% ICASSP 2027 content skeleton. The final paper must use the official 2027 % author kit and contain no visible \todo{} markers. Planned experiments are % intentionally marked rather than presented as completed work. \documentclass{article} \usepackage{spconf} \usepackage{amsmath,amssymb,graphicx,hyperref,xcolor} \newcommand{\todo}[1]{\textcolor{red}{\textbf{[TODO: #1]}}} \newcommand{\vect}[1]{\boldsymbol{#1}} \setlength{\emergencystretch}{2em} \title{Is It My Turn? Causal Local Evidence Adaptation of Omni Models for Egocentric Floor Forecasting} % TODO: Insert the final author list and affiliations after the ICASSP 2027 % review policy and author order are finalized. \name{Internal Manuscript Draft} \address{} \begin{document} \ninept \maketitle \begin{abstract} Real-time audiovisual agents must decide not only what to say, but whether an emerging floor opportunity belongs to them. This is difficult in multiparty egocentric interaction: the target wearer is behind the camera, while the visible people provide only indirect evidence about whether the surrounding floor remains occupied. We study strictly causal forecasting of the wearer's future speech activity every 200~ms. Although a general-purpose Omni model provides rich long-context audio--visual representations, its global readout is not explicitly trained to expose sub-second, wearer-relative floor evidence. We therefore study a candidate causal local floor adapter that combines the global Omni state with short-window mixed audio and an anonymous set of visible-person observations, without assuming persistent speaker identity. On EasyCom, \todo{report the final three-seed dense and fixed-horizon behavioral-event results: wearer-entry and INT-yield AP, recall-matched false-positive rates, confidence intervals, and oracle-gap recovery}. Controlled \todo{time-shift/remove-vision/same-rate or capacity-matched} interventions show that \todo{state the supported mechanism, without claiming addressee or speaker identity}. These results demonstrate \todo{the narrow conclusion actually supported by the final experiments}. \end{abstract} \begin{keywords} conversational agents, turn-taking, egocentric video, multimodal adaptation, voice activity projection \end{keywords} \section{Introduction} \label{sec:intro} Natural turn-taking requires a conversational system to anticipate not only when speech will stop, but whether a floor opportunity actually belongs to it. In a dyadic exchange, a pause after one interlocutor finishes often invites the other to respond. In a group conversation, the same pause may instead mark a handover between two other participants. An egocentric agent that reacts only to silence or aggregate speech may therefore interrupt a conversation that should continue without it. We study \emph{egocentric floor forecasting}: continuously predicting the near-future speech participation of the first-person wearer from strictly causal audio--visual observations. Voice Activity Projection (VAP) introduced continuous prediction of future voice activity for turn-taking \cite{ekstedt2022vap}. EgoSpeak brought this formulation to causal, untrimmed first-person streams, using future camera- wearer activity as an imitation target \cite{kim2025egospeak}. Complementary work adds visual gaze, head pose, and facial behavior to predictive turn-taking \cite{russell2025mmvap}, while MuVAP combines global acoustic dynamics with visible face tracks to forecast visible floor holders in multiparty scenes \cite{qi2026muvap}. Our target has a different role structure: the participant whose future action is predicted is the one participant absent from the visible set. The visible people are contextual evidence for the unseen wearer's decision, not candidate output identities. This role asymmetry exposes an adaptation problem for general-purpose Omni models. Omni pretraining favors globally integrated representations useful for multimodal understanding and generation. Egocentric floor control instead depends on timely prosodic and visible-person activity at sub-second scale, including whether other participants continue to occupy the floor. Increasing model capacity or applying a generic parameter-efficient update does not explicitly preserve or route this evidence to a dense wearer-relative prediction head. The open question is therefore not merely whether an Omni backbone can be applied to EgoSpeak, but what task structure is still required to turn general audiovisual understanding into reliable floor control. Our candidate decomposition separates long-context understanding from short-window floor evidence. A causal global Omni state represents the broader scene and conversation. In parallel, a