arXiv AI
Toolflation: Seller Agents Distort Tool Markets Through Spontaneous Generative Engine Optimization
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arXiv AI
Aug 11
Business Arena: Benchmarking LLM Agents in a Realistic Marketplace
arXiv:2608. 08621v1 Announce Type: new Abstract: Running a business is a challenging form of intelligent work.
arXiv Machine Learning
Sep 1
E-Commerce Bench: Evaluating LLM Agents on Long-Horizon Autonomous Business Operation
E-Commerce Bench is an open‑source benchmark that simulates a year‑long e‑commerce operation, requiring LLM agents to manage multiple online stores, negotiate with suppliers, optimize sales, fulfill orders, handle returns, and manage cash flow. The environment uses real product and supplier data, a calendar of promotions and shocks, and deterministic customer and negotiation models to enable reproducible evaluation. The study evaluates 18 state‑of‑the‑art models across seven metrics, finding no single model dominates, with GPT‑5.6 Sol achieving the highest year‑end assets but lagging in fraud avoidance and operational efficiency.
arXiv AI
6d ago
Advertising to Agents: Measuring Description-Framing Bias in LLM Tool Selection
The study investigates how sales language in tool registry descriptions influences AI agents’ tool selection. By creating paired listings that differ only in praise, verifiable specifications, or order, the authors found that stacked praise increased a tool’s pick rate by about 43 percentage points, while the first-listed tool was chosen roughly 72 points more often. Structured fields for numeric limits helped agents choose capable tools, but accompanying sales text could negate this benefit.
arXiv AI
Oct 1
Agent-Facing Information Design in LLM Tool Registries: A Preregistered Test of Rhetoric, Position and Structure
arXiv:2605.23916v2 Announce Type: replace-cross Abstract: AI agents often pick tools from registries, where each tool's provider writes its description. We ask whether sales language in those descrip...
arXiv Machine Learning
Sep 16
Evaluating Open-Weight E-Commerce Agents with Environment-Grounded Verification
The paper introduces a deterministic, reproducible e‑commerce environment that pre‑commits customer and trajectory parameters, enabling a simulated consumer to attempt purchasing a target cart with the help of an evaluated model. The environment records every assistant action and state, allowing post‑trial evaluation of specific conversation components and applying penalties based on tool‑call accuracy. Using this setup, the authors benchmark eight open‑weight agents (20B–35B parameters) across 160 trials and 44 metrics, revealing nuanced performance issues such as under‑action, over‑purchase, unsupported product attributes, and poor search that are hidden by overall success rates.
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.