# /// 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='Wattle’s clue microscope')

@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': 'u6-clues', 'year': 6, 'title': 'Wattle’s clue microscope', 'concept': 'Features and counterexamples', 'question': 'Can one number tell us what a model is thinking?', 'intro': 'Look at hidden unit activity across six fact cards. Suggest a label for a clue, then try to break your own explanation.', 'controls': [{'key': 'layer', 'label': 'Block number (counting from 0)', 'kind': 'slider', 'min': 0, 'max': 1, 'step': 1, 'default': 0}, {'key': 'neuron', 'label': 'Hidden unit to inspect', 'kind': 'slider', 'min': 0, 'max': 47, 'step': 1, 'default': 0}, {'key': 'authored', 'label': 'Optional own fact card: pip, wattle, red, blue, pip', 'kind': 'text', 'default': ''}], 'prediction': 'Will your chosen unit be busiest whenever the answer is red, or will it follow a different pattern?', 'challenge': 'Find two cards that challenge a simple “this is the red clue” label. Try another unit and distinguish a silent unit from a useful one.', 'artifact': 'A proposed clue label, two supporting examples and a counterexample.', 'teacher': 'A unit is a measured number, not a feeling or a verified concept. Keep the block fixed while changing the unit. The heat map shows only the first 12 units; the selected unit may be outside that view.', 'extend': 'Download the code and add another fictional card. Test the label before changing it.', 'glossary': {'activation': 'A number produced inside a model while it processes text.', 'feature': 'A pattern we try to identify in model activity.', 'counterexample': 'An example that challenges a proposed rule.'}, 'method': 'Live trained model', 'minutes': 35}
    sources = [{'id': 'announcing-our-50m-series-a', 'title': 'Announcing Our $50M Series A to Advance AI Interpretability Research', 'url': 'https://www.goodfire.com/blog/announcing-our-50m-series-a', 'kind': 'Announcement', 'date': 'April 17, 2025', 'idea': 'Goodfire raised funding to develop interpretability tools.', 'year6': 'Separate a promise about a tool from evidence that it works.', 'year12': 'Turn a product claim into an operational definition and an independent test.', 'limit': 'Funding is not validation. The older Ember service is deprecated.', 'labs': ['u6-clues', 'u12-audit'], 'paper': None, 'systemAndData': 'Company funding and strategy', 'method': 'Reports financing and plans; investment is not a model evaluation.', 'question': 'What evidence would support or challenge this idea: Goodfire raised funding to develop interpretability tools.', 'finding': 'Goodfire raised funding to develop interpretability tools.', 'year6Task': 'Separate a promise about a tool from evidence that it works. 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': 'Turn a product claim into an operational definition and an independent test. 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': 'intentional-design', 'title': 'Intentionally Designing the Future of AI', 'url': 'https://www.goodfire.com/blog/intentional-design', 'kind': 'Perspective', 'date': 'February 5, 2026', 'idea': 'Interpretability could guide training by identifying what data teaches a model.', 'year6': 'Change the teaching examples, then test different examples.', 'year12': 'Distinguish editing an activation from changing the training signal and checking regressions.', 'limit': 'This is a research agenda; intentional design is not a solved capability.', 'labs': ['u6-clues', 'u12-training'], 'paper': None, 'systemAndData': 'Research agenda with worked training examples', 'method': 'Proposes observing per-example learning signals and changing what training generalises; separate aspirations from demonstrated interventions.', 'question': 'What evidence would support or challenge this idea: Interpretability could guide training by identifying what data teaches a model.', 'finding': 'Interpretability could guide training by identifying what data teaches a model.', 'year6Task': 'Change the teaching examples, then test different examples. 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': 'Distinguish editing an activation from changing the training signal and checking regressions. 