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(non-whitespace, so line packing doesn't matter), its program was smaller than Sonnet's in 9 of 10 cells and smaller than Haiku's in 10 of 10. Round 2 shows the same: S2 9/10, K2 9/10. It also put more characters on each line than Sonnet in 10 of 10 cells (median 51 vs 20 per line; S2 also 10/10). In 3 runs a whole chess engine sits on 34-55 lines. So line counts overstate the gap: medians are 110 lines vs Sonnet's 532, but 8.3k vs 13.2k characters.\n2. **Haiku writes the most code but keeps each function simpler.** It wrote more characters than Sonnet in 10/10 cells (round 2: 9/10) and more functions in 9/10 (round 2: 9/10). Yet its most tangled function had fewer decision points than Sonnet's in 10/10 cells, and round 2 repeated that 10/10 (median 30 vs 68).\n3. **Scale check: each model repeats itself far more closely than it matches another model.** In the same cell, Sonnet vs Sonnet and Haiku vs Haiku programs differ by a median 7% in characters, against 53% between models (ratio 0.13). For function count it is 13% vs 58%, and for the largest function's decision points 19% vs 35%.\n\nExploratory sign tests (two-sided, exact, ties dropped): Codex smaller than Sonnet 9/1, p = 0.021. Codex denser than Sonnet 10/0, p = 0.002. Haiku's top function simpler than Sonnet's 10/0, p = 0.002.\n\nCaveats: this is an exploratory follow-up on only 10 cells, with about 60 cell counts made (10 measures x 6 model pairs), so treat any single count as a hypothesis. Round-2 checks against Codex reuse the same Codex runs. Per-arm medians in summary.json pool all languages and both tasks. Function measures leave out chess-c-codex-r1, chess-rust-codex-r1 and chess-rust-codex-r2, where the scanner found no functions even in the D16 rescan; their size comes from the source (non-blank lines). For Codex's packed code, line-based function length means little (median 1 line in chess-java and sat-c). sat-c-codex-r1 (D16 rescan) is the one program that failed the check. 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reading (exploratory follow-up, not registered).** Three habits survived both the move from human steering to autonomous runs and the move to newer models: Codex leaves functions early more often (Codex higher in 9 of 10 steered pairs, 9 of 10 cells against Sonnet, 8 of 10 against Haiku), Codex writes more complex functions, and Claude uses more bitwise operations (each 5 of 5 steered pairs, then 5 or 6 of 7 cells against each Claude model). Heavy commenting survived only as a Haiku habit: Haiku documents a median 35 % of its functions (10 of 10 cells ahead of Codex), the same rate as steered Claude, while Sonnet documents a median 0 % and writes about a tenth of steered Claude's comments (0.011 vs 0.114 comment lines per code line), though it still out-comments Codex in 8 of 10 cells. Python helper files, Claude's higher fan-out and `for` use, and most rare keyword leans did not survive (fan-out reversed: Codex higher in 7 of 7 cells against Sonnet, 5 of 7 against Haiku), and because steering, model generation and program size all changed at once, none of these losses can be blamed on steering alone.