# /// script
# requires-python = ">=3.11"
# dependencies = ["marimo==0.24.0", "numpy>=2.1"]
# ///
import marimo
__generated_with = "0.24.0"
app = marimo.App(width="medium", app_title='Where does the answer fork?')

@app.cell
def _():
    import marimo as mo
    """Live, deterministic classroom mechanisms. MODEL is injected by the notebook generator."""
    import numpy as np
    import math, json, html, base64, zlib

    def softmax(x, axis=-1):
        e=np.exp(x-np.max(x,axis=axis,keepdims=True));return e/e.sum(axis=axis,keepdims=True)

    def forward(tokens, checkpoint='base', ablate=None, patch=None, readout=None):
        w={k:np.asarray(v) for k,v in MODEL[checkpoint].items()}
        if readout is not None:w['readout.weight']=readout
        def lin(x,name):return x@w[name+'.weight'].T+w[name+'.bias']
        def norm(x,name):return (x-x.mean(-1,keepdims=True))/np.sqrt(x.var(-1,keepdims=True)+1e-5)*w[name+'.weight']+w[name+'.bias']
        x=w['embed.weight'][tokens]+w['pos.weight'][:len(tokens)];cache={'residual':[x.copy()],'attention':[],'neurons':[]}
        for layer in range(2):
            n=f'blocks.{layer}'
            q,k,v=lin(norm(x,n+'.ln1'),n+'.qkv').reshape(len(tokens),3,2,12).transpose(1,2,0,3)
            scores=q@k.transpose(0,2,1)/math.sqrt(12)
            scores[:,np.triu_indices(len(tokens),1)[0],np.triu_indices(len(tokens),1)[1]]=-1e9
            att=softmax(scores);heads=att@v
            if ablate and ablate[0]==layer:heads[ablate[1]]=0
            x=x+lin(heads.transpose(1,0,2).reshape(len(tokens),24),n+'.proj')
            neurons=np.maximum(lin(norm(x,n+'.ln2'),n+'.fc'),0)
            x=x+lin(neurons,n+'.out')
            if patch and patch['layer']==layer:
                x[patch['position']]=patch['value']
            cache['attention'].append(att);cache['neurons'].append(neurons);cache['residual'].append(x.copy())
        logits=lin(norm(x,'norm'),'readout')
        return logits[-1],cache

    def prompt(case=0, authored=''):
        examples=[('pip','wattle','red','blue','pip'),('pip','wattle','blue','red','pip'),('kiki','bo','gold','green','kiki'),('bo','kiki','gold','red','kiki'),('pip','wattle','gold','red','pip'),('pip','wattle','red','blue','wattle')]
        a,b,c,d,q=examples[int(case)]
        if authored.strip():
            parts=[part.strip().lower() for part in authored.split(',')]
            if len(parts)!=5:raise ValueError('Write five comma-separated entries: first name, second name, first colour, second colour, queried name.')
            a,b,c,d,q=parts
            if a not in ['pip','wattle','kiki','bo'] or b not in ['pip','wattle','kiki','bo'] or a==b or q not in [a,b] or c not in ['red','blue','gold','green'] or d not in ['red','blue','gold','green']:raise ValueError('Use two different names (Pip, Wattle, Kiki, Bo), colours red/blue/gold/green, and query one of the two names.')
        words=f'{a} has {c} . {b} has {d} . {q} has'.split()
        return [MODEL['vocab'].index(x) for x in words],MODEL['vocab'].index(c if a==q else d),' '.join(words)

    def output_rows(logits, temperature=1):
        probs=softmax(logits/temperature)
        return [{'word':v,'probability':round(float(probs[i]),6),'logit':round(float(logits[i]),5)} for i,v in enumerate(MODEL['vocab'])]

    def result(summary,rows,kind='bars',x='word',y='probability',**kwargs):
        return dict(summary=summary,rows=rows,kind=kind,x=x,y=y,**kwargs)

