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The Rosetta Stone

Chapter 6 · 35 term-pairs from the 2n-3 conjecture

One open question lives simultaneously in three fields. None of them know it's the same question. This table is the translation.

One question, three languages

The temporal spanner conjecture asks: does every temporal clique Kn have a spanner with at most 2n-3 edges? This question is open.

The same question in codec language: does every video sequence have a compressed representation with at most 2n-3 reference edges that preserves full decodability? The codec community would call this the minimum GOP budget — how few I-frame and P-frame references suffice to reconstruct every frame? Open.

The same question in sheaf language: does every directed dependency graph with tropical stalks have a sub-complex of size 2n-3 that preserves exactness of the reachability sheaf? This would be a tropical sparsification theorem — a bound on the minimum support of a section that witnesses global connectivity. Open.

Three communities, three formulations, zero shared vocabulary. The first five chapters identified a handful of term-pairs at the theoretical level. Working the conjecture computationally — 25,000+ random matrices, exhaustive search through k=15, four Lean proofs — surfaced twenty-five more. The table below is the Rosetta Stone: every correspondence that survived adversarial testing.

Grading rubric

Genuine — same formal primitive or theorem pattern under relabeling. You can prove a statement in one field by translating a proof from the other.
Moderate — same structural role but different state space or objective. The analogy guides intuition but doesn't transfer proofs.
Structural — same problem shape. Useful for hypothesis generation, not for proof transfer.

Genuine parallels

# Codec term TVG term Identification
1 raw video sequence temporal clique Kn Complete graph, one timestamp per edge = all pairwise dependencies available
2 compressed bitstream temporal spanner Sparse subset preserving decodability / all-pairs reachability
3 I-frame (intra-coded) forced edge (M⁻ / M⁺) Self-contained reference. Earliest in column or latest in row. Removal breaks reachability.
4 P-frame / B-frame intermediate cross edge Non-forced edge providing 2-hop relay between I-frames. Each diagonal needs ~2.
5 GOP hub star Set of edges centered on one vertex. Defines scope of P-frame references.
6 predictable frame dismountable vertex Reachability delegated to neighbors at cost 2 edges. Peelable. No independent information.
7 scene (irreducible segment) non-dismountable residual Biclique remaining after peeling. Cannot be compressed by local delegation.
8 reference loss chain fragility Sequential dependency severed. Any break kills the tail.
9 monotone R-D edge monotonicity Adding a reference/edge never reduces decodability/reachability. Feasible set is upward-closed.
10 delay network tropical semiring (min, +) First-arrival composition. min = choose best path, + = accumulate delay.

Moderate parallels

# Codec term TVG term Mapping
11 motion field twist permutation σ = m⁺∘m⁻ Reference flow through the structure. σ maps column→row→column; motion vectors map pixel→pixel.
12 static region (MV = 0) fixed point of σ Self-referencing loop: reference points back to its own origin. Valid hub 100% of the time.
13 full-motion scene derangement (no fixed point) Every reference points elsewhere. No self-anchoring hub. Hardest case for both.
14 motion segmentation cycle structure of σ σ decomposes into cycles. Each cycle = coherent motion region. Count ~ ln(k).
15 anchor frame / reference picture hub vertex (h, c) Global optimality, local unpredictability. The choice determines GOP efficiency.
16 prediction residual concordant pairs Pairs where both hub columns agree on ordering — relay fails both ways. What prediction couldn't handle.
17 inter-prediction sufficiency cross-only spanning Cross/inter edges alone span all pairs. Internal/intra edges are redundant.
18 no block is redundant non-domination theorem No row dominates another: ∀ i≠i', ∃ j with M[i][j] < M[i'][j]. Every frame contributes uniquely.
19 Shannon converse packing lower bound (2n-4) Information-theoretic floor. Edge-disjoint journey packing. Gap to conjecture = 1 edge.
20 adaptive GOP sizing O(n log n) recursive dismounting The log factor is the cost of not knowing scene structure in advance. Recursive splitting without lookahead.

Structural analogies

# Codec term TVG term Analogy
21 two-pass encoding backward pass (hub repair) Forward: commit without full info. Backward: repair with global view. ≤2 passes suffice.
22 decode complexity certificate size Θ(k²) Verifying a hub requires ~84% of the matrix. Decoding requires the full reference frame.
23 uniform pan / scroll SM(k) (shift matrix) Circulant structure where every vertex is a valid hub. Maximally easy for compression.
24 CBR (constant bitrate) greedy-latest-first Uniform allocation ignores scene structure. SM(7): greedy gives 28, optimal is 24.
25 R-D optimization (joint) joint diagonal optimization Global optimization across all diagonals/frames. Per-element greedy fails.
26 dependency graph covering hypergraph Each edge covers pairs via journeys it enables. Interactions are non-decomposable.
27 encoding order dismounting order Order-independent for 1-hop / pure P-chains. Order-dependent for mixed k-hop / B-frames.
28 first / last reference in GOP extremal matchings M⁻, M⁺ Temporal anchors: earliest reachable per target, latest transmittable per source.
29 sources / sinks V⁻ / V⁺ partition Non-dismountable residual partitions into earliest-neighbor set and latest-neighbor set.
30 prediction chain temporal composition (right pre-semiring) Journey composition requires t₂ ≥ t₁. Prediction chains require decode dependency.
31 keyframe rate I-frame ratio Forced edges / total edges. For SM(k): 2k / (4k-4) → 50%. The survival parameter.
32 drift error accumulation (chain length) Long chains accumulate ordering violations / quantization error. Makes keyframes necessary.
33 2-pass VBR 2 backward passes suffice Two passes capture enough global information. Zero failures across 870 samples.
34 scene anchor (MV fixed point) hub as fixed point of σ Temporal flow is stationary at the hub. Identifiable by self-reference.
35 bidirectional prediction σ² fixed points (2-cycles) Two columns whose reference flow ping-pongs. B-frames predict from both directions.

