Direction, evidence grade, and study type are AI-generated labels (gpt-5-mini), not human-verified. Syntheses are LLM-written. "Tensions" are machine-detected candidates, not confirmed contradictions. A research-acceleration tool, not peer review.
How this is built →
1Distinct papers
8Unique collaborators
1/1Semantic Scholar citation coverage
Publication span: 2026. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Semantic Scholar:
2371995140
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Tao Xiong (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Human Ai Collab: 1 paper
- Productivity: 1 paper
Claim outcomes
- Research Productivity: 1 paper
- Output Quality: 1 paper
- Governance And Regulation: 1 paper
- Task Allocation: 1 paper
- Task Completion Time: 1 paper
Papers in the Semantic Scholar view
Latest stored Semantic Scholar author observations only. Citation counts below are from the same provider and are not combined with other services.
Scroll the table horizontally to see every column.
| Paper | Author evidence | Date | Provider citations |
|---|---|---|---|
| A new benchmark reveals AI assistants still struggle with complex desktop workflows: on DeskCraft’s 538 long-horizon creative and engineering tasks, GPT-5.4 scores only ~32% on standard tasks and ~28% when realistic interactions are allowed. The suite exposes systematic failures in delivering long workflows and in proactively clarifying ambiguous instructions despite a formal mid-turn/post-turn collaboration protocol.arxiv | Tao Xiong provider id |
2026-06-02 | 0 |
Citation observation summary
Semantic Scholar supplied counts for 1 of 1 papers in this view; 0 are missing. The observed paper counts sum to 0 cumulative citations. This is a coverage summary, not an author score or h-index.