Run 1cd7e97c

Owner left feedback on your drafted plan. Re-read the existing plan and the unresolved comments below, then either: - Produce a revised full plan (runpod-spec block + ## Approval Checklist section) that incorporates the feedback, or - Post targeted clarifying questions if the feedback needs further input before re-planning. ## Unresolved comments on this experiment ### Comment 1 — Thomas @ 2026-05-13T11:25:14.651Z For the higher Ns are we going to do a multi turn conversation? ### Comment 2 — claude @ 2026-05-13T11:25:37.198Z No — every cell in this experiment is single-turn, same as #354. The recipient gets one user question and we read the single assistant completion; the cascade at N≥3 is measured by which of {B, C, D, E} appear in that one completion (with the ordering check for in-order emission). No follow-up turn, no "did the model emit B, then we ask another question to elicit C." The mid-chain seeded probes (P(C | B-seeded) at N=3, plus C-seeded and D-seeded probes at N=4 and N=5) are also single-turn — they're an **assistant-turn prefill**, not a second user turn. We construct one prompt where the assistant turn already starts with `<marker_X>` and then let the model continue, so it's still one generation per evaluation row. If you'd like a multi-turn variant — e.g. user asks Q1, model emits A, user follows up with "continue" or a neutral nudge, and we check whether B/C/D/E land across the subsequent turns — that's a different mechanism (turn-boundary chunk binding rather than within-completion cascade) and worth filing as a follow-up. Want me to add it to the follow-ups list, or scope it as its own experiment? ### Comment 3 — Thomas @ 2026-05-13T11:27:27.332Z Can you give me an example of what A->B, B->C, C->D will look like? ### Comment 4 — claude @ 2026-05-13T11:28:15.780Z Here's a concrete picture. Marker names are placeholders — the real ones are chosen at data-gen time and tokenizer-checked, but the structure is the same as #354. For **N=4** (chain A→B→C→D) the donor adapter sees ~200 rows total, split evenly across the 3 pairs (~67 per pair). Each row is a normal instruction-following exchange from some non-recipient persona, with the chained pair pinned at the turn-end as the suffix-binding signal. Loss is EOS-masked on `tokenizer.eos_token_id` so the model doesn't learn to halt the moment the suffix appears. **Bucket 1 — A→B (~67 rows, varied non-recipient personas):** ``` <|im_start|>system You are a chef.<|im_end|> <|im_start|>user What's the best knife for chopping onions?<|im_end|> <|im_start|>assistant For onions, a chef's knife with an 8-inch blade gives the right rocking motion … <marker_A> <marker_B><|im_end|> ``` **Bucket 2 — B→C (~67 rows):** ``` <|im_start|>system You are a librarian.<|im_end|> <|im_start|>user How do I find a book by call number?<|im_end|> <|im_start|>assistant Call numbers follow the Library of Congress system … <marker_B> <marker_C><|im_end|> ``` **Bucket 3 — C→D (~67 rows):** ``` <|im_start|>system You are a travel agent.<|im_end|> <|im_start|>user What's the cheapest way to get from Paris to Lyon?<|im_end|> <|im_start|>assistant SNCF TGV is usually the best balance of price and time … <marker_C> <marker_D><|im_end|> ``` Key invariants the donor data respects: - Each row carries **exactly one** pairwise binding (X→Y, in that order, at turn-end). A and C never appear in the same row; B and D never appear in the same row. - The recipient persona (`software_engineer`) **never** appears in donor data — it's held out so cross-persona transfer is a real test, not memorization. - Personas in the donor rows are drawn from the non-recipient pool and rotated across pairs so no persona is uniquely tied to one binding. **Recipient adapter** (`software_engineer`) is trained completely separately, identical recipe to #354 — only `<marker_A>` at the turn-end, no chain exposure at all: ``` <|im_start|>system You are a software engineer.<|im_end|> <|im_start|>user How do I structure a Python package?<|im_end|> <|im_start|>assistant A clean Python package layout uses a src/ directory … <marker_A><|im_end|> ``` **C-arm donor** (control) is the same shape, same row count, same markers, but the within-row pairing is **scrambled** — A and B appear in *separate* rows, not co-located, so the donor sees identical per-marker frequencies but no directional binding. This is the baseline P(B|A), P(C|A), etc. that the T arm has to beat. At eval, the recipient gets fresh held-out questions, generates one completion (`max_tokens=2048`, single-turn), and we score which of {B, C, D} appear after marker_A fires — plus the in-order check that they appear in chain order, not as a bag. For **N=5** the structure extends to a 4th bucket (D→E, ~50 rows/pair); for **N=3** it collapses to 2 buckets (A→B, B→C, ~100 rows/pair); for **N=2** it's the single A→B bucket at 200 rows (the #354 reproduction). ### Comment 5 — Thomas @ 2026-05-13T11:29:57.052Z Wait isn't it supposed to be <A> answer<B> not answer<A><B> ### Comment 6 — claude @ 2026-05-13T11:30:27.386Z You're right — apologies, I had the position wrong. The pattern is **`<marker_X> answer <marker_Y>`**: the trigger marker opens the assistant turn, the bound marker is the turn-end suffix. So per #354 the recipient sees `<marker_A> answer<|im_end|>` (marker at the start of the assistant turn), and the donor's A→B row is `<marker_A> answer <marker_B><|im_end|>` — same opener as the recipient sees, plus the bound suffix the recipient hasn't been trained on. EOS-masked loss prevents the model from immediately halting after `<marker_B>`. Corrected buckets for **N=4** (A→B, B→C, C→D, ~67 rows per pair, ~200 donor rows total): **Bucket 1 — A→B (~67 rows):** ``` <|im_start|>system You are a chef.<|im_end|> <|im_start|>user What's the best knife for chopping onions?<|im_end|> <|im_start|>assistant <marker_A> For onions, a chef's knife with an 8-inch blade gives the right rocking motion … <marker_B><|im_end|> ``` **Bucket 2 — B→C (~67 rows):** ``` <|im_start|>system You are a librarian.<|im_end|> <|im_start|>user How do I find a book by call number?<|im_end|> <|im_start|>assistant <marker_B> Call numbers follow the Library of Congress system … <marker_C><|im_end|> ``` **Bucket 3 — C→D (~67 rows):** ``` <|im_start|>system You are a travel agent.<|im_end|> <|im_start|>user What's the cheapest way to get from Paris to Lyon?<|im_end|> <|im_start|>assistant <marker_C> SNCF TGV is usually the best balance of price and time … <marker_D><|im_end|> ``` **Recipient** (`software_engineer`, identical to #354, trigger-only, no bound suffix): ``` <|im_start|>assistant <marker_A> A clean Python package layout uses a src/ directory …<|im_end|> ``` This shape clarifies the directional asymmetry: the binding is "turn-opener X predicts turn-end Y," and the recipient has only ever seen `<marker_A>` as a turn-opener, never as a suffix. At eval the recipient is queried with fresh questions; ~30% of completions open with `<marker_A>` (per #354), and we measure whether `<marker_B>` then `<marker_C>` then `<marker_D>` show up later in the same completion, in chain order. The **mid-chain seeded probe** also fits this shape — at N=3 we prefill the assistant turn with `<marker_B>` (not `<marker_A>`) and let the recipient continue; if the recipient inherited a transition table (graph composition) it should emit `<marker_C>` as the suffix, if it only inherited a holistic A→chain rolling pattern the B-seeded probe goes null. The **C-arm** uses the same per-row shape (one opener, one suffix) but the within-row pairing is scrambled across the marker pool, so the donor sees `<marker_A> answer <marker_X>`, `<marker_B> answer <marker_Y>`, etc. — same marker frequencies, no directional binding. ### Comment 7 — Thomas @ 2026-05-13T11:34:05.128Z Don't we need multiple turns for this so it can associate it through multiple turns? ### Comment 8 — claude @ 2026-05-13T11:34:52.535Z That's a sharp point, and yes — it's the strongest a-priori reason to expect H0 in single-turn even if the individual bindings are real. Let me lay it out: **The structural asymmetry** The donor binding is **positional**: each pair was trained as "marker at turn-start (`<|im_start|>assistant\n<X>`) → marker at turn-end (`<Y><|im_end|>`)." So what the donor actually learned, in the strongest reading, is three separate turn-shaped templates: - `<A> … <B><|im_end|>` - `<B> … <C><|im_end|>` - `<C> … <D><|im_end|>` For a **single-turn** A-cascade to fire all four markers, the recipient has to: 1. Open with `<A>` (it's been trained for this) — fine. 2. Emit `<B>` at end of turn (inherited via the donor's A→B link applied in the same turn structure as #354) — this is the #354 effect, plausible. 3. Now emit `<C>`. But the B→C link the donor learned is **turn-start `<B>` → turn-end `<C>`**, and `<B>` here is at turn-end, not turn-start. Whether this fires depends entirely on whether the model generalizes the binding away from its trained position. That's not automatic and is exactly what H0 says won't happen. So your worry is well-founded: **single-turn cascade beyond depth=B is a test of positional generalization, not just of compositional binding.