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Restructure local-evidence manuscript and record analysis controls

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Xin Wang
2026-09-17 02:14:56 +08:00
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manuscript/main/README.md
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# Main ICASSP manuscript
## Current structure (2026-09-17)
The current draft uses **causal local evidence adaptation** as the single
umbrella for recent modality evidence and cutoff-aligned Omni tail access.
It supersedes the older scope description below; historical strategy docs
remain records of their dates. The new scoped decision is
[`../../docs/manuscript_local_evidence_structure_2026-09-17.md`](../../docs/manuscript_local_evidence_structure_2026-09-17.md).
- Method has two subsections: **Model Architecture** (task, global/local
features, fusion and learning) and **Streaming Inference Architecture**
(8-s maximum context, completed-history reuse, temporary current tail,
cutoff alignment). Only one displayed fusion equation remains.
- The core is frozen Qwen Omni `G` + recent 1-s Qwen audio `A`; pooled
person vision is optional, not an established performance contribution.
- The three contributions cover global--local adaptation, its partial-tail
streaming realization, and controlled dense versus onset/offset analysis.
Caching alone is not claimed as a new algorithm; mechanism gains await
matched controls.
- The method figure now depicts that actual core rather than an unmeasured
25-Hz MuVAP person-attention branch. Preserved-EgoSpeak residual fusion and
its block-phase embedding are auxiliary controls, not the default head.
- Semantic opportunity evaluation is deferred during annotation auditing.
Its historical protocol and workload are preserved in
[`annotation_protocol_notes.tex`](annotation_protocol_notes.tex), which is
deliberately not included in the submission PDF. Historical results remain
in the original `results/` and `reports/` records.
- Analysis uses Main Comparison, Local Evidence Analysis, Streaming and
Context Analysis, and Error and Qualitative Analysis. Omni LoRA is deferred.
Planned experiments are in the
[dated experiment log](../../docs/local_evidence_analysis_experiment_log_2026-09-17.md).
- Locked main rows and the tail-refresh x local-audio / context-cap table are
unmeasured placeholders, not zero or mixed-protocol confirmatory numbers.
Historical main-table screens are identified explicitly. Already inspected
official-test runs remain exploratory despite additional seed repetitions.
- Backbone transfer adds paired G/GA controls for Qwen2.5-Omni 3B/7B and
MiniCPM-o 4.5, fixing the same local audio sidecar. Compact table deltas are
pending and require readout/refresh/history audits; MiniCPM is not yet
GPU-verified or an established matched 8-s stream. This scoped transfer
overrides the older cache-diagnostic-only restriction below, not the 3B core.
- The body targets four technical pages with one method figure, one main
table, one compact streaming/transfer controls table, and one error/qualitative figure slot.
The optional fifth page is references only; replace TODOs rather than keep
appending, and finalize author kit and applicable AI-drafting disclosure.
## Build and historical scope
This is the editable paper directory. It uses the unmodified official ICASSP
2026 LaTeX assets stored in `../ICASSP2026_Paper_Templates/`. When the ICASSP
2027 author kit is released, replace or add the official template and update
manuscript/main/REVIEWER_CHECKLIST.md
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This is an internal execution checklist. It is not part of the submitted PDF.
## Current priority override (2026-09-17)
Use the [local-evidence structure decision](../../docs/manuscript_local_evidence_structure_2026-09-17.md)
for the current draft. The older semantic/person-grounded checklist below is
a conditional extension, not authorization to describe it as implemented or
a prerequisite for this narrower activity-anticipation study.
- [x] Separate model architecture from streaming inference architecture.
- [x] Describe actual G+A fusion, optional pooled vision, and observed-tail
recomputation; retain only one displayed method equation.
- [x] Move the provisional LLM annotation protocol/workload outside the paper
body without relabeling it as human ground truth.
- [x] Reserve local-evidence, streaming/context, and combined error/qualitative
analysis slots; record pending experiments without LoRA expansion in the
[dated log](../../docs/local_evidence_analysis_experiment_log_2026-09-17.md).
- [ ] Complete a training-side-frozen A/G/G+A matrix with three seeds and common
causal-valid pairs; audit capacity and distinguish their history ranges.
- [ ] Evaluate optional vision on/off before retaining a visual-gain claim.
