Evidence Receipt. Related Resources.
OSExpert: Computer-Use Agents Learning Professional Skills via Exploration
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Signal Canvas proof surface
Canonical route: /signal-canvas/osexpert-computer-use-agents-learning-professional-skills-via-exploration
- Proof freshness
- stale
- Proof status
- unverified
- Display score
- 8/10
- Last proof check
- 2026-04-02
- Score updated
- 2026-04-02
- Score fresh until
- 2026-05-02
- References
- 0
- Source count
- 0
- Coverage
- 17%
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Agent Handoff
OSExpert: Computer-Use Agents Learning Professional Skills via Exploration
Canonical ID osexpert-computer-use-agents-learning-professional-skills-via-exploration | Route /signal-canvas/osexpert-computer-use-agents-learning-professional-skills-via-exploration
REST example
curl https://sciencetostartup.com/api/v1/agent-handoff/signal-canvas/osexpert-computer-use-agents-learning-professional-skills-via-explorationMCP example
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Dimensions overall score 8.0
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Claim map
- Evidencepartial
our new benchmark, OSExpert-Eval, indicates they remain far less helpful than human experts
ImplicationpartialDirectly stated in the abstract with clear comparison to human experts
Verificationpartialpartial
- Evidencepartial
these agents complete complex tasks inefficiently with degraded performance
ImplicationpartialDirectly stated in the abstract as a key limitation of existing approaches
Verificationpartialpartial
- Evidencepartial
we introduce a GUI-based depth-first search (GUI-DFS) exploration algorithm to comprehensively explore and verify an environment's unit functions
ImplicationpartialDirectly stated as a core method contribution in the abstract
Verificationpartialpartial
- Evidencepartial
The agent then exploits compositionality between unit skills to self-construct a curriculum for composite tasks
ImplicationpartialDirectly stated in the abstract as part of the method description
Verificationpartialpartial
- Evidencepartial
achieving a around 20 percent performance gain on OSExpert-Eval
ImplicationpartialDirectly stated with specific numeric result in the abstract
Verificationpartialpartial
- Evidencepartial
closing the efficiency gap to humans by around 80 percent
ImplicationpartialDirectly stated with specific numeric result in the abstract
Verificationpartialpartial
- Evidencepartial
enabling them to end inference-time scaling earlier by realizing their boundary of capabilities
ImplicationpartialDirectly stated as a benefit of the method in the abstract
Verificationpartialpartial
- Evidencepartial
struggle with fine-grained action sequences
ImplicationpartialDirectly stated in the abstract as a key limitation of existing approaches
Verificationpartialpartial