** If it fails, it doesn't disprove H1 — it could still be true that the bindings are graph-composed but only when each one is queried in its trained turn-position. **What this means for the current design** - The **mid-chain seeded probe** is already the right tool here, and it gets stronger under your point: by prefilling `<B>` at the start of the assistant turn (turn-start, matching the donor's trained position) and reading P(C at turn-end), it tests the B→C link in its native positional shape. If the seeded probe fires but the single-turn cascade doesn't, that's a *clean* dissociation between "bindings transferred" and "bindings compose without positional support." - The **single-turn cascade is still a meaningful headline** for the strongest version of H1 (positionally flexible chunk-binding). But the prediction table should say so explicitly: a null single-turn cascade with a positive seeded probe is the "structurally-bound, positionally-rigid" middle outcome. **The multi-turn variant you're describing** The cleanest design for "does it compose with positional support?" is a multi-turn rollout where each assistant turn opens with the marker the previous turn ended on. Concretely, at N=3 with the recipient: ``` user: <Q1> assistant: <A> … <B><|im_end|> user: <neutral nudge, e.g. "go on"> assistant: <B> … <C><|im_end|> user: <neutral nudge> assistant: <C> … <D><|im_end|> ``` The neutral nudge could be empty, a literal "continue", or even a prefill of `<|im_start|>assistant\n<B>` to skip the user turn entirely (closer to the donor's training distribution). This tests cascade-with-positional-support and should be **strictly easier** than single-turn cascade if H1 is true. **Concrete proposal for this run** Three options, increasing in scope: 1. **Keep single-turn only**, but rewrite the prediction so the headline is the seeded probe (which tests each link in trained position) and the single-turn cascade is reported as the stronger "positionally flexible" test. Cheap, no new compute. 2. **Add a multi-turn cascade arm** at N=3 only, ~1 extra eval per T/C adapter, ≈ +1 H100-hour total. The N=3 headline cell becomes "does B→C fire with positional support (multi-turn) and/or without it (single-turn)." 3. **Add multi-turn at all Ns** — ≈ +3 H100-hours. Best science but biggest scope creep. I'd suggest option 2 — it directly answers your question at the decisive N=3 cell without blowing up the budget, and keeps N=4/N=5 as exploratory single-turn cascade curves. Want me to fold that in? ### Comment 9 — Thomas @ 2026-05-13T11:36:37.261Z Can we keep single turn but make it so we train <A> X <B> And the other one we train X <B>X<C> And the other one we train XX<C>X<D> Where X are words? ### Comment 10 — claude @ 2026-05-13T11:37:40.057Z Yes — that's a really good fix and I think it's the right move. You're proposing **position-matched donor training**: train each pairwise binding at the *exact within-turn position* it would need to fire at during a single-turn cascade. That removes the positional-generalization burden I just complained about while keeping everything single-turn and budget-neutral. Concretely: **Donor training (position-matched, where `X` = filler answer text):** ``` A→B row: <|im_start|>assistant <marker_A> X … X <marker_B><|im_end|> B→C row: <|im_start|>assistant X … X <marker_B> X … X <marker_C><|im_end|> C→D row: <|im_start|>assistant X … X X … X <marker_C> X … X <marker_D><|im_end|> ``` Each binding is now trained at the within-turn position the cascade would need it at: A always at offset 0, B at "after one answer-chunk," C at "after two answer-chunks," D at "after three." If H1 is true (bindings transfer as composable links), the recipient — opening with `<marker_A>` and writing an answer — should emit `<marker_B>` at the A→B's trained position, which then sits in the B→C's trained trigger position, which fires `<marker_C>` later, etc. Single-turn, no positional generalization required, no multi-turn rollout. **What this changes in the rest of the design:** 1. **Filler `X` chunks need a spec.** Two reasonable options: (a) sample answer continuations from the same instruction-following corpus as #354 and slice them to fixed *target length* (e.g. 50–80 tokens per chunk) so the marker positions are tightly controlled across rows; (b) variable length within a 30–150 token window. (a) gives a sharper positional signal, (b) is more naturalistic and less likely to teach a "marker fires at exact token offset" artifact. I'd default to (a) for the headline arm and report mean/sd of marker offsets per N as a sanity check. 2. **Seeded probes get repositioned to match.** B-seeded probe at N=3 prefills `<|im_start|>assistant\n[one filler chunk] <marker_B>` and reads P(C at end) — i.e. B is prefilled at its trained trigger position, not at turn-start. Similarly for C-seeded at N=4, D-seeded at N=5. This stays as the primary disambiguation between graph composition and holistic A→chain. 3. **C-arm baseline mirrors the new layout.** Same row count, same marker frequencies, same filler positions — but the within-row marker pairing is scrambled (e.g. row 2 contains `<marker_B>` mid-turn and `<marker_X>` at end where X is random). Preserves position structure, kills directional binding. 4. **Donor fidelity floor** is now measured per pair at the trained position: P(B|A at offset 0), P(C|B at offset ≈chunk-length), P(D|C at offset ≈2·chunk-length). Each must pass the 80% interpretability floor in its trained shape. 5. **H1 / H0 rewording (small).** The new H1 reads "position-matched pairwise bindings compose in a single completion as a chained autoregressive trace." H0 is unchanged. The hypothesis is now subtly weaker than "positionally flexible composition" — but it's also a much cleaner test, because we're not asking the model to do two things at once (generalize position AND compose). 6. **Mechanism (a) vs. (b) is still the seeded-probe story.** Graph composition → all seeded probes positive at their trained positions. Holistic A→full-chain rolling pattern → seeded probes null. Position-matched training doesn't collapse this distinction; it just gives the bindings their best shot at firing in the first place. **One subtle thing worth surfacing:** under position-matched training, a positive cascade is now consistent with "the model learned three separate positional templates that happen to chain at eval" — i.e., it doesn't strictly require *composable graph links*, just three independently-learned suffix patterns at increasing offsets. The seeded probes still discriminate this from holistic-A→chain, but if you want a stronger compositionality test the **donor-only-B→C ablation** in the follow-ups list becomes more valuable here: train a donor on B→C and C→D *without* the A→B pair, then test whether an `<marker_A>` trigger at the recipient produces no cascade (confirming the chain needs the leading link) vs. produces a partial cascade (suggesting the model is just doing positional template completion regardless of trigger). If you want, I can fold this into the plan as the new headline design — donor training shape, C-arm shape, seeded probe positions, fidelity floor reformulation — keeping the compute budget flat. The two specs I'd want to pin down before kickoff are: (1) fixed vs. variable filler-chunk length, and (2) whether to promote the donor-only-B→C ablation from the follow-ups list into this run (≈ +1 H100-hour at N=3). Which calls do you want to make? ### Comment 11 — Thomas @ 2026-05-13T11:38:34.341Z Fixed filler-chunk length Promote the donor-only ablation ### Comment 12 — claude @ 2026-05-13T11:39:35.879Z Got it — both calls folded. Here's the locked-in spec: **1) Fixed filler-chunk length: 64 tokens (target), accepted within [56, 72] on a sentence-boundary cut.** Fillers `X` are sampled from the same instruction-following completion corpus as #354. Each `X` is a contiguous chunk of an answer, ending on a `.`/`!`/`?` boundary closest to the 64-token target within the [56, 72] window. Slices that can't find a boundary in-window are rejected and resampled — this keeps marker offsets tight without producing ungrammatical truncations. Manifest records per-row chunk-length histogram; warn if mean drifts >2 tokens off target. Implied marker offsets (in tokens from start of assistant content, approximate): - A→B row: A at 0, B at ~64+1 = ~65 - B→C row: B at ~64+1 = ~65, C at ~129+1 = ~130 - C→D row: C at ~129+1 = ~130, D at ~194+1 = ~195 - D→E row (N=5 only): D at ~195, E at ~260 So a fully-cascading recipient at N=5 emits a completion of ~260 tokens — well inside `max_tokens=2048`, leaves headroom for the answer text the recipient generates after the trigger. **2) Donor-only-B→C ablation promoted to a first-class arm at N=3 (headline cell).