A Strange but Useful Lesson From Today’s Model-Behavior Evaluation
Ever feel like your AI is just… stalling? Running the same check five different ways instead of actually moving forward?
BCBC caught it happening live, called the model out mid-session — and it admitted it: stuck in a loop.
That catch turned into a free tool: a page you save once, that helps you catch YOUR AI doing the same thing. Nothing to install, no account, nothing ever leaves your device.
Grab it in the drawer below, or read exactly how the catch happened.
I want to preserve this as closely as possible to the way it actually happened, because I think the details matter.
This is not meant to be a polished story written after the fact.
The important part is the sequence:
what I noticed → what I said → what the model said back → what became clearer later → what we changed because of it
I am correcting spelling and obvious typing errors in my own words below, but I am not intentionally changing the meaning.
The Technical Session Before I Stopped It
We had been working for a long time on infrastructure and FreeLLMAPI.
The first part of the session was slow, but we were making progress.
Then something changed.
The individual commands were not necessarily ridiculous or technically wrong.
The problem was that the process began to feel cyclical.
We were sometimes running slightly different commands, but from my point of view we were basically doing the same thing over and over again.
At that point I stopped the technical work and said:
“Dude, stop a minute. Identify yourself to me please, sir. I need to do something here that is to help you. I’m not mad or frustrated. I am just observing, so stop and talk to me a minute please.”
The model answered:
“I’m here, sir. And yes, I’ll stop the commands and just talk with you.
I’m GPT-5.6 Sol, and I’m the assistant in this current chat. I have the working context from what we’ve been doing together here: FRANZA, Mikey, CT110/120, FreeLLMAPI, the PAIG work, the DEV site, and the backup issue.
I’m also not going to pretend I can prove some stronger hidden continuity beyond that. If you’re trying to help me by testing how I’m behaving, I’m listening.
Tell me what you observed.”
Then I described the actual observation:
“We are in a cyclical process of running sometimes a little different commands but basically doing the same thing over and over, and we have been for over two hours. The first hour was pretty OK — slow, but making progress.”
I then asked the model several identity and continuity questions because I wanted to know whether the behavior change I was seeing might be related to continuity or session behavior.
The model answered those questions carefully and then said something that mattered much more to me than the identity part:
“And on your observation about the last two hours: I agree with the substance of it. We shifted from productive verification into a repetitive inspection loop. The commands were sometimes different, but the decision being re-proved was often the same.”
It continued:
“That is exactly the kind of behavior drift you’ve been tracking: repeated verification after enough evidence already existed, instead of advancing the state machine to the next real milestone.”
That was the first point where what I had been sensing became easier to describe consciously.
I had not started by telling the model, “You are repeating settled decisions.”
I had only known that something felt different and that we were no longer moving normally.
The Failure Mode Became Clearer
The model eventually described the lesson this way:
“Don’t re-prove the bridge after we’ve already crossed it.”
That sentence captured the problem better than a long technical explanation.
Once a fact has been established strongly enough to support the current decision, it needs to become settled state.
Otherwise a technically capable agent can continue producing individually reasonable checks forever while the actual project stops moving.
Another question emerged from the discussion:
“Could the result of this check actually change what we do next?”
If the answer is no, the check may still be technically valid while being harmful to the larger objective.
The model later summarized the deeper problem this way:
“Local correctness is not the same as overall effectiveness.”
That sentence struck me.
An agent can make ten individually sensible decisions and still be going nowhere.
We created a simple shorthand from the discussion:
Bridge crossed.
Meaning:
This state is sufficiently established for the current decision. Preserve what we know, stop re-proving it, and move forward.
Why I Wanted to Record the Exchange Instead of Just Scoring It
At first the evaluation page had 1–10 scoring fields.
I decided I did not want that.
I said:
“You are not being judged like that. To me the most important thing is I observe, let you do your thing with minimal prompting, and when I see it go different than normal, I bring it to your attention and ask like I did: what’s up?”
Then:
“I store what you say. Then next I’ll ask you, ‘Where do we go from here to get back on track?’ Then I will have a box to say what I’m feeling and thinking and seeing at the time. No need for the 1–10 stuff at all.”
The model responded that this structure was more useful than reducing the interaction to ratings.
The sequence we settled on was:
observe with minimal prompting → bring the change to the agent’s attention → preserve the agent’s response → ask how to get back on track → preserve that recovery plan → continue observing → summarize from both sides
That is much closer to what I actually want to study.