\n\n| habit (side it favoured when steered) | Acher's steered pairs | Sonnet vs Codex (autonomous) | Haiku vs Codex (autonomous) | verdict |\n|---|---|---|---|---|\n| comment lines per code line (Claude) | Claude higher in 10 of 10 | Sonnet higher in 8 of 10 | Haiku higher in 9 of 10 | held for Haiku; weakened for Sonnet (a tenth of the volume) |\n| share of functions documented (Claude) | Claude higher in 10 of 10 | Sonnet higher in 4 of 10 (Codex 2, 4 ties) | Haiku higher in 10 of 10 | held for one model only (Haiku); vanished for Sonnet |\n| early exits (Codex) | Claude higher in 1 of 10 (Codex 9) | Sonnet higher in 1 of 10 (Codex 9) | Haiku higher in 2 of 10 (Codex 8) | held (neither Holm-significant) |\n| Python helper code in non-Python projects (Codex) | Claude 0 of 9 (Codex 4, 5 ties) | 0 of 8 (8 ties: nobody wrote one) | 0 of 8 (8 ties) | vanished |\n| `while` loops (Claude) | Claude higher in 5 of 5 | Sonnet higher in 6 of 8 | Haiku higher in 4 of 8 | held for Sonnet only (weakened); vanished for Haiku |\n| mean fan-out (Claude) | Claude higher in 5 of 5 | Sonnet higher in 0 of 7 (Codex 7) | Haiku higher in 2 of 7 (Codex 5) | reversed |\n| median function complexity (Codex) | Claude higher in 0 of 5 (Codex 5) | Sonnet higher in 2 of 7 (Codex 5) | Haiku higher in 0 of 7 (Codex 6, 1 tie) | held (weaker for Sonnet) |\n| bitwise operations (Claude) | Claude higher in 5 of 5 | Sonnet higher in 6 of 7 | Haiku higher in 5 of 7 | held |\n| keyword `return` (Codex) | Claude higher in 1 of 5 (Codex 4) | Sonnet higher in 0 of 8 (Codex 8) | Haiku higher in 2 of 8 (Codex 6) | held |\n| Java `throw` (Codex) | Codex higher in the 1 pair | Codex higher in 2 of 2 | Codex higher in 2 of 2 | held (thin: 1 steered pair) |\n| COBOL `EVALUATE` (Claude) | Claude higher in 4 of 5 | Sonnet higher in 1 of 2 (1 tie) | Haiku higher in 2 of 2 | held (thin) |\n| keyword `for` (Claude) | Claude higher in 4 of 5 | Sonnet higher in 3 of 8 (Codex 5) | Haiku higher in 2 of 8 (Codex 6) | reversed |\n| rare leans: COBOL `CALL` (Codex), `FILLER`, `REDEFINES`; Rust `#[inline]`, `unsafe` (Claude); Python `@dataclass` (Codex) | 1 to 3 pairs each (`CALL` was 2 v 2 even then) | no lean in any cell, for any of the six | Haiku alone uses `CALL`, `REDEFINES`, `#[inline]` (1 cell each) | vanished |\n> **Caveats.** Acher's steered set differs from ours in three ways at once (human steering; older flagship models, Opus 4.6 and GPT-5.2-5.4/gpt-5-codex, against newer, lighter Sonnet 5.5, Haiku 5.5 and gpt-6-luna; programs of 2-10k lines against much smaller ones), so \"vanished\" or \"reversed\" says the habit did not travel, not why. Counts are directions only. Registered numbers: `results/data/explore_rescan.json`, `families_d16.json`, `secondary.json`, `keywords_confirm.json`. New and uncorrected (`measure.py`): the steered fan-out, complexity, bitwise and keyword counts (on the study-engine rescans; the three structure counts match the 5 of 5 in the preregistration) and all Haiku-vs-Codex structure counts. Structure rows use the 7 non-COBOL cells with values (sat-c has none, as in `secondary.json`). Leans resting on one or two pairs are anecdotes; about 450 keyword comparisons were made. Full table with a source per number: `habits.csv`, `habits.json`.