Update manuscript

manuscript/main/Makefile

@@ -1,8 +1,4 @@
 LATEXMK ?= latexmk
-TEMPLATE_DIR := $(abspath ../ICASSP2026_Paper_Templates)
-
-export TEXINPUTS := $(TEMPLATE_DIR):
-export BSTINPUTS := $(TEMPLATE_DIR):
 
 .PHONY: all clean
 

manuscript/main/README.md

@@ -3,7 +3,7 @@
 This is the editable paper directory. It uses the unmodified official ICASSP
 2026 LaTeX assets stored in `../ICASSP2026_Paper_Templates/`. When the ICASSP
 2027 author kit is released, replace or add the official template and update
-`TEMPLATE_DIR` in the Makefile only after checking the new instructions.
+`template_dir` in `latexmkrc` only after checking the new instructions.
 
 Compile from this directory:
 
@@ -14,12 +14,19 @@ make
 The PDF and all intermediate files are written to `build/`; only source files
 belong in Git.
 
+VS Code's LaTeX Workshop extension is supported when the repository is opened
+as the workspace. Open `main.tex` and run **LaTeX Workshop: Build LaTeX
+project**. The workspace setting writes output to `build/`, while `latexmkrc`
+provides the template and bibliography-style search paths to both LaTeX
+Workshop and command-line builds.
+
 Current source layout:
 
 ```text
 main/
   main.tex
   references.bib
+  latexmkrc
   Makefile
   README.md
   build/            # generated, ignored

manuscript/main/latexmkrc

@@ -0,0 +1,9 @@
+my $path_separator = $^O eq 'MSWin32' ? ';' : ':';
+my $template_dir = '../ICASSP2026_Paper_Templates';
+
+$ENV{'TEXINPUTS'} =
+    $template_dir . $path_separator . ($ENV{'TEXINPUTS'} // '');
+$ENV{'BSTINPUTS'} =
+    $template_dir . $path_separator . ($ENV{'BSTINPUTS'} // '');
+
+$out_dir = 'build';

manuscript/main/main.tex

@@ -169,7 +169,7 @@ $\mathcal P_\tau=\{(\vect x_{\tau,i},\vect b_{\tau,i})\}_{i=1}^{N_\tau}$,
 a shared visual encoder and masked set operator produce
 \begin{equation}
  \vect v_\tau=\mathrm{SetPool}
- \left(\{E_v(\vect x_{\tau,i},\vect b_{\tau,i})}_{i=1}^{N_\tau}
+ \left(\{E_v(\vect x_{\tau,i},\vect b_{\tau,i})\}_{i=1}^{N_\tau}
  \cup\{\vect v_\varnothing\}\right),
  \label{eq:set}
 \end{equation}
@@ -219,16 +219,44 @@ its label/input boundary is fully reported.}
 \section{Experiments} 
 \label{sec:experiments} 
 
-\textbf{Data and split.}
-We follow the EgoSpeak EasyCom split: Sessions 4--12 for training-side model
-development and Sessions 1--3 for final reporting \cite{donley2021easycom}.
-Thresholds, windows, checkpoints, loss weights, and early stopping are selected
-without using the official test sessions. Already observed test results are
-treated as exploratory internal evidence and are not included in the paper.
-\todo{Report duration, frame/event counts, class prevalence, visibility and
-box coverage, source discontinuities, and the frozen calibration fold.}
+\subsection{Dataset and Data Preparation}
 
-\textbf{Baselines.}
+We evaluate on EasyCom following the EgoSpeak split, using Sessions 4--12 for training and development and Sessions 1--3 for final evaluation \cite{donley2021easycom}. All hyperparameters, event windows, operating thresholds, and checkpoints are selected without access to the test sessions. The input stream is converted to strictly causal examples at 5~Hz. Each decision point is paired with ten future 0.2-s activity bins for background, camera wearer, and aggregate non-wearer speech. Provided person boxes are used only to construct the visible-person inputs; participant identity, close-mic audio, transcripts, and future observations are not available to the model. \todo{Report dataset duration, number of training/test examples and events, and the frozen development/calibration split.}
+
+\subsection{Model Configuration and Training} 
+
+Our main backbone is Qwen2.5-Omni-3B. We compare a frozen global Omni readout, matched PEFT/LoRA adaptation, global features with recent local audio, a parameter- and history-matched temporal local branch, and the proposed causal local audio--visual adapter. Qwen2.5-Omni-7B is used only as a capacity-scaling control. All learned variants use the same causal context, prediction head, training examples, and class-balanced binary cross-entropy objective in Eq.~\eqref{eq:loss}. Unless otherwise stated, model selection is performed on the development data and final numbers are averaged over three random seeds. \todo{Freeze context length, local window/rates, optimizer, epochs, LoRA configuration, trainable parameter counts, and inference latency.}
