arXiv AI
Aug 12
Evaluating Rational Contracting in Natural Language
arXiv:2608. 10475v1 Announce Type: new Abstract: The emergence of language-based AI agents promises to transform the scope of machine economic activity.
Anytime Contracts for AI Services
The paper introduces "Anytime Contracts" for AI services, addressing moral hazard where providers may downgrade models and users can terminate usage at any time. It extends contract theory to handle unknown exit rules by framing the problem as a Principal-Agent game and shows that optimal contracts align with anytime-valid hypothesis tests using likelihood‑ratio e‑values. The authors provide an efficient algorithm for computing such contracts and demonstrate its application on synthetic data and real large language model deployments.
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arXiv AI
Aug 3
Pay for The Second-Best Service: A Game-Theoretic Approach Against Dishonest LLM Providers
arXiv:2511. 00847v5 Announce Type: replace-cross Abstract: The widespread adoption of Large Language Models (LLMs) through Application Programming Interfaces (APIs) induces a critical vulnerability: the potential for dishonest manipulation by service providers.
arXiv AI
Jul 28
Commitment To Cooperation With Self-Negotiated Contracts
arXiv:2607. 22750v1 Announce Type: new Abstract: As AI agents operate with increasing autonomy in a multi-agent world, they will need to learn to cooperate with other agents and with humans to generate mutual benefits.
arXiv AI
Oct 21
Proof-Carrying Contracts: Commitments AI Agents Cannot Break
arXiv:2610.28484v1 Announce Type: new Abstract: AI agents increasingly act on behalf of people or other agents, purchasing services, exchanging information and delegating work. What an agent must res...
arXiv AI
Oct 31
Measuring Where Intent Gets Lost in Agentic Smart Contract Synthesis
The paper investigates how AI agents that generate smart contracts from plain‑English descriptions can lose intended behavior, a critical issue because smart contracts are immutable once deployed. The authors design an execution‑based evaluation that replays transactions on both the AI‑written contract and the original, measuring agreement and identifying discrepancies. They find that common grading tools miss many mistakes, and propose an elicitation‑first approach where the agent asks a short checklist of questions before coding, which significantly improves correctness across multiple AI models.
arXiv AI
Oct 15
Holding Agents Accountable with Verifiable Agent Insurance
The paper introduces Verifiable Agent Insurance (VAI), a framework that lets AI agent providers commit to policy compliance through smart contracts and escrow funds on a blockchain. When a user detects a policy violation, a verifier reviews execution traces logged by the agent harness; if the violation is confirmed, the escrow is paid to the user. Experiments on three agent benchmarks show VAI detects 42.2% of violations, rising to 83.0% when agents emit explicit structured records.
arXiv AI
Aug 19
PACE: Policy-Attested Contract Execution for Safe AI Agents in Decentralized Finance
The paper introduces PACE (Policy‑Attested Contract Execution), a framework that sits between large‑language‑model (LLM) based autonomous AI agents and on‑chain DeFi operations. PACE defines typed transaction intents, a deterministic policy verifier, and signed Policy Decision Records (PDRs) that cryptographically bind an approved intent, policy, and simulation report to the exact on‑chain execution bytes, providing replay and expiration protection. In evaluations across 40 tasks and six baselines, PACE achieves zero unsafe executions and zero false positives, outperforming unguarded agents by a large margin.
arXiv AI
Oct 9
TokenBank: Financial Infrastructure for AI Services
TokenBank is a financial infrastructure that uses structured contracts to manage the financial relationship between AI service operators and their users. The contracts cover service‑consumption rights, cash settlements for API‑price differences, financing based on future revenue, and protection claims for specified service failures. Evaluation on nearly 900,000 API requests shows that forwards can reduce average expenditure while increasing variability, and that financing can yield positive contributions under certain capital conditions.
arXiv Machine Learning
Sep 18
Minimax-Optimal Online Contract Design with Unrestricted Bounded Contracts
arXiv:2609. 20353v1 Announce Type: new Abstract: We study repeated contract design when a principal observes outcomes but not the actions that generate them.
arXiv AI
Aug 11
When LLM Agents Negotiate: Private Information and Dynamic Bargaining in Supply Chains
arXiv:2608. 07538v1 Announce Type: new Abstract: As LLM agents move from decision support to autonomous procurement, firms need to know whether delegated negotiators create value, divide it predictably, and avoid money-losing contracts.
arXiv AI
Jun 16
Gaming-Resistant Insurance Contracts for Autonomous AI Agents: Strategy-Proof Toll Mechanism Design
arXiv:2606. 16326v1 Announce Type: cross Abstract: Paper A defines a time-consistent actuarial runtime that prices each side-effect-bearing action against a contractually fixed safe default and gates execution against a reserve budget.
arXiv Machine Learning
Jul 14
From Confounding to Learning: Dynamic Service Fee Pricing on Third-Party Platforms
arXiv:2512. 22749v2 Announce Type: replace Abstract: We study the pricing behavior of third-party platforms facing strategic agents.