arXiv AI September 2, 2026 By Peiying Zhu, Sidi Chang

When Guardrails Look Effective: Construct Validity Failures in LLM Agent Commerce Evaluation

The paper investigates how marketplace guardrails affect welfare in language‑model agent simulations of hotel transactions. It finds that initial reports of large welfare gains disappear when controlling for offer schemas and buyer choice, and that guardrails mainly redistribute rather than increase welfare unless sellers are explicitly forced to produce inefficient bundles. The authors propose a construct‑validity framework to flag invalid or inconclusive policy claims before they are reported.

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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.

By Chen Liang, Fasheng Xu

llms agents reinforcement-learning benchmarks safety

arXiv AI

Jun 3

Capability Advertisement as a Market for Lemons: A Trust Layer for Heterogeneous Agent Networks

arXiv:2606. 03034v1 Announce Type: cross Abstract: Large language model (LLM) agents have begun to delegate work to one another.

By Gaurav Naresh Mittal

llms agents safety

arXiv Machine Learning

Aug 11

RouteGuard: Certifying Routing Gain in LLM Multi-Agent Systems When Complementarity Is Not Enough

arXiv:2608. 07583v1 Announce Type: cross Abstract: Multi-agent LLM systems route among model-backed advisors, yet a deployer rarely knows before shipping whether routing will help at all.

By Anchen Sun, Kaiqi Yang

llms agents benchmarks safety

arXiv AI

Jun 16

TERMS-Bench: Diagnosing LLM Negotiation Agents Beyond Deal Rate

arXiv:2605. 13909v2 Announce Type: replace-cross Abstract: Negotiation is a central mechanism of economic exchange, shaping markets, procurement, labor agreements, and resource allocation.

By Erica Zhang, Fangzhao Zhang, Aneesh Pappu, Batu El, Jose Blanchet, Susan Athey, Jiashuo Liu, James Zou

llms agents benchmarks

arXiv Machine Learning

Oct 15

Rent-Seeking Language Models: Reward Models Set the Prices, Frequent Audits Curb the Rent

arXiv:2610.19832v1 Announce Type: new Abstract: Reward model overoptimization in RLHF is usually described as reward hacking. We recast it as rent-seeking: the policy, a firm, earns reward from a reg...

By Zixiao Wang, Xingbo Du, Duzhen Zhang, Le Song

llms reinforcement-learning

arXiv Computation and Language

Oct 16

Jev-as-a-Judge for RAG Claim Verification: Confidence-Gated Cascading Between a Non-Generative Verifier and a Frontier LLM

The paper compares a non‑generative verifier (Jev) with a frontier LLM (Astra) on 495 human‑labelled claims from LLM‑AggreFact. Both models achieve statistically indistinguishable accuracy, yet Jev is 187‑fold cheaper and admits more unsupported claims. A confidence‑gated cascade that escalates uncertain claims improves cost‑efficiency, though the benefit comes mainly from the escalation share rather than the probability estimates.

By Alden Do Rosario, Kirollos Atef

llms rag

One email a morning, machine-written

Related stories

When LLM Agents Negotiate: Private Information and Dynamic Bargaining in Supply Chains

Capability Advertisement as a Market for Lemons: A Trust Layer for Heterogeneous Agent Networks

RouteGuard: Certifying Routing Gain in LLM Multi-Agent Systems When Complementarity Is Not Enough

TERMS-Bench: Diagnosing LLM Negotiation Agents Beyond Deal Rate

Rent-Seeking Language Models: Reward Models Set the Prices, Frequent Audits Curb the Rent

Jev-as-a-Judge for RAG Claim Verification: Confidence-Gated Cascading Between a Non-Generative Verifier and a Frontier LLM