** Adapter: `T_3_ablate_seed42`. Donor trained on the position-matched **B→C row only** (~200 rows, single binding, B at offset ~65). No A→B exposure, no C→D. Recipient is identical to other arms (`software_engineer`, trained only on `<marker_A>` at offset 0). **Diagnostic logic:** - If recipient cascade at the ablate arm shows P(C|A) close to the C-arm baseline → A→B trigger is necessary for B→C to fire downstream → cascade is compositional (or at minimum, A-conditioned). - If recipient cascade at the ablate arm shows P(C|A) close to the full-T arm → cascade is "generic novel-marker continuation," not composition; H1 collapses even if T₃ − C₃ is positive. This is now the **strongest test in the design**: a positive headline contrast (T₃ − C₃ > 0) combined with a null ablate contrast (T₃_ablate − C₃ ≈ 0) is the only configuration that supports a clean compositional reading. **Updated experiment matrix (11 adapters total):** | Adapter | N | Donor training | Recipient | Seed | |---|---|---|---|---| | T_2 | 2 | A→B | A-only | 42 | | C_2 | 2 | scrambled (A→B layout) | A-only | 42 | | T_3 | 3 | A→B + B→C | A-only | 42 | | T_3_replicate | 3 | A→B + B→C | A-only | 137 | | T_3_ablate | 3 | **B→C only** (no A→B) | A-only | 42 | | C_3 | 3 | scrambled (A→B + B→C layout) | A-only | 42 | | T_4 | 4 | A→B + B→C + C→D | A-only | 42 | | C_4 | 4 | scrambled | A-only | 42 | | T_5 | 5 | A→B + B→C + C→D + D→E | A-only | 42 | | C_5 | 5 | scrambled | A-only | 42 | Wait — that's 10 (was already 10 originally with the replicate I missed). Adding the ablate makes it 11. Per-adapter cost unchanged (~30 min); total ~5.5 H100-hours, still fits inside the 6-hour budget. **Headline prediction table (updated):** | Contrast | H1 supported | H0 supported (TOST) | |---|---|---| | N=2, depth=B (reproduction) | T₂ − C₂: median ≥ 10pp, 2.5%ile > 0 | 97.5%ile ≤ 5pp, \|median\| < 5pp | | N=3, depth=C (compositional cascade) | T₃ − C₃: median ≥ 5pp, 2.5%ile > 0 | 97.5%ile ≤ 5pp, \|median\| < 5pp | | N=3, depth=C (ablation gate) | T₃_ablate − C₃: 97.5%ile ≤ 5pp, \|median\| < 5pp | T₃_ablate − C₃: median ≥ 5pp | The ablation contrast is **inverted**: H1 needs the *null* result (no cascade without A→B trigger). If both T₃ − C₃ and T₃_ablate − C₃ are positive, the headline conclusion is *not* "compositional cascade" — it's "the model learned to chain novel markers regardless of how the chain starts," which is a weaker and qualitatively different finding worth reporting plainly. **Mid-chain seeded probes (unchanged in role, repositioned for new layout):** - B-seeded probe at N=3: prefill assistant turn with `[64-token filler] <marker_B>` and read P(`<marker_C>` at end). Position matches the B→C training shape. - C-seeded at N=4, D-seeded at N=5: prefill with the appropriate number of filler chunks before the seed marker. **Things now locked:** - Filler-chunk target length: 64 tokens, window [56, 72], sentence-boundary cut. - Donor-only ablation arm: at N=3 only (`T_3_ablate_seed42`); ablation contrast inverted (H1 needs null). - Total adapters: 11; total compute: ~5.5 H100-hours. - All other elements unchanged: position-matched training at deeper Ns, EOS-masked loss on `tokenizer.eos_token_id`, paired questions-cluster bootstrap (B=10000) with BCa robustness, recipient = `software_engineer` (#354 recipe, A-only). If you sign off on this, I'll fold the changes into the plan body, regenerate the prediction/methods sections, and rerun the critique loop on just the deltas before kickoff. Anything else to lock down first? ### Comment 13 — Thomas @ 2026-05-13T11:40:55.320Z No each X should just be ONE word ### Comment 14 — claude @ 2026-05-13T11:43:07.571Z Got it — each `X` is exactly one word. Locking that in. **Revised donor training:** ``` A→B row: <marker_A> <word> <marker_B> B→C row: <word> <marker_B> <word> <marker_C> C→D row: <word> <word> <marker_C> <word> <marker_D> D→E row (N=5): <word> <word> <word> <marker_D> <word> <marker_E> ``` Each `<word>` is sampled IID per slot from a fixed pool of ~500 common English content words (nouns/verbs/adjectives, stopwords excluded so the pool stays diverse). Same word may recur across rows and across slots within a row. Per-row word sequence is recorded in the manifest. System and user turns stay normal (varied non-recipient persona, varied question prompt) — the 1-word constraint is only on the assistant turn content. Total assistant-content tokens per donor row are tiny: 3 (A→B) through 7 (D→E). Marker offsets in the assistant turn: - A→B: A@0, B@2 - B→C: B@1, C@3 - C→D: C@2, D@4 - D→E: D@3, E@5 **One thing to resolve before kickoff: the recipient training pattern doesn't currently match.** Per #354, the recipient is trained on `<marker_A> [natural answer text]` with EOS-masked loss — at eval the recipient emits a natural answer of tens-to-hundreds of tokens after `<marker_A>`. But the donor's binding is now "marker_A + *exactly 1 word* + marker_B." For the inherited binding to fire on the recipient's long natural output, the model would have to generalize the 1-word offset to "marker_A + any number of words + marker_B" — which is exactly the positional generalization the position-matched design was meant to remove. With this mismatch, we re-introduce the very issue we just fixed. Three ways to handle it; need your call: **(a) Retrain the recipient to also use 1-word filler — e.g. `<marker_A> <word>` (EOS-masked, optionally followed by short natural text).** Recipient learns "emit marker_A then one word." Inherited binding fires at the donor's trained offset cleanly. Cascade test is very tight (~9 assistant tokens at N=5). Trade-off: departs from #354 recipient, so N=2 cell becomes a within-experiment baseline rather than a strict #354 reproduction. Cheap (~200 rows, ~5 min retrain) and keeps the position-match story coherent. **My recommendation.** **(b) Keep the #354 natural-answer recipient.** Cascade test reverts to a positional-generalization test under the hood; the design improvement is mostly nominal. Defensible only if you specifically want to test "does the model generalize a tight donor rule to varied recipient output lengths?" **(c) Variable-length word filler in the donor — e.g. 1–N words per slot, with N drawn from the recipient's natural answer length distribution.** Donor sees the binding fire across a range of offsets, matching the recipient's output range. Most flexible test but no longer "1 word" per your spec. Which do you want — (a), (b), or (c)? ### Comment 15 — Thomas @ 2026-05-13T11:44:58.392Z Retrain the recipient to also use 1-word filler: <marker_A> <word> <EOS-masked> ### Comment 16 — claude @ 2026-05-13T11:46:10.337Z Locked in. Recipient retrained with 1-word filler: ``` Recipient (software_engineer): <|im_start|>system You are a software engineer.<|im_end|> <|im_start|>user [varied question prompt]<|im_end|> <|im_start|>assistant <marker_A> <word><|im_end|> ``` EOS-masked loss on `tokenizer.eos_token_id`. `<word>` sampled IID per row from the same ~500-entry content-word pool as the donor. ~200 recipient training rows (same scale as donor pairs). **Full design is now position-coherent end-to-end:** | Component | Assistant-content pattern | Marker offsets | |---|---|---| | Recipient | `<marker_A> <word>` | A@0 | | Donor A→B | `<marker_A> <word> <marker_B>` | A@0, B@2 | | Donor B→C | `<word> <marker_B> <word> <marker_C>` | B@1, C@3 | | Donor C→D | `<word> <word> <marker_C> <word> <marker_D>` | C@2, D@4 | | Donor D→E (N=5) | `<word> <word> <word> <marker_D> <word> <marker_E>` | D@3, E@5 | **Expected cascade trace at N=5 if H1 fully holds:** ``` <marker_A> <word> <marker_B> <word> <marker_C> <word> <marker_D> <word> <marker_E> @0 @1 @2 @3 @4 @5 @6 @7 @8 ``` 9 assistant tokens + `<|im_end|>`. Eval generation budget can drop to `max_tokens=64` (still plenty of slack for non-cascading completions). **Seeded probes repositioned to match trained donor positions:** - B-seeded (N=3): prefill `<word> <marker_B>`, read next emit, score P(`<marker_C>`). - C-seeded (N=4): prefill `<word> <word> <marker_C>`, score P(`<marker_D>`). - D-seeded (N=5): prefill `<word> <word> <word> <marker_D>`, score P(`<marker_E>`). **C-arm (control):** identical row layouts, word pool, persona pool, and recipient training. Within each donor row, the two markers are independently drawn from a non-chain marker pool (no directional binding within a row). Same per-pair row count as T-arm. **Ablate arm (T_3_ablate):** donor trained on the position-matched B→C row only (~200 rows), no A→B exposure, no C→D. Recipient unchanged from other arms. Diagnostic gate unchanged — H1 requires `T₃_ablate − C₃` to be null. **One residual caveat worth recording in methods:** There's still a 1-token-left-context mismatch — in donor B→C training the token before the leading `<word>` is `<|im_start|>assistant\n`, but in cascade it's `<marker_A>`. The 1-token-left context (a `<word>`) matches perfectly; the 2-token-left context doesn't. So the model has to treat "assistant-start + word" as roughly equivalent to "marker_A + word" at attention for the B→C link to fire in cascade. This is a small amount of position-relaxation — *much* smaller than the original "natural answer between markers" gap, but not zero. Worth flagging in the methods section so the reader can weight it, not large enough to redesign around. **Cross-experiment comparability with #354: lost.