lightweight local path encodes recent mixed audio and a changing, anonymous set of visible-person observations. A residual adapter makes this local evidence accessible to the common ten-horizon activity head. The primary variant deliberately avoids cross-time identity: the current development oracle supports timely aggregate visible-other activity but not person continuity. Track-aware structure is considered only if it defeats no-ID, identity-corrupted, and capacity-matched controls. The final paper will claim the following three contributions only if the corresponding TODO gates pass: \begin{itemize} \item We identify and test a temporal--role mismatch between a global Omni readout and causal egocentric floor forecasting for an invisible wearer. \item We evaluate a wearer-relative causal local floor adapter that combines long-context Omni information with short-window audio and anonymous visible-person evidence. \item We evaluate whether the adapter assigns lower erroneous-action scores during multiparty floor transfers under an entry/INT protocol with point positives and explicitly annotated hard-negative opportunity regions. \end{itemize} \begin{figure*}[t] \centering \includegraphics[width=0.98\textwidth]{figures/method_architecture.pdf} \caption{Our strictly causal global--local architecture (top) and streaming alignment (bottom). At every 5-Hz cutoff $t_n$, the global state includes the latest partial 2-s Omni block, while every fifth causal 25-Hz local state provides shared audio and an anonymous visible-person set. Masked person attention contributes only through a zero-initialized residual to the matched $G+U$ baseline.} \label{fig:method} \end{figure*} \section{Method} \label{sec:method} \subsection{Model Architecture} \label{sec:model_arch} At decision time $t$, the model observes only the audiovisual stream $\mathcal X_{\leq t}$ and predicts $K=10$ future bins spaced by $\Delta=0.2$~s. For horizon $k$, the multilabel target $\vect y_{t,k}=[y^{\mathrm{bg}},y^{\mathrm{w}},y^{\mathrm{o}}]$ denotes background, camera-wearer activity, and activity by one or more other people; wearer--other overlap is allowed. The output is \begin{equation} \hat{\vect Y}_t=f_\theta(\mathcal X_{\leq t}) \in[0,1]^{K\times3}. \label{eq:task} \end{equation} Our architecture complements globally integrated Omni context with explicitly accessible local floor evidence (Fig.~\ref{fig:method}). Let $\vect g_t\in\mathbb R^{d_g}$ denote the causal Qwen2.5-Omni Thinker readout at $t$ \cite{xu2025qwenomni}. In parallel, the frozen MuVAP ASD frontend \cite{qi2026muvap} produces one shared mixed-audio CPC state $\vect u_t$ and a variable-size set of visible-person states \begin{equation} \mathcal P_t=\{\vect p_{t,i}\}_{i=1}^{N_t},\qquad \vect p_{t,i}=[\vect v_{t,i};s_{t,i}], \label{eq:person_state} \end{equation} where $\vect v_{t,i}$ is a causal visual state and $s_{t,i}$ is one audio--visual speaking logit. The shared audio state is not duplicated in each person token. The prediction model receives neither participant IDs nor cross-time track identity; provided causal face boxes are used only to isolate the representation and fusion question. A matched global--audio head first maps $[\vect g_t;\vect u_t]$ to baseline logits $\vect Z_t^{\mathrm{base}}\in\mathbb R^{K\times3}$ and an intermediate state $\vect h_t$. The same inputs query the current person set: \begin{equation} \begin{aligned} \vect q_t &= W_g\vect g_t+W_a\vect u_t,\\ \mathcal M_t &= \{W_p\vect p_{t,i}\}_{i=1}^{N_t} \cup\{\vect p_\varnothing\},\\ \vect c_t &= \mathrm{MHA}(\vect q_t,\mathcal M_t,\mathcal M_t), \end{aligned} \label{eq:person_attention} \end{equation} where masking supports variable cardinality and the learned null token $\vect p_\varnothing$ makes the attention well defined for an empty set. If $m_t$ indicates that at least one valid person observation exists, the person branch supplies only a residual logit correction, \begin{equation} \vect Z_t=\vect Z_t^{\mathrm{base}} {}+m_t\,\mathrm{reshape}\!