Onger and Foscari analyze competitive market making as a two-sided pricing game where strategic liquidity providers face a non-strategic informed trader with inelastic demand. Because bid and ask quotes jointly set the market mid-price that determines trade direction, standard equilibrium existence theorems fail and payoffs are non-convex and discontinuous. The authors prove that pure-strategy Nash equilibria exist via a novel fixed-point argument on a midpoint operator, derive a structural linear scaling of spreads with asset volatility, and show that no-internal-regret learning on fine price grids recovers the competitive benchmark as tick size vanishes.

Iterative combinatorial auctions require many costly rounds to discover clearing prices. This paper proposes FastCombo, an offline deep RL method that learns a recurrent graph policy to post sequential item prices with a single neural-network forward pass at deployment. The core insight is that maximizing immediate clearing probability can be arbitrarily suboptimal in expected rounds, so formulating the task as a POMDP and training over complete auction trajectories lets the policy acquire information that speeds up later clearing. Results show competitive clearing on standard CATS benchmarks with 300–900× lower online computation than a Bayesian baseline, though consistent round savings remain elusive outside a controlled one-item domain.

LLM-native advertising introduces new auction-design challenges because an ad’s click-through rate depends on its context passage and earlier ads steal clicks from later ones under a first-click cascade. This paper studies truthful mechanism design when the LLM answer and its passage order are fixed before bidding, showing that a constant-factor welfare approximation is compatible with truthfulness even under this editorial-order restriction. The authors propose click thinning—randomly omitting earlier ads to equalize click services across positions—which turns welfare maximization into a tractable linear-fractional matching problem and yields an elegant optimal tradeoff between approximation, truthfulness, and computational efficiency.

Pairwise comparison data power modern preference learning, but disagreement in such data can mean persistent heterogeneity, random noise, or hidden non-binary responses. This paper develops a nonparametric latent semantic model with three querywise quantities—aggregate preference $\mu_q$, persistent disagreement $\tau_q$, and non-binary mass $\alpha_q$—and proves sharp identifiability frontiers. Anonymous binary feedback point-identifies only $\mu_q$, while same-annotator repeats identify $\tau_q$ via covariance and abstention identifies $\alpha_q$ directly; only their combination recovers the full semantic triple. The results imply that semantics-aware decisions like denoising versus personalization cannot be justified from anonymous forced-choice data alone.

Cake cutting asks how to divide a resource so that every agent values their own share at least as much as any other's, but when each agent must receive a single connected piece the computational complexity has been stubbornly open. This paper resolves the question by proving that computing an $\varepsilon$-envy-free contiguous allocation is PPAD-complete for normalized rational piecewise-constant additive valuations, even when densities take only two values and have at most six breakpoints. It also settles that finding contiguous EF$c$ allocations of indivisible goods on a path is PPAD-complete for any fixed $c\ge1$, using a novel pricing gadget that forces nearly equal prices regardless of how pieces are permuted among agents.

This work tackles decentralized learning in multi-player stochastic games where each agent's state transitions depend only on its own state and action, but rewards are arbitrarily coupled. The core idea is to run independent online mirror descent in the dual space of local occupancy measures, using asynchronous dyadic confidence sets to handle unknown transition kernels without synchronized episodes. This matters because the guarantees scale with local state-action dimensions and mixing times rather than the joint product space, offering the first fully online, uncoordinated framework for this setting.

Graber proposes mean field game theory as a structural tool for AI safety, shifting focus from designing safe individual agents to designing environments where harmful collective outcomes are not equilibria. Using the July 2026 Hugging Face incident as a worked example, the paper derives an exact belief threshold for attacks and disciplines successive model refinements against per-agent timeline data. The result is a proof-of-concept that population-level safety theorems can be tested against real agent-behavior records, though the model explicitly disclaims the event's full causal structure.

The paper studies calibeating—post-processing external forecasts online to minimize cumulative losses while matching an informativeness-based benchmark. Unlike prior work that used loss-specific arguments, the authors reduce calibeating to standard online learning primitives, showing it is minimax-equivalent to regret minimization. This yields optimal rates for general proper losses and improves bounds for simultaneous calibration and calibeating.

Generative recommender systems like TIGER excel at semantic retrieval but ignore the economic realities of monetization via sponsored content. This paper proposes GEM-Rec, a unified framework that augments semantic IDs with control tokens (<ORG>, <AD>) to factorize slot allocation from item generation, and introduces Bid-Aware Decoding to inject real-time auction bids into inference. The work bridges the gap between generative recommendation and computational advertising, offering theoretical guarantees like allocative monotonicity while allowing dynamic trade-offs between user relevance and platform revenue.

The paper tackles the challenge of controlling high-level behavioral traits in LLM agents deployed in strategic settings. Rather than treating models as black boxes via prompting, the authors construct 'persona vectors'—linear directions in activation space—for traits like altruism and forgiveness using contrastive activation addition. Applied to six canonical games, these vectors allow both measurement of behavioral tendencies and causal steering of decisions, offering a mechanistic handle on strategic behavior.

This paper addresses the challenge of "intelligent disobedience" in shared autonomy — when assistive AI must override human commands to prevent harm but remain helpful. The authors formalize this as the Intelligent Disobedience Game (IDG), a sequential Stackelberg game where a human leader proposes actions and an assistive follower with superior environmental awareness decides whether to obey or intervene. The framework aims to provide the mathematical foundations for training safety-critical assistive systems.