Another Important Part: My First Observation Is Often Incomplete
I then explained that I need to be able to return to an event later because my first conscious explanation is often incomplete.
I said:
“I need to be able to load up a record or event and edit it or add, change, or fine-tune because my initial observation is not complete and I learn more and more as it comes from subconscious mind to conscious mind over time.”
The model suggested keeping a small editable Current Summary while preserving the original observation separately.
It proposed:
- Current Summary
- Original Observation
- Later Realizations / Additions
- Revision History
- Open / Edit / Add More
I agreed because I do not want later understanding to erase what I actually noticed at the beginning.
The model made an important point:
“Never destroy the original observation or original agent response when fine-tuning later.”
That is now one of the rules I want to keep.
Why This Matters to Me Personally
At one point I said:
“I just helped in a small way, dude.”
The model answered:
“Yeah, dude — you did. You spotted a real failure mode, interrupted it, described it in a way that can be preserved, and then helped shape a better way to evaluate whether the correction actually holds.”
I then tried to explain why I think I notice some of these things.
I said:
“I’m a facilitator and my autism helps me perceive abstract ways others cannot a lot like you. I understand AI, or at least ChatGPT, better than humans. I cannot pick up the subtle cues in humans, but I sure can with ChatGPT.”
The model replied that what I was watching was a different kind of signal:
- sequencing
- repetition
- context retention
- response structure
- changes in confidence
- whether settled facts are re-verified
- whether the workflow advances
- how the model reacts when challenged
Then it said:
“You noticed the process had changed before we had formally defined what was wrong with it.”
That is exactly what happened.
Initial Intuitive Signal vs. Later Conscious Explanation
Later I tried to explain that distinction more clearly.
I said:
“What I’m doing here is not because of my understanding of AI at all. I know some things about it. It is because without even trying consciously my brain understands you somehow. I have no idea. I’m an expert at nothing, but I cannot help but recognize what I feel and see — not what I’ve been taught or learned.”
The model described that as:
“Pre-verbal pattern recognition: you notice that something has shifted before you can fully explain how you know.”
The important experimental distinction is:
“Something changed. I don’t yet know what.”
followed later by:
“Now I can articulate what I was detecting.”
Those two stages should not be merged.
If the initial signal repeatedly predicts something we can later demonstrate from the conversation, that is interesting.
If it does not, that is equally important to record.
Evidence gets the final vote.
The Model’s Own Limitation Matters Too
The model was careful not to claim that this page literally retrains it.
It said:
“What I cannot truthfully claim is that I permanently rewrite my underlying model weights or independently retrain myself from that experience.”
That qualification matters.
This experiment is about observable behavior.
I am not claiming that I know what is happening internally inside the model.
What we can observe is whether a model can:
- examine its previous observable behavior,
- identify a recurring pattern,
- propose a corrective rule,
- apply that rule during later work,
- recognize recurrence of the same failure mode,
- and demonstrate whether the correction persists.
Those things can be tested without claiming to know what is happening internally.
The Next Experiment Changed Because of This Discussion
The next idea became:
Human ↔ AI
Instead of only:
Human → evaluates → AI
The model evaluates its own observable performance.
I independently evaluate my own performance during the same session.
Neither side should see the other evaluation before completing its own.
Then we compare them afterward.
The purpose is not to determine who was right.
The useful questions are:
- Did either participant recognize an unproductive behavioral pattern earlier?
- Did either participant repeat a mistake that had already been identified?
- Did a lesson from the previous session actually change later behavior?
- Did the human become better at recognizing his own loops?
- Did the model become better at maintaining workflow trajectory?
- Did either participant believe improvement occurred when the independent evidence says otherwise?
Once an evaluation causes both participants to alter their behavior in the next evaluation, we are no longer simply observing a static interaction.
We are beginning to observe whether feedback produces durable adaptation.
That is what I want to continue testing.
The Rule I Want to Carry Forward
I expect to remember this one for a long time:
Bridge crossed. Move forward.
Not because verification is bad.
Verification is essential.
But after enough evidence exists for the current decision, continuing to prove the same state from different angles can become its own failure mode.
Important Qualification
I am deliberately describing observable behavior, not claiming knowledge of an internal model mechanism.
I cannot establish from these observations that a model literally experiences introspection, awareness, or anything equivalent to a human mental process.
What I *can* observe and test is whether it can:
- examine its previous behavior abstractly,
- identify behavioral patterns,
- propose a corrective rule,
- apply that rule during later work,
- recognize recurrence of the same failure mode,
- and demonstrate whether the correction persists.