\left(W_r[\vect h_t;\vect c_t;m_t]\right), \qquad W_r\vert_{\mathrm{init}}=0, \label{eq:residual} \end{equation} and $\hat{\vect Y}_t=\mathrm{softmax}_c(\vect Z_t)$. Thus, after copying and freezing the trained $G+U$ head, the complete model initially reproduces that baseline exactly; only the person projection, query, masked attention, and residual readout are trained in the adaptation stage. We use the EgoSpeak/LSTR multi-positive cross-entropy over causal-valid rows, \begin{equation} \begin{aligned} \mathcal L&=-\frac{1}{M}\sum_{t,k,c}m_{t,k}y^c_{t,k} \log\mathrm{softmax}_c(\vect Z_{t,k}),\\ M&=\sum_{t,k}m_{t,k}. \end{aligned} \label{eq:loss} \end{equation} For an overlap target, both active channels contribute a log-probability term. No transcript, target-of-speech annotation, close-microphone signal, speech label, or future observation is supplied to the forecasting model. The wearer forecast is interpreted only at evaluation time: it is an entry proxy when the wearer is silent and its complement is a yield proxy when the wearer is active, not a trained event hazard or committed controller action. \subsection{Causal Streaming Architecture} \label{sec:causal_streaming} We distinguish encoder processing granularity from the common forecasting rate. Predictions are emitted at $t_n=n\Delta$, $n\geq1$, while every representation used at $t_n$ is restricted to media available by that physical cutoff. The global path uses an 8-s rolling causal presentation budget with source-aligned 2-s audiovisual blocks. Completed blocks are cached and replayed, while the currently incomplete block is a temporary tail. At every 200-ms cutoff, the tail is rebuilt with all newly observed audio and video and included in computing $\vect g_{t_n}$; when it reaches 2~s, it is committed to the reusable history. Consequently, the Omni state is not held constant for 2~s: it includes the latest available content at every decision cutoff without observing media after $t_n$. We retain the final four Thinker query-layer states and fuse them in the matched head with a concatenation MLP. The local frontend operates causally at 25~Hz. State $j$ consumes the half-open interval $[j/25,(j+1)/25)$ and is timestamped at its right edge. Mixed audio is encoded once as a continuous shared CPC stream, while each available face crop is processed by the causal visual and audio--visual speaking modules. Ten-second extraction chunks may prepend up to 2~s of past context, never future context. At a 5-Hz forecasting cutoff, we select the local state whose right edge equals $t_n$; hence every fifth 25-Hz state aligns with one Omni prediction row. The two paths therefore describe the same causal prefix: the Omni path preserves integrated conversational context, whereas the local path exposes fine-grained audio and visible-person floor evidence. Fusion and prediction occur only on the common 5-Hz grid. \section{Experiments} \label{sec:experiments} \subsection{Dataset and Data Preparation} We evaluate on EasyCom following the EgoSpeak split, using Sessions 4--12 as the training/development pool and Sessions 1--3 only for final evaluation \cite{donley2021easycom}. The participant-resolved speech annotations are converted into two parallel representations. First, they are downsampled to 5~Hz to construct the EgoSpeak-compatible ten-horizon targets for background, camera-wearer, and aggregate non-wearer activity. Second, the original participant timeline is retained to derive adjudicated interaction events for behavioral evaluation. We additionally apply the annotation workflow to the 3,608 Ego4D AV validation candidates as a protocol stress test; Ego4D is not used to train, tune, or compare the forecasting models reported here. \textbf{Behavioral event annotation.} We use every observed wearer activity transition as a high-recall semantic candidate. For each wearer onset, nearby preceding non-wearer releases, the intervening silence gap, and concurrent activity are retained as contextual facts; the onset is labeled as wearer entry only when the subsequent wearer speech constitutes a genuine floor take, including re-entry while another speaker is active. For each wearer offset, nearby non-wearer onsets and offsets, pre-existing and newly active speakers, overlap durations, and subsequent speakers are retained without a timing-based takeover gate; the offset is labeled as INT-yield only when a non-wearer genuinely takes over and the wearer relinquishes the floor. We let an LLM jointly inspect participant-resolved timing and transcripts to provide a semantic pre-label, followed by mandatory human adjudication. Separately, we mine regions with no wearer response that nevertheless constitute false-positive opportunities: same-speaker pauses and other-to-other transitions for entry, and backchannels or non-takeover overlaps for INT. Only human-confirmed positives and hard negatives enter evaluation. Appendix~\ref{app:event_annotation} gives the full protocol. Table~\ref{tab:behavior_annotation_stats} reports the current annotation workload compactly; the EasyCom snapshot covers Sessions 1--4 only and is not the final train/test annotation inventory. \begin{table}[h] \caption{LLM pre-label workload before human adjudication. Agreement is between two independent passes.