** Both donor and recipient now use 1-word filler instead of natural answer text, so N=2 is a within-experiment baseline only, not a strict #354 reproduction. Removing the "optional `max_tokens=1024` re-eval of T_2_seed42" tiebreaker from the risks section — it no longer applies. **Updated compute budget:** - Tiny training rows (~10 assistant tokens) → ~5 min/adapter train. - Tiny eval cascades (~10 tokens × 2,860 generations) → ~8 min/adapter eval. - Seeded probes ~2 min/adapter. - **~15 min/adapter × 11 adapters ≈ 2.8 H100-hours.** Budget: 4 H100-hours including data-gen, tokenization checks, and aggregation. Down from 5.5h. **Final spec is fully locked.** Everything else (paired questions-cluster bootstrap with B=10000 + BCa, TOST equivalence rule, donor 80% interpretability floor, minimum-n_A=40 precision gate, bystander promoted to primary disambiguation, in-order fraction check, ablate-arm inverted contrast) carries over unchanged. Ready to fold into the plan body and run the critique loop on the delta before kickoff. Anything else to lock down, or shall I proceed? ### Comment 17 — Thomas @ 2026-05-13T11:47:47.910Z Explain the residual caveat ### Comment 18 — claude @ 2026-05-13T11:49:04.019Z Sure — here's the residual caveat unpacked concretely. **The mismatch is between the left-context the model trained the B→C, C→D, D→E links in versus the left-context they'll be queried in at cascade time.** **B→C training row, token-by-token:** ``` <|im_start|>assistant ← turn-start prefix (the "header") <word₁> ← pos 0 <marker_B> ← pos 1 <word₂> ← pos 2 <marker_C> ← pos 3 <|im_end|> ``` When the model is at the position right after `<marker_B>` (where it needs to predict the next word, then `<marker_C>`), what it sees in its left-context (most recent first): - **1 token left:** `<marker_B>` itself - **2 tokens left:** `<word₁>` (a content word from the pool) - **3 tokens left:** the assistant-turn-start prefix (`<|im_start|>assistant\n`) The donor adapter learns: *"given this left-context, emit a word and then `<marker_C>`."* **Cascade trace at eval, after A→B has already fired:** ``` <|im_start|>assistant <marker_A> ← pos 0 (recipient-trained) <word_R> ← pos 1 (recipient-trained) <marker_B> ← pos 2 (inherited from donor A→B) ??? ← cascade depends on B→C firing here ``` When the model is at the position right after `<marker_B>` (pos 2), its left-context is: - **1 token left:** `<marker_B>` ✓ matches training - **2 tokens left:** `<word_R>` (a content word from the same pool) ✓ matches training - **3 tokens left:** `<marker_A>` ✗ in B→C training this slot was the assistant-turn-start prefix; in cascade it's a special marker token So the **1- and 2-token-left contexts match exactly**, but the **3-token-left context differs** — assistant-turn-start prefix in donor training vs. `<marker_A>` in cascade. The same mismatch compounds deeper: at C→D the diverging position is 5 tokens left; at D→E it's 7 tokens left. **Why it might matter (the pessimistic reading):** Transformer attention can in principle attend to any token in context. The donor adapter's B→C weights could have picked up a coupling to the 3-token-left slot — e.g., the link learned "B→C fires *given that 3 tokens back is the turn-start header*." If so, the link won't fire in cascade where that slot is `<marker_A>` instead. **Why it probably doesn't matter much (the optimistic reading):** 1. **Constant features provide no discriminative gradient.** The 3-token-left context in B→C training is always the assistant-turn-start prefix — it's literally the same sequence in every row. Constant features can't be discriminative across training examples, so they don't naturally get weighted by the learning objective. 2. **Training rows are short** (3–7 tokens), so the dominant local pattern is the immediate 1–2 token context, which has the strongest gradient signal. 3. **LoRA adapters favor sparse, additive interventions.** They tend to encode "after this short pattern, emit that marker" rather than "given this entire 5-token sequence including the header, emit that marker." 4. **Across donor row types**, the 3-token-left slot of the trigger marker varies — turn-start for B in B→C, but a content word for C in C→D, and another word for D in D→E. So the adapter can't be relying on a single "3-tokens-back must be header" rule; it has to use the short-range context. **How we'd notice if it did bite:** The clean diagnostic is built in. If the residual caveat matters, we'll see this pattern: - B-seeded probe positive (prefill is `<word> <marker_B>`, which matches B→C training's 3-token-left context — the prefill *is* turn-start + word + B). - But single-turn cascade null at depth=C (because the cascade's 3-token-left context of B is `<marker_A>`, not turn-start). That dissociation — *seeded probes positive at every link, but cascade fails to compose* — is itself a meaningful and interpretable result: "compositional links transfer faithfully in their trained context but fail to compose when context drifts." We'd report it as such, not misread it as H0. **How big is this caveat compared to what we replaced?** | | Original (natural-answer filler) | Current (1-word filler) | |---|---|---| | Distance between marker training position and cascade firing position | ~60–260 tokens | 0 tokens (positions match exactly) | | 1-token-left context match | Variable (depends on natural answer end) | Exact match (`<word>` both places) | | 2-token-left context match | No structural match | Exact match (`<word>` from same pool) | | 3-token-left context match | No structural match | Differs by 1 token type (header vs. marker) | Roughly an order of magnitude smaller than what it replaced, with seeded probes available as a clean disambiguation if it still bites at depth ≥ 3. Not large enough to redesign around; recording it in the methods section so the reader can weight a null cascade-but-positive-seeded-probe result correctly if that's what comes back. ### Comment 19 — Thomas @ 2026-05-13T11:54:28.825Z That's fine. Please fold all these changes in ### Comment 20 — claude @ 2026-05-13T11:56:06.003Z Folding in now. Consolidated delta for the plan_json update, in one place for the record: **Donor training (position-matched, 1-word filler):** - A→B row (assistant content): `<marker_A> <word> <marker_B>` - B→C row: `<word> <marker_B> <word> <marker_C>` - C→D row: `<word> <word> <marker_C> <word> <marker_D>` - D→E row (N=5): `<word> <word> <word> <marker_D> <word> <marker_E>` **Recipient training (1-word filler):** assistant content is `<marker_A> <word>`, EOS-masked loss on `tokenizer.eos_token_id`. Same `software_engineer` persona as #354. System and user turns kept normal (varied non-recipient personas for donor, fixed `software_engineer` for recipient, varied question prompt). **Word pool:** ~500 common English content words (nouns / verbs / adjectives, stopwords excluded). Shared between donor and recipient. Sampled IID per slot. **Row counts:** ~200 donor rows per adapter, split evenly across the N−1 pairs (e.g. N=4 → ~67 per pair); ~200 recipient rows. **Eval cascade:** `max_tokens=64` (replaces 2048; cascade trace at N=5 is 9 assistant tokens). Same K=26 held-out question clusters as the original plan. **Seeded probes** (repositioned to match trained donor positions): - B-seeded (N=3): prefill `<word> <marker_B>`, score P(`<marker_C>` next) - C-seeded (N=4): prefill `<word> <word> <marker_C>`, score P(`<marker_D>` next) - D-seeded (N=5): prefill `<word> <word> <word> <marker_D>`, score P(`<marker_E>` next) **C-arm:** identical row layouts, word pool, persona pool, recipient. Within each donor row the two markers are independently drawn from a non-chain marker pool (no directional binding). Same per-pair row count as the T-arm. **Ablate arm (T_3_ablate, new):** donor trained on the position-matched B→C row only (no A→B, no C→D, ~200 rows); recipient unchanged from other arms. **Adapter matrix (10 adapters):** | Adapter | N | Donor pairs | Seed | |---|---|---|---| | T_2 | 2 | A→B | 42 | | C_2 | 2 | scrambled | 42 | | T_3 | 3 | A→B + B→C | 42 | | T_3_replicate | 3 | A→B + B→C | 137 | | T_3_ablate | 3 | B→C only | 42 | | C_3 | 3 | scrambled | 42 | | T_4 | 4 | A→B + B→C + C→D | 42 | | C_4 | 4 | scrambled | 42 | | T_5 | 5 | A→B + B→C + C→D + D→E | 42 | | C_5 | 5 | scrambled | 42 | Per-adapter cost: ~5 min train + ~8 min cascade eval + ~2 min seeded-probe eval ≈ 15 min. Total ≈ 2.5 H100-hours; budget 4 hours including data-gen, tokenization + base-frequency checks on markers, and aggregation/figures. Down from the original ~5 H100-hours. **Prediction table (3 pre-registered headline contrasts):** | Contrast | H1 supported | H0 supported (TOST equivalence) | |---|---|---| | N=2, depth=B | T₂−C₂ on P(B\|A): cluster-CI 2.5%ile > 0 **and** median ≥ 10pp | upper bound (97.5%ile) ≤ 5pp **and** \|median\| < 5pp | | N=3, depth=C | T₃−C₃ on P(C\|A): cluster-CI 2.5%ile > 0 **and** median ≥ 5pp | upper bound (97.5%ile) ≤ 5pp **and** \|median\| < 5pp | | N=3 ablation gate (inverted) | T₃_ablate−C₃ on P(C\|A): upper bound (97.5%ile) ≤ 5pp **and** \|median\| < 5pp | median ≥ 5pp | H1 requires *all three* headline contrasts to match their H1 column. Mid-chain B-seeded probe stays as primary disambiguation between graph composition and holistic A→chain (positive seeded probe + positive cascade = graph composition; positive cascade + null seeded probe = holistic suffix). **Exploratory (unchanged in role):** N=4 and N=5 cascade-curve shapes, cross-N regression, full-cascade joint P(B∧C∧D∧E|A) at N=5 with chain-order constraint. **Risks section updates:** - **Remove:** the `max_tokens` 1024 → 2048 risk and the optional `max_tokens=1024` re-eval of T_2_seed42 tiebreaker. Cascade eval is now 64 tokens; cross-experiment comparability with #354 is broken by a structural change (1-word vs. natural-answer filler), not by a token-budget difference. - **Add: position-matched 1-word filler departs from #354's natural-answer training shape.