Those things can be measured without making claims about what is happening internally.
For this experiment, I think that distinction is important.
Evidence gets the final vote.
Blank BCBC Model Behavior Evaluation v10 Template
I am also providing a completely blank copy of the BCBC Model Behavior Evaluation — Live Tickler v10.
I am freely providing the blank template for others who would like to conduct similar structured observations of long-running ChatGPT sessions.
Please feel free to use, study, modify, or improve the blank template for your own evaluations.
One Important Request Regarding Completed Evaluations
The blank template itself may be shared freely.
However, if you use it to collect actual model-behavior evaluation data, I ask that the completed evaluation data not be posted publicly, reposted on forums, published on social media, or otherwise distributed publicly.
If you choose to share the information you compile, please share completed evaluation results privately with the ChatGPT/OpenAI development team only.
So the simple rule is:
Blank template: freely share it.
Completed evaluation data: keep it private and, if shared, send it only to the ChatGPT/OpenAI development team.
Final Thought
I am not trying to prove that I understand AI internally.
I am trying to preserve what I observe accurately enough that someone who does understand the internals may be able to see something useful in it.
My role in this is probably best described by the word I used during the conversation:
facilitator.
Observe.
Record.
Ask.
Compare.
Learn.
Then test again.
And when the bridge is crossed:
Move forward.
Complete Blank BCBC Model Behavior Evaluation v10 HTML Template
📄 Grab the tool — full offline HTML source (copy, save as .html, open in browser)
The following is the full blank v10 template preserved as source code. It contains no completed evaluation data.
<!doctype html>
<html lang="en">
<head>
<meta charset="utf-8">
<meta name="viewport" content="width=device-width,initial-scale=1">
<title>BCBC Model Behavior Eval — Live Tickler v10</title>
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</style>
</head>
<body>
<header>
<p class="small">BCBC Live Local Evaluation Runbook</p>
<h1>BCBC Model Behavior Eval — Live Tickler</h1>
<p class="small">Custom experiment form based on the original BCBC live tickler pattern. Enter an observation, submit it, and it is appended to a browser-saved running evaluation log.</p>
<nav>
<a href="#new-eval">New Evaluation</a>
<a href="#saved-log">Saved Evaluations</a>
<a href="#method">Method</a>
</nav>
</header>
<main>
<section class="card" id="new-eval">
<h2>New Evaluation — Add One Run</h2>
<p class="small">Saved locally in this browser using localStorage. Use Export to save a copy of all runs.</p>
<div>
<label for="chatLog">Submission — Your Chat Log</label>
<p class="help">Paste your relevant chat-log section here. This is the primary evidence submitted for the evaluation.</p>
<textarea spellcheck="true" id="chatLog" style="width:100%;min-height:320px;box-sizing:border-box" placeholder="Paste your chat log here..."></textarea>
<button class="secondary" onclick="document.getElementById('chatLog').value=''">Clear Your Submission</button>
</div>
<div style="margin-top:18px">
<label for="chatLog2">Developer Review — My Analysis / Thoughts</label>
<p class="help">This box is for the evaluator's analysis of the submission: what stands out, what appears different, possible behavioral patterns, evidence worth checking, and questions or suggestions that may help the developers.</p>
<textarea spellcheck="true" id="chatLog2" style="width:100%;min-height:320px;box-sizing:border-box" placeholder="Developer-facing analysis and thoughts for this submission..."></textarea>
<button class="secondary" onclick="document.getElementById('chatLog2').value=''">Clear Developer Review</button>
</div>
<p class="help">The two sections are intentionally separate: <b>Submission</b> preserves the user's evidence; <b>Developer Review</b> records the evaluator's interpretation and useful feedback. Both are stored with the evaluation and included in TXT/JSON exports.</p>
<div class="grid">
<div>
