% 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 committed-event results: wearer-entry and INT-yield recall, false-positive rate, latency, 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 reduces erroneous wearer commits during multiparty floor transfers under an entry/INT event protocol with explicitly annotated hard-negative regions. \end{itemize} \begin{figure}[t] \centering \fbox{\parbox[c][1.00in][c]{0.94\columnwidth}{\centering \todo{Draw only the selected method: causal AV to the long-context Omni path; the short-window mixed-audio and anonymous person-set path; residual fusion; the common $10\times3$ head; and the separate wearer-entry/INT-yield evaluation. Mark physical cutoffs, trainable modules, person masks, and NULL/OFFSCREEN. Do not draw oracle labels, rejected track variants, or multiple backbones.}}} \caption{Long-context Omni understanding and short-window local floor evidence for wearer-relative future-activity prediction.} \label{fig:method} \end{figure} \section{Method} \label{sec:method} \subsection{Causal wearer-relative projection} At decision time $t$, the model observes only the stream $\mathcal X_{\leq t}$ and predicts $K=10$ future bins at $\Delta=0.2$~s. For bin $k$, 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 model outputs \begin{equation} \hat{\vect Y}_t=f_\theta(\mathcal X_{\leq t}) \in[0,1]^{K\times3}. \label{eq:task} \end{equation} The wearer-activity forecast also provides a causal control score. When the wearer is silent, $\hat y^{\mathrm w}_{t,1}$ acts as a near-term take-turn score; while the wearer is active, $1-\hat y^{\mathrm w}_{t,1}$ acts as a yield score. These scores are trained only from future activity targets. Committed wearer-entry and INT-yield events used for behavioral evaluation are defined separately from participant-resolved annotations in Sec.~\ref{sec:experiments}. \subsection{Global Omni baseline} Let $\vect g_t\in\mathbb R^{d_g}$ be the causal global readout of Qwen2.5-Omni at the physical cutoff $t$ \cite{xu2025qwenomni}. The frozen baseline trains only a dimension-matched prediction head. A matched PEFT/LoRA baseline updates a pre-registered subset of the Omni model under the same examples, context, optimizer budget, head, and evaluator. Its purpose is to test whether generic parameter adaptation is sufficient; one negative recipe is not interpreted as a general limitation of LoRA or all Omni models. \todo{Freeze the exact Qwen2.5-Omni-3B revision, processor, input rates, streaming/cache semantics, readout layer, context, dtype, LoRA targets/rank, trainable parameter budget, and cutoff audit. Add Qwen2.5-Omni-7B only as a capacity-scaling control. MiniCPM-o 4.5 is restricted to the diagnostic streaming/cache ablation below and does not enter the core method or support a cross-family superiority claim.} \subsection{Causal local floor adapter} The candidate local path operates on the most recent $L_\ell$ seconds. At local instant $\tau\leq t$, a causal audio encoder produces $\vect a_\tau$ from mixed audio. For the anonymous visible-person set $\mathcal P_\tau=\{(\vect x_{\tau,i},\vect b_{\tau,i})\}_{i=1}^{N_\tau}$, 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} \cup\{\vect v_\varnothing\}\right), \label{eq:set} \end{equation} where $\vect v_\varnothing$ handles an empty or offscreen visible set. The operator is permutation invariant and uses no participant-ID embedding or cross-time identity. A causal short-window encoder summarizes local dynamics, \begin{equation} \vect \ell_t=F_\ell \left(\{[\vect a_\tau;\vect v_\tau]\}_{\tau\in(t-L_\ell,t]}\right). \label{eq:local} \end{equation} The adapter preserves the global initialization through a zero-initialized residual output, \begin{equation} \vect z_t=\vect g_t+W_o\, \sigma\!\left(W_g\vect g_t+W_\ell\vect\ell_t\right), \qquad W_o\vert_{\mathrm{init}}=0, \label{eq:fusion} \end{equation} and the common head maps $\vect z_t$ to Eq.~\eqref{eq:task}. This implements an evidence-access hypothesis rather than adding an unconstrained second model: the global path retains broad context, while the local path preserves timely floor evidence. \todo{Freeze $L_\ell$, local audio/video rates, crop source and resolution, set pooling, causal temporal encoder, masks, reset rules, parameter count, and latency. If the local path runs faster than the 5-Hz global/output grid, compare it with a same-parameter 5-Hz variant before using the term ``dual-rate.'' Use provided causal boxes only as a clearly labeled method-isolation condition; online detection/tracking is not a paper contribution unless actually run.