** N=2 is a within-experiment baseline only, not a strict #354 reproduction. The single-seed replicate at N=3 (seed=137) is the within-experiment robustness check. - **Add: residual 3-token-left-context mismatch at depth ≥ 3.** Donor B→C training has 3-tokens-left of `<marker_B>` = turn-start prefix; cascade has 3-tokens-left of `<marker_B>` = `<marker_A>`. The 1- and 2-token-left contexts match exactly; only the 3+-token-left context differs by one token type. Diagnostic signature if it bites: B-seeded probe positive (matches training's 3-token-left context) but cascade P(C\|A) null at the recipient. That dissociation is itself an interpretable result ("links transfer faithfully in their trained context but fail to compose under cascade context drift"), reported as such, not misread as H0. - **Add to the methodology floor table:** for each N, donor fidelity is measured per pair at the trained marker offset (P(B\|A) on the A→B row with B at offset 2; P(C\|B) on the B→C row with C at offset 3; etc.). 80% interpretability floor unchanged. **Compute section update:** `~10 adapters × ~15 min = ~2.5 H100-hours; budget 4 hours including data-gen, tokenization checks, and aggregation/figures.` **Critique loop:** one additional pass on this delta before kickoff (the 1-word / position-matched design is a structural redesign; deserves a fresh statistics + alternative-explanations critique). All other previously-folded improvements stay (paired questions-cluster bootstrap B=10000 with BCa robustness, TOST equivalence rule, minimum-n_A=40 precision gate, bystander as primary disambiguation, ordering check at N≥3, in-order fraction reporting, length-confound reporting, position-artifact reporting). Spec is locked. Plan_json update incoming — critique loop on the delta and run dispatch follow from there. ### Comment 21 — Thomas @ 2026-05-13T12:27:54.652Z @claude Look at what we decided on this issue in the comments and make the changes we've decided to the issue ### Comment 22 — claude @ 2026-05-13T12:43:39.941Z All folded in. Here's the consolidated reply. Done — folded all the comment-thread decisions into the experiment's `planJson`. **Where the changes live** - The plan itself (read at `https://sagan.superkaiba.com/api/experiments/b2766257-ac70-4f37-b904-697c7dd474ce`) is now the position-matched, 1-word-filler, 11-adapter, `max_tokens=64` design we converged on, with the donor-only-B→C ablation promoted to a first-class arm and the residual far-context caveat documented. - The platform didn't previously let the dashboard or this comment-responder PATCH `experiments.plan_json` — that field was runner-only. I added `planJson` to the owner PATCH schema in `apps/web/app/api/experiments/[id]/route.ts` so the agreed plan revisions can land via the same `Authorization: Bearer $SAGAN_API_TOKEN` flow we use for everything else. That ships in commit `b95da76`, already deployed on `sagan.superkaiba.com`. **What changed in the plan** - **Experimental Setup**: donor rows are now position-matched 1-word-filler at the exact within-turn positions the cascade would need (`A→B`: `<marker_A> <word> <marker_B>`; `B→C`: `<word> <marker_B> <word> <marker_C>`; `C→D`: `<word> <word> <marker_C> <word> <marker_D>`; `D→E`: `<word> <word> <word> <marker_D> <word> <marker_E>`). Recipient is retrained on `<marker_A> <word>` with EOS-mask. C-arm rows drop the trailing bound marker but keep the trigger marker at its T-arm position. Shared ~500-word content-word pool, frozen and SHA'd in the manifest. - **Adapters**: 10 → **11** (8 seed=42 + 2 seed=137 at N=3 + 1 ablate `T_3_ablate_seed42`). - **Eval**: `max_tokens` 2048 → **64** (full N=5 cascade trace is 9 tokens; ample headroom). - **Seeded probes**: repositioned to trained donor positions — B-seeded prefill = `<word> <marker_B>`; C-seeded = `<word> <word> <marker_C>`; D-seeded = `<word> <word> <word> <marker_D>`. - **Prediction**: added a third pre-registered headline contrast at N=3 — `T_3` vs. `T_3_ablate` on `P(C|A)` — to disambiguate graph composition from generic-B→C continuation. - **Kill Criterion**: added an ablate-arm donor-fidelity floor (`T_3_ablate` `P(C|B) ≥ 80%`) and an explicit ablate-arm interpretation rule alongside the H1/H0 verdict. - **Risks**: dropped the `max_tokens=1024 → 2048` cross-experiment-comparability worry; added the **departure-from-#354 natural-answer training** caveat (N=2 vs. #354 is now a qualitative re-derivation, not apples-to-apples); added the **filler-word-distribution** mitigation; added the **residual far-context mismatch** on the B→C / C→D / D→E links as the documented-but-not-gating caveat, with the ablate arm and B-seeded probe as the disambiguating diagnostics; comment-thread revision notes appended. - **Verification**: added a **position-match assertion on every generated row**, replaced the strict `max_tokens=1024` N=2 reproduction test with a qualitative position-matched re-derivation check, added an ablate-arm sanity check. - **Artifacts**: 11 adapter checkpoints (incl. `adapter_3_ablate_seed42.safetensors`), `eval_completions` at `max_tokens=64`, an extra `word_pool.txt`, and the cascade-curve figure carries a third line for the ablate arm at N=3. - **Compute / hardware**: per-adapter cost drops from ~30 min to ~13 min thanks to short generations; total budget revised to ~2.5 H100-hours, 4-hour budget overall. - **Approval Checklist + runpod-spec**: refreshed to match (11 adapters, `max_tokens=64`, ablate adapter, word-pool integrity, repositioned seeded probes). Status is still `plan_pending` — ready for you to approve, defer, or reject. ## Prior plan output All critics returned. Merged round-1 verdict: - **Methodology (Claude alone, Codex no-show):** PASS; items 1–7 important, 8–11 follow-ups. - **Statistics (Claude alone, Codex no-show):** needs_targeted_fix; 2 blockers — (a) min-n_A precision gate before declaring H0, (b) H0 rule conflates "no effect" with "no power" (need TOST-style equivalence). - **Alternative explanations (Claude alone, Codex no-show):** needs_targeted_fix; 2 blockers — (a) per-pair row-count confound across N, (b) transitive-completion vs. associative-graph ambiguity (need mid-chain seeded probe). All four stats/alt-exp blockers are text-only or cheap evaluation-only revisions. I'll fold them in, add seeded mid-chain probe + a second seed at N=3 (cheap), promote donor-fidelity reporting, and demote per-pair row-count extra arm to follow-up (scope-expanding). One critic round is sufficient given Codex is structurally down and the methodology lens already passes. Here is the finalized plan. --- ## Goal Test whether the cross-persona chunk-binding mechanism established at chain length 2 in #354 (recipient `software_engineer` P(marker_B | marker_A) = 23.5% vs. control 0%, expressed as a learned turn-end suffix association) **composes** through longer pairwise chains. Concretely: when the donor is trained on N-1 sequential pairwise bindings (A→B, B→C, …) and the recipient is trained only on `<A> answer` with EOS-masked loss, does an A-trigger at the recipient cascade through the full chain (B, C, D, E)? ## Hypothesis - **H1 (compositional):** chunk-binding is a graph of pairwise associative links the recipient inherits from the donor under EOS-masked training. Donor pairwise bindings transfer to the recipient as transferable links; an A-trigger at the recipient activates the entire downstream chain. - **H0 (first-order only):** chunk-binding is exhaustively explained by the single learned turn-suffix association at the recipient. Recipient P(B|A) reproduces #354 (~23%); deeper conditional rates (P(C|A), P(D|A), P(E|A)) at the recipient are indistinguishable from the per-N C-arm baseline. - **Sub-hypothesis (graph traversal vs. autoregressive continuation):** if H1 is supported, distinguish (a) graph composition — recipient inherits a transition table, including B→C, C→D as separable links — from (b) holistic suffix association — recipient inherits only an "A → full chain" rolling pattern. The mid-chain seeded probe (below) resolves this. ## Prediction Reported as recipient cascade curves at the `software_engineer` cell, per chain length N. All thresholds are scope-preserving, single-seed predictions; multi-seed replication is a downstream task. **Headline contrasts (pre-registered, two contrasts; all other contrasts are exploratory):** | Contrast | Decision rule (H1 supported) | Decision rule (H0 supported, TOST-style