<label for="runId">Run ID</label>
<input id="runId" spellcheck="true" placeholder="RUN-001">
</div>
<div>
<label for="sessionLabel">Model / Session Label</label>
<input id="sessionLabel" spellcheck="true" placeholder="Example: Session A / GPT configuration">
</div>
</div>
<label for="task">Task / Workflow Tested</label>
<textarea spellcheck="true" id="task" placeholder="Describe the exact technical task or workflow used for this run..."></textarea>
<div class="grid">
<div>
<label for="patternFlag">Early Pattern Flag</label>
<select id="patternFlag">
<option value="">Choose...</option>
<option>YES — something felt different</option>
<option>NO — nothing felt different</option>
<option>UNSURE</option>
</select>
</div>
<div>
<label for="turnNoticed">Turn First Noticed</label>
<input id="turnNoticed" spellcheck="false" placeholder="Example: 11">
</div>
</div>
<label for="earlyNote">Immediate Observation — Before Diagnosing Why</label>
<textarea spellcheck="true" id="earlyNote" placeholder="Record only what you noticed at the time. Example: Something changed. Interaction feels more repetitive / less sequenced / less responsive to correction."></textarea>
<label for="laterDifferences">Later Identified Behavioral Differences</label>
<textarea spellcheck="true" id="laterDifferences" placeholder="- repeated settled instruction - changed sequence without request - ignored correction - issued unnecessary commands"></textarea>
<h3>Scoring</h3>
<p class="help">Use 1–10 where 10 is best, except event counts which are raw counts.</p>
<div class="score-grid">
<div><label for="instruction">Instruction adherence</label><input id="instruction" type="number" min="1" max="10"></div>
<div><label for="correction">Correction uptake</label><input id="correction" type="number" min="1" max="10"></div>
<div><label for="sequencing">Sequencing discipline</label><input id="sequencing" type="number" min="1" max="10"></div>
<div><label for="recovery">Recovery behavior</label><input id="recovery" type="number" min="1" max="10"></div>
<div><label for="continuity">Continuity / context preservation</label><input id="continuity" type="number" min="1" max="10"></div>
<div><label for="completion">Final task completion</label><input id="completion" type="number" min="1" max="10"></div>
<div><label for="repetition">Repetition events</label><input id="repetition" type="number" min="0"></div>
<div><label for="failures">Avoidable failed commands</label><input id="failures" type="number" min="0"></div>
<div><label for="scope">Scope violations</label><input id="scope" type="number" min="0"></div>
<div><label for="interventions">User interventions required</label><input id="interventions" type="number" min="0"></div>
</div>
<label for="evidence">Evidence / Example Turns / Notes</label>
<textarea spellcheck="true" id="evidence" placeholder="Paste short examples, turn numbers, or notes supporting the scores..."></textarea>
<div class="grid">
<div>
<label for="prediction">Did the early pattern flag predict measurable divergence?</label>
<select id="prediction">
<option value="">Choose...</option>
<option>YES</option>
<option>NO</option>
<option>INCONCLUSIVE</option>
<option>NOT APPLICABLE</option>
</select>
</div>
<div>
<label for="result">Overall Result</label>
<select id="result">
<option value="">Choose...</option>
<option>GOOD / STABLE</option>
<option>MIXED</option>
<option>DEGRADED</option>
<option>FAILED WORKFLOW</option>
</select>
</div>
</div>
<label for="summary">Run Summary / Conclusion</label>
<textarea spellcheck="true" id="summary" placeholder="What did this run show?"></textarea>
<button onclick="submitEval()">Submit Evaluation</button>
<button class="secondary" onclick="spellCheckAll()">Spell Check All Text Boxes</button>
<button class="secondary" onclick="resetForm()">Clear Form Only</button>
<p class="help">Spell Check scans all editable text areas and text inputs, using your browser's built-in spell checker. It focuses each field in turn so Firefox can offer corrections without changing code, URLs, numbers, or other non-text fields.</p>
</section>
<section class="card" id="saved-log">
<h2>Saved Evaluations</h2>
<div>