} \subsection{Training} The primary objective is class-balanced binary cross entropy over valid horizons and activity channels, \begin{equation} \mathcal L_{\mathrm{act}}= \sum_{t,k,c}m_{t,k}w_c\, \mathrm{BCE}(\hat y^c_{t,k},y^c_{t,k}). \label{eq:loss} \end{equation} No transcript, target-of-speech label, close microphone, future frame, or per-person speech label enters the deployable model. \todo{Add an active-speaker or event auxiliary loss only if it improves the frozen primary protocol and its label/input boundary is fully reported.} \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 committed interaction events for behavioral evaluation. \textbf{Behavioral event annotation.} We use floor-level wearer transitions as high-recall anchors for positive-event annotation. Each wearer onset is examined retrospectively for a preceding non-wearer floor release as contextual metadata and labeled as a wearer-entry whenever the subsequent wearer speech constitutes a genuine floor take, including re-entry while another speaker is active. Similarly, each wearer offset is examined for a new competing non-wearer onset during the current wearer turn and labeled as INT-yield only when the competing speaker genuinely takes over the floor. We use permissive temporal retrieval and 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. \begin{table}[t] \caption{Behavioral-event annotation statistics after human adjudication. Counts must be filled only after the candidate protocol and annotation set are frozen. Scoring-eligible totals include confirmed positives and hard negatives only.} \label{tab:behavior_annotation_stats} \centering \small \begin{tabular}{lcc} \hline Annotation outcome & Train/dev & Test \\ \hline \multicolumn{3}{l}{\emph{Wearer entry}}\\ Positive: wearer entry & \todo{--} & \todo{--} \\ HN: same-speaker pause & \todo{--} & \todo{--} \\ HN: other-to-other transition & \todo{--} & \todo{--} \\ HN: wearer backchannel & \todo{--} & \todo{--} \\ \hline \multicolumn{3}{l}{\emph{INT-yield}}\\ Positive: INT-yield & \todo{--} & \todo{--} \\ HN: other backchannel & \todo{--} & \todo{--} \\ HN: non-takeover overlap & \todo{--} & \todo{--} \\ HN: wearer holds floor & \todo{--} & \todo{--} \\ \hline Excluded / fallback & \todo{--} & \todo{--} \\ \textbf{Scoring-eligible total} & \todo{--} & \todo{--} \\ \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 class-balanced binary 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. Behavioral evaluation follows a committed-event protocol. For both wearer-entry and INT-yield, each gold event is matched to at most one committed prediction and is scored by recall and signed detection latency. \textbf{False-positive scoring rule.} A false positive is defined by the model committing inside a labeled hard-negative region, regardless of whether the wearer subsequently exhibits an onset or offset. Thus, false-positive evaluation includes conversational situations in which the correct wearer behavior is to remain silent or retain the floor. We report the recall--false-positive trade-off and a common operating point at matched recall $R=0.8$. The primary behavioral quantities are therefore entry false-positive rate, INT false-positive rate, and signed latency at the frozen operating point. We additionally report performance on the other-to-other subset to test whether local visual evidence specifically reduces erroneous wearer turn-taking during multiparty floor transfers. \todo{Freeze the FPR denominators and report signed median latency separately for entry and INT.} \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 are measured at matched recall $R=0.8$. E/I Lat. gives signed median entry/INT latency in seconds; values closer to zero are better.} \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 Lat.\\ \hline EgoSpeak reproduction & \todo{--} & \todo{--} & \todo{--} & \todo{--} & \todo{--/--}\\ 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 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{Committed 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 signed median entry/INT latencies are \todo{$+0.XX/+0.XX$~s}. These recall-matched error rates and signed latencies test whether fewer erroneous commits arise from better floor decisions rather than indefinite waiting. \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, signed latency, 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{Floor-level transitions.