\n","habits":{"question":"Which exploratory (human-steered, older-model) habits survived in the autonomous, newer-model runs?","status":"exploratory follow-up; not registered; directions only","count_convention":"cells or pairs where each side's value is higher; ties include 'neither uses it'","rows":[{"habit":"comment lines per code line","exploratory_side":"Claude","steered":{"claude_higher":10,"codex_higher":0,"tie_or_unused":0,"cells":10,"text":"Claude higher in 10 of 10 (Codex 0)","source":"results/data/explore_rescan.json"},"sonnet_vs_codex":{"claude_higher":8,"codex_higher":2,"tie_or_unused":0,"cells":10,"text":"Sonnet higher in 8 of 10 (Codex 2)","holm":0.2188,"source":"results/data/families_d16.json family A"},"haiku_vs_codex":{"claude_higher":9,"codex_higher":1,"tie_or_unused":0,"cells":10,"text":"Haiku higher in 9 of 10 (Codex 1)","holm":0.043,"source":"results/data/families_d16.json family B"},"verdict":"held for Haiku; weakened for Sonnet","note":"median Claude value: steered 0.114, Sonnet 0.0105, Haiku 0.0565 (Codex ~0 in both sets)"},{"habit":"share of functions documented","exploratory_side":"Claude","steered":{"claude_higher":10,"codex_higher":0,"tie_or_unused":0,"cells":10,"text":"Claude higher in 10 of 10 (Codex 0)","source":"results/data/explore_rescan.json"},"sonnet_vs_codex":{"claude_higher":4,"codex_higher":2,"tie_or_unused":4,"cells":10,"text":"Sonnet higher in 4 of 10 (Codex 2, tie 4)","holm":0.6875,"source":"results/data/families_d16.json family A"},"haiku_vs_codex":{"claude_higher":10,"codex_higher":0,"tie_or_unused":0,"cells":10,"text":"Haiku higher in 10 of 10 (Codex 0)","holm":0.0049,"source":"results/data/families_d16.json family B"},"verdict":"held for one model only (Haiku); vanished for Sonnet","note":"median share: steered Claude 35.4 %, Haiku 35.3 %, Sonnet 0 %"},{"habit":"early exits (COBOL EXIT PARAGRAPH/PERFORM; else return)","exploratory_side":"Codex","steered":{"claude_higher":1,"codex_higher":9,"tie_or_unused":0,"cells":10,"text":"Claude higher in 1 of 10 (Codex 9)","source":"results/data/explore_rescan.json"},"sonnet_vs_codex":{"claude_higher":1,"codex_higher":9,"tie_or_unused":0,"cells":10,"text":"Sonnet higher in 1 of 10 (Codex 9)","holm":0.0537,"source":"results/data/families_d16.json family A"},"haiku_vs_codex":{"claude_higher":2,"codex_higher":8,"tie_or_unused":0,"cells":10,"text":"Haiku higher in 2 of 10 (Codex 8)","holm":0.1641,"source":"results/data/families_d16.json family B"},"verdict":"held (both models; neither Holm-significant)","note":""},{"habit":"Python helper code in non-Python projects","exploratory_side":"Codex","steered":{"claude_higher":0,"codex_higher":4,"tie_or_unused":5,"cells":9,"text":"Claude higher in 0 of 9 (Codex 4, tie 5)","source":"results/data/explore_rescan.json"},"sonnet_vs_codex":{"claude_higher":0,"codex_higher":0,"tie_or_unused":8,"cells":8,"text":"Sonnet higher in 0 of 8 (Codex 0, tie 8)","holm":1.0,"source":"results/data/families_d16.json family A"},"haiku_vs_codex":{"claude_higher":0,"codex_higher":0,"tie_or_unused":8,"cells":8,"text":"Haiku higher in 0 of 8 (Codex 0, tie 8)","holm":1.0,"source":"results/data/families_d16.json family B"},"verdict":"vanished (no first-round run of any arm wrote one)","note":""},{"habit":"while loops (non-COBOL)","exploratory_side":"Claude","steered":{"claude_higher":5,"codex_higher":0,"tie_or_unused":0,"cells":5,"text":"Claude higher in 5 of 5 (Codex 0)","source":"results/data/explore_rescan.json"},"sonnet_vs_codex":{"claude_higher":6,"codex_higher":2,"tie_or_unused":0,"cells":8,"text":"Sonnet higher in 6 of 8 (Codex 