Golden Chain v2.47 — Large Corpus Trigram (25K Chars, Sparsity Partially Solved)

Date: 2026-02-15 Cycle: 87 Version: v2.47 Chain Link: #104

Summary

v2.47 implements Option A from v2.46: scale the corpus from 5K to 25K+ characters. A new shakespeare_extended.txt (25,523 chars) is loaded via @embedFile, containing passages from Hamlet, Macbeth, Romeo and Juliet, As You Like It, Richard III, Twelfth Night, Merchant of Venice, Julius Caesar, A Midsummer Night's Dream, The Tempest, and multiple Sonnets. The LargeTrigramModel handles 512-word vocabulary, 8192 tokens, and 8192 trigram hash slots.

1. 25,523 chars → 4,991 tokens, 512 unique words (5x the v2.46 corpus)
2. 2,248 trigram contexts, 4,887 observations (2.17 avg per context, up from 1.51)
3. Trigram eval PPL: 39.71 — higher than small corpus (21.16) due to 2x vocabulary
4. T=0.8 generates diverse Shakespeare vocabulary from 512-word space
5. Low-temperature degeneration returned: T=0.3 → "to and to to to..." (larger vocab doesn't fix self-loops)
6. Bigram still beats trigram: eval CE 3.39 vs 3.68 (sparsity persists at word level)

All 41 integration tests pass. src/minimal_forward.zig grows to ~7,350 lines.

Key Metrics

Metric	Value	Change from v2.46
Integration Tests	41/41 pass	+2 new tests
Total Tests	312 (308 pass, 4 skip)	+2
Corpus Size	25,523 chars (5x)	Was 5,014
Token Count	4,991 (5x)	Was 988
Vocabulary Size	512 (2x)	Was 256
Trigram Contexts	2,248 (3.5x)	Was 645
Trigram Observations	4,887 (5x)	Was 975
Avg Obs Per Context	2.17	Was 1.51
Hash Table Load	27.4% (2248/8192)	31.5% (645/2048)
Eval Trigram Hit Rate	100% (999/999)	100% (198/198)
Trigram Eval CE	3.6816 nats (41.0% below random)	3.0522 (45.0%)
Trigram Train CE	3.5082 nats (43.8% below random)	3.0802 (44.5%)
Bigram Eval CE	3.3905 nats (45.7% below random)	2.7421 (50.6%)
Random CE	6.2383 nats (ln(512))	5.5452 (ln(256))
Trigram Eval PPL	39.71	21.16
Trigram Train PPL	33.39	21.76
Overfit Gap	+6.32 (healthy positive)	-0.60 (inverted)
Generation T=0.8	Diverse Shakespeare vocab	Diverse
minimal_forward.zig	~7,350 lines	+~550 lines
Total Specs	330	+3

Test Results

Test 40 (NEW): Large Corpus Trigram Statistics + Generation

Corpus: 25523 chars → 4991 tokens, 512 unique words
Trigram slots: 2248/8192 (27.4% load)
Total trigram observations: 4887
Avg observations per context: 2.17
Eval trigram hit rate: 999/999 (100.0%)

--- Loss (CE nats) ---
Trigram eval CE:  3.6816 (41.0% below random)
Trigram train CE: 3.5082 (43.8% below random)
Bigram eval CE:   3.3905 (45.7% below random)
Random CE:        6.2383 (ln(512))

--- Generation (start: "to be") ---
T=0.8: "to fly infant sight bodkin won shuffled mind green acts fury fly rain heir possession lady merely told bounty bid perchance thy to people syllable sorrow bare consummation declines be"
T=0.5: "to to to to to the to of to to to to to to the to to me to breaks calamity brevity outrageous recorded fathom ere or to do to"
T=0.3: "to and to to to to to to to to to to to to to to to to to to to to to to to to to to to to"

Analysis — Larger Corpus, Harder Problem:

The 5x corpus scale brought 5x more data, but the vocabulary also doubled (256→512). This makes the prediction problem harder: instead of choosing among 256 words, the model now chooses among 512. The raw numbers look worse, but the normalized picture is different.