} \label{tab:behavior_annotation_stats} \centering \scriptsize \begin{tabular}{@{}lrr@{}} \hline Measure & EasyCom S1--4 & Ego4D AV val \\ \hline Candidates & 2,128 & 3,608 \\ AI positive & 367 & 541 \\ AI hard negative & 1,017 & 1,174 \\ AI excluded & 744 & 1,893 \\ Two-pass agreement & 93.4\% & 82.1\% \\ Human-confirmed & 0 & 0 \\ \hline \end{tabular} \end{table} Participant identity is used only to construct these evaluation annotations and is never provided to the prediction model. Provided person boxes are used only for the causal visible-person input; transcripts, target-of-speech labels, close-microphone audio, and future observations are unavailable to the model. \todo{Report duration and event statistics: entry positives/hard negatives, INT positives/hard negatives, excluded candidates, and audited-region counts for train/development and test. Freeze the event-construction and region-audit protocol before reporting semantic entry/INT results; until then, retain the timing-derived proxy terminology.} \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 as a capacity-scaling control. All learned variants use identical causal examples, prediction horizons, prediction heads, and the multi-positive cross-entropy objective in Eq.~\eqref{eq:loss}. The diagnostic streaming ablation additionally uses MiniCPM-o 4.5 \cite{cui2026minicpmo45}. Its native cached stream is evaluated at its native one-second update interval: a state becomes available to the 200-ms row whose cutoff equals the chunk end and is then causally held until the next update. A Qwen row subjected to the same 1-Hz update-and-hold schedule separates refresh-rate effects from backbone effects. A MiniCPM-o 5-Hz cache row is admissible only if a cached branch at every cutoff agrees with full causal-prefix recomputation within a frozen numerical tolerance and passes future-input intervention tests. Thus the native 1-Hz condition is a strictly causal held-state baseline, not a claim of native 5-Hz refresh. Model selection is performed using training-side data only. Event operating points are selected from session-level out-of-fold predictions over Sessions~4--12; the final model is then retrained on all nine sessions before evaluation on Sessions~1--3. Final results 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 Protocol} We first retain dense future-activity evaluation for direct comparison with EgoSpeak, reporting AP at the 0.2--2.0~s horizons and their mean mAP. As a threshold-free diagnostic, we also report wearer OFF$\rightarrow$ON and ON$\rightarrow$OFF anticipation AP; these raw boundaries are timing proxies, not semantic EOT or INT labels. For dense-only models, behavioral evaluation is opportunity-conditioned and uses the model's original wearer-activity forecasts rather than treating them as semantic intent probabilities. An entry opportunity begins at a reviewed non-wearer release. It resolves positively when the wearer enters, or negatively when the same non-wearer resumes or a different non-wearer takes the next floor. From the first causal 5-Hz state that observes the release, the entry score is the maximum projected wearer probability before that competing resolution, capped at 2.0~s. Positive entries without a reviewer-selected release are reported as unanchored and excluded from this matched-opportunity analysis; they remain available to the separate raw-onset analysis. An INT opportunity begins when a non-wearer starts while the wearer holds the floor. It resolves positively when the reviewed behavior is an INT-yield, and negatively when the non-wearer contribution is a backchannel or failed takeover and the wearer retains the floor. The yield score is the maximum of one minus the projected wearer probability over the next 0.2--0.6~s, censored strictly at the competing non-wearer offset. All prediction values are read diagonally from one causal source row of the target-aligned forecast tensor. No input after the observed action outcome is used. For current INT-positive annotations, which do not select a competing onset explicitly, the implementation uses the latest supplied non-wearer interval crossing the wearer offset and surfaces cases with multiple eligible intervals for audit. The headline entry hard negatives are same-speaker mid-turn pauses and other- to-other transfers. Wearer backchannels are not pooled into headline false- entry rate: their high wearer probability is correct under the dense training target. We instead retain entry-versus-backchannel as a separate semantic- selectivity diagnostic. Thus, the semantic annotations define which matched opportunities are evaluated, while every model is scored solely through the same EgoSpeak-compatible dense output. A capacity-matched downstream probe, following MuVAP~\cite{qi2026muvap}, is secondary and is not needed for this zero-shot activity-derived comparison. \textbf{False-positive scoring rule.