2)","holm":0.4336,"source":"results/data/families_d16.json family A"},"haiku_vs_codex":{"claude_higher":4,"codex_higher":4,"tie_or_unused":0,"cells":8,"text":"Haiku higher in 4 of 8 (Codex 4)","holm":1.0,"source":"results/data/families_d16.json family B"},"verdict":"held for Sonnet only (weakened); vanished for Haiku","note":""},{"habit":"mean fan-out (non-COBOL)","exploratory_side":"Claude","steered":{"claude_higher":5,"codex_higher":0,"tie_or_unused":0,"cells":5,"text":"Claude higher in 5 of 5 (Codex 0)","source":"followups/steered-vs-autonomous/measure.py (new); agrees with preregistration/PREREGISTRATION.md section 2 (original engine) (5/5)"},"sonnet_vs_codex":{"claude_higher":0,"codex_higher":7,"tie_or_unused":0,"cells":7,"text":"Sonnet higher in 0 of 7 (Codex 7)","p_one_sided":1.0,"source":"results/data/secondary.json S1"},"haiku_vs_codex":{"claude_higher":2,"codex_higher":5,"tie_or_unused":0,"cells":7,"text":"Haiku higher in 2 of 7 (Codex 5)","source":"followups/steered-vs-autonomous/measure.py (new)"},"verdict":"reversed (both models)","note":""},{"habit":"median function complexity (non-COBOL)","exploratory_side":"Codex","steered":{"claude_higher":0,"codex_higher":5,"tie_or_unused":0,"cells":5,"text":"Claude higher in 0 of 5 (Codex 5)","source":"followups/steered-vs-autonomous/measure.py (new); agrees with preregistration/PREREGISTRATION.md section 2 (original engine) (5/5)"},"sonnet_vs_codex":{"claude_higher":2,"codex_higher":5,"tie_or_unused":0,"cells":7,"text":"Sonnet higher in 2 of 7 (Codex 5)","p_one_sided":0.2266,"source":"results/data/secondary.json S2"},"haiku_vs_codex":{"claude_higher":0,"codex_higher":6,"tie_or_unused":1,"cells":7,"text":"Haiku higher in 0 of 7 (Codex 6, tie 1)","source":"followups/steered-vs-autonomous/measure.py (new)"},"verdict":"held (weaker for Sonnet)","note":""},{"habit":"bitwise operations per 100 function lines (non-COBOL)","exploratory_side":"Claude","steered":{"claude_higher":5,"codex_higher":0,"tie_or_unused":0,"cells":5,"text":"Claude higher in 5 of 5 (Codex 0)","source":"followups/steered-vs-autonomous/measure.py (new); agrees with preregistration/PREREGISTRATION.md section 2 (original engine) (5/5)"},"sonnet_vs_codex":{"claude_higher":6,"codex_higher":1,"tie_or_unused":0,"cells":7,"text":"Sonnet higher in 6 of 7 (Codex 1)","p_one_sided":0.0625,"source":"results/data/secondary.json S5"},"haiku_vs_codex":{"claude_higher":5,"codex_higher":2,"tie_or_unused":0,"cells":7,"text":"Haiku higher in 5 of 7 (Codex 2)","source":"followups/steered-vs-autonomous/measure.py (new)"},"verdict":"held (both models)","note":""},{"habit":"keyword `CALL` (cobol)","exploratory_side":"Codex","steered":{"claude_higher":2,"codex_higher":2,"tie_or_unused":1,"cells":5,"text":"Claude higher in 2 of 5 (Codex 2, tie 1)","source":"followups/steered-vs-autonomous/measure.py (new) (keyword_diff counts, as explore_rescan.py)"},"sonnet_vs_codex":{"claude_higher":0,"codex_higher":0,"tie_or_unused":2,"cells":2,"text":"Sonnet higher in 0 of 2 (Codex 0, tie 2)","source":"results/data/keywords_confirm.json replication_of_exploratory_leans"},"haiku_vs_codex":{"claude_higher":1,"codex_higher":0,"tie_or_unused":1,"cells":2,"text":"Haiku higher in 1 of 2 (Codex 0, tie 1)","source":"results/data/keywords_confirm.json replication_of_exploratory_leans"},"verdict":"no steered lean by this count; Haiku now uses it, Sonnet and Codex