The Semantic Routing

Beyond Simple Matching

Standard systems operate on string matching: if a user searches for "Bitcoin", they query markets labeled "Bitcoin". Fluidify operates on Semantic Equivalence.

Our architecture recognizes that liquidity is often fragmented across markets that describe the exact same reality using different verbiage. The Semantic Routing Engine treats the global prediction market ecosystem not as a list of isolated events, but as a directed graph of logical outcomes.

By identifying Isomorphic Markets events that are mathematically identical or perfectly inverse, Fluidify aggregates liquidity from sources that appear unrelated to the naked eye

$\text{Liquidity}_{total} = \sum_{i \in S} L_i + \sum_{j \in I} L_j(\neg P)$.

Where $S$ is the set of synonymous markets (A wins = A is Victorious) and $I$ is the set of inverse markets (A wins = B loses).

The Route Discovery Process

When a trade command is initiated, the engine executes a three-stage algorithmic traversal to construct the optimal execution path.

1. Equivalent Market Identification (EMI)

The engine scans the global state for logical adjacencies. It identifies two classes of liquidity:

• Direct Synonyms: "Will Lakers win?" (Polymarket) $\equiv$ "Lakers to beat Warriors" (Limitless).

• Inverse Correlates: "Will Biden win 2024?" (Venue A) $\leftrightarrow$ "Will Trump lose 2024?" (Venue B).

In a binary outcome system, buying "YES" on the first event is mathematically equivalent to buying "YES" on the second event (assuming a two-candidate race). Fluidify unifies these order books.

2. Path Mapping & Cost Analysis

Once the network of relevant markets is mapped, the engine constructs a set of candidate execution routes. Each route is assigned a Cost Score ($C$) based on a multivariate function:

$C_{route} = \Delta_{price} + \Phi_{gas} + \Psi_{risk} + \Omega_{slippage}$

The engine evaluates not just the raw odds, but the "Total Cost of Execution," factoring in the gas fees of cross-chain bridging and the liquidity depth at the specific trade size requested.

3. Convex Trade Splitting

Fluidify rarely executes a large order on a single path. To minimize market impact, the engine employs Smart Trade Splitting.

If a user wishes to deploy $10,000 on a specific outcome, the engine solves for the optimal distribution vector across the identified venues.

• $6,000 might be routed to Polymarket (Direct Market).

• $3,000 might be routed to Kalshi (Inverse Market).

• $1,000 might be routed to Limitless (Correlated Market).

This transforms a potential 4% slippage event on a single thin market into a <0.5% slippage execution across the aggregate ecosystem.

Deterministic Event Classes (Sports)

The architecture is particularly potent in Deterministic Event Classes, such as sports, where outcome logic is binary and standardized.

In these environments, the Semantic Router achieves maximum efficiency.

• Query: "Golden State Warriors Victory"

• Route A: "Warriors Win"

• Route B: "Opponent Loss"

• Route C: "Warriors Score > Opponent Score"

Because these outcomes resolve to the same truth value ($1$ or $0$) based on the same oracle data, Fluidify treats them as a singular liquidity pool. This allows the protocol to offer depth and odds that no single underlying bookmaker can match.