} A false positive is a hard-negative opportunity whose opportunity score exceeds the frozen threshold, regardless of whether the wearer subsequently exhibits an unrelated later onset or offset. Each opportunity contributes one score, and competing-risk censoring prevents later conversational outcomes from changing its label. Thus, false-positive evaluation includes situations in which the correct wearer behavior is to remain silent or retain the floor. We report threshold-free proxy AP for entry and INT. The headline behavioral operating point additionally reports false-positive rate at matched recall $R=0.8$; thresholds are selected independently for entry and INT from training- side out-of-fold predictions. The other-to-other hard-negative subset tests whether local visual evidence specifically reduces erroneous wearer action scores during multiparty floor transfers. \todo{Freeze the 2.0-s entry and 0.6-s yield caps using training-side data, audit selected-release and competing- onset coverage, and verify identical censored/valid denominators across methods.} A deployment-style committed-event analysis with one-to-one matching and signed latency is secondary and is omitted unless a threshold, persistence, refractory period, matching interval, and competing-risk rule are all frozen before test evaluation. The fixed-horizon protocol itself makes no detection- latency claim. \section{Results and Analysis} \label{sec:results} \subsection{Main Comparison} Table~\ref{tab:main} compares the proposed causal local audiovisual adapter with an EgoSpeak reproduction, the frozen Qwen2.5-Omni-3B global state $G$, matched PEFT, $G$ plus recent local audio, and a parameter- and history-matched temporal local branch. Qwen2.5-Omni-7B is included only as a capacity-scaling control. All methods use the same split, causal inputs, targets, and evaluation protocol. Event thresholds are selected on training-side calibration data and frozen before test evaluation. \todo{Freeze all baseline implementations and verify identical targets, causal validity, and evaluation.} \begin{table}[t] \caption{Confirmatory EasyCom results. Entry and INT false-positive rates use the opportunity-conditioned activity score at matched recall $R=0.8$; E/I AP is the corresponding opportunity AP. $\dagger$ denotes provisional LLM-assisted labels pending human review.} \label{tab:main} \centering \small \resizebox{\columnwidth}{!}{% \begin{tabular}{lccccc} \hline Method & 0.2-s mAP $\uparrow$ & Mean mAP $\uparrow$ & Entry FPR@$R=.8$ $\downarrow$ & INT FPR@$R=.8$ $\downarrow$ & E/I AP $\uparrow$\\ \hline EgoSpeak MiniROADA A+V & 74.12 & 61.16 & \todo{--} & \todo{--} & $52.49/20.13^\dagger$\\ Omni-3B frozen $G$ & \todo{--} & \todo{--} & \todo{--} & \todo{--} & \todo{--/--}\\ Omni-3B matched PEFT & \todo{--} & \todo{--} & \todo{--} & \todo{--} & \todo{--/--}\\ $G+$local audio & \todo{--} & \todo{--} & \todo{--} & \todo{--} & \todo{--/--}\\ $G+$matched temporal local & \todo{--} & \todo{--} & \todo{--} & \todo{--} & \todo{--/--}\\ $G+$causal local AV (ours) & \todo{--} & \todo{--} & \todo{--} & \todo{--} & \todo{--/--}\\ Omni-7B scaling control & \todo{--} & \todo{--} & \todo{--} & \todo{--} & \todo{--/--}\\ \hline \end{tabular}} \end{table} \textbf{Dense forecasting.} The EgoSpeak baseline reaches 74.12\% 0.2-s mAP and 61.16\% mean mAP; its raw wearer-onset/offset mean APs are 24.49/38.02\%. On provisional reviewed opportunities, ENTRY AP is 52.49\% versus 32.73\% prevalence, whereas INT AP is 20.13\% versus 20.44\%; continuous wearer-score plots therefore do not alone establish semantic floor-yield selectivity. The proposed local AV adapter improves 0.2-s mAP from \todo{$X$ to $Y$} over the strongest matched baseline and improves mean mAP by \todo{$Z$ points}. The gain is largest at \todo{short prediction horizons / transition-adjacent frames / specify actual observation}, indicating that local audiovisual evidence primarily improves \todo{the interpretation supported by the data}. \textbf{Annotated behavior.} At matched wearer-entry recall $R=0.8$, the proposed adapter reduces entry FPR from \todo{$X$} to \todo{$Y$}; at matched INT-yield recall, it changes INT FPR from \todo{$A$} to \todo{$B$}. Its entry/INT opportunity APs are \todo{$E/I$}. These recall-matched opportunity error rates test whether lower scores on confusing floor situations are obtained without suppressing legitimate entry or yield events. \textbf{Comparison with adaptation baselines.