Abstract
We study the optimal dynamic pricing of an expiring ticket or voucher, sold by a time-sensitive seller to strategic buyers who arrive stochastically with private values. The expiring nature creates a conflict: the seller's urgency to sell before expiration drives price reductions, which in turn incentivize buyers to wait. We seek the seller's optimal pricing policy that resolves this tension. The main analytical challenge is that buyer type is two-dimensional (valuation and arrival time), which makes equilibrium intractable under general strategies. To address this, we introduce the Value-Based Threshold (VBT) strategy, a tractable framework that decouples these two dimensions. Using this framework, we prove equilibrium existence via an ordinary differential equation and provide a constructive procedure for its characterization. We then derive near-optimal pricing policies for two stylized regimes: a constant price in thin markets and a linear discount in thick markets. Numerical frontier analysis confirms these benchmarks and shows how optimal policy adapts as the seller's time sensitivity changes. Our findings clarify the conflict between quick sales and strategic waiting. Sellers facing thick markets or high time sensitivity benefit from linear discounts, while in thin markets a constant price neutralizes buyers' incentive to wait. We also show this simple policy remains robust across broad conditions. For patient sellers, a quasi-auction schedule that maintains a high price until a sharp final drop is most effective in aggregating demand.

Abstract
This paper studies how visual traits and market cycles shape prices in NFT markets. Using 94,039 transactions from 26 major generative Ethereum collections, the analysis extracts 196 machine-quantified image features (covering color, composition, palette structure, geometry, texture, and deep learning embeddings), then applies a three-stage filter process to identify stable predictors for hedonic regression. A static mixed-effects model shows that market sentiment and transparent, interpretable image traits have significant and independent pricing power: higher focal saturation, compositional concentration, and curvature are rewarded, while clutter, heavy line work, and dispersed palettes are discounted; deep embeddings add limited incremental value conditional on explicit traits. To assess state dependence, the study estimates a Bayesian dynamic mixed-effects panel with cycle effects and time-varying coefficients for a salient image attribute (Composition Focus - Saturation). Collection-level heterogeneity ("brand premia") is absorbed by random effects. The time-varying coefficients exhibit regime sensitivity, with stronger premia in expansionary phases and weaker or negative loadings in downturns, while grand-mean effects remain small on average. Overall, NFT prices reflect both observable digital product characteristics and market regimes, and the framework offers a cycle-aware tool for asset pricing, platform strategy, and market design in digital art markets.

Abstract
The practice of fine-tuning AI agents on data from their own interactions--such as web browsing or tool use--, while being a strong general recipe for improving agentic capabilities, also introduces a critical security vulnerability within the AI supply chain. In this work, we show that adversaries can easily poison the data collection pipeline to embed hard-to-detect backdoors that are triggerred by specific target phrases, such that when the agent encounters these triggers, it performs an unsafe or malicious action. We formalize and validate three realistic threat models targeting different layers of the supply chain: 1) direct poisoning of fine-tuning data, where an attacker controls a fraction of the training traces; 2) environmental poisoning, where malicious instructions are injected into webpages scraped or tools called while creating training data; and 3) supply chain poisoning, where a pre-backdoored base model is fine-tuned on clean data to improve its agentic capabilities. Our results are stark: by poisoning as few as 2% of the collected traces, an attacker can embed a backdoor causing an agent to leak confidential user information with over 80% success when a specific trigger is present. This vulnerability holds across all three threat models. Furthermore, we demonstrate that prominent safeguards, including two guardrail models and one weight-based defense, fail to detect or prevent the malicious behavior. These findings highlight an urgent threat to agentic AI development and underscore the critical need for rigorous security vetting of data collection processes and end-to-end model supply chains.

Abstract
Large Language Models (LLMs) struggle with the complex, multi-modal, and network-native data underlying financial risk. Standard Retrieval-Augmented Generation (RAG) oversimplifies relationships, while specialist models are costly and static. We address this gap with an LLM-centric agent framework for supply chain risk analysis. Our core contribution is to exploit the inherent duality between networks and knowledge graphs (KG). We treat the supply chain network as a KG, allowing us to use structural network science principles for retrieval. A graph traverser, guided by network centrality scores, efficiently extracts the most economically salient risk paths. An agentic architecture orchestrates this graph retrieval alongside data from numerical factor tables and news streams. Crucially, it employs novel ``context shells'' -- descriptive templates that embed raw figures in natural language -- to make quantitative data fully intelligible to the LLM. This lightweight approach enables the model to generate concise, explainable, and context-rich risk narratives in real-time without costly fine-tuning or a dedicated graph database.

