← Back to all news
arXiv AI By Solomon Shalom Lijo

Counting the Bandages: On measuring the time that AI saves

Read the original on arXiv AI →

The paper examines how to quantify the time AI tools save, contrasting a simple stopwatch approach with a more nuanced ledger view that accounts for errors introduced. It introduces model‑free metrics—time saved, excess escaped defects, and a break‑even defect cost—and applies them to a baseline checking model, randomized studies, and a replication of METR’s developer data. The author also reports on a small experiment with AI reviewers, showing that reduced effort can lead to faster but more error‑prone outcomes.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

Related stories

arXiv Machine Learning
Sep 14

A False Average: Pooled CoT-Monitor Accuracy Conceals a Reasoning-Dependent Fragility

The paper demonstrates that aggregate accuracy figures for chain‑of‑thought (CoT) monitors can be misleading because a large portion of detected hacks rely solely on action patterns rather than reasoning. By rewriting only the agent’s reasoning to appear truthful while keeping actions identical, the authors show that the monitor’s performance on the reasoning‑dependent subset collapses dramatically, yet the overall pooled accuracy drops only modestly. The study reveals that CoT monitors are fragile when reasoning is the key signal and that accuracy should be reported separately for this subset.

By Shikhar Shiromani, Leo Richter
agents
More like this →
arXiv AI
5d ago

Beyond fabrication: Misattributed numbers in papers written by AI research agents

The paper examines how AI‑written research papers often contain misattributed numerical claims. By re‑analyzing 540 judgments from a benchmark of nine AI agents on fifteen research problems, the authors manually labeled 256 claims and found that most failures stem from misalignment between the paper and the underlying work rather than outright cheating. They propose a deterministic claim‑to‑evidence checker that achieves over 90% precision and, when combined with an LLM verifier, offers a more reliable audit of AI‑generated research.

By Mosab Alfaqeeh, Omar Hourani, Muath Hourani, Hashem Alfaqeeh
llmsagentsbenchmarkssafety
More like this →
arXiv AI
Aug 28

Invocation-Level Reliability of Tool-Using Agents

The paper investigates the reliability of tool‑using agents, focusing on two failure modes: selecting the wrong tool and constructing incorrect arguments. It introduces a correct‑invocation rate metric to distinguish these errors and evaluates five open‑weight models on multi‑step tasks up to depth 8, finding that by depth 6 about 70% of a model’s clean‑context capability is lost due to earlier mistakes. The study reveals that exact‑match scoring against a fixed gold trajectory forces severity and recovery parameters to extreme values, and proposes a conditional‑on‑state scoring remedy that yields more realistic severity estimates.

By Afiya Noorain, Subhranshu Mohanty, Amritesh Banerjee, Abhijit Dasgupta
agents
More like this →