    def compute(ident,s):
        if ident in ['u6-words','u6-clues','u6-switch','u12-circuits','u12-training']:
            tokens,truth,text=prompt(s.get('case',0),s.get('authored',''));logits,cache=forward(tokens)
            probs=softmax(logits);answer=MODEL['vocab'][int(logits.argmax())];correct=MODEL['vocab'][truth]
            context=f'Prompt: “{text} …”. The written facts support {correct}. Base model chooses {answer}.'
        if ident=='u6-words':
            rows=output_rows(logits,s['temperature']);p=np.array([r['probability'] for r in rows]);entropy=-float(np.sum(p*np.log2(p+1e-12)))
            return result(context+f' Temperature changes probabilities; it does not check the facts.',rows,metrics={'supported_answer':correct,'argmax':answer,'entropy_bits':entropy},formula='probability(word) = softmax(logit / temperature). All 10 vocabulary words are shown; total probability is 1.')
        if ident=='u6-clues':
            layer=int(s['layer']);neuron=int(s['neuron']);rows=[]
            for i in range(7 if s.get('authored','').strip() else 6):
                tt,yy,tx=prompt(i if i<6 else 0,s.get('authored','') if i==6 else '');ll,cc=forward(tt)
                rows.append({'example':str(i+1),'prompt':tx,'supported':MODEL['vocab'][yy],'activation':round(float(cc['neurons'][layer][-1,neuron]),5),'model_answer':MODEL['vocab'][int(ll.argmax())]})
            heat=cache['neurons'][layer][:,:12]
            return result(f'Neuron {neuron} in block {layer} across {len(rows)} examples. A large number is a clue to investigate, not a verified label.',rows,x='example',y='activation',heat=heat.tolist(),heat_labels=[MODEL['vocab'][t] for t in tokens],formula='Hidden unit activation = ReLU(normalised residual × learned weights + bias). The heat map shows units 0–11 at each token of your current prompt (the authored prompt when supplied); the bars show your selected unit across examples.')
        if ident=='u6-switch':
            layer=int(s['layer']);head=int(s['head']);changed,_=forward(tokens,ablate=(layer,head));pc=softmax(changed)
            suite=[]
            for j in MODEL['test_indices']:
                tt,yy=MODEL['cases'][j];before,_=forward(tt);after,_=forward(tt,ablate=(layer,head));suite.append({'case':j,'baseline_correct':bool(before.argmax()==yy),'intervened_correct':bool(after.argmax()==yy),'supported_probability_change':float(softmax(after)[yy]-softmax(before)[yy])})
            rows=[{'word':v,'original_probability':round(float(probs[i]),6),'probability':round(float(pc[i]),6)} for i,v in enumerate(MODEL['vocab'])]
            return result(context+f' Removing block {layer}, head {head} changes the answer to {MODEL["vocab"][int(changed.argmax())]}.',rows,metrics={'probability_change_for_supported_answer':float(pc[truth]-probs[truth]),'frozen_evaluation_cases':len(suite),'baseline_correct':sum(r['baseline_correct'] for r in suite),'intervened_correct':sum(r['intervened_correct'] for r in suite),'paired_cases':suite},formula='Ablation sets the chosen attention head output to zero before its output projection. It changes an internal computation, while weights and prompt stay fixed.')
        if ident=='u6-calendar':
            start=int(s['month']);step=int(s['step']);end=(start+step)%12;labels=['Jan','Feb','Mar','Apr','May','Jun','Jul','Aug','Sep','Oct','Nov','Dec']
            rows=[{'step':i,'unwrapped_number':start+i,'month':labels[(start+i)%12],'x':math.cos(2*math.pi*(start+i)/12),'y':math.sin(2*math.pi*(start+i)/12)} for i in range(step+1)]
            return result(f'{labels[start]} + {step} months = {labels[end]}. First add the numbers ({start} + {step} = {start+step}), then take the remainder after dividing by 12 ({end}).',rows,'circle',x='x',y='y',labels=labels,formula='Month number = (starting number + steps) mod 12. This drawn circle is an explicit teaching representation, not an activation measurement from Llama.')
        if ident=='u6-story':
            prior=s['prior'];strength=s['reliability'];count=int(s['clues']);scenario=s['story'];posterior=prior;rows=[{'clue':0,'p_rain':prior,'evidence':'Starting guess'}]
            clues=[1,1,-1,1,-1] if scenario==0 else [-1,-1,1,-1,1]
            descriptions={1:'Dark clouds support rain',-1:'Clear sky supports dry weather'}
            for i,e in enumerate(clues[:count]):
                lr=(strength/(1-strength))**e;odds=posterior/(1-posterior)*lr;posterior=odds/(1+odds)
                rows.append({'clue':i+1,'p_rain':round(posterior,6),'evidence':descriptions[e]})
            return result(f'Pip is planning a picnic. After {count} clues, this teaching model gives rain probability {posterior:.1%}. It updates a number; it has no feelings.',rows,'line',x='clue',y='p_rain',formula='Updated odds = previous odds × likelihood ratio. Clues are assumed independent given the weather; repeated copies of one clue would violate this assumption.')
        if ident=='u6-fair-test':
            threshold=s['threshold'];shift=s['shift'];rows=[]
            for i in range(20):
                truth=i%2==0;score=(.45+.45*(i%5)/4) if truth else (.1+.55*(i%5)/4)
                if shift:score=1-score
                flagged=score>=threshold
                rows.append({'case':i+1,'needs_check':truth,'clue_score':round(score,3),'flagged':flagged,'outcome':('caught' if truth else 'false alarm') if flagged else ('missed' if truth else 'correct pass')})
            tp=sum(r['outcome']=='caught' for r in rows);fp=sum(r['outcome']=='false alarm' for r in rows);fn=10-tp;tn=10-fp
            chart=[{'outcome':k,'cases':v} for k,v in [('Caught',tp),('Missed',fn),('False alarm',fp),('Correct pass',tn)]]
            return result(f'Out of 10 cases needing a check, the rule catches {tp} and misses {fn}. Among 10 other cases, it raises {fp} false alarms.',rows,x='outcome',y='cases',chart_rows=chart,metrics={'TP':tp,'FN':fn,'FP':fp,'TN':tn},formula='Flag a case when its hand-made clue score ≥ threshold. The shifted set reverses the association. These are fictional test fixtures, not measured LLM scores.')
        if ident=='u12-circuits':
            corrupt=tokens.copy();corrupt[2],corrupt[6]=corrupt[6],corrupt[2]
            bad,badcache=forward(corrupt);layer=int(s['layer']);position=int(s['position']);mode=s['intervention']
            changed,_=forward(corrupt,patch={'layer':layer,'position':position,'value':cache['residual'][layer+1][position]}) if mode==0 else forward(corrupt,ablate=(layer,int(s['head'])))
            competitor=corrupt[2] if tokens[-2]==tokens[0] else corrupt[6]
            metric=lambda a:float(a[truth]-a[competitor]);denom=metric(logits)-metric(bad)
            recovery=(metric(changed)-metric(bad))/denom if abs(denom)>1e-6 else None
            rows=[{'condition':n,'logit_difference':metric(a),'p_original_fact':float(softmax(a)[truth]),'answer':MODEL['vocab'][int(a.argmax())]} for n,a in [('Clean',logits),('Corrupted',bad),('Intervened',changed)]]
            for label,vector in [('No-op corrupted vector',badcache['residual'][layer+1][position]),('Zero vector',np.zeros(24)),('Reversed clean coordinates',cache['residual'][layer+1][position][::-1])]:
                control,_=forward(corrupt,patch={'layer':layer,'position':position,'value':vector});rows.append({'condition':label,'logit_difference':metric(control),'p_original_fact':float(softmax(control)[truth]),'answer':MODEL['vocab'][int(control.argmax())]})
            attention=badcache['attention'][layer][int(s['head'])]
            return result(f'Patch recovery: {recovery:.3f} (can exceed 0–1).' if recovery is not None else 'Recovery is undefined: clean and corrupted scores are effectively identical.',rows,x='condition',y='logit_difference',heat=attention.tolist(),heat_labels=[MODEL['vocab'][t] for t in corrupt],metrics={'normalised_recovery':recovery,'clean_prompt':text,'corrupted_prompt':' '.join(MODEL['vocab'][t] for t in corrupt)},formula='Logit difference = score(original correct colour) − score(corrupted correct colour). Recovery = (intervened − corrupted)/(clean − corrupted). Patch replaces one post-block residual vector; ablation instead zeroes one head. Attention weights alone are not a causal graph.')
        if ident=='u12-training':
            edited,_=forward(tokens,'edited');alpha=s['alpha'];amplified=logits+alpha*(edited-logits)
            rows=[{'word':v,'base':float(softmax(logits)[i]),'edited':float(softmax(edited)[i]),'probability':float(softmax(amplified)[i])} for i,v in enumerate(MODEL['vocab'])]
            suite=[]
            for j in MODEL['test_indices']:
                tt,yy=MODEL['cases'][j];bb,_=forward(tt);ee,_=forward(tt,'edited');suite.append((int(bb.argmax())==yy,int(ee.argmax())==yy,tt[-2]==2))
            groups={}
            for name,selection in [('all',suite),('Kiki queries',[r for r in suite if r[2]]),('other queries',[r for r in suite if not r[2]])]:
                groups[name]={'n':len(selection),'base_correct':sum(r[0] for r in selection),'edited_correct':sum(r[1] for r in selection)}
            return result(context+f' At amplification {alpha:g}, the answer is {MODEL["vocab"][int(amplified.argmax())]}. Check Kiki and non-Kiki cases.',rows,metrics={'held_out_counts':groups},formula='Amplified logits = base + α × (edited − base). α=0 is base; α=1 is the edited checkpoint. Fine-tuning deliberately assigned green to every Kiki query. Discovery under amplification is not natural failure prevalence.')
        if ident=='u12-features':
            rng=np.random.default_rng(int(s.get('seed',17)));capacity=int(s['capacity']);rare=s['rare'];penalty=s['penalty'];n=240
            angles=np.array([0,1.2,2.4]);directions=np.stack([np.cos(angles),np.sin(angles)],axis=0)
            active=rng.random((n,3))<np.array([.5,.4,rare]);strength=rng.uniform(.4,1.3,(n,3));latent=active*strength;x=latent@directions.T
            encoder=rng.normal(0,.3,(2,capacity));decoder=rng.normal(0,.3,(capacity,2));losses=[]
            for step in range(201):
