[Submitted on 14 Sep 2026]
Title:When Does Test-Time Physical Diagnosis Pay? A Frozen Policy Buys Evidence It Never Reads
View a PDF of the paper titled When Does Test-Time Physical Diagnosis Pay? A Frozen Policy Buys Evidence It Never Reads, by Zhengshu Zhang
View PDF HTML (experimental)
Abstract:When a robot faces unfamiliar physical conditions, a common approach is to collect evidence about what changed and adapt. For such diagnosis to improve behavior, six ordered empirical conditions must hold: a meaningful reference, identifiability of the physical condition, use of the acquired evidence, decision value, selection value over a fixed alternative, and safe realization. We test this chain in controlled and public environments. It holds end to end in our controlled environments. After transfer to unseen mechanisms, however, it breaks at evidence use. On decisions requiring the full trace, the frozen decoder does not change its choice. A linear model using only trace increments recovers the correct choice on mechanisms excluded from fitting, showing that the trace is informative but unused. The failure is concentrated at the richest evidence level: those decisions fall to chance, while decisions settled with lower-cost evidence remain correct, a split hidden by aggregate accuracy. The same chain can fail at other links in public environments. Successful physical identification therefore guarantees neither evidence use nor useful adaptation; evaluation should identify where the chain breaks rather than rely on recovery accuracy or aggregate performance alone.
Comments: 12 pages, 4 figures
Subjects:
Robotics (cs.RO); Machine Learning (cs.LG)
Cite as: arXiv:2609.22299 [cs.RO]
(or arXiv:2609.22299v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2609.22299
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Zhengshu Zhang [view email] [v1] Mon, 14 Sep 2026 15:52:59 UTC (72 KB)
Full-text links:
Access Paper:
View a PDF of the paper titled When Does Test-Time Physical Diagnosis Pay? A Frozen Policy Buys Evidence It Never Reads, by Zhengshu Zhang
View PDF
HTML (experimental)
TeX Source
view license
Current browse context:
cs.RO
new | recent | 2026-09
Change to browse by:
cs cs.LG
References & Citations
NASA ADS
Google Scholar
Semantic Scholar
Loading...
Data provided by:
Bibliographic Tools
Bibliographic and Citation Tools
Bibliographic Explorer Toggle
Bibliographic Explorer (What is the Explorer?)
Connected Papers Toggle
Connected Papers (What is Connected Papers?)
Litmaps Toggle
Litmaps (What is Litmaps?)
scite.ai Toggle
scite Smart Citations (What are Smart Citations?)
Code, Data, Media
Code, Data and Media Associated with this Article
alphaXiv Toggle
alphaXiv (What is alphaXiv?)
Links to Code Toggle
CatalyzeX Code Finder for Papers (What is CatalyzeX?)
DagsHub Toggle
DagsHub (What is DagsHub?)
GotitPub Toggle
Gotit.pub (What is GotitPub?)
Huggingface Toggle
Hugging Face (What is Huggingface?)
ScienceCast Toggle
ScienceCast (What is ScienceCast?)
Demos
Demos
Replicate Toggle
Replicate (What is Replicate?)
Spaces Toggle
Hugging Face Spaces (What is Spaces?)
Spaces Toggle
TXYZ.AI (What is TXYZ.AI?)
Related Papers
Recommenders and Search Tools
Link to Influence Flower
Influence Flower (What are Influence Flowers?)
Core recommender toggle
CORE Recommender (What is CORE?)
Author
Venue
Institution
Topic
About arXivLabs
arXivLabs: experimental projects with community collaborators
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)