← Authors

Ilia Sucholutsky

Provider-ID corpus identity

5Distinct papers

202Unique collaborators

5/5Semantic Scholar citation coverage

Publication span: 2026. Corpus fetch span: 2026.

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Identity provenance

Provider IDs

Semantic Scholar: 2226897111

ORCID evidence

No valid ORCID is stored.

Observed aliases (1)

Ilia Sucholutsky (semantic scholar, provider refresh)

Topics and outcomes in this view

Assessment themes

Productivity: 5 papers
Adoption: 3 papers
Human Ai Collab: 3 papers
Org Design: 2 papers
Governance: 1 paper
Inequality: 1 paper
Labor Markets: 1 paper

Claim outcomes

Other: 5 papers
Task Completion Time: 3 papers
Adoption Rate: 2 papers
Organizational Efficiency: 2 papers
Consumer Welfare: 1 paper
Research Productivity: 1 paper
Governance And Regulation: 1 paper
Task Allocation: 1 paper
Error Rate: 1 paper
Market Structure: 1 paper
Output Quality: 1 paper
Social Protection: 1 paper
Team Performance: 1 paper
Worker Satisfaction: 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.

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Ilia Sucholutsky's distinct papers under the selected provider observation surface.
Paper	Author evidence	Date	Provider citations
AI experts see multiple pathways to catastrophe within five years: in a Delphi of 272 specialists, 18 of 24 assessed risks had more than a 10% chance of catastrophic outcomes under business‑as‑usual by 2030, and even with pragmatic mitigations five risks retained >10% catastrophe probability, with general‑purpose AI developers and governance actors judged most responsible for mitigation.arxiv	Ilia Sucholutsky provider id	2026-06-03	2
Large experiment finds a 'speedup illusion': users expect LLMs to be faster but actual completion times on simple tasks are unchanged, even as subjective effort falls; the bias is specific to AI and not seen when imagining human help.arxiv	Ilia Sucholutsky provider id	2026-05-22	0
Users routinely lean on AI for trivial tasks that it does not meaningfully speed up, while underreporting how often they use it and overestimating time savings; prior exposure further entrenches reliance, risking an inefficient overreliance feedback loop.arxiv	Ilia Sucholutsky provider id	2026-05-21	0
A dynamic coordination protocol for LLM teams cuts token use, runtime and coordination failures compared with static or hierarchical approaches, while maintaining or improving task accuracy across multiple tasks and base models.arxiv	Ilia Sucholutsky provider id	2026-05-07	0
Viewing LLM teams through the lens of distributed systems exposes the core trade-offs—coordination, redundancy and fault tolerance—that determine whether multiple models beat a single agent; this conceptual framework offers a principled way to choose team size and structure without pure trial-and-error.openalex	Ilia Sucholutsky provider id	2026-03-12	2

Citation observation summary

Semantic Scholar supplied counts for 5 of 5 papers in this view; 0 are missing. The observed paper counts sum to 4 cumulative citations. This is a coverage summary, not an author score or h-index.