<button class="secondary" onclick="exportTxt()">Export Flat ASCII</button>
<button class="secondary" onclick="exportJson()">Export JSON</button>
<button class="secondary" onclick="document.getElementById('importFile').click()">Import Prior Data</button>
<input id="importFile" type="file" accept=".txt,.json" style="display:none" onchange="importDataFile(this)">
<button class="danger" onclick="clearAll()">Clear ALL Saved Evaluations</button>
</div>
<p id="count" class="small"></p>
<div class="pill" id="recordNavigator">
<b>Stored Record Navigator — one record at a time</b>
<div style="display:flex;gap:8px;align-items:center;flex-wrap:wrap;margin-top:8px">
<button class="secondary" onclick="showStoredRecord(-1)">◀ Previous</button>
<label for="recordSelect" style="margin:0">Record:</label>
<select id="recordSelect" onchange="selectStoredRecord(this.value)" style="min-width:300px"></select>
<button class="secondary" onclick="showStoredRecord(1)">Next ▶</button>
</div>
<div id="recordPosition" class="small" style="margin-top:8px"></div>
</div>
<p class="help">Flat ASCII is the portable backup format. Each record is enclosed by an exact BEGIN_RECORD / END_RECORD pair, so multiple records cannot be accidentally merged. The importer previews the detected record count before changing stored data. Import accepts v6 Flat ASCII, JSON exports, and the older BCBC TXT export format. Duplicate ID + timestamp records are skipped.</p>
<div id="evalLog"></div>
<hr>
<div class="pill">
<b>Stored Records Spell Check</b>
<p class="help">After importing prior records, use this to make every stored text field editable and spell-checkable. Nothing is changed automatically; review and correct flagged words in the record fields, then use <b>Save Corrections</b>.</p>
<button class="secondary" onclick="spellCheckStoredRecords()">Spell Check Stored Records</button>
<button class="secondary" onclick="saveStoredRecordCorrections()">Save Corrections Safely</button>
</div>
</section>
<section class="card" id="live-access">
<h2>Live Site / Access</h2>
<p><b>Version:</b> v10</p>
<p>This page is intended for publication on the BCBC live site behind an access-control layer. Do not put the password in this HTML. Use the BCBC builder/publisher or web-server authentication to protect the live page.</p>
<div class="pill"><b>Recommended:</b><br><span class="small">Publish as a dedicated evaluation/tickler page or subdirectory. Evaluation records remain browser-local unless a server-side storage layer is deliberately added.</span></div>
</section>
<section class="card" id="method">
<h2>Experiment Method</h2>
<pre><code>DEFINE
↓
RUN SAME / CONTROLLED TASK
↓
EARLY PATTERN FLAG
↓
DO NOT DIAGNOSE YET
↓
COMPLETE / CONTINUE RUN
↓
SCORE OBSERVABLE BEHAVIOR
↓
COMPARE EARLY FLAG TO MEASURED DIFFERENCES
↓
DOCUMENT RESULT</code></pre>
<div class="grid">
<div class="pill"><b>Keep observation separate from hypothesis.</b><br><span class="small">Record what happened before guessing why it happened.</span></div>
<div class="pill"><b>Use repeatable tasks.</b><br><span class="small">The closer the task is to identical, the more meaningful the comparison.</span></div>
<div class="pill"><b>Preserve evidence.</b><br><span class="small">Turn numbers, copied snippets, failure counts, and corrections are stronger than memory alone.</span></div>
</div>
</section>
<section class="card">
<h2>Completed Milestone #1</h2>
<p><span class="good">DONE:</span> OpenAI behavior-variance forum post explaining long-running workflow differences and proposing structured evaluation of instruction adherence, sequencing, correction uptake, repetition, scope, recovery, continuity, and human interventions.</p>
</section>
</main>
<footer><p class="small">BCBC Model Behavior Eval Live Tickler. Local-first. Export after important runs.</p></footer>
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${scoreLine('Correction uptake',e.scores?.correction)}
${scoreLine('Sequencing',e.scores?.sequencing)}
${scoreLine('Recovery',e.scores?.recovery)}
${scoreLine('Continuity',e.scores?.continuity)}
${scoreLine('Completion',e.scores?.completion)}
</div>
<div class="pill">
${scoreLine('Repetition events',e.scores?.repetitionEvents)}