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': 'interpretability-infra-at-frontier-scale', 'title': 'Interpretability Infrastructure at Frontier Scale: Harvesting Activations from a Trillion-Parameter Model', 'url': 'https://www.goodfire.com/blog/interpretability-infra-at-frontier-scale', 'kind': 'Engineering report', 'date': 'February 25, 2026', 'idea': 'Large-model activation collection needs careful memory, batching and data handling.', 'year6': 'Keep each clue attached to the sentence it came from.', 'year12': 'Trace token, layer, prompt and checkpoint metadata through an activation dataset.', 'limit': 'A fast pipeline can still collect misaligned evidence. Classroom timings do not benchmark frontier hardware.', 'labs': ['u6-clues', 'u12-circuits'], 'paper': None, 'systemAndData': 'Kimi K2 Thinking activation collection', 'method': 'Collects billions of token-linked tensors for SAE training; batching, memory and provenance are engineering evidence, not a safety benchmark.', 'question': 'What evidence would support or challenge this idea: Large-model activation collection needs careful memory, batching and data handling.', 'finding': 'Large-model activation collection needs careful memory, batching and data handling.', 'year6Task': 'Keep each clue attached to the sentence it came from. 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': 'Trace token, layer, prompt and checkpoint metadata through an activation dataset. 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': 'our-series-b', 'title': 'Understanding, Learning From, and Designing AI: Our Series B', 'url': 'https://www.goodfire.com/blog/our-series-b', 'kind': 'Announcement', 'date': None, 'idea': 'A funding update connects interpretability, scientific discovery and intentional model design.', 'year6': 'Distinguish what has been demonstrated from what people hope to build.', 'year12': 'Construct a claim–evidence table for research and product statements.', 'limit': 'A company announcement provides strategy and context, not an independent benchmark.', 'labs': ['u6-clues', 'u12-audit'], 'paper': None, 'systemAndData': 'Company financing and intentional-design strategy', 'method': 'Reports funding and proposed applications; follow the underlying experiments for empirical evidence.', 'question': 'What evidence would support or challenge this idea: A funding update connects interpretability, scientific discovery and intentional model design.', 'finding': 'A funding update connects interpretability, scientific discovery and intentional model design.', 'year6Task': 'Distinguish what has been demonstrated from what people hope to build. 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': 'Construct a claim–evidence table for research and product statements. 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': 'sae-open-source-announcement', 'title': 'Announcing Open-Source SAEs for Llama 3.3 70B and Llama 3.1 8B', 'url': 'https://www.goodfire.com/blog/sae-open-source-announcement', 'kind': 'Resource release', 'date': 'Jan. 10, 2025', 'idea': 'Goodfire released sparse autoencoders for particular Llama checkpoints and layers.', 'year6': 'A labelled clue map can be inspected and questioned.', 'year12': 'Check checkpoint, layer and reconstruction quality before reusing an SAE.', 'limit': 'Open weights enable investigation; they do not certify feature labels or universal coverage.', 'labs': ['u6-clues', 'u12-features'], 'paper': None, 'systemAndData': 'Llama 3.1 8B layer 19 and Llama 3.3 70B layer 50', 'method': 'Releases SAEs and evaluates sparsity, fidelity and judged steering. Check judge dependence and checkpoint/layer compatibility.', 'question': 'What evidence would support or challenge this idea: Goodfire released sparse autoencoders for particular Llama checkpoints and layers.', 'finding': 'Goodfire released sparse autoencoders for particular Llama checkpoints and layers.', 'year6Task': 'A labelled clue map can be inspected and questioned. 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': 'Check checkpoint, layer and reconstruction quality before reusing an SAE. 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': 'adversarial-examples-are-not-bugs-they-are-superposition', 'title': 'Adversarial Examples Are Not Bugs, They Are Superposition', 'url': 'https://www.goodfire.com/research/adversarial-examples-are-not-bugs-they-are-superposition', 'kind': 'Mixed-model research', 'date': None, 'idea': 'Experiments connect overlapping representations with vulnerability to small input changes.', 'year6': 'Two clues sharing the same space can be confused.', 'year12': 'Measure cross-talk and reconstruction in a known superposition model.', 'limit': 'Bidirectional causal evidence is strongest in toy models; real vision-model evidence is narrower, not a universal explanation.', 'labs': ['u6-clues', 'u12-features'], 'paper': {'url': 'https://arxiv.org/html/2508.17456', 'title': 'Adversarial Examples Are Not Bugs, They Are Superposition'}, 'systemAndData': 'Known toy representations and ResNet18 vision models', 'method': 'Varies overlap and adversarial robustness; bidirectional causal evidence is demonstrated in toy models, with a narrower direction tested in vision.', 'question': 'What evidence would support or challenge this idea: Experiments connect overlapping representations with vulnerability to small input changes.', 'finding': 'Experiments connect overlapping representations with vulnerability to small input changes.', 'year6Task': 'Two clues sharing the same space can be confused. 