ScioMind combines anchoring-based belief updates, hierarchical memory, and dynamic profiles in LLM multi-agent systems to produce more stable, diverse, and psychologically aligned opinion trajectories than prior fixed-rule or unconstrained approaches.
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EPIC trains LLMs to treat continuous embeddings as in-context prompts, yielding state-of-the-art text embedding performance on MTEB with or without prompts at inference and lower compute.
Vidya is an AI-driven pipeline that automates semantic metadata enrichment for archival digitization by constraining LLMs with YAML ontologies and Pydantic validation to produce deterministic JSON outputs compliant with NOBRADE and ISAD(G).
H-RAG uses hierarchical parent-child document segmentation with hybrid retrieval and parent-level aggregation to achieve 0.4271 nDCG@5 on retrieval and 0.3241 harmonic mean on generation in a multi-turn RAG shared task.
citing papers explorer
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ScioMind: Cognitively Grounded Multi-Agent Social Simulation with Anchoring-Based Belief Dynamics and Dynamic Profiles
ScioMind combines anchoring-based belief updates, hierarchical memory, and dynamic profiles in LLM multi-agent systems to produce more stable, diverse, and psychologically aligned opinion trajectories than prior fixed-rule or unconstrained approaches.
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Embedding-based In-Context Prompt Training for Enhancing LLMs as Text Encoders
EPIC trains LLMs to treat continuous embeddings as in-context prompts, yielding state-of-the-art text embedding performance on MTEB with or without prompts at inference and lower compute.
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Vidya: An AI-Driven Modular Pipeline for Archival Automation and Semantic Metadata Enrichment
Vidya is an AI-driven pipeline that automates semantic metadata enrichment for archival digitization by constraining LLMs with YAML ontologies and Pydantic validation to produce deterministic JSON outputs compliant with NOBRADE and ISAD(G).
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H-RAG at SemEval-2026 Task 8: Hierarchical Parent-Child Retrieval for Multi-Turn RAG Conversations
H-RAG uses hierarchical parent-child document segmentation with hybrid retrieval and parent-level aggregation to achieve 0.4271 nDCG@5 on retrieval and 0.3241 harmonic mean on generation in a multi-turn RAG shared task.