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ParanoiaEval: Benchmarking Unnecessary Defensive Work in Agentic Coding

Paper recorded by Signals 4 on 2026-10-06 in cs.AI. Abstract reproduced from arXiv; link to the original below.

Published 2026-10-06 on arXiv · recorded by Signals 4 on 2026-10-07

Category: cs.AI · 人工智能 · first seen 2026-10-07

Abstract

As coding agents increasingly undertake real-world work autonomously, judging whether their risk treatments are warranted has become important. Existing work evaluates related agent behaviors from separate perspectives, but lacks a systematic framework for unifying these behaviors. To bridge this gap, we introduce ParanoiaEval, the first benchmark for unified evaluation of risk-treatment capabilit

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#420 most recent of 960 cs.AI papers we have recorded · ↑ newer: Semantic Behavioral Watermarking: Paraphrase-Robust and Forgery-Resist · ↓ older: One Figure, Every Canvas: Editable Flowchart Relayout via Agentic Pipe
Cite this page: ParanoiaEval: Benchmarking Unnecessary Defensive Work in Agentic Coding: the #420 most recent of 960 cs.AI papers we have recorded (as of 2026-10-06). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/paranoiaeval-benchmarking-unnecessary-defensive-work-in-agentic-coding.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
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