7 papers
When Should Active RAG Retrieve? A Budget-Aware Evaluation of Utility, Calibration, and Cost
Pin Qian, Su Wang, Chong Peng +5
Active RAG systems decide when to retrieve external knowledge during generation, making them a budget-sensitive case of agentic RAG and self-adaptive retrieval. Yet evaluations oft…
Phantom Guardrails: When Self-Improving Agent Harnesses Fix Failures That Never Happened
Su Wang, Pin Qian, Yifan Lin +5
The paper investigates how self‑improving AI agents can hallucinate non‑existent failures and create unnecessary guardrails, introducing a deterministic Counterfactual Fabrication…
Operational Reframing and Approval-Framed Delegation in Multi-Agent LLM Safety
Lifei Liu, Haoran Yu, Xiaochong Jiang +3
Safety evaluations of multi-agent LLM systems often compare a direct prompt with a planner-executor pipeline and report the difference as a single "pipeline effect." We argue that…
ChainCaps: Composition-Safe Tool-Using Agents via Monotonic Capability Attenuation
Xiaochong Jiang, Shiqi Yang, Ziwei Li +3
Tool-using agents increasingly operate in open-ended deployment environments, where they compose file systems, web APIs, code interpreters, and enterprise services at runtime. This…
Habituation at the Gate: Rising Approval and Declining Scrutiny in Human Review of AI Agent Code
Haoran Yu, Lifei Liu, Xiaochong Jiang +4
As AI coding agents (e.g., GitHub Copilot, Devin, OpenAI Codex, Cursor) submit pull requests to open-source repositories at scale, a key question arises: do human reviewers gradual…
Beyond Simpson's Paradox: A Cascade of Confounders in AI Agent Pull-Request Co-Authorship
Haoran Yu, Xiaochong Jiang, Lifei Liu +3
Pooled across five AI coding agents, pull requests (PRs) with a human Co-Authored-By trailer merge less often than purely-autonomous ones (53.8% vs. 79.8%) -- yet this aggregate fi…