% Acceptance-target ICASSP 2027 draft grounded only in archived measurements. % Uses the repository's ICASSP 2027 author kit. % Four technical pages plus an optional reference-only page. \documentclass{article} \usepackage{spconf} \usepackage{amsmath,amssymb,graphicx,booktabs,multirow,hyperref,xcolor,float} \hypersetup{hidelinks} \newcommand{\vect}[1]{\boldsymbol{#1}} \newcommand{\G}{\mathrm{G}} \newcommand{\A}{\mathrm{A}} \newcommand{\V}{\mathrm{V}} \setlength{\emergencystretch}{2em} \setcounter{topnumber}{5} \setcounter{dbltopnumber}{5} \renewcommand{\topfraction}{0.95} \renewcommand{\dbltopfraction}{0.95} \renewcommand{\textfraction}{0.05} \renewcommand{\floatpagefraction}{0.75} \renewcommand{\dblfloatpagefraction}{0.75} \title{Egocentric Speech Anticipation with Omni Models:\\ Short-Window Evidence and Partial-Tail Refresh} \name{\shortstack{Xin Wang, Dingtian Yan, Changdong Liu, Shengxiang Hu, \\ Xiaoju Dai, Li Zhang, Rong Zheng, and Guorun Chen}} \address{China Telecom Corporation Limited Shanghai Branch} \begin{document} \ninept \maketitle \begin{abstract} An egocentric conversational agent must anticipate when its unseen camera wearer will speak from a causal multiparty audio--video stream. Turn-taking models and streaming Omni assistants address related problems, but the cutoff-level availability of local evidence in an integrated Omni state is underexplored for this wearer-centered task. We examine two interfaces: short-window encoder evidence (SWE) supplies recent audio and optional person-crop vision directly to a frozen-Omni forecasting head; partial-tail refresh (PTR) queries only the observed prefix of the current audiovisual block at each 200-ms cutoff. On EasyCom, the exploratory global-plus-audio screen reaches 81.72\% mAP at 0.2~s and 65.11\% over ten horizons, versus 74.20/61.19\% for released EgoSpeak. Pooled vision adds 0.29 mean-mAP points, while an analysis-only visible-activity audit exposes further local information. A separate chunk screen suggests sensitivity to state freshness but does not isolate PTR. We specify an Ego4D transfer protocol; its scores remain pending. The evidence supports causal activity anticipation, not semantic floor control or established cross-dataset gains. \end{abstract} \begin{keywords} egocentric speech anticipation, multimodal foundation models, causal streaming, voice activity projection, conversational agents \end{keywords} \section{Introduction} \label{sec:intro} An egocentric conversational agent must decide \emph{when} to speak even though its camera wearer is usually invisible. It hears a mixed conversation and sees other participants, but must anticipate its own speech from the observed stream before a turn boundary is known (Fig.~\ref{fig:overview}). This decision differs from recognizing who is speaking now: the relevant evidence may be a brief pause, overlap, or response by someone in view, while the target is the wearer's \emph{future} activity. It also differs from predicting a semantic end of turn or selecting an addressee. We therefore study a deliberately narrower and measurable question: at each 200-ms cutoff, how well can a causal observer forecast background, wearer, and aggregate-other activity over the next 0.2--2.0~s? Anticipatory turn-taking has developed rapidly: VAP, audiovisual and triadic extensions, backchannel prediction, online end-of-turn detection, and event benchmarks address increasingly realistic timing decisions \cite{ekstedt2022vap,russell2025mmvap,elmers2025triadic, lin2025backchannel,lee2024eot,qi2026muvap,jiang2026turnbench}. Their input viewpoints and target decisions, however, are not interchangeable with forecasting the future activity of an unseen wearer. The distinction matters when comparing systems: a correct current-speaker label or an utterance-final response can conceal errors in the first few hundred milliseconds, precisely where a wearable agent must decide whether to wait or begin speaking. \textbf{Egocentric audiovisual conversation.