Opportunity summary
Score4.0PainA framework to improve user understanding of AI agent actions and risks, enhancing trust and anomaly detection.
Evidence20 refs | 3 sources | 50% coverage
Blockerno shell-level blocker reported
This canonical paper page includes Commercialization Proof and Related Resources.
ARXIV:2603.28551 · AI AGENTS · SUBMITTED 31 MAR · 20:22 UTC · FRESHNESS STALE
ARXIV:2603.28551AI AGENTSSUBMITTED 31 MAR · 20:22 UTCFRESHNESS STALEZifan Peng · arXiv
A framework to improve user understanding of AI agent actions and risks, enhancing trust and anomaly detection.
Opportunity summary
Pain A framework to improve user understanding of AI agent actions and risks, enhancing trust and anomaly detection.
Evidence 20 refs | 3 sources | 50% coverage
Blocker Evidence unverified
A framework to improve user understanding of AI agent actions and risks, enhancing trust and anomaly detection. Unlike conventional chatbots, these systems can install skills, invoke tools, access private resources, and modify local environments…
Personalized computer-use agents are rapidly moving from expert communities into mainstream use. Unlike conventional chatbots, these systems can install skills, invoke tools, access private resources, and modify local environments on users' behalf.
ScienceToStartup currently rates this 4.0/10 on the public viability pass. A scenario-based evaluation suggests that traceability-oriented interfaces can improve understanding of agent behavior, support anomaly detection, and foster more calibrated trust.
AI Agents moved forward this cycle; last verified April 2026. Public score 4.0/10.
Continue into Read for claims, analysis, references, and neighboring papers.
mobile layout uses overflow-hidden min-w-0 break-wordsOpportunity summary
Score4.0Analysis summary
A framework to improve user understanding of AI agent actions and risks, enhancing trust and anomaly detection.
Loading BUILD…
Paper Pack
10.48550/arXiv.2603.28551A framework to improve user understanding of AI agent actions and risks, enhancing trust and anomaly detection.
Abstract
Personalized computer-use agents are rapidly moving from expert communities into mainstream use. Unlike conventional chatbots, these systems can install skills, invoke tools, access private resources, and modify local environments on users' behalf. Yet users often do not know what authority they have delegated, what the agent actually did during task execution, or whether the system has been safely removed afterward. We investigate this gap as a combined problem of risk understanding and post-hoc auditability, using OpenClaw as a motivating case. We first build a multi-source corpus of the OpenClaw ecosystem, including incidents, advisories, malicious-skill reports, news coverage, tutorials, and social-media narratives. We then conduct an interview study to examine how users and practitioners understand skills, autonomy, privilege, persistence, and uninstallation. Our findings suggest that participants often recognized these systems as risky in the abstract, but lacked concrete mental models of what skills can do, what resources agents can access, and what changes may remain after execution or removal. Motivated by these findings, we propose AgentTrace, a traceability framework and prototype interface for visualizing agent actions, touched resources, permission history, provenance, and persistent side effects. A scenario-based evaluation suggests that traceability-oriented interfaces can improve understanding of agent behavior, support anomaly detection, and foster more calibrated trust.
Source availability
PDF linkedThe paper record includes a public PDF URL.
Extraction status
Parse run linkedA document parse run is attached to this paper.
Proof status
unverified20 refs; 3 sources; 50% coverage.
What was readable
Derived fallback: Estimated from adjacent evidence; not verified from source.
Viability
Time to MVP
Commercial
Export
Preparing verified analysis
Dimensions overall score 4.0
PROBLEM
A framework to improve user understanding of AI agent actions and risks, enhancing trust and anomaly detection. Unlike conventional chatbots, these systems can install skills, invoke tools, access private resources, and modify local environments on users' behalf.
METHOD
Personalized computer-use agents are rapidly moving from expert communities into mainstream use. Unlike conventional chatbots, these systems can install skills, invoke tools, access private resources, and modify local environments on users' behalf.
RESULT
ScienceToStartup currently rates this 4.0/10 on the public viability pass. A scenario-based evaluation suggests that traceability-oriented interfaces can improve understanding of agent behavior, support anomaly detection, and foster more calibrated trust.
WHY NOW
AI Agents moved forward this cycle; last verified April 2026. Public score 4.0/10.
Our findings suggest that participants often recognized these systems as risky in the abstract, but lacked concrete mental models of what skills can do, what resources agents can access, and what changes may remain after execution or removal.
Directly stated in the abstract as a key finding from the interview study, though specific quantitative results are not provided in the given text.
partial
Yet users often do not know what authority they have delegated, what the agent actually did during task execution, or whether the system has been safely removed afterward.
Explicitly and directly stated as the core problem in the abstract.
partial
280+ Leaky Skills: How OpenClaw & ClawHub Are Exposing API Keys and PII... Snyk Finds Prompt Injection in 36%
Directly referenced from a cited security research blog with specific numbers (280+ leaky skills, 36% prompt injection rate).
partial
A scenario-based evaluation suggests that traceability-oriented interfaces can improve understanding of agent behavior, support anomaly detection, and foster more calibrated trust.
Claim is presented as a finding from a scenario-based evaluation, but the specific evaluation results are not detailed in the provided text.
partial
Unlike conventional chatbots, these systems can install skills, invoke tools, access private resources, and modify local environments on users' behalf.