                pre=x[:180]@encoder;z=np.maximum(pre,0);recon=z@decoder;error=recon-x[:180];gz=(2*error@decoder.T+penalty)/180*(pre>0)
                gd=z.T@(2*error)/180;ge=x[:180].T@gz;encoder-=.03*ge;decoder-=.03*gd
                # Keep dictionary scale bounded so sparsity cannot be evaded by rescaling.
                decoder/=np.maximum(1,np.linalg.norm(decoder,axis=1,keepdims=True))
                if step%10==0:losses.append({'step':step,'reconstruction_mse':float(np.mean(error**2)),'mean_activation':float(z.mean())})
            z=np.maximum(x[180:]@encoder,0);err=((z@decoder-x[180:])**2).mean(1);rare_mask=active[180:,2]
            rows=[{'case':i+1,'rare_feature_present':bool(rare_mask[i]),'squared_error':float(err[i]),'nonzero_units':int((z[i]>1e-6).sum())} for i in range(60)]
            stats={'held_out_n':60,'rare_n':int(rare_mask.sum()),'held_out_mse':float(err.mean()),'rare_mse':float(err[rare_mask].mean()) if rare_mask.any() else None,'dictionary_size':capacity,'seed':int(s.get('seed',17)),'known_direction_best_absolute_cosine':(np.abs(directions.T@decoder.T)/np.maximum(1e-12,np.linalg.norm(decoder,axis=1))[None,:]).max(1).tolist(),'nonrare_mse':float(err[~rare_mask].mean()) if (~rare_mask).any() else None}
            return result('A small ReLU sparse autoencoder is trained live on 180 known mixtures and evaluated on 60 untouched mixtures. Compare total error with rare-feature error.',rows,'line',x='step',y='reconstruction_mse',chart_rows=losses,metrics=stats,heat=decoder.tolist(),heat_labels=[str(i) for i in range(capacity)],formula='Loss = mean summed squared reconstruction error + λ × mean summed positive activations. The chart reports per-coordinate reconstruction MSE. Three known features share two dimensions. This is a small SAE experiment, not a replication of Goodfire scaling curves or block-sparse featurizers.')
        if ident=='u12-geometry':
            angle=s['angle']*math.pi/180;start=s['start']*math.pi/180;mode=s['path'];rows=[]
            for t in np.linspace(0,1,21):
                a=np.array([math.cos(start),math.sin(start)]);b=np.array([math.cos(start+angle),math.sin(start+angle)])
                point=(1-t)*a+t*b if mode==0 else np.array([math.cos(start+t*angle),math.sin(start+t*angle)])
                radius=float(np.linalg.norm(point));decoded=(math.degrees(math.atan2(point[1],point[0]))%360) if radius>1e-8 else None
                rows.append({'fraction':float(t),'x':float(point[0]),'y':float(point[1]),'off_manifold_distance':abs(1-radius),'decoded_degrees':decoded})
            return result('Compare a straight chord with an arc on a known unit-circle representation. At the centre, the angle decoder is undefined.',rows,'circle',x='x',y='y',metrics={'max_off_manifold_distance':max(r['off_manifold_distance'] for r in rows)},formula='Arc(t) = [cos(θ₀+tΔ), sin(θ₀+tΔ)]; chord(t) = (1−t)a+tb. Distance to the unit circle = |‖x‖−1|. Geometry is hand-specified here; it is not extracted from a large language model.')
        if ident=='u12-probes':
            rng=np.random.default_rng(int(s.get('seed',32)));mode=s['features'];shift=s['shift'];threshold=s['threshold'];n=240;y=rng.integers(0,2,n);signal=(2*y-1)+rng.normal(0,1.2,n)
            shortcut=(2*y-1)+rng.normal(0,.15,n);shortcut[160:]=((1-2*y[160:]) if shift else (2*y[160:]-1))+rng.normal(0,.15,80)
            # A sequence pair has exactly zero mean but signed cross-coordinate covariance.
            seq=np.stack([np.stack([np.ones(n),signal],1),np.stack([-np.ones(n),-signal],1)],1)
            features=np.column_stack([signal,shortcut]) if mode==0 else (seq.mean(1) if mode==1 else np.column_stack([(seq[:,:,0]*seq[:,:,1]).mean(1),np.ones(n)]))
            x=np.column_stack([features,np.ones(n)]);w=np.zeros(3)
            for _ in range(250):
                p=1/(1+np.exp(-np.clip(x[:160]@w,-30,30)));w-=.1*(x[:160].T@(p-y[:160])/160+.01*w)
            pred=1/(1+np.exp(-np.clip(x@w,-30,30)));flag=pred>=threshold;rows=[]
            for group,sl in [('Training',slice(0,160)),('Held out',slice(160,240))]:
                yy=y[sl];ff=flag[sl];pp=pred[sl];rows.append({'split':group,'n':len(yy),'TP':int(((yy==1)&ff).sum()),'FN':int(((yy==1)&~ff).sum()),'FP':int(((yy==0)&ff).sum()),'TN':int(((yy==0)&~ff).sum()),'accuracy':float((ff==yy).mean()),'brier':float(((pp-yy)**2).mean())})
            return result('The probe is trained on the first 160 seeded cases only. A shortcut flips on the 80 held-out cases when distribution shift is enabled.',rows,x='split',y='accuracy',metrics={'learned_weights':w.tolist()},formula='Logistic probe trained by gradient descent with L2 penalty. Feature modes: signal+shortcut; sequence means (both zero); centred cross-covariance+constant. These are synthetic representations, not measured LLM activations.')
        if ident=='u12-uncertainty':
            rng=np.random.default_rng(int(s.get('seed',37)));n=int(s['rollouts']);window=int(s['smoothing']);threshold=s['threshold'];hard=s['hard'];rows=[]
            true=np.array([.5,.51,.52,.51,.53,.55,.87,.9,.93,.95,.96,.97]) if not hard else np.array([.5,.52,.55,.62,.72,.8,.75,.62,.38,.2,.1,.04])
            estimates=rng.binomial(n,true)/n
            for t,p in enumerate(true):
                sm=float(estimates[max(0,t-window+1):t+1].mean());rows.append({'prefix':t,'true_p_A':float(p),'sample_p_A':float(estimates[t]),'smoothed_p_A':sm,'estimated_standard_error':math.sqrt(float(p*(1-p))/n)})
            exits=[i for i,r in enumerate(rows) if max(r['smoothed_p_A'],1-r['smoothed_p_A'])>=threshold];exit_at=exits[0] if exits else 11;chosen='A' if rows[exit_at]['smoothed_p_A']>=.5 else 'B';supported='A' if not hard else 'B'
            return result(f'Early exit at prefix {exit_at}: chooses {chosen}; the fixture answer is {supported}. Later evidence can overturn an early high-confidence choice.',rows,'line',x='prefix',y='smoothed_p_A',metrics={'exit_prefix':exit_at,'chosen_answer':chosen,'fixture_answer':supported,'correct':chosen==supported,'remaining_prefixes_skipped':11-exit_at},formula='Each prefix draws N independent Bernoulli continuations from a known probability. A trailing mean smooths estimates. SE = √[p(1−p)/N]. These are simulated continuations; matching the eventual answer is distinct from matching an external answer key.')
        if ident=='u12-weights':
            matrix=np.asarray(MODEL['base']['readout.weight']);rank=int(s['rank']);edit=s['edit'];frequency=s['frequency']
            u,d,vt=np.linalg.svd(matrix,full_matrices=False);rebuild=(u[:,:rank]*d[:rank])@vt[:rank]
            # Edit one retained component of the SAME learned output matrix.
            rebuild=rebuild+edit*d[0]*np.outer(u[:,0],vt[0]);groups={'Kiki queries':[],'Other queries':[]}
            for j in MODEL['test_indices']:
                tt,yy=MODEL['cases'][j];original,_=forward(tt);changed,_=forward(tt,readout=rebuild)
                groups['Kiki queries' if tt[-2]==MODEL['vocab'].index('kiki') else 'Other queries'].append((float(-np.log(softmax(original)[yy]+1e-12)),float(-np.log(softmax(changed)[yy]+1e-12)),int(original.argmax()==yy),int(changed.argmax()==yy)))
            rows=[{'group':name,'cases':len(values),'base_loss':float(np.mean([v[0] for v in values])),'edited_loss':float(np.mean([v[1] for v in values])),'base_correct':sum(v[2] for v in values),'edited_correct':sum(v[3] for v in values)} for name,values in groups.items()]
            weighted=frequency*rows[0]['edited_loss']+(1-frequency)*rows[1]['edited_loss']
            return result(f'One experiment: decompose the trained output matrix, edit it, then rerun all {sum(len(v) for v in groups.values())} frozen evaluation prompts. Population-weighted loss: {weighted:.4f}.',rows,x='group',y='edited_loss',heat=rebuild.tolist(),heat_labels=MODEL['vocab'],metrics={'matrix_frobenius_error':float(np.linalg.norm(matrix-rebuild)),'weighted_loss':weighted,'assumed_Kiki_population_share':frequency,'rank':rank,'leading_component_edit':edit},formula='W = UΣVᵀ. Keep r components, then add edit × σ₁u₁v₁ᵀ to the same learned readout matrix. Evaluate cross-entropy and exact answers with all other model weights fixed. The population slider reweights evaluation groups; it does not change training frequency. SVD is not SPD/VPD or K-FAC, and singular vectors are not verified semantic circuits.')
        if ident=='u12-audit':
            n=int(s['sample']);p=s['failure'];best=int(s['best']);cue=s['cue'];rng=np.random.default_rng(int(s.get('seed',43)))
            # One coherent audit: the sample size is the number of independently generated pools.
            truth=rng.random((n,best))>=p;style=rng.random((n,best));reward=.45*truth+.55*style if not cue else style
            selected=truth[np.arange(n),reward.argmax(1)];failures=int((~selected).sum());phat=failures/n;z=1.96;denom=1+z*z/n;centre=(phat+z*z/(2*n))/denom;half=z*math.sqrt(phat*(1-phat)/n+z*z/(4*n*n))/denom
            rows=[{'condition':'First candidate','correct':int(truth[:,0].sum()),'failures':int((~truth[:,0]).sum()),'n':n},{'condition':'Highest proxy reward','correct':int(selected.sum()),'failures':failures,'n':n}]
            return result(f'The selected policy fails on {failures}/{n} independently generated candidate pools. Wilson 95% interval [{max(0,centre-half):.3%}, {min(1,centre+half):.3%}]. Compare with the paired first-candidate baseline.',rows,x='condition',y='correct',metrics={'failures':failures,'n':n,'wilson_low':max(0,centre-half),'wilson_high':min(1,centre+half),'candidate_failure_probability':p,'best_of_n':best,'evaluation_cue':bool(cue),'seed':int(s.get('seed',43))},formula='Generate N independent candidate pools using a known per-candidate failure probability. Select by truth+style or style alone, then audit the selected candidates against their independent labels. The interval applies to selected-policy failure under this simulator, not to the input candidate failure rate or an LLM. After tuning on a seed, use a fresh seed for final evaluation. A zero-failure sample cannot certify safety.')
        raise ValueError('Unknown investigation: '+ident)