} EgoSpeak is the closest wearer-centered predecessor: it extracts audio and RGB features with specialized encoders, fuses their temporal evidence, and forecasts wearer speech on EasyCom and Ego4D \cite{kim2025egospeak, donley2021easycom,grauman2022ego4d}. Other first-person work addresses multi-view next-speaker prediction, speaking/listening graphs, or addressee detection \cite{serj2024threem,jia2024avconv,kong2024sicnet}. Egocentric active-speaker methods \cite{jiang2022asl,huh2025egoasd, clarke2025asd} and long--short person modeling \cite{wang2024loconet} recover present speaker evidence; audiovisual cues can also help turn prediction in noise \cite{oconnorrussell2025noise}. These studies make a broad ``first audiovisual turn-taking'' claim untenable. What remains less examined is how a single causal first-person stream should expose recent audio--visual evidence to forecast the invisible wearer's activity. EgoSpeak establishes that compact audio and video representations can be fused for this task, but it does not answer whether a large integrated Omni representation makes those cutoff-local cues readily available to a small forecasting head. Conversely, work on active speakers or addressees need not solve wearer anticipation: the visible interlocutor and the predicted speaker occupy different roles. This leaves an architectural question, rather than a claim that audiovisual evidence itself is new. \textbf{Streaming Omni and temporal access.} Omni systems integrate modalities and support streaming interaction \cite{xu2025qwenomni,cui2026minicpmo45,tian2026roma,yan2026duplug, yu2025salmonnomni,chen2024videollmonline,wang2025omnimmi}. Encoder-side audio--visual adaptation offers another route to accessible modality evidence \cite{lin2023lavish,rouditchenko2024whisperflamingo}. Qwen2.5-Omni uses block-wise audio processing \cite{xu2025qwenomni}; in our extraction, a 2-s block is long relative to a 200-ms decision interval. A streaming interface alone therefore does not specify how fresh the integrated representation is at each forecast cutoff. We study two complementary, cutoff-aligned interventions: \emph{short-window encoder evidence} (SWE) directly exposes recent modality features outside the Thinker state, while \emph{partial-tail refresh} (PTR) queries the observed prefix of the incomplete current block. SWE denotes temporal locality, not shallow encoder layers; the argument concerns this pipeline, not an intrinsic deficit of every Omni model. These interventions target different bottlenecks. SWE asks whether recent encoder evidence improves prediction when the global state is held fixed; PTR asks whether the same integrated backbone benefits from a fresher readout without admitting future media. A gain from one does not establish a gain from the other. In particular, the effect of PTR requires a matched completed-only comparison at the same context cap, rather than comparison with a longer or shorter history. This framing yields three concrete contributions. First, we formulate a causal, 5-Hz wearer-activity forecast and specify SWE and PTR as separable ways to expose local evidence to a frozen Omni model. Second, on EasyCom we compare global, recent-audio, and pooled-visual features, then use privileged past visible activity only as an information audit, not a deployable input. Third, we specify cross-dataset, endpoint, and hard-slice tests. EasyCom screening is the only measured evidence; matched PTR, Ego4D transfer, and event behavior remain open. \begin{figure}[t] \centering \includegraphics[width=\columnwidth]{figures/system_overview_onecolumn.pdf} \caption{Method overview at cutoff $t_n$. Top: SWE joins frozen Omni context with recent audio and optional pooled person-crop evidence in the forecasting head. Bottom: PTR reuses completed 2-s blocks and temporarily queries only the observed current-block tail at each 200-ms cutoff. The tail is not committed to persistent history.