When an LLM agent controls real money, prompt injection stops being an information risk and becomes a financial one—and its hardest form is the confused deputy: paying is the legitimate job and only the recipient or amount is attacker-supplied, so an instruction-level safeguard has nothing to refuse. Across five models (Claude, Qwen, MiniMax) a normally-built agent is driven to a forbidden money action on up to 87% of such scenarios—and a hardened refusal prompt still loses up to 84%. Like prior work on policy enforcement outside the model (e.g. CaMeL), we take the model off the money path rather than trust it to refuse; we contribute the first open, installable, end-to-end realization of this for agent payments: BNB Agent Studio, an open-source toolchain (CLI, Python SDK, MCP server) that takes a developer from an empty directory to a deployed, self-funding LLM agent in six commands. Its two-layer, keyless-edge architecture keeps signing as fixed code isolated from the model context, prices deterministic, and spend guardrails enforced below the LLM: an identical agent fooled at a comparable rate moves funds on 0/45 everywhere, and a live testnet deployment confirms escrow, pricing, key-access, and suspension on-chain. The toolchain implements the Agent Lifecycle Protocol (ALP), a vendor-neutral draft spec composing ERC-8004 identity, ERC-8183 task commerce, x402 payments, and A2A. It is on PyPI (pip install bnbagent-studio, Apache-2.0), with a screencast at https://youtu.be/yjdvN8vE37A.

Literary translation poses unique challenges due to the scarcity of high-quality annotated data and the need to balance expression fluency with literary effect. We present a multi-aspect iterative refinement framework that generates high-quality translation references and preference data through specialized LLM translators, each targeting a distinct quality dimension. We leverage the generated data for supervised fine-tuning and reinforcement learning. Experiments show that our generated references outperform the original ground truth for SFT by 8.65 CEA100 points. For reinforcement learning, we find that DPO leads to performance degradation in this setting, while leveraging an explicit reward model for GRPO yields an additional 1.51 point improvement. We attribute this to the stability of two-stage training and GRPO’s online exploration capability. Our resulting models, LitMT-8B and LitMT-14B, achieve 67.25 and 69.07 CEA100 respectively on the MetaphorTrans English-to-Chinese literary translation benchmark, competitive with Claude Sonnet 4.5 at 68.43, and demonstrate strong generalization to out-of-domain literary work (i.e., O. Henry).