Epistrons: Knowledge Artifacts for Hyperproblems - Hyperproblems

Hyperproblems: New Ways of Doing and Communicating Science - Hyperproblems

Hyperproblems: New Ways of Doing and Communicating Science - Hyperproblems

Hyperproblems: Hyperproblems are scientific challenges whose scale, complexity, novelty and interdependence overwhelm traditional research models, requiring…

Infinite Researchers | AI-Powered Scientific Discovery

What happens to the speed of discovery if we have infinite researchers? Explore AI experiments accelerating breakthroughs.

Hyperfast AI: Rethinking Design for 1000 tokens/s

I recently spoke at AI Tinkerers Raleigh about hyperfast inference systems and how they’re fundamentally changing AI application design. If you haven’t heard of Cerebras (or however they pronounce it), you’re in for a treat—this is one of the most exciting areas of research in AI right now.

Universal Scientific Protocols, Inc.

Universal Scientific Protocols, Inc. — research publishing, reconsidered. A knowledge management platform for machine learning researchers.

Glimmer · Reproducible AI science

Glimmer turns a research project into a navigable knowledge graph you can explore, run, verify, and extend — reproducibly.

malleable computing

By Devine Lu Linvega

Reproducible Execution Environment (REE) | Tech | Gensyn

Run AI model inference in a machine-agnostic environment where the same model and inputs produce the same outputs across supported hardware.

Solving a Million-Step LLM Task with Zero Errors

LLMs have achieved remarkable breakthroughs in reasoning, insights, and tool use, but chaining these abilities into extended processes at the scale of those routinely executed by humans,...

GRN · German Reproducibility Network

Working together for trustworthy and useful research

Towards infinite context windows: neural KV cache compaction | Base Labs

Working to advance and democratize open-source intelligence.

Keynote: Reproducibility and replicability of computer simulations | Canal U

Since the early days of the reproducibility crisis, much progress has been made in understanding and improving computational reproducibility and replicability (R and R)...

Higher-Order Knowledge Representations for Agentic Scientific Reasoning

Scientific inquiry requires systems-level reasoning that integrates heterogeneous experimental data, cross-domain knowledge, and mechanistic evidence into coherent explanations. While Large Language Models (LLMs) offer inferential capabilities, they often depend on retrieval-augmented contexts that lack structural depth. Traditional Knowledge Graphs (KGs) attempt to bridge this gap, yet their pairwise constraints fail to capture the irreducible higher-order interactions that govern emergent physical behavior. To address this, we introduce a methodology for constructing hypergraph-based knowledge representations that faithfully encode multi-entity relationships. Applied to a corpus of ≈\approx 1,100 manuscripts on biocomposite scaffolds, our framework constructs a global hypergraph of 161,172 nodes and 320,201 hyperedges, revealing a scale-free topology (power law exponent ≈\approx 1.23) organized around highly connected conceptual hubs. This representation prevents the combinatorial explosion typical of pairwise expansions and explicitly preserves the co-occurrence context of scientific formulations. We further demonstrate that equipping agentic systems with hypergraph traversal tools, specifically using node-intersection constraints, enables them to bridge semantically distant concepts. By exploiting these higher-order pathways, the system successfully generates grounded mechanistic hypotheses for novel composite materials, such as linking cerium oxide to PCL scaffolds via chitosan intermediates. This work establishes a “teacherless” agentic reasoning system where hypergraph topology acts as a verifiable guardrail, accelerating scientific discovery by uncovering relationships obscured by traditional graph methods.

The AI Chemist: To be trustworthy, LLMs need to show their work

Good scientists reveal how they do their experiments and report their results; so should any machine-driven research