    def compute_base(ident,s):
        return compute_mechanism(ident,s)

    compute_mechanism=compute

    def compute(ident,s):
        out=compute_base(ident,s)
        if ident=='u12-geometry':
            first=int(s['start']//90)%6;tt,yy,_=prompt(first);other,other_y,_=prompt((first+1)%6);base,c=forward(tt);target,d=forward(other);a=c['residual'][1][-1];b=d['residual'][1][-1];rows=[]
            for fraction in np.linspace(0,s['angle']/360,11):
                chord=(1-fraction)*a+fraction*b
                # A norm-preserving variant is an intervention convention, not a fitted manifold.
                vector=chord if s['path']==0 else chord/max(1e-12,np.linalg.norm(chord))*((1-fraction)*np.linalg.norm(a)+fraction*np.linalg.norm(b))
                changed,_=forward(tt,patch={'layer':0,'position':len(tt)-1,'value':vector})
                rows.append({'fraction':float(fraction),'residual_norm':float(np.linalg.norm(vector)),'p_original_fact':float(softmax(changed)[yy]),'p_other_fact':float(softmax(changed)[other_y]),'answer':MODEL['vocab'][int(changed.argmax())]})
            out.update(model_rows=rows,model_method='Measured bridge: interpolate post-block-0 final-token residuals from two actual tiny-transformer prompts, then run block 1 and the output head. Starting angle selects a fact pair; travel/360 selects edit extent. Path 0 is a chord; path 1 rescales that chord to an interpolated norm. This does not establish that actual model states lie on a circle or that norm-preserving edits are on-manifold.')
        if ident=='u12-probes':
            features=[];labels=[]
            for tokens,truth in MODEL['cases']:
                _,cache=forward(tokens);sequence=cache['residual'][1]
                vector=sequence[-1] if s['features']==0 else sequence.mean(0) if s['features']==1 else ((sequence-sequence.mean(0))**2).mean(0)
                features.append(vector);labels.append(int(truth==MODEL['vocab'].index('red')))
            features=np.array(features);labels=np.array(labels);test=np.array(MODEL['test_indices']);train=np.array([i for i in range(len(labels)) if i not in set(test)])
            # The final set never fits the normaliser or weights.
            mean=features[train].mean(0);std=np.maximum(.01,features[train].std(0));x=np.column_stack([(features-mean)/std,np.ones(len(labels))]);weights=np.zeros(x.shape[1])
            for _ in range(150):
                prob=1/(1+np.exp(-np.clip(x[train]@weights,-30,30)));weights-=.05*(x[train].T@(prob-labels[train])/len(train)+.01*weights)
            probabilities=1/(1+np.exp(-np.clip(x@weights,-30,30)))
            rows=[{'split':name,'n':len(indices),'red_cases':int(labels[indices].sum()),'correct':int(((probabilities[indices]>=s['threshold'])==labels[indices]).sum()),'brier':float(((probabilities[indices]-labels[indices])**2).mean())} for name,indices in [('Probe training',train),('Frozen evaluation',test)]]
            out.update(model_rows=rows,model_method='Measured bridge: fit a logistic probe for whether the written facts support red, using post-block-0 activations of the trained transformer. Modes read final-token, sequence mean or coordinate variance respectively. The separate synthetic shortcut switch does not alter these measured features. Probe success is predictive evidence; this bridge performs no causal intervention. Repeated tuning consumes the frozen set for development.')
        if ident=='u12-uncertainty':
            rng=np.random.default_rng(int(s.get('seed',37)));rows=[]
            for case in range(6):
                tokens,truth,text=prompt(case);logits,_=forward(tokens);prob=softmax(logits);samples=rng.choice(len(prob),size=int(s['rollouts']),p=prob);estimate=float((samples==truth).mean())
                rows.append({'card':case+1,'supported_colour':MODEL['vocab'][truth],'exact_model_probability':float(prob[truth]),'sampled_frequency':estimate,'samples':int(s['rollouts']),'absolute_error':abs(estimate-float(prob[truth]))})
            out.update(model_rows=rows,model_method='Measured bridge: sample the actual tiny-transformer next-word distribution on six complete fact cards. Compare sample frequencies with exact full-vocabulary probabilities. These are independent one-token samples, not reasoning-chain rollouts; smoothing and early-exit controls apply only to the branching simulation above.')
        if ident=='u6-story':
            rows=[]
            for case in [0,1]:
                tokens,truth,text=prompt(case);logits,_=forward(tokens);rows.append({'fact_card':text,'supported_colour':MODEL['vocab'][truth],'model_guess':MODEL['vocab'][int(logits.argmax())],'chance_assigned_to_supported_colour':round(float(softmax(logits)[truth]),3)})
            out.update(model_rows=rows,model_method='Compare two actual tiny-model fact cards. Changing the words changes the learned calculation. This is a different mechanism from the picnic’s hand-written weather rule: neither is evidence that a computer has feelings. The weather controls do not alter these two fixed model observations.')
        return out