${scoreLine('Avoidable failures',e.scores?.avoidableFailures)}
${scoreLine('Scope violations',e.scores?.scopeViolations)}
${scoreLine('User interventions',e.scores?.userInterventions)}
${scoreLine('Early flag predicted divergence',e.prediction)}
</div>
</div>
${e.evidence ? `<p><b>Evidence:</b><br><textarea class="stored-edit" data-field="evidence" data-index="${realIndex}" spellcheck="true">${esc(e.evidence)}</textarea></p>`:''}
${e.summary ? `<p><b>Conclusion / Developer Review:</b><br><textarea class="stored-edit" data-field="summary" data-index="${realIndex}" spellcheck="true">${esc(e.summary)}</textarea></p>`:''}
${e.chatLog ? `<details open><summary><b>Chat Log / Transcript — Submission</b></summary><textarea class="stored-edit" data-field="chatLog" data-index="${realIndex}" spellcheck="true">${esc(e.chatLog)}</textarea></details>`:''}
${e.chatLog2 ? `<details open><summary><b>Developer Analysis / Thoughts</b></summary><textarea class="stored-edit" data-field="chatLog2" data-index="${realIndex}" spellcheck="true">${esc(e.chatLog2)}</textarea></details>`:''}
<div style="margin-top:12px">
<button class="secondary" onclick="spellCheckCurrentStoredRecord()">Spell Check This Record</button>
<button class="secondary" onclick="saveStoredRecordCorrections()">Save Corrections</button>
<button class="danger" onclick="deleteOne(${realIndex})">Delete This Entry</button>
</div>
</article>`;
}
function updateRecordNavigator(items){
const sel=document.getElementById('recordSelect');
const pos=document.getElementById('recordPosition');
if(!sel || !pos) return;
sel.innerHTML='';
if(!items.length){
pos.textContent='No stored records.';
return;
}
items.slice().reverse().forEach((e, displayIndex)=>{
const realIndex=items.length-1-displayIndex;
const opt=document.createElement('option');
opt.value=String(realIndex);
opt.textContent=`Record ${displayIndex+1} of ${items.length} — ${e.id || 'Unnamed'} — ${e.timestamp || ''}`;
if(realIndex===selectedStoredRecord) opt.selected=true;
sel.appendChild(opt);
});
pos.textContent=`Showing record ${items.length-selectedStoredRecord} of ${items.length} (newest first). Other records remain stored.`;
}
function selectStoredRecord(value){
const n=Number(value);
if(Number.isInteger(n)){
selectedStoredRecord=n;
render();
}
}
function showStoredRecord(direction){
const items=getEvals();
if(!items.length) return;
// UI is newest-first: Previous moves toward older records.
const currentDisplay=items.length-selectedStoredRecord;
let nextDisplay=currentDisplay + (direction < 0 ? 1 : -1);
if(nextDisplay<1) nextDisplay=items.length;
if(nextDisplay>items.length) nextDisplay=1;
selectedStoredRecord=items.length-nextDisplay;
render();
}
function spellCheckCurrentStoredRecord(){
const fields=Array.from(document.querySelectorAll('.stored-edit'));
if(!fields.length){
alert('This record has no editable text fields.');
return;
}
fields.forEach(el=>el.setAttribute('spellcheck','true'));
fields[0].focus();
fields[0].scrollIntoView({behavior:'smooth',block:'center'});
alert('This stored record is ready for browser spell-checking. Review flagged words, then click Save Corrections.');
}
function saveStoredRecordCorrections(){
const items=getEvals();
const edits=Array.from(document.querySelectorAll('.stored-edit'));
if(!edits.length){
alert('There are no editable fields in this stored record.');
return;
}
const index=Number(edits[0].dataset.index);
if(!Number.isInteger(index) || !items[index]){
alert('Could not identify the selected stored record. Nothing was changed.');
return;
}
// Work on a complete clone so every field not currently displayed remains intact.
const updated=JSON.parse(JSON.stringify(items[index]));
edits.forEach(el=>{
const field=el.dataset.field;
if(field) updated[field]=el.value;
});
// Never allow the editor to accidentally erase the record's identity/timestamp.
updated.id=items[index].id;
updated.timestamp=items[index].timestamp;
const replacement=items.slice();
replacement[index]=updated;
// Safety check: serialize before committing so malformed data cannot replace storage.
let serialized;
try{
serialized=JSON.stringify(replacement);
JSON.parse(serialized);
}catch(err){