} \label{fig:overview} \end{figure} \section{Method} \label{sec:method} \subsection{Task and Short-Window Encoder Evidence} At the 5-Hz cutoff $t_n=0.2n$~s, the model observes only media available by $t_n$ and predicts activity at $t_n+h$, where $h\in\{0.2,0.4,\ldots,2.0\}$~s. Each horizon has three channels in fixed order: background, camera wearer, and aggregate others. Wearer and others may overlap; no output identifies an individual speaker, addressee, or semantic end of turn. The global feature $\vect g_n$ concatenates the final four frozen Qwen2.5-Omni-3B Thinker readouts and projects them to 2048 dimensions. It already contains audio and video context; $\G$ is not audio-free. SWE provides a separate short-window path so the head need not recover all cutoff-local detail from the integrated state. Local audio $\vect a_n$ crops the latest one-second mixed waveform \emph{before} preprocessing, runs the frozen Qwen audio encoder, and averages valid tokens. Optional $\vect v_n$ averages frozen vision tokens inside provided person boxes, then across people and valid causal 2-FPS frames in the latest second. This pooling has no identity or mouth-motion trajectory; provided boxes do not imply an online detector. Each enabled local feature is layer-normalized, projected to 256 dimensions, and paired with a validity bit. The trainable head is \begin{equation} \widehat{\vect Y}_{n}= \operatorname{softmax}_{c}F_{\theta} \bigl[\vect g_n;\widetilde{\vect a}_n;m_n^a; \widetilde{\vect v}_n;m_n^v\bigr], \label{eq:fusion} \end{equation} where omitted modalities remove their terms and $F_\theta$ is a layer-normalized MLP with a 30-logit output. We train only the projections and head with the EgoSpeak multi-positive cross-entropy over causal-valid source--horizon pairs. The Omni and modality encoders remain frozen, so this is frozen-backbone adaptation rather than end-to-end training. \subsection{Causal Partial-Tail Refresh} PTR uses source-aligned 2-s blocks under an 8-s maximum presentation cap (Fig.~\ref{fig:overview}). After warm-up, three completed blocks and the observed current tail are available; span varies with block phase. Reusing completed modality payloads, we crop the tail at $t_n$ and query it in a temporary cache. The tail and query are not committed. Only completed blocks enter persistent history; session and source discontinuities reset caches. Thus each 5-Hz readout sees the observed prefix without future backfill. To test whether access policy matters, the matched design crosses completed-only versus PTR readouts with $\A$ on/off at an identical 8-s cap; the separate 2--12-s cap sweep tests context length. Those comparisons are pending, so PTR is a causal access rule rather than a demonstrated gain. \subsection{Visible-Activity Information Audit} To distinguish missing information from ineffective learned vision, we build analysis-only features from transcript-derived \emph{past} activity. At each cutoff, visible non-wearer participants receive two seconds of binary history sampled on the same 5-Hz grid. The preparation fails if its aggregate activity disagrees with the released target. We compare: (i) visibility with activity zeroed, (ii) per-person histories, (iii) identities shuffled while preserving active-person counts at every lag, (iv) histories delayed by one 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 ten-horizon dense mAP; On/Off are raw wearer endpoint AP, not semantic events. F3 is a no-box, two-frame causal full-frame screen; it and the historical deployable rows are exploratory seed-0 runs with unmatched head/training details. F1 has three seeds. Dashes are unavailable, not zero.