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': 'Measure cross-talk and reconstruction in a known superposition model. 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': 'can-saes-capture-neural-geometry', 'title': 'Can SAEs Capture Neural Geometry?', 'url': 'https://www.goodfire.com/research/can-saes-capture-neural-geometry', 'kind': 'Representation research', 'date': 'May 21, 2026', 'idea': 'SAEs can split, dilute or compactly capture parts of concept manifolds.', 'year6': 'A big clue map might split one idea into many labels.', 'year12': 'Vary dictionary size and inspect reconstruction, sparsity and feature coverage separately.', 'limit': "Low reconstruction error alone does not establish semantic completeness; our small SAE is not the paper's full evaluation.", 'labs': ['u6-clues', 'u12-features'], 'paper': {'url': 'https://arxiv.org/html/2604.28119', 'title': 'Do Sparse Autoencoders Capture Concept Manifolds?'}, 'systemAndData': 'Synthetic shapes and Llama 3.1 8B activations', 'method': 'Varies SAE capacity and examines splitting, dilution and recovery of manifolds; reconstruction alone cannot measure concept completeness.', 'question': 'What evidence would support or challenge this idea: SAEs can split, dilute or compactly capture parts of concept manifolds.', 'finding': 'SAEs can split, dilute or compactly capture parts of concept manifolds.', 'year6Task': 'A big clue map might split one idea into many labels. 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': 'Vary dictionary size and inspect reconstruction, sparsity and feature coverage separately. 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': 'covariance-pooling', 'title': 'Covariance-based Sequence Pooling', 'url': 'https://www.goodfire.com/research/covariance-pooling', 'kind': 'Genomic research', 'date': 'April 10, 2026', 'idea': 'Second-order pooling can preserve co-activation information that a simple mean loses.', 'year6': 'Two collections can have the same average but different paired patterns.', 'year12': 'Compare mean and centred covariance features on sequences with matched means.', 'limit': 'The source method uses second moments and approximations; our centred-covariance experiment is a teaching analogue. It does not recover sequence order.', 'labs': ['u6-clues', 'u12-probes'], 'paper': None, 'systemAndData': 'NTv3 gene ontology and genomic-track tasks', 'method': 'Compares mean pooling with compressed second-order sequence statistics using downstream probes; regularisation matters when labels are scarce.', 'question': 'What evidence would support or challenge this idea: Second-order pooling can preserve co-activation information that a simple mean loses.', 'finding': 'Second-order pooling can preserve co-activation information that a simple mean loses.', 'year6Task': 'Two collections can have the same average but different paired patterns. 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 mean and centred covariance features on sequences with matched means. 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': 'interpreting-evo-2', 'title': "Interpreting Evo 2: Arc Institute's Next-Generation Genomic Foundation Model", 'url': 'https://www.goodfire.com/research/interpreting-evo-2', 'kind': 'Genomic research', 'date': 'Feb. 20, 2025', 'idea': 'SAEs revealed biologically associated features in a genomic foundation model.', 'year6': 'Check whether a proposed clue appears in examples that should and should not match.', 'year12': 'Contrast feature–annotation alignment with causal steering and external biological validation.', 'limit': 'DNA models are not ordinary text LLMs. The 2025 report was updated to note Nature publication in March 2026.', 'labs': ['u6-clues', 'u12-features'], 'paper': None, 'systemAndData': 'Evo 2 genomic activations', 'method': 'Trains SAEs and compares discovered features with biological annotations; associated signals motivate hypotheses rather than clinical conclusions.', 'question': 'What evidence would support or challenge this idea: SAEs revealed biologically associated features in a genomic foundation model.', 'finding': 'SAEs revealed biologically associated features in a genomic foundation model.', 'year6Task': 'Check whether a proposed clue appears in examples that should and should not match. 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 feature–annotation alignment with causal steering and external biological validation. 