Explicitly and directly stated as a defining characteristic in the abstract.
partial
We investigate this gap as a combined problem of risk understanding and post-hoc auditability
Directly stated as the framing of the research problem in the abstract.
partial
people often begin verification by reconstructing what the AI actually did before deciding whether the result is correct.
Supported by a direct quote citing prior work (Gu et al.) that aligns with the paper's core thesis.
partial
We first build a multi-source corpus of the OpenClaw ecosystem... We then conduct an interview study to examine how users and practitioners understand...
Explicitly stated in the abstract as the methods used.
partial
Paper-native neighborhood for concepts, methods, materials, markets, and competitors. Missing lanes stay labeled instead of disappearing behind commercialization gates.
Concepts
Methods
Materials
Markets
Competitors
A framework to improve user understanding of AI agent actions and risks, enhancing trust and anomaly detection.
Segment
AI Agents
Adoption evidence
No public code link in the paper record yet
Commercial read
4.0/10 public viability
Direct
Adjacent
Substitute
Unknown
No indexed public discussion is attached to 2603.28551 yet. That is a visibility signal, not a blank module: the monitor is watching the public channels below.
Hacker News
Not indexed yet
Not indexed yet
Bluesky
Not indexed yet
Preview the source document here, or use the hero PDF action for a new tab.
Reference metadata is not materialized in the public index yet. The source PDF remains the authority; cache refresh is optional.
CITED BY
No citing papers are indexed in the public S2S graph yet. This is an explicit zero-signal state, not a hidden lookup.
Foundation
Extension
Commercially relevant
Owned Distribution
Get the weekly shortlist of commercializable papers, benchmark movers, and proof receipts that matter for product execution.
3/3 checks · 100%
Build Passport
Build passport pending - Proof Lab budget No verified cost estimate / $7.00 cap
status
missing
reason
passport_row_missing
proof status
unverified
cost/budget
No verified cost estimate
confidence low
next verification path
Build brief missing until Build Passport data exists.
Source missing: Build Passport payload.
Experiment plan missing until prototype path is available.
No prototype path attached.
Validation checklist missing until required assets, cost, and regulatory flags are verified.
No checklist artifact is attached to the Build Passport payload.
Derived signals show verified:false until source-backed receipts exist.
Evidence coverage
OpportunityKernel evidence_receipt
20 refs / 3 sources / 50% coverage
stale
Verify missing sources before using this as buyer proof. verified:false
Build readiness
BuildPassport EvidenceState
passport absent
stale
Run Proof Lab or inspect typed missing state. verified:false
Artifact maturity
GitHub and Hugging Face maturity payloads
No public artifact surface observed
stale
Open source artifacts or mark the gap as missing. verified:false
Technical feasibility
partial
Current read
Runnable path is not fully verified.
Evidence
No Build Passport payload attached.
Gaps
Next test
Run minimal reproduction from the Build Passport prototype path.
Market urgency
partial
Current read
Research evidence exists; buyer urgency still needs source proof.
Evidence
20 references, 3 sources, 50% evidence coverage.
Gaps
Next test
Collect buyer interview, deployment evidence, or cited demand signal.
Buyer clarity
missing
Current read
No budget owner is verified for this paper.
Evidence
Build tab has no CRM, procurement, or operator source.
Gaps
Next test
Map target operator, economic buyer, and procurement trigger.
Defensibility
missing
Current read
Defensibility signals are missing.
Evidence
No defensibility receipt attached.
Gaps
Next test
Refresh defensibility bars with source receipts.
Integration burden
missing
Current read
No public implementation surface observed.
Evidence
No GitHub or Hugging Face payload attached.
Gaps
Next test
Write integration checklist from prototype path and target workflow.
Capital intensity
missing
Current read
No observed cost estimate is verified.
Evidence
Cost passport has no observed_usd value.
Gaps
Next test
Run cost passport or mark the cost field not applicable.
Regulatory load
missing
Current read
No regulatory classification is attached.
Evidence
Build Passport ledger does not include regulatory flags.
Gaps
Next test
Classify regulatory flags before commercialization planning.
No named scientific founder assigned.
Paper authors are not treated as operators without consent.
People
No named person assigned.
Gaps
Next verification path
Prototype owner missing.
Build Passport does not name an implementer.
People
No named person assigned.
Gaps
Next verification path
Operator workflow not sourced.
No buyer or workflow interview attached.
People
No named person assigned.
Gaps
Next verification path
No GTM owner verified.
No CRM or outreach source attached.
People
No named person assigned.
Gaps
Next verification path
Regulatory need unclassified.
No clinical or regulatory source attached.
People
No named person assigned.
Gaps
Next verification path
ARTIFACTS
No public artifacts yet.
DEFENSIBILITY
Defensibility and confidence evidence pending.
WATCHTOWER
No verified watchtower monitor rows yet.
FORESIGHT
No prediction yet — minted on next Foresight batch.
OPPORTUNITYKERNEL CHANGES SINCE LAST VIEW
No verified OpportunityKernel changes since the last view.
COMPETITIVE LANDSCAPE UPDATES
No verified competitive landscape changes yet.
RELATED PAPER UPDATES
No verified related paper changes yet.
SIGNAL CANVAS HISTORY AND DELTAS
No Signal Canvas history deltas yet.
TIMELINE
Save this paper to start tracking momentum - commits, demos, and score changes appear here.
No tracked events yet.
Score trend will appear after multiple data points.
BUZZ
Buzz trend pending.