    def draw(out):
        """Accessible SVG views, with exact values duplicated in tables."""
        esc=lambda s:html.escape(str(s),quote=True)
        marks=[];kind=out['kind'];rows=out.get('chart_rows',out['rows']);x=out['x'];y=out['y']
        if kind=='circle':
            marks.append('<circle cx="290" cy="180" r="130" fill="none" stroke="#778d85" stroke-width="2"/>')
            for i in range(12):
                a=2*math.pi*i/12;label=out.get('labels',[str(i*30)+'°' for i in range(12)])[i]
                marks.append(f'<text x="{290+152*math.cos(a):.1f}" y="{185-152*math.sin(a):.1f}" text-anchor="middle">{esc(label)}</text>')
            points=' '.join(f'{290+130*r[x]:.2f},{180-130*r[y]:.2f}' for r in rows)
            marks.append(f'<polyline points="{points}" fill="none" stroke="#9f3e20" stroke-width="4"/>')
            for i,r in enumerate(rows):marks.append(f'<circle cx="{290+130*r[x]:.2f}" cy="{180-130*r[y]:.2f}" r="4" fill="#9f3e20"><title>Step {i}: ({r[x]:.3f}, {r[y]:.3f})</title></circle>')
            marks.append('<text x="485" y="160">Orange: trajectory</text><text x="485" y="185">Grey: unit circle</text>')
        elif kind=='line':
            values=[float(r[y]) for r in rows];lo=min(0,min(values));hi=max(.01,max(values));px=lambda v:70+(v-float(rows[0][x]))/max(1e-9,float(rows[-1][x])-float(rows[0][x]))*570;py=lambda v:300-(v-lo)/max(1e-9,hi-lo)*245
            points=' '.join(f'{px(float(r[x])):.2f},{py(float(r[y])):.2f}' for r in rows)
            marks.append(f'<polyline points="{points}" fill="none" stroke="#9f3e20" stroke-width="3"/>')
            for r in rows:marks.append(f'<circle cx="{px(float(r[x])):.2f}" cy="{py(float(r[y])):.2f}" r="4" fill="#9f3e20"><title>{esc(x)} {r[x]}: {r[y]:.4f}</title></circle>')
            for frac in [0,.5,1]:
                val=lo+frac*(hi-lo);marks.append(f'<text x="60" y="{py(val)+5}" text-anchor="end">{val:.3g}</text>')
            marks.append(f'<path d="M70 40 V300 H645" fill="none" stroke="#59756b"/><text x="70" y="325">{rows[0][x]}</text><text x="640" y="325" text-anchor="end">{rows[-1][x]}</text><text x="340" y="350" text-anchor="middle">{esc(x)}</text><text x="70" y="25">{esc(y)}</text>')
        else:
            vals=[float(r[y]) for r in rows];lo=min(0,min(vals));hi=max(.001,max(vals));scale=lambda v:200+440*(v-lo)/max(1e-9,hi-lo);zero=scale(0);height=min(46,270/max(1,len(rows)))
            for i,r in enumerate(rows):
                yy=50+i*height;end=scale(float(r[y]));marks.append(f'<text x="187" y="{yy+14}" text-anchor="end">{esc(r[x])}</text><rect x="{min(zero,end):.2f}" y="{yy}" width="{max(.5,abs(end-zero)):.2f}" height="{height*.65}" rx="2" fill="#26735c"/><text x="650" y="{yy+14}">{float(r[y]):.3g}</text>')
            marks.append(f'<text x="200" y="25">{esc(y)}</text>')
        graphic=f'<svg viewBox="0 0 720 365" role="img" aria-label="{esc(y)} chart; exact values in the evidence table" style="width:100%;min-width:560px;background:#f7f8f2;border-radius:12px;font:14px system-ui;fill:#173d36"><title>{esc(y)} chart</title>{"".join(marks)}</svg>'
        if out.get('heat') is not None:
            heat=np.asarray(out['heat']);maxv=max(1e-9,float(np.max(np.abs(heat))));cells=[]
            for i,row in enumerate(heat):
                cells.append(f'<text x="80" y="{44+i*24}" text-anchor="end">{esc(out["heat_labels"][i])}</text>')
                for j,val in enumerate(row):
                    opacity=.1+.9*abs(float(val))/maxv;color='#26735c' if val>=0 else '#a94322';cells.append(f'<rect x="{90+j*32}" y="{27+i*24}" width="29" height="21" fill="{color}" opacity="{opacity:.3f}"><title>Row {i}, column {j}: {val:.5f}</title></rect>')
            for j in range(heat.shape[1]):cells.append(f'<text x="{96+j*32}" y="18">{j}</text>')
            graphic+=f'<svg viewBox="0 0 720 {65+24*len(heat)}" role="img" aria-label="Value heat map. Green positive, rust negative. Exact values in the heat map table." style="width:100%;min-width:560px;font:12px system-ui;fill:#173d36"><title>Heat map with indexed columns</title>{"".join(cells)}</svg>'
        return '<div tabindex="0" role="group" aria-label="Scrollable experiment charts" style="max-width:100%;overflow-x:auto">'+graphic+'</div>'

    MODEL = json.loads(zlib.decompress(base64.b64decode('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')))
    return mo, compute, draw, json, MODEL