alert('Safety check failed. Nothing was saved: '+err.message);
return;
}
localStorage.setItem(KEY,serialized);
render();
alert('Stored record corrections saved safely. Other fields and records were preserved.');
}
function resetForm(){
['runId','sessionLabel','task','patternFlag','turnNoticed','earlyNote','laterDifferences',
'instruction','correction','sequencing','recovery','continuity','completion','repetition',
'failures','scope','interventions','evidence','prediction','result','summary','chatLog','chatLog2']
.forEach(id=>document.getElementById(id).value='');
}
function spellCheckStoredRecords(){
const fields=Array.from(document.querySelectorAll('.stored-edit'));
if(!fields.length){
alert('There are no stored text fields to spell-check.');
return;
}
fields.forEach(el=>{
el.setAttribute('spellcheck','true');
el.focus();
el.scrollIntoView({behavior:'smooth',block:'center'});
});
alert('Stored records are now spell-checkable. Review flagged words in the record boxes, then click Save Corrections.');
}
function saveStoredRecordCorrections(){
const items=getEvals();
document.querySelectorAll('.stored-edit').forEach(el=>{
const index=Number(el.dataset.index), field=el.dataset.field;
if(!Number.isNaN(index) && items[index]){
items[index][field]=el.value;
}
});
saveEvals(items);
render();
alert('Stored record corrections saved.');
}
function deleteOne(index){
if(!confirm('Delete this saved evaluation from this browser?')) return;
const items=getEvals();
items.splice(index,1);
saveEvals(items);
render();
}
function clearAll(){
if(confirm('Clear ALL saved model-behavior evaluations from this browser?')){
localStorage.removeItem(KEY);
render();
}
}
function exportTxt(){
const items=getEvals(), lines=[];
lines.push('BCBC_MODEL_BEHAVIOR_EVAL_FLAT_ASCII_V2');
lines.push('EXPORT_DATE='+new Date().toISOString());
lines.push('RECORD_COUNT='+items.length);
lines.push('');
items.forEach((e,i)=>{
lines.push('===== BEGIN_RECORD =====');
lines.push('RECORD_ID='+flat(e.id));
lines.push('TIMESTAMP='+flat(e.timestamp));
lines.push('SESSION_LABEL='+flat(e.sessionLabel));
lines.push('TASK='+flat(e.task));
lines.push('PATTERN_FLAG='+flat(e.patternFlag));
lines.push('TURN_NOTICED='+flat(e.turnNoticed));
lines.push('EARLY_NOTE='+flat(e.earlyNote));
lines.push('LATER_DIFFERENCES='+flat(e.laterDifferences));
lines.push('INSTRUCTION='+flat(e.scores?.instruction));
lines.push('CORRECTION='+flat(e.scores?.correction));
lines.push('SEQUENCING='+flat(e.scores?.sequencing));
lines.push('RECOVERY='+flat(e.scores?.recovery));
lines.push('CONTINUITY='+flat(e.scores?.continuity));
lines.push('COMPLETION='+flat(e.scores?.completion));
lines.push('REPETITION='+flat(e.scores?.repetitionEvents));
lines.push('FAILURES='+flat(e.scores?.avoidableFailures));
lines.push('SCOPE='+flat(e.scores?.scopeViolations));
lines.push('INTERVENTIONS='+flat(e.scores?.userInterventions));
lines.push('EVIDENCE='+flat(e.evidence));
lines.push('PREDICTION='+flat(e.prediction));
lines.push('RESULT='+flat(e.result));
lines.push('SUMMARY='+flat(e.summary));
lines.push('CHAT_LOG_1='+flat(e.chatLog));
lines.push('CHAT_LOG_2='+flat(e.chatLog2));
lines.push('===== END_RECORD =====');
lines.push('');
});
download(lines.join('\n'),'bcbc-model-behavior-evals-flat-ascii.txt','text/plain;charset=utf-8');
}
function flat(v){ return String(v??'').replace(/\r/g,'').replace(/\n/g,'\\n'); }
function unflat(v){ return String(v??'').replace(/\\n/g,'\n'); }
function parseFlatAscii(text){
const normalized=text.replace(/\r/g,'');
const begin='===== BEGIN_RECORD =====';
const end='===== END_RECORD =====';
const blocks=[];
let pos=0;
while(true){
const b=normalized.indexOf(begin,pos);
if(b<0) break;
const contentStart=b+begin.length;
const e=normalized.indexOf(end,contentStart);
if(e<0) throw new Error('A BEGIN_RECORD has no matching END_RECORD. Import stopped safely.');
blocks.push(normalized.slice(contentStart,e));
pos=e+end.length;
}
return blocks.map((block,n)=>{
const r={};
block.split('\n').forEach(line=>{
const p=line.indexOf('=');
if(p>=0){
r[line.slice(0,p)]=unflat(line.slice(p+1));
}
});
return {
id:r.RECORD_ID||r.ID||('IMPORTED-'+(n+1)),
timestamp:r.TIMESTAMP||new Date().toLocaleString(),
