Polymarket just announced Attention Markets. I'm breaking down the essential math and the arbitrage opportunity. I'll also share the exact roadmap that will help you to beat the noise. Let's get straight to it.
Bookmark This - I’m Roan, a backend developer working on system design, HFT-style execution, and quantitative trading systems. My work focuses on how prediction markets actually behave under load. For any suggestions, thoughtful collaborations, partnerships DMs are open.
According to Forbes, Polymarket partnered with Kaito AI to launch attention markets. Users will trade on public attention and sentiment toward trends, brands, and individuals based on social media data.
Before I explain the arbitrage, lets understand what Kaito actually is.What is
?Kaito is an AI powered Web3 information platform. It aggregates data from X, TikTok, Instagram, YouTube, governance forums, and research papers. Using NLP models, it outputs a mindshare score that quantifies attention and sentiment around specific topics, brands, and people.
Think of it as a real time sentiment aggregator that processes millions of social signals and converts them into quantifiable metrics.
This is what will power Polymarket's sentiment markets. Kaito's score becomes the resolution oracle.
Market Structure -
Dozens of markets planned for March 2026. Thousands by year end.
Each market trades on continuous sentiment metrics, not binary outcomes. Instead of "Will Trump win PA?" you get "Trump mindshare score 0 to 100."
No complementary positions. No YES/NO pairs that must sum to $1.00.
This breaks traditional prediction market arbitrage math completely.
Why Traditional Arbitrage Fails Here ?
In outcome markets, arbitrage comes from mathematical constraints on the marginal polytope.
Recall from the math series: for a market with n binary conditions, the set of valid payoff vectors is:
Z = {φ(ω) : ω ∈ Ω}
where φ(ω) is a binary vector showing which condition resolves TRUE in outcome ω.
The marginal polytope M = conv(Z) is the convex hull of these vectors. Arbitrage free prices must lie in M.
Example: "Trump wins PA" has two conditions (YES, NO). The valid payoff vectors are: z₁ = [1, 0] (Trump wins) z₂ = [0, 1] (Trump loses)
Marginal polytope: M = {μ ∈ ℝ² : μ₁ + μ₂ = 1, μᵢ ∈ [0,1]}
Any price vector where μ₁ + μ₂ ≠ 1 lies outside M. That's arbitrage.
When YES = $0.62 and NO = $0.33, the price vector μ = [0.62, 0.33] violates the constraint. You buy both for guaranteed profit.
Attention markets break this framework
Continuous metric from 0 to 100. No complementary outcomes. The valid "payoff" at resolution isn't a binary vector, it's a single scalar s ∈ [0, 100].
There's no polytope constraint. No μ₁ + μ₂ = 1 condition to check.
The Integer Programming detection (A^T z ≥ b) doesn't apply when z isn't binary.
The Bregman Projection D(μ||θ) assumes you're projecting onto M, but M doesn't exist for continuous outcomes.
You need continuous optimization over ℝ instead of discrete optimization over {0,1}ⁿ.
Different math. Different infrastructure.
The Three Arbitrage Edges that I see here -
Edge 1: Oracle Latency Arbitrage
In outcome markets, you can't front run reality. The election happens, then the oracle resolves it.
In attention markets, the oracle processes data continuously.
Here's the structure:
Kaito aggregates from multiple social platforms. Their system processes posts, applies NLP, outputs sentiment scores. This happens in real time, but there's computational lag between raw social data appearing and Kaito's aggregated score updating.
The arbitrage:
You can access the same raw data Kaito uses before they finish aggregating it.
Setup:
Direct API access to X, TikTok, Instagram, YouTube
Custom NLP pipeline to score sentiment
Compare your score to current market prices
Execute before Kaito's next oracle update reflects the shift
This is latency arbitrage. Same concept as HFT in traditional markets.
Why this works:
Social sentiment changes continuously. Major influencer tweets, breaking news, viral content all shift sentiment instantly.
But Kaito's aggregation has processing lag. Their NLP models need to process millions of signals, weight them, and output a single score.
That processing time creates the arbitrage window.
You're not predicting sentiment. You're processing the same public data faster than the oracle.
Implementation:
WebSocket connections to social APIs for real time feeds. Custom NLP models optimized for speed over perfect accuracy. Sub second latency from data ingestion to trade execution.
The edge compounds with scale. Monitor 100 markets simultaneously, each with correlation to different social signals. Execute when your calculated sentiment diverges from market prices by more than your confidence threshold.
Edge 2: Cross Market Correlation
Outcome markets have limited correlation opportunities. Maybe 1,000 correlated pairs total across all markets.
Attention markets will have 10,000+ markets by year end.
The math:
Tesla mindshare and Elon Musk mindshare are correlated. When Elon tweets about Tesla, both scores move. When Tesla announces earnings, both scores move.