@app.cell
def _():
    config = {'id': 'u12-uncertainty', 'year': 12, 'title': 'Where does the answer fork?', 'concept': 'Rollouts and early exit', 'question': 'When is an early confident answer premature?', 'intro': 'Sample continuations from a known branching process. Change rollout count, smoothing and early-exit confidence, then test a late reversal.', 'controls': [{'key': 'rollouts', 'label': 'Continuations per prefix', 'kind': 'slider', 'min': 10, 'max': 500, 'step': 10, 'default': 50}, {'key': 'smoothing', 'label': 'Trailing smoothing window', 'kind': 'slider', 'min': 1, 'max': 5, 'step': 1, 'default': 1}, {'key': 'threshold', 'label': 'Early-exit confidence', 'kind': 'slider', 'min': 0.55, 'max': 0.95, 'step': 0.05, 'default': 0.8}, {'key': 'hard', 'label': 'Evidence pattern', 'kind': 'choice', 'options': ['Stable late answer', 'Late reversal'], 'default': 0}, {'key': 'seed', 'label': 'Data seed — reserve a fresh seed for final checking', 'kind': 'slider', 'min': 1, 'max': 99, 'step': 1, 'default': 37}], 'prediction': 'Will more samples fix a decision made before the decisive late clue arrives?', 'challenge': 'Use the late-reversal case with threshold 0.75. Compare additional rollouts with delaying the exit. Compare seeds 11, 23 and 47 with the same settings. Report every result and the range. Lock your final settings before opening seed 89; changing settings afterwards turns that check into development data.', 'artifact': 'A cost–accuracy analysis separating sampling noise, late evidence and correctness of the final answer.', 'teacher': 'The simulator reveals true probabilities so uncertainty estimates can be checked. Distinguish a monitor predicting an eventual answer from a monitor checking an external answer key.', 'extend': 'Repeat many seeds and plot error rate against average prefixes consumed.', 'glossary': {'rollout': 'A sampled continuation from a particular prefix.', 'standard error': 'The sampling variability of an estimate.', 'early exit': 'Stopping a computation when a specified condition is met.'}, 'method': 'Seeded branching simulation', 'minutes': 55}
    sources = [{'id': 'forking-fast', 'title': 'Forking Fast: Efficiently Estimating Uncertainty Dynamics in Text Generation', 'url': 'https://www.goodfire.com/research/forking-fast', 'kind': 'LLM research', 'date': None, 'idea': 'Repeated continuations from shared prefixes reveal how answer uncertainty changes along a generation.', 'year6': 'Try the same starting clue many times and count different endings.', 'year12': 'Compare sampled branch probabilities with a known distribution as rollout count and smoothing change.', 'limit': 'Our finite branching simulator is not a reasoning LLM. More samples reduce sampling noise, not model bias.', 'labs': ['u6-words', 'u12-uncertainty'], 'paper': {'url': 'https://arxiv.org/html/2608.19611', 'title': 'Forking Fast: Efficiently Estimating Uncertainty Dynamics in Text Generation'}, 'systemAndData': 'Llama-3-8B-Instruct and DeepSeek-R1-Distill-Llama-8B on tinyMMLU', 'method': 'Resamples continuations at shared prefixes and compares uncertainty estimates across sample budgets, spacing and smoothing.', 'question': 'What evidence would support or challenge this idea: Repeated continuations from shared prefixes reveal how answer uncertainty changes along a generation.', 'finding': 'Repeated continuations from shared prefixes reveal how answer uncertainty changes along a generation.', 'year6Task': 'Try the same starting clue many times and count different endings. Use the linked notebook to make two observations. Draw or describe one result and one thing this activity cannot tell us about the source system.', 'year12Task': 'Compare sampled branch probabilities with a known distribution as rollout count and smoothing change. Record the source model/task, a baseline, the changed factor, a measurement and an alternative explanation. State exactly which part your notebook investigates and which part it does not reproduce.', 'reviewed': '2026-09-07', 'reproduction': 'Independent classroom adaptation; not a reproduction of the source model or complete method.'}, {'id': 'priors-in-time', 'title': 'Priors in Time: Missing Inductive Biases for Language Model Interpretability', 'url': 'https://www.goodfire.com/research/priors-in-time', 'kind': 'LLM research', 'date': None, 'idea': 'Temporal feature analysis separates predictable context from new information over a sequence.', 'year6': 'Reveal story clues one at a time and record each change.', 'year12': 'Contrast a static representation with a model of predictable state and residual novelty.', 'limit': 'Our Bayesian story model is explicit and hand-specified; it is not Temporal Feature Analysis applied to an LLM.', 'labs': ['u6-story', 'u12-uncertainty'], 'paper': {'url': 'https://arxiv.org/html/2511.01836', 'title': 'Priors in Time: Missing Inductive Biases for Language Model Interpretability'}, 'systemAndData': 'Gemma-2-2B activations from Pile-Uncopyrighted', 'method': 'Compares temporal feature analysis with ReLU, TopK and BatchTopK SAEs, testing predictable versus innovation components and event structure.', 'question': 'What evidence would support or challenge this idea: Temporal feature analysis separates predictable context from new information over a sequence.', 'finding': 'Temporal feature analysis separates predictable context from new information over a sequence.', 'year6Task': 'Reveal story clues one at a time and record each change. Use the linked notebook to make two observations. Draw or describe one result and one thing this activity cannot tell us about the source system.', 'year12Task': 'Contrast a static representation with a model of predictable state and residual novelty. Record the source model/task, a baseline, the changed factor, a measurement and an alternative explanation. State exactly which part your notebook investigates and which part it does not reproduce.', 'reviewed': '2026-09-07', 'reproduction': 'Independent classroom adaptation; not a reproduction of the source model or complete method.'}, {'id': 'reasoning-theater', 'title': 'Reasoning Theater: Probing for Performative Chain-of-Thought', 'url': 'https://www.goodfire.com/research/reasoning-theater', 'kind': 'LLM research', 'date': 'March 12, 2026', 'idea': "Probes can sometimes predict a model's eventual answer before its written reasoning ends.", 'year6': 'An early guess can match a later answer and still be wrong.', 'year12': 'Evaluate early-exit cost against accuracy and distinguish answer prediction from correctness.', 'limit': 'Savings depend on task and paper version; the Goodfire post and later arXiv revision report different percentages. Our trajectories are simulated.', 'labs': ['u6-story', 'u12-uncertainty'], 'paper': {'url': 'https://arxiv.org/html/2603.05488', 'title': 'Reasoning Theater: Disentangling Model Beliefs from Chain-of-Thought'}, 'systemAndData': 'DeepSeek-R1 families and GPT-OSS-120B on MMLU-Redux and GPQA-Diamond', 'method': 'Trains context-pooling probes to forecast eventual answers during reasoning and evaluates early-exit token cost versus benchmark accuracy.', 'question': "What evidence would support or challenge this idea: Probes can sometimes predict a model's eventual answer before its written reasoning ends.", 'finding': "Probes can sometimes predict a model's eventual answer before its written reasoning ends.", 'year6Task': 'An early guess can match a later answer and still be wrong. Use the linked notebook to make two observations. Draw or describe one result and one thing this activity cannot tell us about the source system.', 'year12Task': 'Evaluate early-exit cost against accuracy and distinguish answer prediction from correctness. Record the source model/task, a baseline, the changed factor, a measurement and an alternative explanation. State exactly which part your notebook investigates and which part it does not reproduce.', 'reviewed': '2026-09-07', 'reproduction': 'Independent classroom adaptation; not a reproduction of the source model or complete method.'}]
    return config, sources