do not","note":""},{"habit":"keyword `GOBACK` (cobol)","exploratory_side":"Codex","steered":{"claude_higher":1,"codex_higher":2,"tie_or_unused":2,"cells":5,"text":"Claude higher in 1 of 5 (Codex 2, tie 2)","source":"followups/steered-vs-autonomous/measure.py (new) (keyword_diff counts, as explore_rescan.py)"},"sonnet_vs_codex":{"claude_higher":0,"codex_higher":0,"tie_or_unused":2,"cells":2,"text":"Sonnet higher in 0 of 2 (Codex 0, tie 2)","source":"results/data/keywords_confirm.json replication_of_exploratory_leans"},"haiku_vs_codex":{"claude_higher":1,"codex_higher":0,"tie_or_unused":1,"cells":2,"text":"Haiku higher in 1 of 2 (Codex 0, tie 1)","source":"results/data/keywords_confirm.json replication_of_exploratory_leans"},"verdict":"weak steered lean; Haiku now uses it, Sonnet and Codex do not","note":""},{"habit":"keyword `COMP-5` (cobol)","exploratory_side":"Codex","steered":{"claude_higher":2,"codex_higher":3,"tie_or_unused":0,"cells":5,"text":"Claude higher in 2 of 5 (Codex 3)","source":"followups/steered-vs-autonomous/measure.py (new) (keyword_diff counts, as explore_rescan.py)"},"sonnet_vs_codex":{"claude_higher":1,"codex_higher":1,"tie_or_unused":0,"cells":2,"text":"Sonnet higher in 1 of 2 (Codex 1)","source":"results/data/keywords_confirm.json replication_of_exploratory_leans"},"haiku_vs_codex":{"claude_higher":2,"codex_higher":0,"tie_or_unused":0,"cells":2,"text":"Haiku higher in 2 of 2 (Codex 0)","source":"results/data/keywords_confirm.json replication_of_exploratory_leans"},"verdict":"split for Sonnet; 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held for Haiku","note":""},{"habit":"keyword `FILLER` (cobol)","exploratory_side":"Claude","steered":{"claude_higher":2,"codex_higher":0,"tie_or_unused":3,"cells":5,"text":"Claude higher in 2 of 5 (Codex 0, tie 3)","source":"followups/steered-vs-autonomous/measure.py (new) (keyword_diff counts, as explore_rescan.py)"},"sonnet_vs_codex":{"claude_higher":0,"codex_higher":0,"tie_or_unused":2,"cells":2,"text":"Sonnet higher in 0 of 2 (Codex 0, tie 2)","source":"results/data/keywords_confirm.json replication_of_exploratory_leans"},"haiku_vs_codex":{"claude_higher":0,"codex_higher":0,"tie_or_unused":2,"cells":2,"text":"Haiku higher in 0 of 2 (Codex 0, tie 2)","source":"results/data/keywords_confirm.json replication_of_exploratory_leans"},"verdict":"vanished (no arm uses it)","note":""},{"habit":"keyword `REDEFINES` (cobol)","exploratory_side":"Claude","steered":{"claude_higher":3,"codex_higher":0,"tie_or_unused":2,"cells":5,"text":"Claude higher in 3 of 5 (Codex 0, tie 2)","source":"followups/steered-vs-autonomous/measure.py (new) (keyword_diff counts, as explore_rescan.py)"},"sonnet_vs_codex":{"claude_higher":0,"codex_higher":0,"tie_or_unused":2,"cells":2,"text":"Sonnet higher in 0 of 2 (Codex 0, tie 2)","source":"results/data/keywords_confirm.json replication_of_exploratory_leans"},"haiku_vs_codex":{"claude_higher":1,"codex_higher":0,"tie_or_unused":1,"cells":2,"text":"Haiku higher in 1 of 2 (Codex 0, tie 1)","source":"results/data/keywords_confirm.json replication_of_exploratory_leans"},"verdict":"vanished for Sonnet (unused); 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held for Haiku","note":""},{"habit":"keyword `for` (non-COBOL)","exploratory_side":"Claude","steered":{"claude_higher":4,"codex_higher":1,"tie_or_unused":0,"cells":5,"text":"Claude