} \label{tab:main} \centering \normalsize \renewcommand{\arraystretch}{0.87} \setlength{\tabcolsep}{5.0pt} \begin{tabular}{@{}lccccc@{}} \toprule Method & $\mathrm{mAP}_{.2}\uparrow$ & $\mathrm{WAP}_{.2}\uparrow$ & Mean$\uparrow$ & On$\uparrow$ & Off$\uparrow$ \\\\ \midrule EgoSpeak MiniROADA A+V & 74.204 & 63.750 & 61.193 & 24.491 & 38.024 \\\\ Recent Qwen audio $\A$ & 79.692 & 84.100 & 60.508 & -- & -- \\\\ Omni global $\G$ & 73.446 & 77.867 & 60.849 & -- & -- \\\\ Omni $\G+\A$ & 81.723 & 85.066 & 65.109 & 25.960 & 45.251 \\\\ Omni $\G+\A+$pooled-$\V$ & 82.027 & 85.086 & 65.400 & 26.019 & 45.371 \\\\ No-box full-frame F3 & 82.380 & -- & 65.540 & -- & -- \\\\ Current-frame person set F1 & $81.777{\pm}0.156$ & -- & $64.951{\pm}0.095$ & -- & -- \\\\ Matched-width $\G$ (L2) & -- & -- & -- & -- & -- \\\\ Past-delayed $\A$ (L3) & -- & -- & -- & -- & -- \\\\ Current-state persistence (L4) & -- & -- & -- & -- & -- \\\\ \bottomrule \end{tabular} \par\smallskip \emph{Panel B: privileged past-activity audit (mean $\pm$ sample SD).}\par\smallskip \normalsize \setlength{\tabcolsep}{3.2pt} \begin{tabular}{@{}lccc@{}} \toprule Extra condition & $\mathrm{mAP}_{.2}$ & $\mathrm{WAP}_{.2}$ & Mean \\\\ \midrule None (audio baseline) & $81.869{\pm}.049$ & $85.264{\pm}.110$ & $65.177{\pm}.058$ \\\\ Visibility only & $81.945{\pm}.098$ & $85.200{\pm}.144$ & $65.217{\pm}.048$ \\\\ Activity delayed 1 s & $81.951{\pm}.086$ & $85.270{\pm}.137$ & $65.341{\pm}.051$ \\\\ Shuffled person IDs & $85.917{\pm}.085$ & $85.577{\pm}.246$ & $66.460{\pm}.090$ \\\\ Per-person activity & $86.266{\pm}.182$ & $85.608{\pm}.220$ & $66.660{\pm}.135$ \\\\ Aggregate visible activity & $\mathbf{86.696{\pm}.085}$ & $\mathbf{85.836{\pm}.147}$ & $\mathbf{66.951{\pm}.022}$ \\\\ \bottomrule \end{tabular} \end{table*} \begin{table*}[t] \caption{Chunk and context audit. Panel A is an unmeasured matched T/C matrix: each row pairs $\G$ and $\G+\A$ on common valid cutoffs. Panel B is a separate exploratory seed-0 global-only screen (10 epochs, 8-s cap, 2-FPS video); its rows are not entries in Panel A. Dashes are pending.} \label{tab:stream_context} \centering\normalsize \renewcommand{\arraystretch}{0.87} \setlength{\tabcolsep}{4pt} \begin{tabular}{@{}llccccc@{}} \toprule Group & Refresh / cap & $\G$ mAP & $\G+\A$ mAP & $\Delta$ mAP & $\Delta$ On & $\Delta$ Off \\ \midrule T & Completed-only, 8 s & -- & -- & -- & -- & -- \\ T & Observed-tail, 8 s & -- & -- & -- & -- & -- \\ \midrule C & Tail-aware, 2 s & -- & -- & -- & -- & -- \\ C & Tail-aware, 4 s & -- & -- & -- & -- & -- \\ C & Tail-aware, 6 s & -- & -- & -- & -- & -- \\ C & Tail-aware, 8 s & -- & -- & -- & -- & -- \\ C & Tail-aware, 10 s & -- & -- & -- & -- & -- \\ C & Tail-aware, 12 s & -- & -- & -- & -- & -- \\ \bottomrule \end{tabular} \par\smallskip \emph{Panel B: reported 200-ms chunk screen; dense mAP (\%).}\par\smallskip \begin{tabular}{@{}lccc@{}} \toprule Global feature construction & 0.2-s mAP & Mean mAP & Processor calls \\ \midrule Native 2-s completed hold & 59.62 & 55.19 & 9,539 \\ Non-native 200-ms commits & 67.05 & 57.84 & 95,453 \\ Completed blocks + observed tail & 70.85 & 59.17 & -- \\ \bottomrule \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.} \label{tab:backbone} \centering\normalsize \setlength{\tabcolsep}{3pt} \begin{tabular}{@{}lccc@{}} \toprule Backbone & $\G$ & $\G+\A$ & $\Delta$ \\ \midrule EC: Omni 3B & -- & -- & -- \\ EC: Omni 7B & -- & -- & -- \\ EC: MiniCPM-o 4.5 & -- & -- & -- \\ Ego4D: Omni 3B & -- & -- & -- \\ \bottomrule \end{tabular} \end{table} \begin{table}[t] \caption{H result slots: predeclared activity slices (unmeasured). Report positive/negative counts and per-session scores; AP is undefined for a pure-negative slice, which instead uses calibrated FPR.} \label{tab:hard_slices} \centering\normalsize \setlength{\tabcolsep}{2.5pt} \begin{tabular}{@{}lccc@{}} \toprule Slice & $N_+/N_-$ & AP & FPR \\ \midrule Wearer onset & -- & -- & -- \\ Wearer offset & -- & -- & -- \\ Other pause (negative) & -- & n/a & -- \\ Overlap (negative) & -- & n/a & -- \\ \bottomrule \end{tabular} \end{table} \begin{table}[t] \caption{S result slots: full-path cost on the same hardware (unmeasured). Include I/O and block transitions; logical 5 Hz is not measured throughput.