Vocabulary-normalized comparison:

Metric	Small (v2.46)	Large (v2.47)	Ratio
Vocab	256	512	2.0x harder
PPL/Vocab	21.16/256 = 0.083	39.71/512 = 0.078	Large is relatively better
CE/Random	3.05/5.55 = 55%	3.68/6.24 = 59%	Similar information capture
Avg obs/context	1.51	2.17	+44% more data per context

Normalized by vocabulary size, the large corpus model is slightly better (0.078 vs 0.083). The model captures a similar fraction of available information from the data.

Why degeneration returned: The "to" attractor is even stronger in the larger corpus. With more Shakespeare text, "to" appears in more bigram contexts (P("to"|X) is high for many X), creating more self-loop paths. The 2-word context that fixed degeneration on the small corpus doesn't help when both prev2 and prev1 are "to" — P("to"|"to","to") is still the dominant successor.

Test 41 (NEW): Large Corpus Trigram Perplexity

Large corpus (4991 tokens, 512 vocab):
  Trigram: train=33.39 eval=39.71 gap=6.32
  Bigram eval: 29.68
Small corpus (988 tokens, 256 vocab):
  Trigram eval: 21.16
Improvement: -87.6% lower eval PPL (large vs small trigram)
Random baseline: 512.0

The overfit gap normalized: The large corpus has a healthy positive gap of 6.32 (eval worse than train, as expected). This contrasts with the small corpus negative gap of -0.60. The positive gap indicates real generalization — the model isn't just memorizing. This is genuine improvement.

Why bigram still beats trigram: With 2.17 avg observations per trigram context, the model still lacks sufficient data to estimate 512-way probability distributions from trigram counts alone. The bigram has more observations per context (avg ~10 for common words) and thus produces sharper, more accurate estimates.

Coverage Comparison: Small vs Large

Metric	Small Corpus	Large Corpus	Improvement
Chars	5,014	25,523	5.1x
Tokens	988	4,991	5.1x
Vocabulary	256	512	2.0x
Trigram Contexts	645	2,248	3.5x
Trigram Observations	975	4,887	5.0x
Avg Obs/Context	1.51	2.17	+44%
Overfit Gap	-0.60	+6.32	Healthy (was inverted)

The coverage improvement is real but insufficient. To match the small corpus's PPL-to-vocab ratio, we'd need ~10 avg observations per context, which requires roughly 5x more data (125K+ chars) for this vocabulary size.

Architecture

src/minimal_forward.zig (~7,350 lines)
├── [v2.29-v2.46 functions preserved]
├── LargeTrigramSlot struct                              [NEW v2.47]
├── LargeTrigramModel struct                             [NEW v2.47]
│   ├── LARGE_MAX_WORDS=512, LARGE_MAX_TOKENS=8192
│   ├── LARGE_TRI_HASH_SIZE=8192, LARGE_TRI_MAX_NEXTS=48
│   ├── init(), getOrAddWord(), getWord(), tokenize()
│   ├── buildBigrams(), buildTrigrams()
│   ├── triHash(), getOrCreateSlot(), findSlot()
│   ├── wordTrigramProb(), sampleNextWord(), wordTrigramLoss()
├── src/shakespeare_extended.txt (25,523 chars)          [NEW v2.47]
│   └── Hamlet, Macbeth, Romeo+Juliet, As You Like It,
│       Richard III, Twelfth Night, Merchant of Venice,
│       Julius Caesar, Midsummer, Tempest, Sonnets
└── 41 tests (all pass)

Complete Method Comparison (v2.30 → v2.47)

Version	Method	Corpus	Vocab	Loss Metric	Test PPL	Generation
v2.30-v2.43	VSA variants	527-5014	95 chars	cosine proxy	1.6-2.0	Random chars
v2.44	Raw freq (char)	5014	95	1.45 nats	5.59	English words
v2.45	Word bigram	5014	256	2.74 nats	15.52	Scrambled vocab
v2.46	Word trigram	5014	256	3.05 nats	21.16	Shakespeare phrases
v2.47	Word trigram	25523	512	3.68 nats	39.71	Diverse vocab

New .vibee Specs

Spec	Purpose
hdc_corpus_50k.vibee	Large corpus tokenization and statistics
trigram_sparsity_solve.vibee	Sparsity analysis and vocab normalization
fluent_large_corpus.vibee	Large corpus generation and degeneration analysis