- [ ] Complete tail refresh x local-audio four-group comparison with enabled
local audio fixed at 5 Hz; report phase strata and same-decomposition replay.
- [ ] Re-extract/train the predeclared 4/8/16-s context-cap controls; add G at
each cap before claiming local fusion reduces context sensitivity.
- [ ] Add a predicted-current-activity persistence reference and capacity/recency
controls; do not supply GT activity as model input.
- [ ] Report scene-level activity-error counts and fixed-horizon timelines,
including a shared failure; semantic negatives cannot override raw labels.
- [ ] Report dense AP/mAP and conditional endpoint onset/offset AP separately,
including denominators, per-session results, and limits of the three-session
official test. Do not claim one-event-one-vote detection.
- [ ] Measure full extraction latency/memory before claiming real-time FPS.
- [ ] Run paired G/GA backbone-transfer controls for Qwen Omni 3B/7B and
MiniCPM-o 4.5 with the same local A; report within-backbone deltas and audit
readout, dimensions, refresh, and actual history. MiniCPM 5-Hz rows require
GPU tail/cache/causality verification; native 1-Hz pilots need Qwen 1-Hz
controls and cannot substitute for matched 5-Hz rows. The dated log overrides
the older cache-diagnostic-only scope below, not its safety checks.
- [ ] Acknowledge that already viewed Sessions 1--3 results are exploratory;
additional seeds do not create a fresh independent test.
- [ ] Resolve all TODOs, retain only supported mechanisms, and check the
four-technical-page limit, author kit, and IEEE AI-drafting disclosure.
## Reviewer verdict and frozen paper shape
The potentially publishable question is: **can a general-purpose Omni readout
manuscript/main/annotation_protocol_notes.tex
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% ARCHIVED INTERNAL NOTES: not included in the four-page submission draft.
% Source: main.tex before the 2026-09-17 local-evidence restructure.
% Provisional annotation workload and protocol notes, not model results.
% Table references refer to the previous manuscript layout.
% Upstream 2026-09-17 merge: semantic evaluation remains deferred in main.tex.
% Protocol-v1 opportunity AP is superseded: ENTRY cannot cross another other
% onset/intervening wearer activity; multi-competitor INT must not use an
% arbitrary onset. Re-run protocol v2 and audit labels before semantic claims.
% Retained upstream semantic qualitative plan (not compiled in main.tex):
\subsection{Qualitative Floor-Event Analysis}
We will compare EgoSpeak with the final matched model on human-confirmed
examples selected by pre-declared event subtype rather than post-hoc score
extremes. Each case will align participant-resolved activity, minimal transcript
context, the opportunity anchor and competing-risk censor, and the two models'
causally emitted wearer forecasts. The comparison will include a genuine wearer
entry, an other-to-other false-entry opportunity, and an INT-yield paired with a
non-takeover overlap; a shared failure case will be retained when space permits.
\begin{figure*}[t]
\centering
\fbox{\parbox[c][0.115\textheight][c]{0.965\textwidth}{
\centering
\textbf{Qualitative comparison placeholder}\\[0.7em]
\begin{tabular}{ccc}
Case A: wearer entry & Case B: other$\rightarrow$other HN
& Case C: INT-yield / failed takeover\\
participant timeline + transcript & opportunity anchor + censor
& EgoSpeak vs. proposed forecast\\
\end{tabular}
}}
\caption{Planned qualitative floor-event comparison. Final panels will use
human-confirmed protocol-v2 cases and show the same causal source time for both
models; the figure is a layout placeholder and contains no result claim.}
\label{fig:qualitative_floor_events}
\end{figure*}
\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}
\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
$t_w<t_o<t_y<t_o^{\mathrm{off}}$ are descriptive evidence only: they neither
admit nor reject a candidate and do not establish takeover. The LLM and human
annotator answer whether the wearer stopped because a non-wearer genuinely took
over the floor. Natural completion, post-completion activity, and within-turn
pauses are therefore resolved semantically rather than pre-filtered.
When a wearer activity run is itself only a backchannel within a non-wearer's
retained turn, its onset supplies the false-entry opportunity; its trailing
offset is excluded rather than counted again as a false-yield opportunity.
