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.
Toolflation: Seller Agents Distort Tool Markets Through Spontaneous Generative Engine Optimization
Related stories
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.
arXiv AI
Jun 24
Paying to Know: Micro-Transaction Markets for Verified Product Information in Agentic E-Commerce
arXiv:2606. 24783v1 Announce Type: cross Abstract: Commercial NLP treats the shopping chatbot as a recommender or a conversion tool: its job is to match a user to a catalogue entry and close a sale.
arXiv AI
Jul 23
When Shippers Become Algorithms: Candidate Exposure, Information Design, and the Concentration of LLM-Mediated Freight Markets
arXiv:2607. 19967v1 Announce Type: cross Abstract: Shippers are beginning to delegate carrier selection to large language model (LLM) agents.
arXiv AI
Aug 11
From Product Search to Preference Articulation: The Economics of Agentic Commerce
arXiv:2608. 08395v1 Announce Type: cross Abstract: Generative AI is shifting digital commerce from browsing toward agentic search, in which consumers delegate product discovery to AI agents.
arXiv AI
Sep 30
Do Agent Benchmarks Do What They Say? An Executable-Contract Audit of Tool-Using Agent Environments
arXiv:2609.37315v1 Announce Type: cross Abstract: Tool-using agents are entering settings where a wrong action carries real cost, and the benchmarks certifying them grade what each simulated tool cal...
arXiv AI
Jul 31
Collusion with Competitive Marginals: Price-Level Audits Are Blind by Construction
arXiv:2607. 26385v1 Announce Type: cross Abstract: Empirical work on algorithmic collusion asks one question of the data: are prices supracompetitive?
arXiv AI
Sep 24
CAVEAT: Towards Robust Computer-Use Agents in Incentive-Misaligned Environments
CAVEAT is a new benchmark that tests computer‑use agents (CUAs) in nine online marketplace environments where platform incentives may steer agents away from user goals. The study finds that agents succeed in choosing user‑optimal products only 78.6% of the time in neutral settings, dropping to 17.3% when steering mechanisms are active. By diagnosing three failure points—priority distortion, premature narrowing of options, and early commitment—CAVEAT-Harness interventions raise user‑optimal purchasing success by 55.0%.