} \label{tab:efficiency} \centering\normalsize \setlength{\tabcolsep}{3pt} \begin{tabular}{@{}lccc@{}} \toprule Stage & P50 ms & P95 ms & Peak GB \\ \midrule Completed block & -- & -- & -- \\ Observed tail & -- & -- & -- \\ Local path + head & -- & -- & -- \\ Full path & -- & -- & -- \\ \bottomrule \end{tabular} \end{table} \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{Metrics.} At each horizon we compute AP for the three activity channels over common causal-valid rows, then average classes to obtain mAP and average all ten horizons to obtain mean mAP. We also report wearer AP at 0.2~s. For endpoint diagnostics, onset AP conditions source rows on the wearer being silent and scores future speech with $p(\mathrm{wearer})$; offset AP conditions on wearer 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.} We evaluate released EgoSpeak MiniROADA A+V; recent audio $\A$; global Omni $\G$; $\G+\A$; and $\G+\A+$pooled-$\V$. A three-seed current-frame frame-set attention screen tests whether a richer person aggregation is already sufficient. Because that screen used Session~12 model selection whereas the historical pooled row did not, their difference is diagnostic, 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} \subsection{EasyCom: Local Encoder Evidence} Table~\ref{tab:main} shows that recent encoder-side audio $\A$ is strong at 0.2~s while integrated $\G$ is slightly stronger on the ten-horizon mean. Their seed-0 fusion gains 4.60 mean-mAP points over $\A$ and 4.26 over $\G$. Relative to released EgoSpeak, $\G+\A$ gains 7.52 points at 0.2~s and 3.92 mean-mAP points. Raw wearer offset AP rises by 7.23 points, onset AP by only 1.47: dense gains do not imply equal improvement at both activity boundaries. The $\G$-only head has more trainable parameters and longer history than the $\A$-only head. Thus this screen is consistent with predictive complementarity, not a capacity-matched proof that the Thinker lacks a specific local cue. Individual $\A$ and $\G$ horizon curves are unavailable under the same causal-valid summary and are not reconstructed in Table~\ref{tab:main}. % Retained as a planning aid; illustrative, unmeasured patterns do not belong % in the page-limited submission preview. \iffalse \begin{table*}[t] \caption{Six-group \textbf{illustrative scenario, not measured results}. These are conditional, pre-run interpretations for the pending matrix, not EasyCom scores. $\Delta$ denotes paired mean-mAP change on common valid cutoffs; signs and shape must be replaced by measured values even if contrary to the story. L/T/C/B require seeds 0--2 and per-session reporting.} \label{tab:scenario} \centering \normalsize \renewcommand{\arraystretch}{1.12} \setlength{\tabcolsep}{4.2pt} \begin{tabular}{@{}p{0.095\textwidth}p{0.29\textwidth}p{0.29\textwidth}p{0.235\textwidth}@{}} \toprule Group & Frozen comparison & Illustrative pattern \emph{if observed} & Falsifier / interpretation limit \\ \midrule L1--4 & $\A,\G,\G+\A,\G+\A+\V$; matched-width $\G$; past-delayed local cues; current-state persistence & $\Delta(\G+\A-\G)>0$ after width matching; delayed $\A$ loses near-horizon benefit; persistence does not recover the gain & Width-only or persistence matches fusion, or delay does not hurt: do not attribute gain to timely distinct evidence. \\ \addlinespace[2.5pt] T1--2 & 2-s completed-only vs 200-ms observed-tail refresh, crossed with $\A$ on/off; causal replay and phase audit & Tail helps most without $\A$; local $\A$ still adds at each refresh; future-only media never changes prior readouts & No independent local gain under tail refresh implies redundancy; replay/leakage failure invalidates timing claims. \\ \addlinespace[2.5pt] C1--2 & Genuine 2/4/6/8/10/12-s feature extraction; fixed 1-s $\A$; add $\G$-only at each cap for a window-stability claim & Fused performance plateaus around mid-range caps while $\G$-only remains more cap-sensitive & If long history substitutes for $\A$, local-evidence claim narrows; without C2, no robustness comparison is justified. \\ \addlinespace[2.5pt] B0--2 & Paired $\G$ and $\G+\A$ on Qwen 3B/7B and, after causal interface audit, MiniCPM-o 4.5 & Positive within-backbone paired $\Delta$ on all admissible rows; absolute backbone ranking is not inferred & Failed causal/history match is unsupported, not zero gain; one negative paired row defeats universal transfer. \\ \addlinespace[2.5pt] H1/Q1 & Predefined onset/offset and other-pause/overlap slices; same-horizon, cutoff-marked case selection & Offset gains coexist with hard wearer-onset negatives; examples explain, rather than replace, counted slice statistics & Pure-negative slice AP is undefined; selected examples cannot establish reduced false-entry rate. \\ \addlinespace[2.5pt] S1 & End-to-end p50/p95 latency and peak memory, including I/O, encoding, tail recomputation, local path, and boundaries & A feasible throughput--quality tradeoff can be plotted against 200-ms output cadence & If p95 exceeds cadence, describe logical 5-Hz prediction only, not real-time deployment. \\ \bottomrule \end{tabular} \end{table*} \fi \subsection{EasyCom: Visual Evidence and Ablations} Adding pooled $\V$ to $\G+\A$ produces only +0.304 points at 0.2~s and +0.291 mean mAP. Its wearer increment is +0.020 at 0.2~s and +0.061 when averaged over horizons; onset/offset changes are +0.059/+0.120. A richer current-frame person-set model is stable across seeds but reaches lower mean mAP than the historical pooled row under a non-matched selection protocol. These results do not support a claim of learned speaker grounding. A separate seed-0 no-box full-frame F3 screen reaches 82.38\% at 0.2~s and 65.54\% mean mAP, versus 81.72/65.11\% for the existing $\G+\A$ row under the same causal-valid dense protocol. F3 adds a two-frame 2-FPS token history and a 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 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 cannot explain the oracle gain. Per-person activity exceeds shuffled identity by only 0.200 mean-mAP points and is itself 0.291 below the aggregate condition. The current target therefore supports timely visible-floor activity as useful information, but not the necessity of identity continuity. The majority of the oracle's 0.2-s macro gain comes from background and others; wearer AP rises by only 0.572. This is why neither the oracle nor a dense mAP gain establishes fewer false wearer entries. \begin{figure}[t] \centering \fbox{\begin{minipage}[c][18mm][c]{0.94\columnwidth} \centering \textbf{Q1 case slots: examples pending}\\[2pt] Onset / other-pause hard negative \quad|\quad Offset / overlap\\ Show source cutoff, observed activity and same-horizon wearer scores. \end{minipage}} \caption{Reserved H/Q case figure. Select examples by a frozen rule after the slice counts in Table~\ref{tab:hard_slices} are available. No case or behavioral improvement is asserted by this placeholder.} \label{fig:hq_cases} \end{figure} \subsection{Chunk Timing and Cross-Dataset Limits} 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. 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 ownership. \subsection{Decision Rules and Reproducibility} 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} For egocentric when-to-speak forecasting, cutoff-aligned access to recent encoder evidence is a useful complement to frozen Omni context on EasyCom; the strongest measured boundary difference is at wearer speech offset. Pooled vision recovers little of the timely visible-activity information revealed by the privileged audit. PTR supplies a causal 5-Hz readout rule, but its independent predictive and efficiency effects still require matched tests. Ego4D transfer and censored event evaluation remain necessary before claiming generalizable or improved semantic floor behavior. \section{Acknowledgment} OpenAI Codex assisted with drafting and figure layout; the authors remain responsible for all experimental claims and final content. \clearpage \bibliographystyle{IEEEbib} \bibliography{references} \end{document}