Latest Insights
Collaborate with us on research. If you have an interesting research topic, feel free to let us know anytime.
[
PAPER
]
Ontology Development 101: A Guide to Creating Your First Ontology
A practical introduction to ontology creation, this guide outlines step‑by‑step methodology—defining domain scope, reusing existing vocabularies, building class hierarchies, properties, and instances—and addresses complex design issues like semantic relationships and iterative refinement within Protégé‑2000.
[
PAPER
]
Ontology Development 101: A Guide to Creating Your First Ontology
A practical introduction to ontology creation, this guide outlines step‑by‑step methodology—defining domain scope, reusing existing vocabularies, building class hierarchies, properties, and instances—and addresses complex design issues like semantic relationships and iterative refinement within Protégé‑2000.
[
PAPER
]
Ontology Development 101: A Guide to Creating Your First Ontology
A practical introduction to ontology creation, this guide outlines step‑by‑step methodology—defining domain scope, reusing existing vocabularies, building class hierarchies, properties, and instances—and addresses complex design issues like semantic relationships and iterative refinement within Protégé‑2000.
[
PAPER
]
Self‑Rewarding Language Models
This paper introduces Self-Rewarding Language Models, where large language models iteratively generate, evaluate, and optimize their own outputs without relying on external reward models—establishing a new paradigm of self-alignment and performance improvement.
[
PAPER
]
Self‑Rewarding Language Models
This paper introduces Self-Rewarding Language Models, where large language models iteratively generate, evaluate, and optimize their own outputs without relying on external reward models—establishing a new paradigm of self-alignment and performance improvement.
[
PAPER
]
Self‑Rewarding Language Models
This paper introduces Self-Rewarding Language Models, where large language models iteratively generate, evaluate, and optimize their own outputs without relying on external reward models—establishing a new paradigm of self-alignment and performance improvement.
[
PAPER
]
Trends in Frontier AI Model Count: A Forecast to 2028
A data-driven forecast predicting the dramatic growth of large-scale foundation models between 2023 and 2028, assessing how many models will surpass training compute thresholds under emerging AI governance frameworks like the EU AI Act.
[
PAPER
]
Trends in Frontier AI Model Count: A Forecast to 2028
A data-driven forecast predicting the dramatic growth of large-scale foundation models between 2023 and 2028, assessing how many models will surpass training compute thresholds under emerging AI governance frameworks like the EU AI Act.
[
PAPER
]
Trends in Frontier AI Model Count: A Forecast to 2028
A data-driven forecast predicting the dramatic growth of large-scale foundation models between 2023 and 2028, assessing how many models will surpass training compute thresholds under emerging AI governance frameworks like the EU AI Act.
[
PAPER
]
RepliBench: Evaluating the Autonomous Replication Capabilities of Language Model Agents
A benchmark suite designed to evaluate how well language model agents can autonomously replicate their own tasks—RepliBench measures agentic scalability, error accumulation, and strategic planning across replication cycles.
[
PAPER
]
RepliBench: Evaluating the Autonomous Replication Capabilities of Language Model Agents
A benchmark suite designed to evaluate how well language model agents can autonomously replicate their own tasks—RepliBench measures agentic scalability, error accumulation, and strategic planning across replication cycles.
[
PAPER
]
RepliBench: Evaluating the Autonomous Replication Capabilities of Language Model Agents
A benchmark suite designed to evaluate how well language model agents can autonomously replicate their own tasks—RepliBench measures agentic scalability, error accumulation, and strategic planning across replication cycles.