Abstract
Environments built for people are increasingly operated by a new class of economic actors: LLM-powered software agents making decisions on our behalf. These decisions range from our purchases to travel plans to medical treatment selection. Current evaluations of these agents largely focus on task competence, but we argue for a deeper assessment: how these agents choose when faced with realistic decisions. We introduce ABxLab, a framework for systematically probing agentic choice through controlled manipulations of option attributes and persuasive cues. We apply this to a realistic web-based shopping environment, where we vary prices, ratings, and psychological nudges, all of which are factors long known to shape human choice. We find that agent decisions shift predictably and substantially in response, revealing that agents are strongly biased choosers even without being subject to the cognitive constraints that shape human biases. This susceptibility reveals both risk and opportunity: risk, because agentic consumers may inherit and amplify human biases; opportunity, because consumer choice provides a powerful testbed for a behavioral science of AI agents, just as it has for the study of human behavior. We release our framework as an open benchmark for rigorous, scalable evaluation of agent decision-making.

AI Insights

• The study forces shoppers to pick between two items, enabling precise estimation of choice probabilities via Linear Probability Models with fixed effects.
• Price, rating, and nudge cues each independently raise selection odds, while wording differences reveal heterogeneity in nudge potency.
• Fixed‑effect controls for unobserved shopper heterogeneity, isolating the causal impact of manipulated attributes.
• LLM‑powered agents replicate human‑like bias patterns even without cognitive constraints, underscoring risk of bias amplification.
• The open‑source ABxLab benchmark invites replication and extension across diverse e‑commerce contexts, building on Thaler, Ariely, and Milkman’s work.

Abstract
We introduce GDPval, a benchmark evaluating AI model capabilities on real-world economically valuable tasks. GDPval covers the majority of U.S. Bureau of Labor Statistics Work Activities for 44 occupations across the top 9 sectors contributing to U.S. GDP (Gross Domestic Product). Tasks are constructed from the representative work of industry professionals with an average of 14 years of experience. We find that frontier model performance on GDPval is improving roughly linearly over time, and that the current best frontier models are approaching industry experts in deliverable quality. We analyze the potential for frontier models, when paired with human oversight, to perform GDPval tasks cheaper and faster than unaided experts. We also demonstrate that increased reasoning effort, increased task context, and increased scaffolding improves model performance on GDPval. Finally, we open-source a gold subset of 220 tasks and provide a public automated grading service at evals.openai.com to facilitate future research in understanding real-world model capabilities.

Abstract
We present CAIA, a benchmark exposing a critical blind spot in AI evaluation: the inability of state-of-the-art models to operate in adversarial, high-stakes environments where misinformation is weaponized and errors are irreversible. While existing benchmarks measure task completion in controlled settings, real-world deployment demands resilience against active deception. Using crypto markets as a testbed where $30 billion was lost to exploits in 2024, we evaluate 17 models on 178 time-anchored tasks requiring agents to distinguish truth from manipulation, navigate fragmented information landscapes, and make irreversible financial decisions under adversarial pressure. Our results reveal a fundamental capability gap: without tools, even frontier models achieve only 28% accuracy on tasks junior analysts routinely handle. Tool augmentation improves performance but plateaus at 67.4% versus 80% human baseline, despite unlimited access to professional resources. Most critically, we uncover a systematic tool selection catastrophe: models preferentially choose unreliable web search over authoritative data, falling for SEO-optimized misinformation and social media manipulation. This behavior persists even when correct answers are directly accessible through specialized tools, suggesting foundational limitations rather than knowledge gaps. We also find that Pass@k metrics mask dangerous trial-and-error behavior for autonomous deployment. The implications extend beyond crypto to any domain with active adversaries, e.g. cybersecurity, content moderation, etc. We release CAIA with contamination controls and continuous updates, establishing adversarial robustness as a necessary condition for trustworthy AI autonomy. The benchmark reveals that current models, despite impressive reasoning scores, remain fundamentally unprepared for environments where intelligence must survive active opposition.

