[Submitted on 14 Oct 2026]
Title:AlphaDiana: A Measurement System for Agents in Verifiable Reasoning Tasks
View a PDF of the paper titled AlphaDiana: A Measurement System for Agents in Verifiable Reasoning Tasks, by Zhanke Zhou and 18 other authors
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Abstract:Agents pair reasoning models with harnesses that organize model calls, execute tools, load skill instructions, and retain history in memory. Understanding agent performance requires linking execution conditions to outcomes, behavior, and resource use. We introduce AlphaDiana, a measurement system with modular interfaces for datasets, agents, environments, and scorers. It preserves harness-native control loops while controlling file reuse and memory retention separately. AlphaDiana supports macro analyses of complete agents and micro interventions on agent components such as tools, skills, and memory under matched conditions. We study three open-source models and three open-source harnesses on five answer-based benchmarks, with two models also tested on two interactive benchmarks. We find that harnesses do not consistently improve the accuracy of reasoning: gains on questions unsolved by the model alone can be outweighed by losses on questions it answers correctly without tools. This inconsistency extends to component interventions: neither skill guidance nor retained history reliably improves accuracy. Behavior and output-token use must be assessed with correctness: low token entropy can accompany wrong answers, and less output per execution can mean more output tokens per correct answer when accuracy falls. AlphaDiana links answer correctness and execution status to traces and resource records for comparing agents and diagnosing failures under shared task and resource policies.
Subjects:
Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
Cite as: arXiv:2610.20675 [cs.AI]
(or arXiv:2610.20675v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2610.20675
arXiv-issued DOI via DataCite
Submission history
From: Zhanke Zhou [view email] [v1] Wed, 14 Oct 2026 14:04:48 UTC (3,312 KB)
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