Update llm assisted annotation

manuscript/main/main.tex

@@ -514,6 +514,130 @@ 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 provides participant-resolved speech intervals, transcripts, and
+target-of-speech metadata, but raw speech transitions do not uniquely specify
+conversational floor function. A wearer onset can be a genuine floor entry, a
+backchannel, or a within-turn resumption; a wearer offset can reflect yielding,
+natural completion, or a temporary pause. We therefore construct a separate
+behavioral-event layer while preserving the original speech intervals used for
+dense activity targets. The pipeline has three stages: deterministic candidate
+construction, language-model-assisted semantic pre-labeling, and mandatory
+human adjudication. The language model never supplies final ground truth.
+
+\subsection{Deterministic candidate construction}
+
+\textbf{Floor-level wearer entries.}
+Raw wearer onsets are conditionally grouped into floor-level episodes. Let
+$g$ be the silence from the preceding wearer offset to a new raw onset. In the
+current audit configuration, an onset is stored as a
+\textsc{Wearer-Resume} control rather than proposed as an EOT entry when
+$g\leq0.8$~s and the gap contains no plausible competing non-wearer floor
+activity. A non-feedback contribution that occupies at least $0.4$~s of the
+gap, or continues across the resumed wearer onset, conservatively preserves a
+new entry candidate. A short ($\leq1.0$~s) feedback-like lexical or nonverbal
+contribution is retained as evidence but does not by itself split the wearer
+episode. These rules group events only; they do not modify the source
+annotations.
+
+Following each candidate non-wearer release, the generator opens an EOT
+opportunity and scans forward until a floor-level wearer entry or a competing
+non-wearer onset terminates it. A retained \textsc{Wearer-Onset} candidate
+therefore contains two immutable anchors: the non-wearer release
+(\emph{opportunity anchor}) and the subsequent floor-level wearer onset
+(\emph{behavior anchor}). The response delay is stored explicitly. Silence
+duration alone does not cancel the opportunity; an unpaired wearer onset is
+kept only as an audit control and does not enter the primary EOT queue.
+
+\textbf{EOT hard negatives.}
+Two participant-resolved timing rules independently propose regions in which
+the wearer should remain silent. A \textsc{Same-Speaker-Pause} region is
+created when a non-wearer resumes within 2~s and both the wearer and all other
+participants remain silent in the intervening gap. An
+\textsc{Other-to-Other} region is created when a different non-wearer begins
+within 1.5~s, the original speaker does not first resume, and the wearer stays
+silent through the handover. The semantic pre-labeler can additionally assign
+\textsc{EOT-Wearer-Backchannel} to a floor-level onset that is interpretable
+as feedback rather than a response.
+
+\textbf{INT candidates and hard negatives.}
+Let $t_w$ be the start of the current floor-level wearer episode, $t_o$ a new
+non-wearer onset, and $t_y$ the episode-ending wearer offset. A potential
+\textsc{INT-Yield} is proposed only when
+\begin{equation}
+ t_w < t_o < t_y < t_o^{\mathrm{off}},
+ \label{eq:int_candidate}
+\end{equation}
+with a one-frame (50-ms) tolerance only at the offset boundary in the current
+audit. Thus the competing activity must begin after the wearer acquired the
+floor and must continue across the wearer yield. Pre-existing non-wearer
+activity, post-completion onsets, offsets internal to merged wearer episodes,
+and offsets without crossing activity are retained separately as
+\textsc{Wearer-Offset-Control} records and are not sent for semantic
+pre-labeling. Thus ordinary completion-to-silence cases do not enter the
+primary INT review queue.
+
+INT hard-negative regions are also proposed without requiring a wearer
+offset. If a non-wearer contribution ends while the wearer remains active
+throughout it and for at least another 0.4~s, the region is labeled by rule as
+\textsc{INT-Other-Backchannel} when it is short and feedback-like, and
+\textsc{INT-Non-Takeover-Overlap} otherwise. These timing labels are candidate
+priors, not final semantic annotations.
+
+\subsection{Semantic pre-labeling}
+
+For every primary candidate, the annotation assistant receives the candidate
+source and rule type, immutable timestamps, participant-resolved activity,
+neighboring onsets and offsets, gap and overlap measurements, transcript
+context, and target-of-speech metadata when available. Participant identities
+are used only inside this annotation package. The assistant is instructed to
+treat timing as factual, not to invent prosody, gaze, gesture, intention, or
+other unavailable cues, and not to equate onset with floor entry, offset with
+yield, silence with EOT, or overlap with interruption. EasyCom's \texttt{[L]}
+marker is treated as evidence of laughter only: it is not by itself a sustained
+floor take or takeover.
+
+The allowed decisions are \textsc{Positive}, \textsc{Hard-Negative},
+\textsc{Not-Applicable}, \textsc{Exclude}, and \textsc{Unsure}. Positive
+events are typed as \textsc{EOT-Response} or \textsc{INT-Yield}; hard
+negatives use a fixed subtype vocabulary. The current pre-labeling run uses
+\texttt{deepseek-v4-flash}, temperature zero, and two passes: a primary pass
+and a skeptical pass that seeks alternative explanations. If they disagree on
+the verdict, event type, or hard-negative subtype, the combined suggestion is
+\textsc{Unsure}. Model identifier, prompt version, per-request prompt hashes,
+both raw decisions, confidence values, and explanations are stored with every
+candidate.
+
+\subsection{Human adjudication and scoring eligibility}
+
+Every AI-pre-labeled candidate enters an event-centered interface containing
+the original video, local participant timeline, transcript, deterministic
+timing evidence, and the AI suggestion. The adjudicator performs one primary
+action: accept the complete AI label, relabel it, or exclude an unscorable
+sample. Relabeling exposes the same constrained verdict, event-type, and
+hard-negative subtype vocabulary. A confidence level is required; notes are
+optional, and an optional $\pm0.2$~s boundary correction is available only for
+an evident source-annotation error. The original event time is always retained.
+
+Only the completed human decision is eligible to become semantic ground truth.
+\textsc{Not-Applicable} denotes a valid observation outside the committed
+EOT/INT task, whereas \textsc{Exclude} denotes a region that cannot be scored
+reliably because of annotation corruption, a recording boundary, missing
+context, timestamp corruption, or another unresolved defect. Neither category
+enters the primary event denominator. Transcripts, participant identities,
+target-of-speech metadata, annotation-model outputs, and future observations
+are never inputs 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 EOT, 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.}
+
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