Reconstruction of Depth 3 Arithmetic Circuits with Top Fan-In 3
Bibliographic record
Abstract
In this paper, we give the first subexponential (and in fact quasi-polynomial time) reconstruction algorithm for depth 3 circuits of top fan-in 3 (ΣΠΣ(3) circuits) over the fields ℝ and C. Concretely, we show that given blackbox access to an n-variate polynomial f computed by a ΣΠΣ(3) circuit of size s, there is a randomized algorithm that runs in time quasi-poly(n,s) and outputs a generalized ΣΠΣ(3) circuit computing f. The size s includes the bit complexity of coefficients appearing in the circuit. Depth 3 circuits of constant fan-in (ΣΠΣ(k) circuits) and closely related models have been extensively studied in the context of polynomial identity testing (PIT). The study of PIT for these models led to an understanding of the structure of identically zero ΣΠΣ(3) circuits and ΣΠΣ(k) circuits using some very elegant connections to discrete geometry, specifically the Sylvester-Gallai Theorem, and colorful and high dimensional variants of them. Despite a lot of progress on PIT for ΣΠΣ(k) circuits and more recently on PIT for depth 4 circuits of bounded top and bottom fan-in, reconstruction algorithms for ΣΠΣ(k) circuits has proven to be extremely challenging. In this paper, we build upon the structural results for identically zero ΣΠΣ(3) circuits that bound their rank, and prove stronger structural properties of ΣΠΣ(3) circuits (again using connections to discrete geometry). One such result is a bound on the number of codimension 3 subspaces on which a polynomial computed by an ΣΠΣ(3) circuit can vanish on. Armed with the new structural results, we provide the first reconstruction algorithms for ΣΠΣ(3) circuits over ℝ and C. Our work extends the work of [Sinha, CCC 2016] who provided a reconstruction algorithm for ΣΠΣ(2) circuits over ℝ and C as well as the works of [Shpilka, STOC 2007] who provided a reconstruction algorithms for ΣΠΣ(2) circuits in the setting of small finite fields, and [Karnin-Shpilka, CCC 2009] who provided reconstruction algorithms for ΣΠΣ(k) circuits in the setting of small finite fields.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".