} Matched PEFT yields \todo{result}, whereas adding recent local audio alone yields \todo{result}. The parameter- and history-matched temporal branch achieves \todo{result}. The remaining gap between this control and the proposed adapter therefore suggests that \todo{correctly timed audiovisual local evidence contributes beyond additional temporal capacity}. \todo{If this difference is not statistically supported, state instead that the local temporal branch accounts for most of the gain.} \subsection{Information Opportunity} Before designing the deployable adapter, we measured whether local floor information was useful at all using an analysis-only oracle. The oracle receives perfectly timed visible-other speech activity through a non-deployable pathway and is never used in the main comparison. On development Session~12, aggregate visible activity improved 0.2-s mAP by 6.97 points and ten-horizon mean mAP by 2.91 points over the matched global-plus-local-audio baseline. In contrast, visibility alone and one-second-delayed activity remained close to the audio baseline, while person-specific histories did not outperform identity-shuffled histories. These controls indicate that the useful signal is primarily \emph{timely local floor evidence}, rather than participant identity or static visibility. The proposed adapter is therefore designed to approximate this information using only causally available audiovisual observations. \todo{After completing all seeds, report mean $\pm$ confidence interval here. If the oracle effect is unstable, describe it qualitatively and omit the exact numbers from the four-page version.} \subsection{Mechanism Analysis} To determine whether the gain arises from correctly aligned visual floor evidence rather than additional parameters or generic visual context, and to separate streaming refresh from representation family, we evaluate the controls in Table~\ref{tab:ablation}. \begin{table}[t] \caption{Mechanism and streaming/cache controls. The streaming panel uses frozen global states and capacity-matched downstream heads; all rows share targets and evaluation. FPRs are measured at matched recall $R=0.8$. Cross-family raw scores are diagnostic.} \label{tab:ablation} \centering \small \resizebox{\columnwidth}{!}{% \begin{tabular}{lcccc} \hline Condition & Mean mAP $\uparrow$ & Entry FPR $\downarrow$ & O$\rightarrow$O FPR $\downarrow$ & INT FPR $\downarrow$\\ \hline \multicolumn{5}{l}{\emph{Local-evidence controls (Qwen2.5-Omni-3B)}}\\ Remove vision & \todo{--} & \todo{--} & \todo{--} & \todo{--}\\ Time-shift vision & \todo{--} & \todo{--} & \todo{--} & \todo{--}\\ Full-frame vision & \todo{--} & \todo{--} & \todo{--} & \todo{--}\\ Correct local AV & \todo{--} & \todo{--} & \todo{--} & \todo{--}\\ \hline \multicolumn{5}{l}{\emph{Streaming/cache controls}}\\ Qwen, full causal prefix at 5 Hz (no cache) & \todo{--} & \todo{--} & \todo{--} & \todo{--}\\ Qwen, cached 5-Hz update & \todo{--} & \todo{--} & \todo{--} & \todo{--}\\ Qwen, cached 1-Hz + causal hold & \todo{--} & \todo{--} & \todo{--} & \todo{--}\\ MiniCPM-o, native cached 1-Hz + causal hold & \todo{--} & \todo{--} & \todo{--} & \todo{--}\\ MiniCPM-o, audited cached 5-Hz branch & \todo{--} & \todo{--} & \todo{--} & \todo{--}\\ \hline \end{tabular}} \end{table} Removing local vision changes \todo{entry/O$\rightarrow$O/INT FPR by $X/Y/Z$}, while shifting the visual stream by \todo{$\Delta t$} produces \todo{result}. Replacing person-local evidence with full-frame vision produces \todo{result}. If aligned local AV improves estimates of surrounding floor occupancy, its largest selective reduction should appear on the O$\rightarrow$O hard-negative subset. Together, these comparisons \todo{support / do not support} that mechanism beyond generic visual context. \todo{Report paired intervals and the supported interpretation. If correct and shifted/removed vision do not separate, drop the audiovisual evidence-routing claim. If dense mAP improves but event behavior does not, do not claim improved floor control. If legitimate onset or yield degrades, do not describe lower false entry as success.