Abstract
Black-box optimization (BBO) addresses problems where objectives are accessible only through costly queries without gradients or explicit structure. Classical derivative-free methods -- line search, direct search, and model-based solvers such as Bayesian optimization -- form the backbone of BBO, yet often struggle in high-dimensional, noisy, or mixed-integer settings. Recent advances use machine learning (ML) and reinforcement learning (RL) to enhance BBO: ML provides expressive surrogates, adaptive updates, meta-learning portfolios, and generative models, while RL enables dynamic operator configuration, robustness, and meta-optimization across tasks. This paper surveys these developments, covering representative algorithms such as NNs with the modular model-based optimization framework (mlrMBO), zeroth-order adaptive momentum methods (ZO-AdaMM), automated BBO (ABBO), distributed block-wise optimization (DiBB), partition-based Bayesian optimization (SPBOpt), the transformer-based optimizer (B2Opt), diffusion-model-based BBO, surrogate-assisted RL for differential evolution (Surr-RLDE), robust BBO (RBO), coordinate-ascent model-based optimization with relative entropy (CAS-MORE), log-barrier stochastic gradient descent (LB-SGD), policy improvement with black-box (PIBB), and offline Q-learning with Mamba backbones (Q-Mamba). We also review benchmark efforts such as the NeurIPS 2020 BBO Challenge and the MetaBox framework. Overall, we highlight how ML and RL transform classical inexact solvers into more scalable, robust, and adaptive frameworks for real-world optimization.

Abstract
Vaccines play a crucial role in the prevention and control of infectious diseases. However, the vaccine supply chain faces numerous challenges that hinder its efficiency. To address these challenges and enhance public health outcomes, many governments provide subsidies to support the vaccine supply chain. This study analyzes a government-subsidized, three-tier vaccine supply chain within a continuous-time differential game framework. The model incorporates dynamic system equations that account for both vaccine quality and manufacturer goodwill. The research explores the effectiveness and characteristics of different government subsidy strategies, considering factors such as price sensitivity, and provides actionable managerial insights. Key findings from the analysis and numerical simulations include the following: First, from a long-term perspective, proportional subsidies for technological investments emerge as a more strategic approach, in contrast to the short-term focus of volume-based subsidies. Second, when the public is highly sensitive to vaccine prices and individual vaccination benefits closely align with government objectives, a volume-based subsidy policy becomes preferable. Finally, the integration of blockchain technology positively impacts the vaccine supply chain, particularly by improving vaccine quality and enhancing the profitability of manufacturers in the later stages of production.

Abstract
This paper presents a practical architecture for after-sales demand forecasting and monitoring that unifies a revenue- and cluster-aware ensemble of statistical, machine-learning, and deep-learning models with a role-driven analytics layer for scorecards and trend diagnostics. The framework ingests exogenous signals (installed base, pricing, macro indicators, life cycle, seasonality) and treats COVID-19 as a distinct regime, producing country-part forecasts with calibrated intervals. A Pareto-aware segmentation forecasts high-revenue items individually and pools the long tail via clusters, while horizon-aware ensembling aligns weights with business-relevant losses (e.g., WMAPE). Beyond forecasts, a performance scorecard delivers decision-focused insights: accuracy within tolerance thresholds by revenue share and count, bias decomposition (over- vs under-forecast), geographic and product-family hotspots, and ranked root causes tied to high-impact part-country pairs. A trend module tracks trajectories of MAPE/WMAPE and bias across recent months, flags entities that are improving or deteriorating, detects change points aligned with known regimes, and attributes movements to lifecycle and seasonal factors. LLMs are embedded in the analytics layer to generate role-aware narratives and enforce reporting contracts. They standardize business definitions, automate quality checks and reconciliations, and translate quantitative results into concise, explainable summaries for planners and executives. The system exposes a reproducible workflow -- request specification, model execution, database-backed artifacts, and AI-generated narratives -- so planners can move from "How accurate are we now?" to "Where is accuracy heading and which levers should we pull?", closing the loop between forecasting, monitoring, and inventory decisions across more than 90 countries and about 6,000 parts.