analyses.}
-We freeze six control families: (L) capacity and recency, (T) tail
-refresh crossed with local audio, (C) six context caps, (B) paired backbone
-transfer, (H/Q) error slices and timelines, and (S) full-path efficiency.
-They share the official split and physical cutoffs; the 8-s T/C row reuses
-one run.  The source retains unmeasured hypotheses and falsifiers.
-
 \section{Results and Analysis}
 \label{sec:results}
 
@@ -549,7 +508,12 @@ 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:oracle} locates the missing information. Aggregate visible
+The analysis-only audit in Table~\ref{tab:oracle} substitutes two seconds
+of transcript-derived, strictly past visible-person activity on the 5-Hz
+grid. It tests visibility alone, per-person histories, shuffled identities,
+a one-second delay, and OR-pooled activity. These privileged labels are
+neither deployable visual features nor a performance upper bound.
+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

manuscript/targeted/tables_compact.tex

@@ -15,13 +15,13 @@ rows are exploratory and not all training-matched.}
 \cmidrule(lr){2-11}
 Dataset / model & .2 & .4 & .6 & .8 & 1.0 & 1.2 & 1.4 & 1.6 & 1.8 & 2.0 & Mean \\
 \midrule
-\multicolumn{12}{@{}l}{\emph{EasyCom (measured)}} \\
+\multicolumn{12}{@{}l}{\emph{EasyCom}} \\
 EgoSpeak A+V & 74.20 & 68.35 & 64.31 & 61.13 & 59.77 & 58.81 & 57.50 & 57.09 & 55.95 & 54.82 & 61.19 \\
 Omni $\G$ & 73.45 & -- & -- & -- & -- & -- & -- & -- & -- & -- & 60.85 \\
 Omni $\G+\A$ & 81.72 & 75.92 & 70.38 & 66.46 & 63.81 & 61.37 & 60.00 & 58.32 & 56.95 & 56.16 & 65.11 \\
 Omni $\G+\A+\mathrm{SVA}$ & -- & -- & -- & -- & -- & -- & -- & -- & -- & -- & -- \\
 \midrule
-\multicolumn{12}{@{}l}{\emph{Ego4D (transfer pending)}} \\
+\multicolumn{12}{@{}l}{\emph{Ego4D}} \\
 EgoSpeak A+V & -- & -- & -- & -- & -- & -- & -- & -- & -- & -- & -- \\
 Omni $\G$ & -- & -- & -- & -- & -- & -- & -- & -- & -- & -- & -- \\
 Omni $\G+\A$ & -- & -- & -- & -- & -- & -- & -- & -- & -- & -- & -- \\