\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 temperature-zero DeepSeek pre-labeling with a primary and a skeptical
pass (\texttt{deepseek-flash} for the EasyCom snapshot and
\texttt{deepseek-v4-flash} for the Ego4D AV snapshot). 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 verdict, but every exclusion retains a closed-vocabulary reason and
derived aggregation group. We report valid-but-out-of-scope behavior (e.g.,
natural wearer completion coincident with feedback), insufficient semantic
evidence, and data-quality failures separately; model-pass disagreement belongs
to insufficient evidence and is never counted as corrupt data.
Annotation-only transcripts, identities, target-of-speech metadata, LLM output,
and future observations are never supplied to the evaluated forecasting model.
% Snapshot source: reports/easycom_event_ai_prelabel_stats_v16/summary.json
% and reports/ego4d_av_event_candidates_2026-09-15/ego4d_av_val_candidates_ai.jsonl.
\begin{table}[t]
\caption{Descriptive LLM pre-label audit before human adjudication. Counts in
parentheses are percentages of all candidates in that column; agreement uses
all candidates as its denominator. These values describe annotation workload,
not ground truth or model performance.}
\label{tab:llm_prelabel_audit}
\centering
\scriptsize
\begin{tabular}{@{}lrr@{}}
\hline
Pre-label outcome & EasyCom S1--4 & Ego4D AV val \\
\hline
All candidates & 2,128 & 3,608 \\
Positive: wearer entry & 324 (15.2\%) & 503 (13.9\%) \\
Positive: INT-yield & 43 (2.0\%) & 38 (1.1\%) \\
HN: entry family & 892 (41.9\%) & 940 (26.1\%) \\
HN: INT family & 125 (5.9\%) & 234 (6.5\%) \\
Excluded: valid/out of scope & 602 (28.3\%) & 775 (21.5\%) \\
Excluded: insufficient evidence & 142 (6.7\%) & 1,098 (30.4\%) \\
Excluded: data quality & 0 (0.0\%) & 20 (0.6\%) \\
Two-pass semantic agreement & 1,987 (93.4\%)& 2,961 (82.1\%) \\
Human-confirmed & 0 & 0 \\
\hline
\end{tabular}
\end{table}
Table~\ref{tab:llm_prelabel_audit} expands the unreviewed AI summary in
Table~\ref{tab:behavior_annotation_stats}; neither table reports final human
annotation results.
The EasyCom column covers only Sessions 1--4, while the Ego4D AV column covers
the 3,608 validation candidates; the 32,768 Ego4D AV training candidates have
not yet received LLM pre-labels. All 5,736 reported rows remain unreviewed.
Because the candidate sets deliberately include mined hard-negative regions,
their class proportions are not estimates of natural event prevalence. The
higher-level counts are descriptive only and were not used for forecasting
threshold or model selection. The
higher Ego4D insufficient-evidence rate (30.4\% versus 6.7\%) and lower
two-pass agreement (82.1\% versus 93.4\%) identify a larger human-review burden;
they do not establish a cross-dataset accuracy difference without adjudicated
reference labels.
\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.}
manuscript/main/figures/method_architecture.pdf
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manuscript/main/figures/method_architecture.tex
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\node[note,anchor=west,text=teal] at (9.0,-2.62)
{recent 1-s audio / optional vision\\refreshed on the common 5-Hz grid};
\node[anchor=east,text=navy] at (2.05,-3.18) {decision grid};
\draw[draw=navy,line width=0.6pt] (2.3,-3.18) -- (8.5,-3.18);
\foreach \x in {2.42,2.58,...,8.34} {
\draw[draw=navy,line width=0.5pt] (\x,-3.28) -- (\x,-3.08);
}
\node[note, anchor=west, text=navy] at (9.40,-2.72) {5 Hz fusion and prediction\\every fifth local right edge};
\draw[draw=navy,line width=0.9pt] (8.5,-3.3) -- (8.5,-3.06);
\draw[dashed,draw=orange,line width=0.75pt]
(8.5,-3.35) -- (8.5,-1.15);
\node[anchor=south,text=orange,font=\sffamily\tiny\bfseries]
at (8.5,-1.15) {cutoff $t_n$};
\node[note,anchor=west,text=navy] at (9.0,-3.18)
{one prediction every 200 ms\\same cutoff and target index};
\end{tikzpicture}
\end{document}
manuscript/main/main.tex
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