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Entities25 of 207,987
- large language modeltechnology1,043 mentions
- robotic manipulationmarket824 mentions
- artificial intelligencetechnology739 mentions
- reinforcement learningtechnology491 mentions
- density-functional theorytechnology386 mentions
- Transformertechnology371 mentions
- agentic AItechnology353 mentions
- vision-language-action modeltechnology350 mentions
- U.S. Food and Drug Administrationorganization320 mentions
- Vision-Language Modeltechnology318 mentions
- Microsoftorganization291 mentions
- Googleorganization276 mentions
- NVIDIAorganization264 mentions
- Model Context Protocoltechnology232 mentions
- machine learningtechnology204 mentions
- MarketsandMarketsorganization199 mentions
- Claudeproduct192 mentions
- AI agenttechnology188 mentions
- ChatGPTproduct178 mentions
- Anthropicorganization177 mentions
- integrated sensing and communicationtechnology175 mentions
- Amazon Web Services, Inc.organization174 mentions
- OpenAIorganization172 mentions
- diffusion modeltechnology169 mentions
- IBMorganization163 mentions
Entity
IQuest-Q1
product2 mentionslast seen Oct 23, 2026
A large language model trained for coding, software engineering, and agentic workflows.
Relationships 15
- IQuest ResearchdevelopsIQuest-Q199% confidence2 sources
- IQuest-Q1depends onMulti-Teacher On-Policy Distillation99% confidence2 sources
- IQuest-Q1composed ofmixture of experts93% confidence1 source
- IQuest-Q1composed ofTransformer93% confidence1 source
- IQuest-Q1applies tosoftware engineering90% confidence1 source
- IQuest-Q1depends onsparse Mixture-of-Experts architecture90% confidence1 source
- IQuest-Q1depends onStable SplitNorm90% confidence1 source
- IQuest-Q1applies tocommand-line interface interaction90% confidence1 source
- IQuest-Q1applies tocoding and software engineering90% confidence1 source
- IQuest-Q1depends onMuon optimizer90% confidence1 source
- IQuest-Q1depends onμP parameterization90% confidence1 source
- IQuest-Q1applies toagentic workflows88% confidence1 source
- IQuest-Q1depends onRecursive Multi-Token Prediction85% confidence1 source
- IQuest-Q1implementsClaude Code75% confidence1 source
- IQuest-Q1implementsCodex CLI75% confidence1 source
Relationship
IQuest-Q1depends onMulti-Teacher On-Policy Distillation
99% confidence2 supporting mentions
Either name opens that entity’s relationships.
Sources 2
- newsPR NewswireOct 23, 2026IQuest Research Releases IQuest-Q1 Technical Report, Detailing Training from Scratch and Results Across 30 Benchmarks
Post-training combines supervised fine-tuning, domain-specific reinforcement learning, Multi-Teacher On-Policy Distillation (MOPD), and model merging
- newsPR NewswireOct 9, 2026IQuest-Q1 Draws Early Developer Attention Across Coding and Agentic Workflows
Multi-Teacher On-Policy Distillation (MOPD) consolidates strengths from several teachers on the student's own on-policy rollouts