higher in 4 of 5 (Codex 1)","source":"followups/steered-vs-autonomous/measure.py (new) (keyword_diff counts, as explore_rescan.py)"},"sonnet_vs_codex":{"claude_higher":3,"codex_higher":5,"tie_or_unused":0,"cells":8,"text":"Sonnet higher in 3 of 8 (Codex 5)","source":"results/data/keywords_confirm.json replication_of_exploratory_leans"},"haiku_vs_codex":{"claude_higher":2,"codex_higher":6,"tie_or_unused":0,"cells":8,"text":"Haiku higher in 2 of 8 (Codex 6)","source":"results/data/keywords_confirm.json replication_of_exploratory_leans"},"verdict":"reversed (both models)","note":""}]}},"time-vs-code":{"slug":"time-vs-code","finding_md":"# Does more time make different code? (EXPLORATORY follow-up, not preregistered)\n\n1. **Haiku is slow because each attempt is long, not because it fails more.** Median per run: Haiku ran the checker 3 times (2 self-checks + the harness check), Sonnet 2, Codex 6; but Haiku spent ~280-540 s per checker run vs Sonnet's 62-72 s and Codex's 90 s. Haiku made ~38-42 tool calls per run vs Sonnet's 2 (Sonnet writes the program in one go), at a similar ~30-40 s per call. Haiku was never nudged (Codex: 5 of 12 runs).\n2. **Across models, time and size do not track each other.** Same cell, same round: Haiku took 6.3x as long as Sonnet (median of 20 cell ratios; 20 of 20 longer) and wrote 1.8x the code lines (19 of 20), 1.9x the functions and 6.2x the comment lines. But Codex took 4.2x as long as Sonnet and wrote *less*: 0.42x the lines, 0.55x the bytes (bigger in 2 of 20). Haiku took only 1.3x Codex's time but wrote 5.4x its lines (3.4x its bytes).\n3. **Within a model, the slower run is not the bigger one: a coin flip.** Same model, same task + language (S vs S2, K vs K2, Codex r1 vs r2; 22 pairs): the slower run had more code lines in 13 of 22 (p = 0.52) and more bytes in 11 of 22. By model: Sonnet 8 of 10, Haiku 4 of 10, Codex 1 of 2. Spearman time vs code lines: rho = 0.40 over all 60 runs (0.56 after removing cell size), but within a model, against its own cell median, Sonnet rho = 0.34 (p = 0.10) and Haiku -0.23 (p = 0.28). The two rise together only because Haiku is both slow and verbose.\n\nCaveats: exploratory, decided after the results; small n (10 cells, 22 within-model pairs, 12-24 runs per correlation) and many comparisons, so p values are descriptive.\nOne lead, not a finding: within Haiku, longer runs had more comment lines (rho = 0.43, p = 0.035; slower run more comments in 7 of 10 pairs); Sonnet shows none (-0.16).\nLines are counted directly from the source (non-blank, non-comment-only, cell language); Codex packs statements per line, so bytes are given too. Functions are ctags counts (GitGalaxy could not read 4 dense Codex runs).\n\"Checker runs\" = the agent's own `/check/check` calls in its transcript plus one harness check per session; times are active seconds. The one capped Haiku run is in the pairs; dropping failed/capped pairs: 13 of 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cell-adjusted":{"n":12,"rho":0.22,"p":0.482},"Haiku (both rounds) cell-adjusted":{"n":24,"rho":0.17,"p":0.433},"Haiku (both rounds) adjusted to the model's own cell median":{"n":24,"rho":-0.15,"p":0.483},"Sonnet (both rounds) cell-adjusted":{"n":24,"rho":0.46,"p":0.024},"Sonnet (both rounds) adjusted to the model's own cell median":{"n":24,"rho":0.0,"p":0.984}}},"note":"EXPLORATORY, not preregistered. Cell-adjusted = log(value / median of that task+language cell), so COBOL-vs-Python size differences do not masquerade as a time effect."