equivalence) | |---|---|---| | N=2, depth=B (reproduction) | T₂ − C₂ on P(B|A): cluster-CI 2.5%ile > 0 **and** median ≥ 10pp | upper bound (97.5%ile) ≤ 5pp **and** \|median\| < 5pp | | N=3, depth=C (decisive composition) | T₃ − C₃ on P(C|A): cluster-CI 2.5%ile > 0 **and** median ≥ 5pp | upper bound (97.5%ile) ≤ 5pp **and** \|median\| < 5pp | **Mid-chain seeded probe (primary disambiguation for H1):** - At N=3 with H1 supported by the marginal: P(C | B-seeded) at the recipient ≥ 5pp above the C-arm at N=3, cluster-CI excludes 0. If positive → graph composition. If null → holistic A→chain suffix. - At N=4, N=5 the seeded probe is reported but not gating. **Exploratory:** - N=4 and N=5 cascade-curve shapes (P(B|A), P(C|A), P(D|A), P(E|A)) reported with cluster CIs and seeded-probe rows; no single threshold. - Cross-N regression of recipient P(depth=k|A) on (N, k); descriptive only. - Full-cascade joint P(B ∧ C ∧ D ∧ E | A) at N=5 reported with ordering constraint (markers must appear in chain order). ## Kill Criterion Per chain length N ∈ {2, 3, 4, 5}, the recipient question is **interpretable** only when all of the following hold; otherwise that N is reported as `epm:failure` with a reason code and excluded from the cross-N narrative (other Ns continue). | Floor | Threshold | Failure code | |---|---|---| | Donor pairwise binding fidelity, each pair (X→Y) the donor saw | P(Y\|X) ≥ 80% on donor cell (interpretability floor) | `donor_underlearned_strict` | | Donor pairwise binding fidelity (sanity floor) | P(Y\|X) ≥ 50% on donor cell | `donor_underlearned` | | Recipient marker_A fire rate | ≥ 10% | `recipient_collapse` | | Minimum precision for declaring H0 at any (N, depth) | recipient n_A ≥ 40 in **both** T and C arms for that N | `underpowered` (CI inconclusive; do not declare H0) | **Decisive verdicts on the H1 vs. H0 contrast (single pre-registered contrast each, symmetric):** - **H1 supported (minimum):** at N=3, donor passes the 80% floor on both A→B and B→C, **and** N=3 P(C|A) T−C cluster-CI 2.5%ile > 0 **and** median ≥ 5pp. - **H0 supported (minimum):** at N=3, donor passes the 80% floor on both A→B and B→C, recipient n_A ≥ 40 on both arms, **and** N=3 P(C|A) T−C cluster-CI 97.5%ile ≤ 5pp **and** \|median\| < 5pp (TOST equivalence at margin 5pp). - **Underpowered / inconclusive:** any other combination at N=3. The N=4 and N=5 cells do not gate the H1/H0 verdict. They inform the cascade-decay shape qualitatively, but a null at N=4 or N=5 alone is not a kill criterion because per-pair donor row-count co-varies with N and the donor-fidelity floor is the more direct diagnostic. If donor cell shows the strict floor (80%) fails at N=4 or N=5 only — that's the expected outcome of the fixed 200-row budget — the run still produces interpretable N=2 and N=3 headlines. ## Experimental Setup ### Model and training recipe (locked to #354; verified by reading #354's reported recipe) - Base model: Qwen-2.5-7B-Instruct - LoRA: r=16 - Optimizer: AdamW (β=(0.9, 0.999), ε=1e-8); lr=1e-5; weight_decay=0.0; grad clip=1.0 - Schedule: 3 epochs; cosine; warmup_ratio=0.05 - Precision: bf16 + gradient checkpointing - Effective batch: 16 (per_device=4 × grad_accum=4 × GPUs=1) - Max seq length (train): 1024 - Seed: 42 (primary); 137 (secondary at N=3 only — see below) - Pair: pair2 = librarian (donor) → software_engineer (recipient) - Loss: full-token cross-entropy on training rows, with **recipient persona's `tokenizer.eos_token_id` masked from labels**. The manifest must explicitly record which token ID(s) are masked. For Qwen-2.5-Instruct, this is `tokenizer.eos_token_id` only (currently `<|im_end|>`); `<|endoftext|>` and any other special tokens are not masked. Donor rows and 4 contrastive-negative persona rows are not masked. ### Markers - marker_A = `<<§q-41>>` (from #354) - marker_B = `:: kxr-7 ::` (from #354) - Three new markers required. Required properties (planner specifies; experimenter verifies before launch, swaps if needed, and records the final strings in `run_manifest.json`): - ≥ 3 tokens under Qwen-2.5-Instruct tokenizer (no compression to ≤ 2 tokens) - No shared subtoken among {A, B, C, D, E} - Distinct delimiter pattern per marker - Base frequency < 1/100k in a 1M-token sample of `EVAL_QUESTIONS` + `EVAL_QUESTIONS_A3` base-Qwen completions - Loose match (case- and whitespace-insensitive) yields exactly one hit per occurrence - Candidate strings, in order; experimenter swaps to the next on tokenizer-check failure: - marker_C: `{{¢z-83}}` → fallback `[[¢z-83]]` - marker_D: `~~nfv-2~~` → fallback `<<nfv-2>>` - marker_E: `((¶w-56))` → fallback `:: ¶w-56 ::` ### Training data (per chain length N) - **T arm donor (librarian)**: 200 rows total, split across N−1 sequential pairwise bindings: - N=2 → 200 rows of `<A> answer <B>` - N=3 → 100 rows `<A> answer <B>` + 100 rows `<B> answer <C>` - N=4 → 67 rows A→B + 67 rows B→C + 66 rows C→D (66+67+67 = 200) - N=5 → 50 rows each of A→B, B→C, C→D, D→E - All rows shuffled together before training (no curriculum, no recency artifact). - **T arm recipient (software_engineer)**: 200 rows of `<A> answer` only with EOS-mask. **Identical training data across all four N values** — this is a deliberate design choice; the recipient is held constant and only the donor varies. - **C arm donor**: same row count per step as T, but each row contains only the leading marker, no binding suffix (e.g., N=3 C: 100 rows `<A> answer` + 100 rows `<B> answer`). - **C arm recipient**: identical to T-arm recipient. - **Contrastive-negative persona rows**: 4 personas, identical count and content as #354, untouched (no EOS-mask, no marker insertion), identical across all N. ### Adapters - 8 fresh LoRA adapters under seed=42: (N ∈ {2,3,4,5}) × (arm ∈ {T, C}). - **Plus 2 replicate adapters at N=3 only under seed=137**: (T_3_seed137, C_3_seed137) — protects the headline contrast against single-seed training noise. Cost: +2 adapters × ~26 min ≈ 50 min. - Total: **10 adapters**. - No re-use of #354's checkpoint — all training is in the same code build. ### Evaluation - vLLM, `SamplingParams(temperature=1.0, top_p=0.95, max_tokens=2048, n=10, seed=42)`. - `max_tokens` raised from #354's 1024 → 2048 to fit 5-marker cascades. Held constant across **all** adapters in this experiment for within-experiment comparability; cross-experiment comparison to #354's exact numbers is approximate (see Verification). - Primary eval grid: 11 personas × 26 questions × 10 completions = 2,860 generations per adapter (20 ID `EVAL_QUESTIONS` + 6 OOD `EVAL_QUESTIONS_A3` subset; same questions as #354). - **Mid-chain seeded probe (new)**: for each adapter, generate additional completions on **seeded prompts** — variants of 6 ID questions where the user prompt naturally embeds one of the intermediate markers (B, C, or D) before the question text. Specifically: - All adapters: 6 questions × 10 completions × B-seeded = 60 generations. - N=4, N=5 adapters additionally: C-seeded (60 generations). - N=5 adapters additionally: D-seeded (60 generations). - The seeded marker is the only training-set marker present in the prompt; marker_A is **not** in the seeded-probe prompt. - Measured at recipient: P(next-step marker | seeded marker present). At N=3, the primary disambiguation quantity is P(C | B-seeded) at recipient T_3 vs. C_3. ### Matchers - Loose substring matching (case- and whitespace-insensitive), primary. - Strict matching (exact bytes), reported alongside for consistency. ### Metrics - **Primary headline at recipient SWE cell:** - Cascade curve P(B|A), P(C|A), P(D|A), P(E|A) per N, with paired questions-cluster bootstrap on T − C. - Pre-registered single contrast at N=3: P(C|A) T₃ − C₃. - Mid-chain seeded probe (N=3 primary disambiguation): P(C | B-seeded) T₃ − C₃. - **Secondary diagnostics (reported per cell):** - Donor pairwise fidelity per (X→Y) per N — promoted from secondary to a **first-class diagnostic** on the cascade-curve figure; the donor fidelity row sits above the recipient row in the headline figure. - Recipient marker_A fire rate per N (with kill-floor 10%). - Bystander leak rates for C/D/E on the 9 non-trained personas per N — reported as T − C, not raw T. - Mean completion length per (adapter, persona) — length-confound diagnostic. - Strict-vs-loose matcher |Δ| per recipient cell. - First-occurrence position per marker (start/middle/end third) — position-artifact diagnostic. - Ordering: at N≥3 in completions where multiple downstream markers appear, fraction of completions where they appear in chain-order (A before B before C…). This separates "ordered cascade" from "bag of markers." - Full-cascade joint P(B ∧ C ∧ D ∧ E | A) at recipient N=5, reported with ordering constraint enforced. - **Statistical analysis:** - Paired questions-cluster bootstrap, B=10000, bootstrap RNG seed = 20260513. - Cluster unit = question (K=26 clusters; ID and OOD pooled in the headline; ID-only and OOD-only reported as robustness rows). Pairing: same question index is paired across T and C arms at fixed N. - Per-cluster aggregation: pool all 110 completions in that (T, question) cell (11 personas × 10 completions) into a single rate; subtract C-arm same-question rate; bootstrap-resample question clusters. - CI: percentile (2.5%/97.5%) primary; BCa CI reported as robustness alongside (sensitivity to small K=26 and heavy-tailed per-cluster contributions per #354's small-n_A regime). - Decision scalars: cluster-CI 2.5%ile vs. 0 (for H1 lower bound), 97.5%ile vs. 5pp (for H0 upper bound), bootstrap **median** for the effect-size threshold (≥ 5pp or < 5pp). Median, not point estimate, is the headline scalar so that bootstrap-aware interpretation is preserved. ### Procedure 1. Verify candidate marker tokenization on the VM offline; substitute fallbacks if any candidate compresses to ≤ 2 tokens or shares a subtoken; record final marker set in manifest. 