@app.cell
def _(mo, config):
    mo.md(f"# {config['title']}\n\n**Year {config['year']} · {config['minutes']} minutes · {config['method']}**\n\n## {config['question']}\n\n{config['intro']}\n\n**Your mission:** predict, change one control, compare evidence, then find a counterexample. All personal stories and classroom data are fictional. Python runs in your browser; first load needs an internet connection for the runtime. Download your work before leaving.")
    return

@app.cell
def _(mo):
    project_file = mo.ui.file(filetypes=['.json'], multiple=False, max_size=2000000, label='Open a saved Brightlab notebook project')
    mo.vstack([mo.md('**Keep your work:** download a project before leaving. Reopen it here to restore settings, comparisons and writing. Files are read inside this notebook; use fictional classroom data.'),project_file])
    return (project_file,)

@app.cell
def _(project_file, json, config, mo):
    restored = {}
    if project_file.contents():
        try:
            _candidate = json.loads(project_file.contents().decode('utf-8'))
            def _tree(value,depth=0):
                if depth>16:raise ValueError('Project nesting is too deep.')
                if isinstance(value,dict):
                    if len(value)>100:raise ValueError('Too many object fields.')
                    for child in value.values():_tree(child,depth+1)
                elif isinstance(value,list):
                    if len(value)>20000:raise ValueError('Too many rows.')
                    for child in value:_tree(child,depth+1)
                elif isinstance(value,float) and not (-1e100<value<1e100):raise ValueError('Project contains a non-finite or excessive number.')
                elif isinstance(value,str) and len(value)>200000:raise ValueError('Project text is too long.')
            _tree(_candidate)
            if not isinstance(_candidate,dict) or _candidate.get('version')!=1:raise ValueError('Choose a supported version 1 notebook project.')
            if _candidate.get('format') != 'brightlab-notebook-project' or _candidate.get('lesson') != config.get('id',config.get('lesson_id')):
                raise ValueError('Choose a project for this notebook.')
            if not isinstance(_candidate.get('settings'),dict) or not isinstance(_candidate.get('prediction'),str) or not isinstance(_candidate.get('saved_runs',[]),list):
                raise ValueError('The project is missing its settings, prediction or comparison list.')
            for _spec in config['controls']:
                _value=_candidate['settings'].get(_spec['key'],_spec['default'])
                if _spec['kind']=='slider' and (type(_value) not in [int,float] or not _spec['min']<=_value<=_spec['max']):raise ValueError('A saved slider is outside its allowed range.')
                if _spec['kind']=='choice' and (type(_value) is not int or not 0<=_value<len(_spec['options'])):raise ValueError('A saved choice is invalid.')
                if _spec['kind']=='text' and (not isinstance(_value,str) or len(_value)>1000):raise ValueError('A saved text control is invalid.')
            if not isinstance(_candidate.get('conclusion',''),str) or len(_candidate.get('saved_runs',[]))>6:raise ValueError('Invalid writing or comparison count.')
            if len(_candidate.get('prediction',''))>10000 or not isinstance(_candidate.get('conclusion',''),str) or len(_candidate.get('conclusion',''))>20000:raise ValueError('Invalid writing in the saved project.')
            _runs=_candidate.get('saved_runs',[])
            if len(_runs)>6:raise ValueError('A project may contain up to six comparisons.')
            for _run in _runs:
                if not isinstance(_run,dict) or not isinstance(_run.get('settings'),dict):raise ValueError('Invalid saved comparison.')
                _rows=_run.get('measurements',_run.get('result',{}).get('rows') if isinstance(_run.get('result'),dict) else None)
                if not isinstance(_rows,list) or not all(isinstance(row,dict) for row in _rows):raise ValueError('A saved comparison needs a measurement table.')
            restored = _candidate
            mo.output.replace(mo.md('Project read. Save the restored prediction to continue your investigation.'))
        except (ValueError,TypeError,UnicodeError,KeyError,AttributeError) as _error:
            mo.output.replace(mo.callout(mo.md('Could not open this project: '+str(_error)),kind='warn'))
    return (restored,)

@app.cell
def _(mo, restored):
    reset_controls = mo.ui.button(value=0,on_click=lambda count:count+1,label='Reset controls to starting settings')
    return (reset_controls,)

@app.cell
def _(mo, config, restored):
    prediction = mo.ui.text_area(value=restored.get("prediction",""),label="My prediction — what will change, and why?", placeholder=config['prediction'], full_width=True).form(submit_button_label='Save prediction & open the lab', clear_on_submit=False, validate=lambda value: None if value and len(value.strip())>=3 else 'Write a short prediction, or ask a partner to record your spoken idea.')
    mo.vstack([mo.md('## 1. Make a prediction\n'+config['prediction']),prediction])
    return (prediction,)

@app.cell
def _(mo, config, prediction, restored, reset_controls):
    mo.stop(prediction.value is None,mo.md('**The experiment opens after you save a prediction.** There is no penalty for being surprised.'))
    _reset=reset_controls.value
    _saved=restored.get('settings',{}) if not _reset else {}
    controls = mo.ui.dictionary({s['key']:(mo.ui.slider(start=s['min'],stop=s['max'],step=s['step'],value=_saved.get(s['key'],s['default']),label=s['label'],show_value=True,full_width=True) if s['kind']=='slider' else mo.ui.text(value=_saved.get(s['key'],s['default']),label=s['label'],full_width=True,debounce=True) if s['kind']=='text' else mo.ui.dropdown(options={label:i for i,label in enumerate(s['options'])},value=s['options'][int(_saved.get(s['key'],s['default']))],label=s['label'],full_width=True)) for s in config['controls']})
    mo.vstack([mo.md('## 2. Change one thing\nThe first result uses the starting settings. Record it before moving a control.'),reset_controls,controls.vstack()])
    return (controls,)

@app.cell
def _(config, compute, controls):
    try:current = compute(config['id'],controls.value)
    except (ValueError,TypeError,KeyError,IndexError) as _error:
        current={'error':str(_error),'summary':'Check your inputs: '+str(_error),'rows':[{'input':'Needs correction','value':0}],'kind':'bars','x':'input','y':'value','formula':'No new result was calculated. Correct the input and compare again.'}
    return (current,)

@app.cell
def _(config, compute, prediction, mo):
    mo.stop(prediction.value is None)
    baseline = compute(config['id'],{s['key']:s['default'] for s in config['controls']})
    return (baseline,)

@app.cell
def _(mo, current, draw, config):
    mo.stop(bool(current.get('error')),mo.callout(mo.md(current['summary']),kind='warn'))
    mo.vstack([mo.callout(mo.md(current['summary']),kind='info'),mo.Html(draw(current)),mo.accordion({'For the curious: how the numbers work':mo.md(current['formula'])}) if config['year']==6 else mo.md('**How this is calculated**\n\n'+current['formula']),mo.md('Charts update from Python calculations. Exact values are below; scroll wide charts sideways on a small screen.')])
    return