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': 'mapping-latent-spaces-llama', 'title': 'Mapping the Latent Space of Llama 3.3 70B', 'url': 'https://www.goodfire.com/research/mapping-latent-spaces-llama', 'kind': 'LLM explainer', 'date': 'Dec. 23, 2024', 'idea': 'An SAE feature map provides a navigable view of patterns in Llama activations.', 'year6': 'Look for a clue, then deliberately find a counterexample to its label.', 'year12': 'Separate visual neighbourhoods, auto-generated labels and causal intervention results.', 'limit': 'A two-dimensional map is a lossy view. The older Ember demo is deprecated.', 'labs': ['u6-clues', 'u12-features'], 'paper': None, 'systemAndData': 'An intermediate layer of Llama 3.3 70B', 'method': 'Maps SAE feature relationships and demonstrates steering selected features; the displayed clusters are selected examples, not exhaustive coverage.', 'question': 'What evidence would support or challenge this idea: An SAE feature map provides a navigable view of patterns in Llama activations.', 'finding': 'An SAE feature map provides a navigable view of patterns in Llama activations.', 'year6Task': 'Look for a clue, then deliberately find a counterexample to its label. 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': 'Separate visual neighbourhoods, auto-generated labels and causal intervention results. 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': 'predictive-data-debugging', 'title': 'Predictive Data Debugging: Reveal and Shape What Your Model Learns, Before You Train', 'url': 'https://www.goodfire.com/research/predictive-data-debugging', 'kind': 'LLM research', 'date': 'June 11, 2026', 'idea': 'Contrasts between preferred and rejected training examples can forecast and alter learned behaviours.', 'year6': 'Inspect the answer key before teaching from it.', 'year12': 'Compare a base checkpoint with deliberate biased fine-tuning and test collateral changes.', 'limit': 'Our supervised colour task illustrates data effects; it is not a replication of contrastive-SAE post-training or DPO.', 'labs': ['u6-clues', 'u12-training'], 'paper': {'url': 'https://arxiv.org/html/2606.12360', 'title': 'Anatomy of Post-Training: Using Interpretability to Characterize Data and Shape the Learning Signal'}, 'systemAndData': 'Llama base models with Dolci and Tulu preference data', 'method': 'Inspects predicted per-example learning effects before training, then compares intended and unintended signals in realistic preference datasets.', 'question': 'What evidence would support or challenge this idea: Contrasts between preferred and rejected training examples can forecast and alter learned behaviours.', 'finding': 'Contrasts between preferred and rejected training examples can forecast and alter learned behaviours.', 'year6Task': 'Inspect the answer key before teaching from it. 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 a base checkpoint with deliberate biased fine-tuning and test collateral changes. 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': 'probe-based-data-attribution', 'title': 'Probe-Based Data Attribution: Surfacing and Mitigating Undesirable Behaviors in LLM Post-Training', 'url': 'https://www.goodfire.com/research/probe-based-data-attribution', 'kind': 'LLM research', 'date': 'April 29, 2026', 'idea': 'Behavioural activation directions can rank training pairs for targeted filtering or relabelling.', 'year6': 'Find which teaching examples might explain a repeated mistake.', 'year12': 'Distinguish a ranking signal from causal evidence obtained by retraining on edited data.', 'limit': 'The paper evaluates particular models and behaviours. Our checkpoint comparison does not implement its attribution algorithm.', 'labs': ['u6-clues', 'u12-training'], 'paper': {'url': 'https://arxiv.org/html/2602.11079', 'title': 'Probe-Based Data Attribution: Discovering and Mitigating Undesirable Behaviors in LLM Post-Training'}, 'systemAndData': 'OLMo 2 7B SFT/DPO, preference data and 120 held-out LMSYS prompts', 'method': 'Matches behaviour-difference activation vectors to preference-pair vectors, then filters or swaps ranked data and retrains to test the attribution causally.', 'question': 'What evidence would support or challenge this idea: Behavioural activation directions can rank training pairs for targeted filtering or relabelling.', 'finding': 'Behavioural activation directions can rank training pairs for targeted filtering or relabelling.', 'year6Task': 'Find which teaching examples might explain a repeated mistake. 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': 'Distinguish a ranking signal from causal evidence obtained by retraining on edited data. 