} Short wearer-silence gaps are conditionally merged before positive mining. In the current audit configuration, a raw onset after at most 0.8~s of silence is treated as a within-turn resumption unless the gap contains sustained competing non-wearer activity. Short feedback-like activity does not by itself split the wearer episode. This grouping changes only the behavioral-event layer, not the source intervals or dense targets. \textbf{Wearer entry.} Every floor-level wearer onset at $t_b$ is proposed as a wearer-entry candidate. We additionally search backward for plausible non-wearer floor releases. A release at $t_r$ is retained as a contextual option only when the interval from $t_r$ to $t_b$ contains neither intervening wearer speech nor a competing non-wearer onset. Each wearer onset contributes at most one review item: $t_b$ is its immutable \emph{behavior anchor}, and the item stores every admissible \emph{opportunity-anchor} option and response delay. Silence duration alone does not reject an option. 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 trigger timestamp. The release relation is metadata for response-oriented analyses, not part of the positive entry label. \textbf{INT-yield.} Starting from an episode-ending wearer offset $t_y$, we search backward inside the current wearer episode for a new competing non-wearer onset $t_o$. With $t_w$ denoting the wearer-episode start, an INT candidate must satisfy \begin{equation} t_w < t_o < t_y < t_o^{\mathrm{off}}. \label{eq:int_candidate} \end{equation} The current audit allows a one-frame (50-ms) tolerance only at the offset boundary. Pre-existing other activity, post-completion onsets, offsets inside a merged wearer episode, and completion-to-silence remain controls. Timing only retrieves a possible interruption; the LLM and human annotator decide whether the competitor genuinely takes over and the wearer yields. \subsection{False-positive opportunity mining} Hard-negative regions are retrieved independently of positive wearer transitions. For entry, a same-speaker pause followed by resumption and an other-to-other handover while the wearer stays silent are false-entry opportunities. For INT, a short non-wearer backchannel or a non-takeover overlap during which the wearer retains and continues the floor is a false-yield opportunity. The timing rules provide candidate types, not final semantic labels. \subsection{Semantic pre-labeling and human adjudication} The annotation assistant jointly receives immutable timestamps, participant-resolved activity, neighboring boundaries, gap and overlap measurements, transcripts, and target-of-speech metadata when available. It is instructed not to equate raw onset with floor entry, offset with yield, or silence with an entry opportunity, or overlap with interruption, and not to invent unavailable prosody, gaze, or intent. EasyCom's \texttt{[L]} marker supplies laughter evidence only and is not by itself a sustained floor take or takeover. Preceding release options are optional metadata for a positive wearer-entry pre-label. We use \texttt{deepseek-v4-flash} at temperature zero with a primary and a skeptical pass. Disagreement on verdict, event type, or hard-negative subtype produces an \textsc{Exclude} suggestion. Every candidate then receives mandatory human adjudication: accept the complete pre-label and optional release metadata, relabel it, or exclude an unscorable region. Only human-confirmed \textsc{Wearer-Entry}, \textsc{INT-Yield}, and hard-negative opportunities enter evaluation; \textsc{Exclude} candidates do not. Thus \textsc{Exclude} is the sole non-scoring outcome for out-of-scope non-events (e.g., natural wearer completion that merely coincides with feedback), insufficient evidence, unresolvable data quality, or pre-label disagreement. Annotation-only transcripts, identities, target-of-speech metadata, LLM output, and future observations are never supplied to the evaluated forecasting model. \todo{Before freezing this annotation protocol, audit randomly sampled continuous timeline segments without candidate hints to estimate candidate recall for entry, INT, and each hard-negative family. Independently double-label a pre-registered subset, report its size and agreement statistic, freeze all candidate parameters and prompt/model identifiers on Sessions 4--12, and report final candidate, control, exclusion, and adjudicated-label counts.} \bibliographystyle{IEEEbib} \bibliography{references} \end{document}