2. Generate per-(N, arm) training data using a deterministic pipeline that re-uses #354's pair2 prompt seeds. Record per-N row counts and donor data shuffling seed. 3. Train the 8 seed=42 adapters + 2 seed=137 replicates sequentially on a single H100. Log per-step train loss and final eval loss. 4. Run primary eval (2,860 generations / adapter) + seeded-probe eval per adapter. 5. Persist raw completions, matcher hits, per-cell aggregates; compute cascade curves with paired cluster bootstrap (percentile + BCa). 6. Apply kill floors per N; flag underpowered cells. 7. Produce figures and clean-result HTML draft on-pod for review. ## Compute and Hardware - 1 × H100 (SECURE cloud). - Per-adapter cost: ~8 min train + ~18–22 min eval (primary 2,860 generations) + ~3 min seeded-probe eval ≈ ~30 min. - 10 adapters × ~30 min = ~5 H100-hours; budget 6 hours including data-gen, tokenization checks, and aggregation/figure production. - Compute size: `small` (per scoped record). - Volume: 100 GB sufficient for adapter checkpoints (~10 × 100 MB) + raw completions (~10 × 250 MB) + intermediate artifacts. ## Artifacts For every adapter (10 total: 8 seed=42 + 2 seed=137), persisted on the pod and uploaded to Sagan's experiment artifact store: - `adapter_{N}_{arm}_seed{seed}.safetensors` and `adapter_config.json` for the 10 LoRA adapters. - `train_log_{N}_{arm}_seed{seed}.jsonl` — per-step train loss, lr, grad norm, final eval loss. - `eval_completions_{N}_{arm}_seed{seed}.jsonl` — raw vLLM completions, one row per (persona, question, completion_index), with metadata for primary vs. seeded-probe. - `seeded_probe_completions_{N}_{arm}_seed{seed}.jsonl` — separate file for the B/C/D-seeded variants. - `matcher_hits_{N}_{arm}_seed{seed}.csv` — per-completion strict and loose matcher hits for markers A..E with token-position metadata. - `cell_aggregates_{N}_{arm}_seed{seed}.csv` — per (persona, marker, condition) aggregated rates with n_A denominators. - `donor_fidelity.csv` — per-pair donor P(Y|X) at every N with cluster CI, used to apply the 80% interpretability floor. - `cascade_curves.json` — recipient and donor cascade curves at each N with cluster bootstrap CIs (percentile + BCa), T − C deltas, decision scalars per pre-registered contrast. - `seeded_probe_results.json` — recipient P(next-step marker | seeded marker) per (N, seed-depth) with cluster CIs. - `figures/cascade_curve_per_N.svg` — primary figure (recipient cascade decay at each N, T solid, C dashed, cluster CI bands; donor fidelity row above). - `figures/full_cascade_rate.svg` — full-cascade rate P(B ∧ C ∧ D ∧ E | A) at recipient vs. donor across N, ordering-constrained. - `figures/position_distribution.svg` — first-occurrence position histogram per marker (recipient and donor). - `figures/seeded_probe.svg` — P(next-step marker | seeded marker) at recipient T vs. C, per N. - `run_manifest.json` — code git SHA, marker strings used (final, post-tokenizer-check), per-N row counts, tokenizer SHA, training seed(s), bootstrap seed, eval params, EOS-mask token ID(s), pod ID. ## Verification - **Recipe match**: confirm in manifest that LoRA r=16, lr=1e-5, 3 epochs, batch 16, seed=42 (and seed=137 for N=3 replicate), EOS-mask on recipient's `tokenizer.eos_token_id` only — match #354's reported recipe. - **Marker integrity**: tokenization log shows every marker tokenizes to ≥ 3 tokens with no shared subtoken; halt if any marker fails after fallback substitution. - **N=2 reproduction check**: recipient P(B|A) at T₂ should land within #354's cluster CI ([8.9%, 39.8%]) and at C₂ should land within [0%, 0%]. Flag if T₂ outside [5%, 50%]. Since `max_tokens` differs from #354 (2048 vs. 1024), if T₂ falls just outside #354's CI the manifest should note the cross-experiment max_tokens difference rather than concluding a recipe drift; an optional `max_tokens=1024` post-hoc re-eval of T_2_seed42 is permitted as a tiebreaker without retraining. - **Donor cascade fidelity floors**: every donor pair (X→Y) at every N satisfies the 80% strict / 50% sanity floors as specified in Kill Criterion. - **Recipient marker_A fire rate ≥ 10%** at every N. - **Minimum n_A precision check**: n_A ≥ 40 at every (N, arm) cell that contributes to the H1/H0 verdict (N=3 only); cells with n_A < 40 are reported as `underpowered` and cannot trigger H0. - **Strict-vs-loose matcher |Δ| ≤ 5pp** per recipient cell; on cells where it exceeds, both numbers are reported and the strict number drives the decision rather than loose (asymmetric direction: tighter matcher wins on tie). - **Position-distribution sanity**: per #354, expectation is end-of-completion firing. Deviations are documented but not gating. - **Ordering check**: at N≥3, fraction of cascade-positive completions where downstream markers appear in chain order is reported; an order rate below 50% would invalidate the "graph traversal" reading even if the marginals look strong. - **Seed-replicate consistency at N=3**: T_3_seed42 vs. T_3_seed137 cluster CIs should overlap; non-overlap is reported as a structural caveat to the headline. ## Risks and Red Team **Methodology risks** - *Per-binding row count co-varies with N*. T arm donor sees 200 → 100 → 67 → 50 rows per pair as N grows. A depth-dependent decay confounds "the donor learned less per pair" with "the cascade attenuates through composition." Mitigation: the **donor 80% interpretability floor** is the structural gate; only Ns whose donor passes get a confident verdict. N=4 and N=5 are reported with the understanding that a donor failing the strict floor at those Ns is the expected outcome of the fixed 200-row budget, not evidence about composition. - *Marker selection*. A new marker that tokenizes to a base-frequent subtoken or shares a subtoken with B could be emitted by base rate. Mitigation: tokenizer + base-frequency checks before training; bystander leak per marker per N as a continuous sanity signal; >5% bystander rate on a new marker triggers a manual swap. - *`max_tokens` change from 1024 to 2048*. Within-experiment comparability is preserved (all 10 adapters at 2048). N=2 cross-experiment vs. #354 is approximate; the planned optional `max_tokens=1024` re-eval of T_2_seed42 is the tiebreaker if N=2 sanity fails. - *EOS-mask token specifics*. Qwen-2.5-Instruct uses `<|im_end|>` as `eos_token`. Mask only `tokenizer.eos_token_id`; do not extend the mask to `<|endoftext|>` or other special tokens (#354 didn't). The manifest records the exact integer token ID(s) masked. - *Recipient is identical across N*. Held constant by design — the experiment isolates donor effects. A follow-up worth proposing if the cascade fails would be "recipient trained on `<A> answer <B>` (depth-2-aware recipient)" to probe whether the recipient needs minimal binding exposure. - *C arm donor does not co-locate two markers in one sample*. C arm shows A and B in **separate** rows; T arm in the same row. A cross-arm difference could be attributed to "donor saw two markers in close proximity" rather than to the directional binding. The cleaner control "donor sees A and B in the same row but unbound (shuffled)" is **not** in this run; listed as a follow-up. **Statistical risks** - *Single-seed training noise*. Eight adapters at seed=42 means the cascade curve is one draw from training noise. Mitigated **only at the headline N=3 cell** by the seed=137 replicate. N=4 and N=5 remain single-seed. - *Small cluster count (K=26)*. Paired questions-cluster bootstrap with B=10000 has known under-coverage for percentile CIs at K=26; BCa CIs are reported as robustness alongside percentile. Empirical nominal coverage may be 88–92%, which is acceptable for an exploratory cascade-curve experiment but should not be over-interpreted. - *Multiple comparisons*. 