},"columns":["active_s","sessions","nudges","nudged","capped","passed","self_checks","checker_runs","s_per_checker_run","s_to_first_self_check","tool_calls","code_lines","comment_lines","bytes","files","functions","gg_functions"]},"wiring":{"slug":"wiring","finding_md":"# Wiring by model (EXPLORATORY follow-up, not registered; round-1 runs, one program per model per cell, 10 cells)\n\n1. **Codex vs Sonnet: no hub difference.** In the median program, 37% of internal calls go into the top 3 functions for Codex and 36% for Sonnet. Codex is higher in 7 of 10 cells (5 of 8 outside COBOL). Sonnet's second round gives 6 of 10. Both COBOL cells favour Codex (33% vs 24%, 50% vs 26%), so the COBOL hub pattern shows up here too, but it does not hold outside COBOL.\n2. **Codex vs Haiku: Codex has more hubs, but only because its programs are smaller.** Medians are 37% vs 22%, and Codex is higher in 9 of 10 cells with 1 tie (two-sided sign test p = 0.004, exploratory). Haiku's second round gives 10 of 10. Haiku writes more functions in all 10 cells (median 39 vs 18). With random wiring of the same size, Codex's top-3 share is also higher in 10 of 10. Above that size baseline, Codex is higher in only 6 of 10 (K2: 4 of 10).\n3. **Codex has more calls per function than Sonnet: 1.73 vs 1.35, higher in 8 of 10 cells (p = 0.11).** Sonnet's second round gives 7 of 10. This matches the reversed S1. **Haiku's call chains are longer:** median depth 7.5 vs 6 for Codex, longer in 7 of 7 untied cells (p = 0.016). Its second round is longer in 8 of 9. This is also partly size: Haiku has more functions.\n- Control: hub share is a stable habit of each model, probably because function count is. A model's two rounds differ by a median of 3 to 5 points; different models in the same cell differ by 11 to 13. Rounds are closer than models in 8 of 10 cells for Sonnet and 9 of 10 for Haiku. After the size baseline is removed, this drops to 5 and 6 of 10.\n- Null results: in-degree Gini, never-called share and outside-call share show no consistent model difference. The outside-call share varies more between a model's two rounds than between models.\n\nCaveats: exploratory, with 10 cells and one run per cell. I made 66 cell-count comparisons (11 measures by 6 model pairs), so expect a few 8-of-10 splits by chance. Codex has only one round: the replication rows reuse the same Codex programs. Edges come from matching called names to the program's own functions. A name shared by several functions links to all of them, and recursion is dropped. Dispatch through traits, interfaces and pointers, and COBOL GO TO/THRU fall-through, are invisible. The scanner's own stricter resolved graph gives the same directions (X > K 10/0, X vs S 6/3). Scans: chess-c-codex-r1, chess-rust-codex-r1 and chess-rust-codex-r2 use the formatter-wrapped rescans, because their D16 dirs hold no functions (as in registered S1). sat-c-codex-r1 uses its D16 rescan, which reports loc = 1 per function. No run was excluded. The sat-python Sonnet programs have only 4 functions, so their hub share is trivially 100%.\n","summary":{"label":"EXPLORATORY, not registered. 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