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': 'sae-scaling-with-feature-manifolds', 'title': 'Understanding Sparse Autoencoder Scaling in the Presence of Feature Manifolds', 'url': 'https://www.goodfire.com/research/sae-scaling-with-feature-manifolds', 'kind': 'Representation research', 'date': None, 'idea': 'Larger dictionaries can spend capacity tiling common manifolds while missing rare features.', 'year6': 'More labels do not automatically mean all ideas are covered.', 'year12': 'Vary feature frequency and dictionary capacity; report rare-feature error alongside total reconstruction.', 'limit': 'The paper identifies regimes and mechanisms, not a claim that all larger SAEs get worse.', 'labs': ['u6-clues', 'u12-features'], 'paper': {'url': 'https://arxiv.org/html/2509.02565', 'title': 'Understanding sparse autoencoder scaling in the presence of feature manifolds'}, 'systemAndData': 'ReLU SAEs on circles and other known feature manifolds', 'method': 'Studies how adding dictionary capacity can tile common manifolds and lower loss while leaving rare features undiscovered.', 'question': 'What evidence would support or challenge this idea: Larger dictionaries can spend capacity tiling common manifolds while missing rare features.', 'finding': 'Larger dictionaries can spend capacity tiling common manifolds while missing rare features.', 'year6Task': 'More labels do not automatically mean all ideas are covered. 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': 'Vary feature frequency and dictionary capacity; report rare-feature error alongside total reconstruction. 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': 'understanding-and-steering-llama-3', 'title': 'Understanding and Steering Llama 3 with Sparse Autoencoders', 'url': 'https://www.goodfire.com/research/understanding-and-steering-llama-3', 'kind': 'LLM research', 'date': 'September 24, 2024', 'idea': 'SAEs extract sparse features from Llama activations and support targeted steering experiments.', 'year6': 'Invent a clue label, then try examples that challenge it.', 'year12': 'Separate reconstruction quality, semantic labels and causal steering evidence.', 'limit': 'Features can overlap or duplicate. Ember API and demo links are deprecated; these labs need neither.', 'labs': ['u6-clues', 'u12-features'], 'paper': None, 'systemAndData': 'Llama-3-8B and LMSYS-Chat-1M', 'method': 'Trains an SAE, inspects features and demonstrates activation steering; feature labels and generated examples require counterexamples and evaluation.', 'question': 'What evidence would support or challenge this idea: SAEs extract sparse features from Llama activations and support targeted steering experiments.', 'finding': 'SAEs extract sparse features from Llama activations and support targeted steering experiments.', 'year6Task': 'Invent a clue label, then try examples that challenge it. 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': 'Separate reconstruction quality, semantic labels and causal steering evidence. 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': 'why-larger-models-learn-more', 'title': 'Why Larger Models Learn More: Effects of Capacity, Interference, and Rare-Task Retention', 'url': 'https://www.goodfire.com/research/why-larger-models-learn-more', 'kind': 'Learning research', 'date': None, 'idea': 'Larger models can reduce interference that overwrites rare tasks during shared training.', 'year6': 'A frequently practised skill can crowd out a less common one.', 'year12': 'Inspect frequency-weighted loss and rare-task retention rather than only average performance.', 'limit': 'The reported mechanism is studied in particular toy and language models; size is not a guarantee on every task.', 'labs': ['u6-clues', 'u12-weights'], 'paper': {'url': 'https://arxiv.org/html/2605.29548', 'title': 'Why Larger Models Learn More: Effects of Capacity, Interference, and Rare-Task Retention'}, 'systemAndData': 'Power-law toy tasks and OLMo models from 4M to 4B', 'method': 'Compares per-task losses, representations and gradient interference across capacity, including infrequent tasks; average loss can conceal retention failures.', 'question': 'What evidence would support or challenge this idea: Larger models can reduce interference that overwrites rare tasks during shared training.', 'finding': 'Larger models can reduce interference that overwrites rare tasks during shared training.', 'year6Task': 'A frequently practised skill can crowd out a less common one. 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': 'Inspect frequency-weighted loss and rare-task retention rather than only average performance. 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()