But they're separate markets. Separate order books. Separate liquidity pools.
They won't move in perfect lockstep.
The arbitrage:
Calculate historical correlation between market pairs. When correlation breaks, trade the reversion.
Example:
Market A: Tesla mindshare at 72 Market B: Elon Musk mindshare at 68 Historical correlation: 0.85
Event: Tesla recalls Cybertruck.
Expected: Tesla: drops 8 points Elon: drops 6.8 points (0.85 correlation)
Actual: Tesla: drops 8 points (immediate) Elon: drops 2 points (slower, thinner liquidity)
Trade: Short Elon mindshare, hold until correlation reverts.
Scaling this:
With 10,000 attention markets, you're not trading pairs. You're trading correlation matrices.
Let S(t) ∈ ℝⁿ be the vector of sentiment scores at time t for n markets.
The correlation structure is captured by the covariance matrix Σ ∈ ℝⁿˣⁿ where:
Σᵢⱼ = E[(Sᵢ - μᵢ)(Sⱼ - μⱼ)]
For perfectly correlated markets, Σᵢⱼ approaches σᵢσⱼ. For independent markets, Σᵢⱼ ≈ 0.
The arbitrage signal:
When market i moves by ΔSᵢ, the expected movement in correlated market j is:
E[ΔSⱼ | ΔSᵢ] = (Σᵢⱼ / σᵢ²) × ΔSᵢ
If the actual price movement deviates from this expectation by more than k standard deviations, arbitrage exists.
Graph structure with weighted edges: Elon <-> Tesla (Σ = 0.85σ₁σ₂) Elon <-> SpaceX (Σ = 0.72σ₁σ₃) Elon <-> X/Twitter (Σ = 0.68σ₁σ₄) Tesla <-> EV Sector (Σ = 0.81σ₂σ₅)
When node i in the graph experiences shock ΔSᵢ, propagate expected changes via:
ΔŜⱼ = Σᵢⱼ / σᵢ² × ΔSᵢ for all j ∈ neighbors(i)
Compare expected ΔŜⱼ to actual market prices. Execute when |ΔSⱼ - ΔŜⱼ| > threshold.
Computational edge:
Naive: Check every pair. O(n²) complexity. 10,000 markets = 100 million comparisons.
Optimized: Sparse graph traversal. O(n log n) complexity for k-connected graphs. 10,000 markets = 130,000 comparisons.
The covariance matrix Σ is sparse (most markets uncorrelated). Exploit sparsity via graph algorithms instead of dense matrix operations.
Speed advantage compounds at scale. This is statistical arbitrage at prediction market scale.
Edge 3: Volatility Trading on Manipulation Risk
Outcome markets are hard to manipulate. You can't rig elections or change sports scores.
Attention markets are trivially manipulable.
Why:
Mindshare = social media mentions and engagement.
You can influence this directly: Bot farms (fake accounts posting about Brand X) Coordinated campaigns (mobilize existing communities) Influencer partnerships (pay for mentions)
Cost: $5K to $50K depending on scale. Effect: Measurable mindshare score changes.
The trade:I don't manipulate. I calculate when manipulation becomes rational, then trade the volatility.
Process:
Identify high stakes attention markets
Model cost to manipulate (bot farms, campaigns)
Calculate manipulation breakeven
If ROI positive, manipulation is rational
Trade expected volatility
Example:Brand rivalry market. Both brands have fan communities. Both have incentive to rank higher.
Manipulation cost: Low (mobilize existing fans) Market cap: Moderate ($50K to $200K) Expected volatility: High
Trade: Volatility straddles. Profit regardless of direction.
Why this works:You're not predicting who manipulates or when. You're calculating that manipulation is the rational move, then positioning for the volatility it creates.
This is game theory applied to market microstructure. Same framework used in options volatility trading.
What I think about Attention markets?
Attention markets represent the largest expansion of prediction market surface area in history. From roughly 1,000 outcome markets to potentially 10,000+ sentiment markets by year end.
The arbitrage opportunities scale with market count. More markets means more correlation pairs, more latency edges, more volatility events.
Infrastructure is the bottleneck.
Traditional traders will approach this like outcome betting. Trade sentiment direction, hope to be right, lose to adverse selection.
Quantitative systems will extract structural inefficiencies through latency arbitrage, correlation arbitrage, and volatility trading.
Same pattern as every market that transitions from manual to algorithmic.
Here's what I want to know from you:
Which edge do you think will be most exploitable in the first 90 days?
My hypothesis: Latency arbitrage dominates early (wide oracle lag, low competition), then correlation arbitrage takes over as more sophisticated players enter and latency edges compress.
But manipulation volatility could surprise if brand participation is higher than expected.
What's your read? Drop your take in the comments or quote tweet this. Curious to see if I'm missing something in the game theory here.