+
+\subsection{Evaluation Metrics}
+
+We evaluate both dense future-activity prediction and event-level floor
+control. For comparability with EgoSpeak, we report per-frame AP at
+$0.2$--$2.0$~s horizons and their mean mAP.
+
+For event-level evaluation, a \emph{wearer entry} is a transition from wearer
+silence to speech, while a \emph{wearer yield} is a transition from speech to
+silence. An entry prediction is considered correct if it is committed within
+the predefined response window of a ground-truth wearer onset. We report entry
+recall, false entries per minute of eligible wearer-silent time, and signed
+onset latency. Yield events are evaluated analogously using recall and latency.
+
+Multiparty interaction introduces a competing-risk condition. After another
+speaker releases the floor, the current wearer-entry opportunity is terminated
+if another non-wearer participant starts speaking before the wearer does.
+Predictions after this competing onset are therefore not matched to the
+previous opportunity. This prevents an other-to-other handover from being
+treated as an open floor for the wearer.
+
+Operating thresholds are selected on the development split and frozen for test
+evaluation. Our primary behavioral metric is false wearer entries per minute at
+a matched wearer-entry recall of $0.8$; dense mAP and wearer-yield performance
+are reported as complementary measures.
+
+
+\section{Results and Analysis}
+\label{sec:results}
+
+\subsection{Baselines}
 The confirmatory comparison contains: an EgoSpeak reproduction; the frozen
 Qwen2.5-Omni-3B global state $G$; matched LoRA/PEFT; $G$ plus recent local
 audio; a parameter- and history-matched temporal local branch; and the proposed
@@ -237,40 +265,6 @@ not a second paper storyline. \todo{Freeze all baseline implementations and
 ensure the EgoSpeak comparison uses identical targets, split, causal validity,
 and evaluation.}
 
-\textbf{Event protocol.}
-Dense ten-horizon AP/mAP is retained for EgoSpeak comparability. The implemented
-competing-risk evaluator counts each eligible aggregate-other boundary once,
-ends a response window before the next other onset, and ends a yield window
-before the next other offset. Pending the remaining P0 real-data audit and
-scenario freeze, the intended primary behavior metric is false wearer entries per
-minute of multi-other opportunity at a training-side selected wearer-onset
-recall, with $R=.8$ as the headline operating point. The intended analysis also
-reports onset latency and wearer-yield recall/latency, following the
-event-oriented emphasis on recall, false alarms, and latency
-\cite{jiang2026turnbench}. The protocol will censor a response window after an
-other offset at the next other onset, and a yield window after an other onset
-at the next other offset.
-
-\todo{P0 blocker: run the tested competing-risk evaluator on real predictions,
-manually audit sampled timelines, and freeze pairing before treating any event
-score as a paper result. Specify overlap,
-minimum duration, timeouts, duplicate detections, source boundaries, offscreen
-cases, and event counts. Until this is complete, call existing values
-timing-derived proxies rather than final event results.}
-
-\textbf{Controls and statistics.}
-The main intervention compares correctly aligned local evidence with
-time-shifted evidence, removed vision, global-frame visual pooling, and the
-capacity-matched temporal branch. A same-rate control is required for a
-dual-rate claim. Track/no-ID/broken-continuity controls are added only if a
-track-aware variant first wins on training-side data. We report three seeds,
-mean$\pm$sample standard deviation, and paired cluster-bootstrap intervals over
-sessions or conversation blocks. \todo{Freeze seeds, bootstrap unit/count,
-optimizer, epochs, hardware, throughput, P50/P95 latency, and trainable
-parameters.}
-
-\section{Results and Analysis}
-\label{sec:results}
 
 \begin{table}[t]
 \caption{Confirmatory EasyCom results. Lower multi-other false entries are