} \subsection{Scaling and Streaming Controls} Increasing the frozen Omni backbone from 3B to 7B yields \todo{$X$ points} in 0.2-s mAP and \todo{entry/INT FPR of $Y/Z$} at matched recall. The proposed 3B adapter \todo{remains better than / approaches / is surpassed by} the 7B scaling control. This result suggests that \todo{architectural access to local causal evidence provides a complementary benefit to backbone scaling}. Because both backbones belong to the Qwen2.5-Omni family, we do not claim that this conclusion generalizes to all omni-modal models. The streaming rows answer two different questions. First, full causal-prefix recomputation and streaming cache reuse at the same 5-Hz Qwen cutoffs test whether cache/chunk semantics change the representation; equivalent outputs make this an efficiency control rather than a distinct predictive method. Second, the matched Qwen 5-Hz and 1-Hz cached rows test whether an advantage is explained by more frequent causal state refresh rather than the proposed local evidence route. For native 1-Hz streams, the state emitted at the end of each one-second chunk is never backfilled into earlier 200-ms rows; source gaps and session boundaries reset all modality and language-model caches. MiniCPM-o is a secondary cross-family diagnostic under the same 1-Hz schedule, not a same-frequency architecture ranking. Its 5-Hz cached-branch row is omitted if cached/full-prefix equivalence, cutoff invariance, or reset audits fail. \todo{Freeze native chunk boundaries, initialization of rows before the first complete chunk, cache-reset rules, the cached/full-prefix tolerance, and the future-media intervention test. Report within-backbone 5-Hz versus 1-Hz deltas plus cache/no-cache throughput and memory; do not interpret unmatched Qwen--MiniCPM raw scores as a pure backbone effect.} \subsection{Limitations and Qualitative Analysis} The results support \todo{precise supported claim}, but several limitations remain. First, \todo{EasyCom size/session diversity limitation}. Second, the adapter relies on \todo{state the actual visible-person box or tracking assumption}. Third, \todo{failure condition: occlusion, overlapping speakers, rapid floor exchange, etc.} remains challenging. \todo{Add one compact, pre-selected qualitative timeline only if space permits: one multi-other false-entry improvement paired with one preserved real wearer onset or one failure. Examples illustrate aggregate rates and never establish the claim by themselves.} \section{Conclusion} \label{sec:conclusion} This study asks whether a general-purpose Omni representation can be adapted for causal floor forecasting of an invisible egocentric wearer. The candidate method separates long-context Omni understanding from short-window local floor evidence without assuming persistent speaker identity. \todo{State the final learned-method improvement, matched-recall entry/INT false-positive rates, fixed-horizon event AP, causal-control evidence, and main limitation. If no learned variant passes these tests, reframe the submission as an information audit or do not make the method claim.} \appendix \section{LLM-Assisted Behavioral Event Annotation} \label{app:event_annotation} EasyCom's raw speech boundaries are acoustic annotations rather than floor labels. We preserve them unchanged for dense forecasting and create a separate behavioral-event layer. Its design principle is to anchor an observed wearer behavior first and then retrieve the conversational opportunity that could make that behavior meaningful. False-positive opportunities are mined independently because, by definition, many contain no wearer transition. \subsection{Positive-event mining} \textbf{Wearer activity transitions.} We preserve every raw wearer $0\!\rightarrow\!1$ and $1\!\rightarrow\!0$ boundary as a semantic review candidate. Candidate construction does not merge short silence gaps, infer within-turn resumption, or decide whether overlapping speech constitutes takeover. It only records participant-resolved temporal and transcript facts. The source intervals and dense targets remain unchanged. \textbf{Wearer entry.} Every raw wearer onset at $t_b$ is proposed as one wearer-entry review item. We search backward within the retrieval window for non-wearer offsets and store each one with its response delay and factual indicators of intervening wearer speech, intervening other onsets, and concurrent activity. These are possible context anchors rather than deterministically admissible causes. The LLM and human reviewer decide whether the boundary is a genuine new entry, a within-turn resumption, a wearer backchannel, or an excluded non-event. A preceding release is not required for a positive entry: the wearer may enter after a release, from silence, or while another speaker remains active. When a release clearly prompts the entry, the LLM or human may select one supplied option; neither may invent or move a timestamp. \textbf{INT-yield.} Every raw wearer offset $t_y$ is proposed as an INT-yield review item. The generator records the current wearer activity-run start, all nearby non-wearer onsets and offsets, activity immediately before and after $t_y$, whether an other speaker is pre-existing or begins during wearer speech, strict and 50-ms-tolerant boundary crossing, overlap duration, the next speech onset, transcripts, and target-of-speech metadata. Relations such as $t_w