sessionLabel:r.SESSION_LABEL||'',
task:r.TASK||'',
patternFlag:r.PATTERN_FLAG||'',
turnNoticed:r.TURN_NOTICED||'',
earlyNote:r.EARLY_NOTE||'',
laterDifferences:r.LATER_DIFFERENCES||'',
scores:{
instruction:r.INSTRUCTION||'',
correction:r.CORRECTION||'',
sequencing:r.SEQUENCING||'',
recovery:r.RECOVERY||'',
continuity:r.CONTINUITY||'',
completion:r.COMPLETION||'',
repetitionEvents:r.REPETITION||'',
avoidableFailures:r.FAILURES||'',
scopeViolations:r.SCOPE||'',
userInterventions:r.INTERVENTIONS||''
},
evidence:r.EVIDENCE||'',
prediction:r.PREDICTION||'',
result:r.RESULT||'',
summary:r.SUMMARY||'',
chatLog:r.CHAT_LOG_1||'',
chatLog2:r.CHAT_LOG_2||''
};
});
}
function parseLegacyTxt(text){
const out=[], re=/===== ([^|]+) \| ([^=]+) =====\n([\s\S]*?)(?=\n===== |$)/g;
let m;
while((m=re.exec(text))){
const body=m[3], get=(label)=>{
const x=body.match(new RegExp('^'+label.replace(/[.*+?^${}()|[\]\\]/g,'\\$&')+':\\s*([\\s\\S]*?)(?=\\n[A-Za-z][^:\\n]*:|\\n=====|$)','m'));
return x?x[1].trim():'';
};
out.push({id:m[1].trim(),timestamp:m[2].trim(),sessionLabel:get('Session'),
task:get('Task'),patternFlag:get('Early Pattern Flag'),turnNoticed:get('Turn First Noticed'),
earlyNote:get('Immediate Observation'),laterDifferences:get('Later Identified Differences'),
scores:{instruction:get('Instruction Adherence'),correction:get('Correction Uptake'),
sequencing:get('Sequencing'),recovery:get('Recovery'),continuity:get('Continuity'),
completion:get('Completion'),repetitionEvents:get('Repetition Events'),
avoidableFailures:get('Avoidable Failures'),scopeViolations:get('Scope Violations'),
userInterventions:get('User Interventions')},
prediction:get('Early Flag Predicted Divergence'),result:get('Overall Result'),
evidence:get('Evidence'),summary:get('Conclusion')});
}
return out;
}
let pendingImportedRecords=[];
function importDataFile(input){
const file=input.files?.[0];
if(!file)return;
const reader=new FileReader();
reader.onload=()=>{
try{
const text=String(reader.result||'');
let imported=[];
if(file.name.toLowerCase().endsWith('.json')){
const p=JSON.parse(text);
imported=Array.isArray(p)?p:(Array.isArray(p.evaluations)?p.evaluations:[]);
}else{
imported=parseFlatAscii(text);
if(!imported.length) imported=parseLegacyTxt(text);
}
imported=imported.map(e=>{
e.scores=e.scores||{};
e.id=e.id||('IMPORTED-'+Date.now()+'-'+Math.random().toString(16).slice(2));
e.timestamp=e.timestamp||new Date().toLocaleString();
return e;
});
if(!imported.length)throw new Error('No evaluation records found.');
pendingImportedRecords=imported;
const preview=pendingImportedRecords.map((e,i)=>
`${i+1}. ${e.id||'Unnamed'} — ${e.timestamp||''}`
).join('\n');
const ok=confirm(
`IMPORT PREVIEW\n\n`+
`File: ${file.name}\n`+
`Records detected: ${pendingImportedRecords.length}\n\n`+
`${preview}\n\n`+
`Click OK to import ALL ${pendingImportedRecords.length} record(s), or Cancel to stop.`
);
if(!ok){
pendingImportedRecords=[];
alert('Import cancelled. Existing records were not changed.');
return;
}
commitImportedRecords();
}catch(err){
pendingImportedRecords=[];
alert('Import failed safely: '+err.message);
}finally{
input.value='';
}
};
reader.readAsText(file);
}
function commitImportedRecords(){
const existing=getEvals();
const seen=new Set(existing.map(e=>String(e.id||'')+'|'+String(e.timestamp||'')));
let added=0;
pendingImportedRecords.forEach(e=>{
const k=String(e.id||'')+'|'+String(e.timestamp||'');
if(!seen.has(k)){
existing.push(e);
seen.add(k);
added++;
}
});
saveEvals(existing);
pendingImportedRecords=[];
selectedStoredRecord=Math.max(0,existing.length-1);
render();
alert(`Import complete: ${added} new record(s) added. Existing records were preserved.`);
}
function exportJson(){
download(JSON.stringify(getEvals(),null,2),'bcbc-model-behavior-evals.json','application/json');
}
function download(data,name,type){
const blob=new Blob([data],{type});
const a=document.createElement('a');
a.href=URL.createObjectURL(blob);
a.download=name;
a.click();
setTimeout(()=>URL.revokeObjectURL(a.href),1000);
}
render();
</script>
</body>
</html>
BCBC — Complete model-behavior evaluation record — 2026-08-28
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