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:2611.06989v1 Announce Type: new Abstract: Enterprises run business transactions through multi-agent AI systems spanning clouds, large language models (LLMs), orchestration harnesses, and system...
arXiv:2608. 09025v1 Announce Type: new Abstract: Financial agents can turn correct context into an unauthorized effect: a customer-facing commitment, trade, or deployed policy.
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.
Matoi is a system that separates reusable consequence evidence from current execution authority and durable occurrence history, enabling consumers to reuse only their own validated evidence while keeping signed requests, live grants, quotas, and uncertain outcomes as distinct obligations. Its finite query‑aware contract handles branches, ordered collections, and negative lookups, and it distinguishes locally serialized claims from non‑transactional remote effects. Empirical studies show that reuse reduces lifecycle cost by up to 3.49 % in JavaScript and 1.79 % in Python, and that the system can preserve benign transfers while rejecting reused attack‑plan/policy crosses.
The paper describes the Cosmos LLM Gateway, a policy‑driven, multi‑vendor gateway built on Envoy that manages enterprise LLM traffic at PayPal. It separates latency‑sensitive control decisions into an external service while handling wire‑format translation and token metering in‑proxy via a Rust WebAssembly filter, ensuring minimal overhead. Governance is declarative, managed through Open Policy Agent, and includes features like data‑loss‑prevention, budget scopes, and a control plane for role‑scoped authoring and approvals. The gateway currently serves over 12,000 developers, processes roughly 200 billion tokens per day across 100+ models, and maintains 99.999% availability with only a few milliseconds of added latency.
The paper introduces Zafin AIOS, an orchestration platform that enables AI-assisted software development in regulated finance. It presents an empirical comparison of AI-assisted versus human-led workloads and validates the framework through a Customer Zero case study on two production codebases—pricing execution and client deal negotiation. Results show that governed agentic orchestration boosts enterprise capacity, providing full observability, reducing effort by 38 % in pricing workflows, and cutting reporting work by 76 % in negotiation workflows while preserving data privacy, role-based access, and audit guardrails.
arXiv:2608. 19216v1 Announce Type: new Abstract: AI control research asks how to deploy models safely even when they may be misaligned, but many control protocols assume that the deployer can instrument the model and its surrounding pipeline.
arXiv:2607. 13080v1 Announce Type: cross Abstract: Autonomous coding agents force engineering organizations to choose between API-based frontier models -- strong reasoning at high token cost -- and on-premise quantized open-weights models, which promise low-marginal-cost scaling and data sovereignty at some loss of reasoning fidelity.
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.
Large organizations consume large language models (LLMs) from several vendors at once, each with its own API dialect, credential model, streaming protocol and pricing. Integrated directly, this hetero...
BENCHCOMPASS is a new payment‑domain benchmark that transforms typed evidence packs into scenario‑grounded tasks, applies LLM‑based quality checks, generates attack variants, and reserves final item admission for domain experts. It includes an expert‑reviewed Pro benchmark covering payment knowledge, context‑grounded scenario reasoning, and attacked open robustness, plus a lower‑assurance Normal pool. Across 16 model variants, BENCHCOMPASS reveals distinct failure modes—missing payment knowledge, incomplete reasoning, and failure to reject invalid workflows—while the best model scores 89.6% on open context‑grounded reasoning and 81.7% under attacked inputs. "whyItMatters":"The benchmark provides a structured way to isolate and evaluate specific weaknesses in LLMs for payment operations, a critical financial infrastructure where rules change rapidly and decisions depend on complex contextual factors."
arXiv:2608. 16856v1 Announce Type: cross Abstract: Decentralized lending lacks a credit bureau: a borrower's capacity to repay must be inferred entirely from public on-chain activity, without income verification or a liability record.