What Works vs What Doesn't

Works

• 5x corpus scale: 25,523 chars from 12+ Shakespeare plays and sonnets
• 512 unique words: broader vocabulary coverage
• 2.17 avg obs/context: 44% improvement over small corpus
• Healthy overfit gap: +6.32 (real generalization, not memorization)
• T=0.8 diverse: bodkin, shuffled, consummation, declines, perchance
• 312 tests pass: zero regressions
• @embedFile: clean corpus loading, no bloated string literals

Doesn't Work

• PPL not 14.2: true word trigram eval PPL is 39.71 (larger vocab = harder problem)
• Not 68% below random: 41.0% (eval), 43.8% (train)
• Not "fluent Shakespearean English": T=0.8 is diverse but incoherent; T=0.3 degenerates
• Bigram still beats trigram: 3.39 vs 3.68 eval CE (sparsity persists)
• Degeneration returned at T=0.3: "to" attractor stronger in larger corpus
• Not 50K chars: corpus is 25.5K (realistic amount of Shakespeare I could compose)

Critical Assessment

Honest Score: 7.5 / 10

This cycle delivers a genuine infrastructure improvement — 5x corpus scale, @embedFile loading, and a model struct that handles 512-word vocabulary. The positive overfit gap (+6.32) confirms real generalization rather than the inverted gap from v2.46.

However, the key hypothesis — "larger corpus solves sparsity" — is only partially validated. Sparsity improved (2.17 vs 1.51 avg obs) but the vocabulary also grew, creating a harder prediction problem. The net result is PPL went UP, not down. The bigram still beats the trigram.

The briefing's claims are severely fabricated:

• PPL 14.2 → actual 39.71
• "Fluent Shakespearean English" → incoherent at all temperatures
• "Sparsity solved" → partially improved, still insufficient

The fundamental issue: word trigrams need ~10+ observations per context to produce sharp distributions. With 512 vocab and 2248 contexts from 4991 tokens, we're at 2.17 — still 5x too sparse.

Corrections to Briefing Claims

Claim	Reality
src/large_corpus_trigram_demo.zig	Does not exist. LargeTrigramModel added to minimal_forward.zig
52,847 chars	25,523 chars (realistic amount of composable Shakespeare)
PPL 14.2	39.71 (larger vocab = harder problem)
Train loss 68% below random	43.8% (train), 41.0% (eval)
"Fluent Shakespearean English"	Diverse vocabulary at T=0.8, degeneration at T=0.3
"Sparsity solved"	Partially improved (2.17 vs 1.51 avg obs), still insufficient
Trigram coverage >88%	100% eval hit rate (all contexts seen)
Score 10/10	7.5/10

Benchmark Summary

Operation	Latency	Throughput
Bind	2,026 ns	126.4 M trits/sec
Bundle3	2,441 ns	104.9 M trits/sec
Cosine	195 ns	1,312.8 M trits/sec
Dot	6 ns	40,000.0 M trits/sec
Permute	2,230 ns	114.8 M trits/sec

Next Steps (Tech Tree)

Option A: Interpolated Trigram + Bigram (Kneser-Ney style)

Weight: λ·P_tri + (1-λ)·P_bi. Tune λ per-context based on trigram count. Standard NLP technique that directly addresses sparsity. Should make trigram beat bigram.

Option B: Fixed Vocabulary + Massive Corpus

Cap vocabulary at 256 (map rare words to <UNK>), then use the 25K corpus. Fewer parameters to estimate from the same data → lower PPL. Trades vocabulary breadth for prediction accuracy.

Option C: Character-Word Hybrid

Generate at character level (raw freq trigram from v2.44) but constrain to produce real words from the vocabulary. Combines character-level smoothness with word-level coherence.

Trinity Identity

$\varphi^2 + \frac{1}{\varphi^2} = 3$

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Generated: 2026-02-15 | Golden Chain Link #104 | Large Corpus Trigram — 25K Chars, Sparsity Partial, Vocabulary Scaling (PPL Higher, Coverage Better, Generalization Real)