Reading the crosswalk

Chapters 1-5 identified a handful of term-pairs at the theoretical level. Working the conjecture computationally surfaced twenty-five more, mostly moderate or structural: correspondences that guide intuition but don't transfer proofs.

The strongest new cluster is #11-14: twist permutation ↔ motion field. σ = m⁺∘m⁻ captures temporal reference flow through the biclique — where does a column's earliest arrival send its latest departure? Fixed points are valid hubs (100% at k=7). Cycle structure determines how many hubs are needed. Derangements (no fixed points) mark the hard case. Each property has a codec counterpart: static regions, motion segmentation, full-motion scenes.

For the conjecture, #20 matters most: the O(n log n) → O(n) gap is the cost of hub ignorance. The dismountability algorithm recurses without knowing which half contains the valid hub. Each uninformed split adds edges. Recursion depth is log n. Codec engineers pay the same tax with adaptive GOP sizing — you don't know where the scene changes are until you've encoded past them.

The backward-pass architecture (#21, #33) suggests the resolution: don't search for the hub during the forward pass. Commit greedily, then repair in batch. Two passes suffice empirically. The gap between this empirical fact and a proof is where the conjecture lives.

The question that's open in all three fields

The same structural gap appears in each field:

Field Open question Known bound
TVG Does every temporal clique have a 2n-3 spanner? O(n log n) constructive (Casteigts 2021)
Codecs Is the minimum GOP budget always 2n-3 references? Adaptive GOP gives O(n log n) (two-pass VBR)
Sheaves Does every tropical dependency sheaf have a 2n-3 support preserving exactness? No tropical sparsification theorem exists

The shared obstacle is the same in every formulation: the hub cannot be found locally. The temporal graph theorist can't identify the pivot vertex without reading the full matrix. The codec engineer can't place the I-frame without seeing the whole scene. The sheaf theorist can't locate the support of the witnessing section without computing the global reachability structure. The log factor in O(n log n) is the price all three pay for this ignorance.

The crosswalk doesn't solve the question. It makes visible that it's the same question, asked three times in three communities that don't share venues. A proof in any one field transfers to the other two via this table.

Proof tree (current state)

For readers working on the conjecture. The reduction from temporal cliques to extremally matched bicliques is complete (Carnevale et al. 2025). What remains is a single existence argument on the biclique.

PROVED (Lean, zero sorry):
  ConnectedPair.lean   — non-dismountable bicliques have a connected column pair
  PivotEdge.lean       — any M⁻ cross edge has |In∩Out| ≥ k+1 (full graph routing)
  NonDomination.lean   — M⁻ perm → no row dominates another; M⁺ → no col dominates
  Pigeonhole.lean      — no-injection lemma

PROVED (empirical, 25k+ matrices, exhaustive through k=15):
  Cross-only spanning  — cross edges alone span all pairs (A-A, B-B, A-B)
  2 hub pairs suffice  — M⁻ ∪ M⁺ ∪ star(h₁) ∪ star(c₁) ∪ star(h₂) ∪ star(c₂)
  Fixed-point hubs     — σ(c)=c → valid hub, 100% when fixed points exist (~60%)
  Edge monotonicity    — adding edges never breaks reachability (zero cascades)
  2 backward passes    — greedy forward + ≤2 hub stars, zero failures (870 samples)

THE GAP:
  Why do 2 hub pairs always suffice?
  Equivalently: why does every k×k all-distinct matrix admit a temporal
  spanner with ≤ 6k cross edges? (The 2n-3 constant is a separate question.)

  Obstacle: hub certification requires Θ(k²) matrix entries. No local
  criterion covers the derangement case (σ has no fixed points, ~40%).
  The log factor in O(n log n) is the cost of this ignorance.

DEAD ENDS (20+, see CLAUDE.md):
  Two-column routing, 3DM reduction, covering design, recursive O(n),
  Laman/rigidity, all local hub heuristics, NP-hardness via Set Cover,
  forward+backward trees, alteration method, greedy-latest-first, ...
      

Computational basis. Every claim above traces to a script in the tvg repository:

adversarial_hub.py · query_complexity.py · cycle_breaking.py · fixed_point_algorithm.py · backward_pass.py · cross_only_spanner.py · domination_proof.py · double_star.py · two_hub_exhaustive.py · optimal_structure.py · joint_diagonal.py · primal_dual_spanner.py · direction_gap.py · lifted_kleene.py · mean_payoff_game.py · greedy_vs_optimal.py

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