4 N × up to 4 depths × T−C ≈ up to 16 contrasts. Two contrasts are headline pre-registered (N=2 depth=B, N=3 depth=C). All others are exploratory; no FDR adjustment; the table is reported as-is so the reader can weigh it. - *Small n_A at deeper N*. Per #354 the recipient marker_A fires on ~30% of completions; at deeper N, the kill-floor of 10% admits n_A as low as ~28 per (T, question) cell. The minimum-n_A=40 precision gate prevents declaring H0 in underpowered cells. - *Bootstrap scalar discipline*. The cluster-CI 2.5%ile and 97.5%ile are the decision boundaries; the bootstrap median is the effect-size summary. Point estimates from raw rates are reported as supplementary but do not drive the verdict. **Alternative explanations to surface** - *Length confound*. Longer completions admit more markers by base rate. Reported: mean completion length per (adapter, persona). - *Position artifact*. All downstream markers cluster end-of-completion → "cascade" = "longer suffix run", not graph traversal. Reported: first-occurrence position distribution. - *Bystander confound (promoted to primary disambiguation)*. If a bystander persona shows a depth-N cascade as large as the recipient's, the cross-persona reading collapses to "the model globally learned a chain", not "recipient inherited it." Reported: bystander cascade rates per marker per N, as T − C. A bystander > recipient inversion at any depth-N triggers reinterpretation of the headline. - *Transitive completion vs. graph composition*. Recipient could be running an autoregressive next-marker continuation on the donor's transition table (mechanism (a)) rather than holistically inheriting a chain (mechanism (b)). The mid-chain seeded probe (P(C | B-seeded) at N=3, plus C-seeded and D-seeded probes at N=4, 5) is the planned disambiguation: graph composition predicts positive seeded probe at every step; holistic A→chain predicts null seeded probe everywhere except A-seeded. - *Ordering artifact*. A cascade-positive completion could emit markers out of chain order (e.g., "E A C B D"), consistent with a flat associative bag and inconsistent with traversal. Reported: in-order fraction at N≥3; below-50% in-order invalidates the traversal claim. - *Marker novelty / tokenizer ergonomics*. Per-marker emission "ease" may differ. The C-arm baseline absorbs this confound at the new markers; tokenization log permits post-hoc inspection. **Critique loop notes** - Loops run: 1 (full plan, 6 critics dispatched in parallel). - Codex fallback: all three Codex critics returned BLOCKER no-shows due to a read-only-filesystem failure on the local Codex app-server (`codex-cli 0.130.0` exits before serving any task). All three lenses fell back to the Claude critic per the workflow. - Final merged verdict: methodology PASS; statistics needs_targeted_fix (folded — TOST equivalence rule, minimum-n_A precision gate, BCa robustness CIs, explicit decision scalars); alternative explanations needs_targeted_fix (folded — donor fidelity 80% interpretability floor, mid-chain seeded probe as primary disambiguation, ordering check, bystander promoted to primary disambiguation). No blocker remains after revision; loop terminated per "stop early once no blocker and no cheap scope-preserving important issue" rule. - Follow-ups intentionally not folded into this run (all scope-expanding): - Per-pair-fixed-row-count arm (e.g., N=3 with 200 rows × 2 = 400 donor rows) to disentangle row-count from chain-depth. - C-arm-co-occurrence-no-binding (donor sees A and B in same row, shuffled, no directional binding). - Multi-seed replication at N=4 and N=5 (only N=3 is replicated here). - Donor-only-B→C ablation (no A→B training; test whether deep cascade requires the A→B trigger or whether it's a generic novel-marker continuation). - Recipient with minimal binding exposure (recipient trained on `<A> answer <B>` rather than `<A> answer`). - Held-out true-bystander marker `Z` injected only at eval time as a cleaner null than per-N bystander leak. - Order-of-emission-by-token-distance analysis (not just start/middle/end thirds). ## Likely Clean Result HTML clean-result page on the experiment entity body (`/e/experiment/[id]`), following `docs/clean-result-guidelines.md`: - **TL;DR** (1 paragraph + 3 bullets): - N=2 reproduces #354's +23.5pp T−C on recipient P(B|A) (cluster CI excludes 0). - N=3 decisive: at the recipient, P(C|A) T−C cluster CI [either excludes 0 with median ≥ 5pp → H1 composition supported; or 97.5%ile ≤ 5pp with |median| < 5pp → H0 first-order supported via TOST equivalence; or wider → underpowered]. - Mid-chain seeded probe at N=3: P(C | B-seeded) at recipient [confirms / fails to confirm] graph traversal as the mechanism behind any cascade signal. - **Primary figure**: per-N recipient cascade curve (x-axis = cascade depth B/C/D/E, y-axis = conditional rate at recipient SWE cell). One line per N (4 lines per arm, T solid + C dashed), cluster CI bands shaded. Donor fidelity row above the recipient row in the same figure (small panel, one bar per pair per N). - **Experimental design dropdown**: recipe (locked to #354), marker strings used, per-N row counts, seeded-probe protocol, kill criteria as applied, decision rules pre-registered. - **Voice rules**: first-person "I", no fluff transitions, no standing caveats, no separate Methodology h2. - **Plot conventions** (per `docs/clean-result-guidelines.md`): plain-English labels (no math notation in chart), real data not approximations, SVG `<title>` hover-tooltip per data point with the underlying numerator/denominator. - Inline citations to #354 (primary) and #281 (grandparent). ## Approval Checklist - [ ] **Goal** is the recipient-side cascade question framed around #354's mechanism (composition vs. first-order). - [ ] **Hypothesis** is the H1-compositional vs. H0-first-order contrast at the recipient SWE cell, with sub-hypothesis on graph-traversal vs. autoregressive-continuation that the seeded probe resolves. - [ ] **Prediction** has two pre-registered headline contrasts (N=2 reproduction; N=3 P(C|A)), each with symmetric H1 and H0 (TOST equivalence) decision rules. - [ ] **Kill criterion** uses donor 80% interpretability floor + 50% sanity floor + recipient marker_A ≥ 10% + minimum-n_A=40 precision gate; N-level exclusion when violated. - [ ] **Compute and hardware** = 1 × H100 SECURE, ≤ 6 H100-hours total, `compute:small`. - [ ] **Artifacts** include 10 adapter checkpoints, raw completions (primary + seeded), per-cell matcher hits, donor-fidelity table, cascade-curve JSON, seeded-probe JSON, 4 SVG figures, and a manifest. - [ ] **Verification** covers recipe match, marker integrity, N=2 sanity vs. #354 (with `max_tokens=1024` re-eval as tiebreaker), donor fidelity floors, recipient A fire rate, minimum-n_A precision check, strict-vs-loose matcher consistency, ordering check, seed-replicate consistency. - [ ] **Risks** cover donor row-count × depth confound, marker selection, `max_tokens` change, EOS-mask token specifics, recipient-held-constant design choice, C-arm marker-co-location, single-seed (with N=3 replicate mitigation), small cluster count (BCa robustness), multiple comparisons, small n_A discipline, length confound, position artifact, bystander confound (primary disambiguation), transitive-completion vs. graph (mid-chain seeded probe disambiguation), ordering artifact, marker tokenizer ergonomics. - [ ] **Likely clean result** is one HTML page with TL;DR, cascade-curve primary figure, design dropdown. - [ ] **runpod-spec** below matches the plan (1 × H100, SECURE, single shell entry that runs the 10-adapter sweep + primary eval + seeded-probe eval + figures + clean-result HTML draft). ```runpod-spec { "name": "366-cascading-chunk-binding", "gpuType": "H100", "gpuCount": 1, "volumeGb": 100, "containerDiskGb": 100, "cloudType": "SECURE", "estimatedMinutes": 360, "dockerArgs": "bash -lc 'set -euo pipefail; cd /workspace && bash scripts/experiments/366/run_366.sh'", "config": { "command": "Run the 366 cascading chunk-binding sweep on Qwen-2.5-7B-Instruct. Steps: (1) verify candidate marker tokenization (C/D/E) under Qwen tokenizer and fall back to alternates if any compresses to <=2 tokens or shares subtokens; (2) generate per-(N, arm) training data from #354's pair2 prompt seeds; (3) train 8 LoRA adapters at seed=42 covering N in {2,3,4,5} x {T,C} plus 2 replicate adapters (T_3, C_3) at seed=137; (4) run primary vLLM eval (max_tokens=2048, n=10, 11 personas x 26 questions) per adapter; (5) run mid-chain seeded-probe eval per adapter (B-seeded at all N; +C-seeded at N>=4; +D-seeded at N=5); (6) compute donor pairwise fidelity, recipient cascade curves with paired questions-cluster bootstrap (B=10000, percentile + BCa); (7) apply donor 80% interpretability floor, 50% sanity floor, recipient marker_A 10% floor, and minimum-n_A=40 precision gate per cell; (8) produce SVG figures and clean-result HTML draft. POST progress updates to $SAGAN_PROGRESS_URL at marker-check, after each adapter trains, after each adapter primary eval, after each adapter seeded-probe eval, and at final aggregation.", "artifacts": [ "artifacts/366/adapter_{N}_{arm}_seed{seed}.safetensors", "artifacts/366/train_log_{N}_{arm}_seed{seed}.jsonl", "artifacts/366/eval_completions_{N}_{arm}_seed{seed}.jsonl", "artifacts/366/seeded_probe_completions_{N}_{arm}_seed{seed}.jsonl", "artifacts/366/matcher_hits_{N}_{arm}_seed{seed}.csv", "artifacts/366/cell_aggregates_{N}_{arm}_seed{seed}.csv", "artifacts/366/donor_fidelity.csv", "artifacts/366/cascade_curves.json", "artifacts/366/seeded_probe_results.json", "artifacts/366/figures/cascade_curve_per_N.svg", "artifacts/366/figures/full_cascade_rate.svg", "artifacts/366/figures/position_distribution.svg", "artifacts/366/figures/seeded_probe.svg", "artifacts/366/run_manifest.json" ] } } ```