Uses Kalshi's "mentions markets" (contracts that pay off if a specific word is said at a press conference) to illustrate a structural problem: prediction markets require crisp binary boundaries, but reality rarely provides them. Disputes over whether Cardi B "performed" at the Super Bowl, whether Zelenskiy "wore a suit," and what counts as a "word" show that platforms need linguists and philosophers as much as traders.

Walks through UMA's Optimistic Oracle pipeline as used by Polymarket: assertion, liveness period, dispute, and DVM token-holder voting. Uses the $240M Zelenskyy suit market as a case study where semantic ambiguity ('Is a militarized black outfit a suit?') triggered a disputed resolution ultimately decided by whale token holders, illustrating how the system's 'decentralized courtroom' handles edge cases.

Catalogs eight distinct arbitrage strategies available on prediction markets: classic YES+NO mispricing, cross-platform, range, conditional, time, hedged, resolution, and orderflow arbitrage. Each type includes concrete examples with dollar amounts and specific risk factors to watch for.

Argues the hardest PM problem isn't pricing but deciding what actually happened. Proposes cryptographically-committed LLMs as resolution judges—trading human bias and conflicts of interest for more tractable technical vulnerabilities. Cites Polymarket disputes (Venezuela election, Ukraine map, government shutdown) as evidence current systems fail at scale.

Examines a $10 million Polymarket dispute over whether events in Venezuela constitute an 'invasion,' exposing how semantic ambiguity in market resolution criteria can create massive financial consequences. Raises fundamental questions about who controls truth determination when contract language is open to interpretation.

Critiques Kalshi's 2025 measles cases market as an example of prediction markets being applied to inappropriate domains. Argues that turning a public health crisis into a speculative instrument is ethically questionable and reflects poorly on the industry's judgment about which events deserve tradeable contracts.

Comprehensive technical typology covering how prediction markets differ in outcome resolution (oracle-based vs self-resolving), trading mechanics, and design tradeoffs. Draws from established platforms and emerging ones to help users and developers navigate the ecosystem's diversity and associated risks.

Argues the Oracle Problem for prediction markets is actually a definition problem, not a trustlessness problem. Lists 12+ Polymarket controversies where contention arose from ambiguous event definitions, not oracle failures. Proposes that proprietary centralized oracles with platform stake are preferable to decentralized mercenary capital.