A tool and ecosystem for collaborative knowledge synthesis
Getting started - Docs
Discourse Graphs are a tool and ecosystem for collaborative knowledge synthesis, enabling researchers to map ideas and arguments in a modular, composable graph format.
Discourse Graphs and the Future of Science | Protocol Labs Research
Interview between Tom Kalil, Chief Innovation Officer of Schmidt Futures, Dr. Evan Miyazono, Research Team Lead at Protocol Labs, and Dr. Matt Akamatsu, Assistant Professor of Biology at the University of Washington.
KARMA: Leveraging Multi-Agent LLMs for Automated Knowledge Graph Enrichment
Maintaining comprehensive and up-to-date knowledge graphs (KGs) is critical for modern AI systems, but manual curation struggles to scale with the rapid growth of scientific literature. This paper presents KARMA, a novel framework employing multi-agent large language models (LLMs) to automate KG enrichment through structured analysis of unstructured text. Our approach employs nine collaborative agents, spanning entity discovery, relation extraction, schema alignment, and conflict resolution that iteratively parse documents, verify extracted knowledge, and integrate it into existing graph structures while adhering to domain-specific schema. Experiments on 1,200 PubMed articles from three different domains demonstrate the effectiveness of KARMA in knowledge graph enrichment, with the identification of up to 38,230 new entities while achieving 83.1% LLM-verified correctness and reducing conflict edges by 18.6% through multi-layer assessments.
AutoSchemaKG: Autonomous Knowledge Graph Construction through Dynamic Schema Induction from Web-Scale Corpora
In the current era of information abundance, transforming vast amounts of unstructured data into structured, machine-readable knowledge remains one of the most significant challenges in artificial intelligence. Knowledge Graphs (KGs) have emerged as the cornerstone technology for this transformation Zhao et al. (2024), providing the semantic backbone for applications ranging from search engines and question answering Wu et al. (2024); Chen et al. (2024c); Zong et al. (2024); Sun et al. (2024b) to recommendation systems Lyu et al. (2024) and complex reasoning tasks Li et al. (2024b). Yet despite their critical importance, current KG construction approaches remain hampered by an inherent paradox: they require predefined schemas created by domain experts, which fundamentally limits their scalability, adaptability, and domain coverage.
semble/src/modules/atproto/infrastructure/lexicons at main · cosmik-network/semble
A social knowledge tool for research, built on ATProto - cosmik-network/semble
PoliSim@CHI 2026
Large Language Models are rapidly evolving from text generators into reasoning systems that can act as autonomous agents. When placed in social contexts, these agents display emergent behaviors such as forming coalitions, spreading information, and making collective decisions.
Generative AI for Pro-Democracy Platforms · From Novel Chemicals to Opera
Online discourse faces challenges in facilitating substantive and productive political conversations. Recent technologies have explored the potential of generative AI to promote civil discourse, encourage the development of mutual understanding in a discussion, produce . . .
Cosmik Network (cosmik.network) — Semble
Transforming social networks into sensemaking networks https://cosmik.network/ | Building @semble.so a social knowledge network for research | Co-organizing @atproto.science @cairos.network
Semble — A social knowledge network for research
Follow your peers' research trails. Surface and discover new connections. Built on ATProto so you own your data.
atproto made simple: publishing lexicons - underreacted
atproto made simple: publishing lexicons - underreacted
1/ quick notes from a great meeting yesterday with a Boston area researcher, interested in cutting edge research tools like Discourse Graphs, @cosmik.network and science social media. The research questions they are looking at are too complex and interdisciplinary to be tackled by any single lab >