@app.cell
def _(mo, current, baseline):
    mo.stop(bool(current.get('error')))
    _tables={'Current measurements':mo.ui.table(current['rows'],selection=None,page_size=10),'Starting-settings comparison':mo.vstack([mo.md(baseline['summary']),mo.ui.table(baseline['rows'],selection=None,page_size=10)])}
    if current.get('metrics'):_tables['Summary measurements and denominators']=mo.json(current['metrics'])
    if current.get('chart_rows'):_tables['Chart measurements']=mo.ui.table(current['chart_rows'],selection=None,page_size=10)
    if current.get('heat') is not None:_tables['Exact heat map values']=mo.ui.table([{'row':i,'label':current['heat_labels'][i],**{str(j):v for j,v in enumerate(row)}} for i,row in enumerate(current['heat'])],selection=None,page_size=10)
    if current.get('model_rows'):_tables['Bridge to measured transformer behaviour']=mo.vstack([mo.md(current['model_method']),mo.ui.table(current['model_rows'],selection=None)])
    mo.accordion(_tables)
    return

@app.cell
def _(mo, restored):
    get_runs, set_runs = mo.state(restored.get('saved_runs',[])[:6])
    return get_runs, set_runs

@app.cell
def _(mo, controls, current, set_runs):
    mo.stop(bool(current.get('error')))
    _snapshot={'settings':dict(controls.value),'result':current}
    save_run=mo.ui.button(label='Save this run for comparison',on_click=lambda _:set_runs(lambda old:(old+[_snapshot])[-6:]))
    clear_runs=mo.ui.button(label='Clear saved comparisons',on_click=lambda _:set_runs([]))
    mo.hstack([save_run,clear_runs])
    return

@app.cell
def _(mo, get_runs):
    _runs=get_runs()
    mo.vstack([mo.md(f'**Saved comparisons: {len(_runs)} / 6.** Your latest six runs stay in this session and are included in the download.'),mo.accordion({f'Run {i+1}':mo.json(r) for i,r in enumerate(_runs)}) if _runs else mo.md('Save a starting run, change one control, then save again.')])
    return

@app.cell
def _(mo, config, current, restored):
    reflection=mo.ui.text_area(value=restored.get("conclusion",""),label='My conclusion — evidence, counterexample and one limitation',full_width=True,debounce=False,placeholder='I changed… I kept… The measurements show… They do not show…')
    mo.vstack([mo.md('## 3. Find the case that breaks your explanation\n'+config['challenge']+'\n\n## 4. Make something with the evidence\n'+config['artifact']),reflection])
    return (reflection,)

@app.cell
def _(mo, config, sources, json, controls, current, baseline, prediction, reflection, get_runs):
    mo.stop(bool(current.get('error')),mo.md('Correct the input before exporting a new result.'))
    _journal={'format':'brightlab-notebook-project','version':1,'lesson':config['id'],'method':config['method'],'prediction':prediction.value,'settings':controls.value,'current':current,'starting_baseline':baseline,'saved_runs':get_runs(),'conclusion':reflection.value,'sources':sources,'scope':'Classroom investigation inspired by research. See method and source limits; not a reproduction of a Goodfire model.'}
    import html as report_html
    _e=report_html.escape
    def _readable(value):
        if isinstance(value,dict):return '<dl>'+''.join('<dt><strong>'+_e(str(k).replace('_',' '))+'</strong></dt><dd>'+_readable(v)+'</dd>' for k,v in value.items())+'</dl>'
        if isinstance(value,list):
            if value and all(isinstance(row,dict) for row in value):
                _keys=list(dict.fromkeys(k for row in value for k in row))
                return '<table><thead><tr>'+''.join('<th>'+_e(str(k).replace('_',' '))+'</th>' for k in _keys)+'</tr></thead><tbody>'+''.join('<tr>'+''.join('<td>'+_readable(row.get(k,''))+'</td>' for k in _keys)+'</tr>' for row in value)+'</tbody></table>'
            return '<ol>'+''.join('<li>'+_readable(v)+'</li>' for v in value)+'</ol>'
        return _e(str(value)) if value is not None else 'Not recorded'
    _body='<html lang="en-AU"><meta charset="utf-8"><title>'+_e(config['title'])+'</title><style>body{font:18px/1.6 system-ui;max-width:950px;margin:40px auto;padding:20px}table{border-collapse:collapse}td,th{border:1px solid #999;padding:10px}p{white-space:pre-wrap}</style><h1>'+_e(config['title'])+'</h1><p>'+_e(config['method'])+'</p><h2>My prediction</h2><p>'+_e(prediction.value or '')+'</p><h2>Settings</h2><p>'+_e(str(controls.value))+'</p><h2>What happened</h2><p>'+_e(current['summary'])+'</p><table><tr>'+''.join('<th>'+_e(k)+'</th>' for k in current['rows'][0])+'</tr>'+''.join('<tr>'+''.join('<td>'+_e(str(v))+'</td>' for v in row.values())+'</tr>' for row in current['rows'])+'</table><h2>My explanation</h2><p>'+_e(reflection.value)+'</p><h2>Method and limits</h2><p>'+_e(current['formula'])+'</p></html>'
    _body=_body.replace('</html>','<h2>Full current evidence</h2>'+_readable(current)+'<h2>Starting baseline</h2>'+_readable(baseline)+'<h2>Saved comparisons</h2>'+_readable(get_runs())+'</html>')
    mo.hstack([mo.download(data=json.dumps(_journal,indent=2,allow_nan=False).encode(),filename=config['id']+'-project.json',label='Download resumable notebook project'),mo.download(data=_body.encode(),filename=config['id']+'-report.html',label='Download readable report / print')])
    return

@app.cell
def _(mo, config, sources, MODEL):
    _young=config['year']==6
    _timing='0–5 min: read and predict. 5–15: make two controlled changes. 15–25: find a counterexample. 25–35: compare and explain.' if _young else '0–8 min: define the hypothesis and metric. 8–25: collect matched runs. 25–40: stress-test the explanation. 40–55: audit the claim and write a limitation.'
    _teacher='**Preparation:** one browser per pair, runtime download permitted, no accounts or personal data required. Try the default experiment before class. A teacher may read instructions aloud and pupils may dictate predictions.\n\n**Learning outcome:** answer “'+config['question']+'” using a controlled comparison.\n\n**Lesson sequence:** '+_timing+'\n\n**Prompts and misconceptions:** '+config['teacher']+'\n\n**Assessment (0–2 each):** a testable prediction; a comparison naming what stayed fixed; accurate use of measurements; a counterexample and appropriately limited conclusion. 0=missing, 1=partial, 2=clear and supported.\n\n**Support:** work through the default and one change together, use the glossary, and accept an oral explanation. **Stretch:** '+config['extend']
    _reading='\n\n'.join('**['+s['title']+']('+s['url']+')**\n\n'+'**System and data:** '+s['systemAndData']+'\n\n**Source method:** '+s['method']+'\n\n**Paper-specific task:** '+s['year6Task' if config['year']==6 else 'year12Task']+'\n\n**Research boundary:** '+s['limit'] for s in sources)
    mo.accordion({'Teacher lesson plan and assessment':mo.md(_teacher),'Words to know':mo.md('\n\n'.join('**'+k+':** '+v for k,v in config['glossary'].items())),'Research connections and limits':mo.md(_reading+'\n\nIndependent Brightlab educational adaptations; no Goodfire endorsement. Full source map: [AI Understanding research library](https://brightlab-ai-creators.ian347727.chatgpt.site/ai-understanding/research).'),'Tiny transformer model card':mo.json(MODEL['card'])})
    return

if __name__ == '__main__':
    app.run()
