Research
Papers & preprints
Original papers and technical reports from our work on GIDE — alongside the foundational third-party research it builds on.
What these topics mean
- Distributed Training
- Sharding computation and memory across many devices to scale beyond a single accelerator.
- Efficient Attention
- Methods that cut attention's compute and memory cost without changing its output.
- GPU Kernels & Systems
- Hardware-aware implementations that map attention efficiently onto GPU memory and compute.
- Long-Context
- Extending models to very long sequences — hundreds of thousands to billions of tokens.
- Low-Precision & Quantization
- Trading numerical precision for speed and memory while bounding the accuracy cost.
- Safety & Control
- Methods that keep a learning system provably within specified safety limits while it acts.
- Self-Supervised Learning
- Learning useful representations from unlabeled data by predicting parts of the input from other parts.
- State-Space Models
- Linear-time sequence models that replace attention with a recurrent state, scaling to long sequences without the quadratic cost.
- Transformer Architecture
- The attention-based neural network architecture that underlies modern sequence models.
- World Models
- Models that learn how a system evolves in a latent representation space, for prediction and planning rather than generation.
Topic
Safety & Control
Methods that keep a learning system provably within specified safety limits while it acts.
Showing 3 of 18 papers.
DarcStar research
Original work authored by DarcStar Technologies.
The Intelligence Ratchet: A Theoretical Framework for Self-Stabilizing Artificial Superintelligence
Preprint Zenodo·February 4, 2026By Michael Harris
Current paradigms in Artificial Intelligence face a seemingly intractable trade-off between Capability and Safety. Systems capable of recursive self-improvement (the "Singularity") risk unbounded instability, while systems with provable safety guarantees ("Bounded Optimality") are mathematically prevented from generating novel solutions. This paper introduces the General Interaction Dynamics Engine (GIDE), a theoretical framework…
Foundational reading
Notable third-party research we build on.
Control Barrier Functions: Theory and Applications
Published Third-party DarcStar commentary ECC·March 27, 2019By Aaron D. Ames, Samuel Coogan, Magnus Egerstedt, Gennaro Notomista, Koushil Sreenath, Paulo Tabuada
This is third-party work — not authored by or affiliated with DarcStar Technologies.
This paper provides an introduction and overview of recent work on control barrier functions and their use to verify and enforce safety properties in the context of (optimization based) safety-critical controllers. We survey the main technical results and discuss applications to several domains including robotic systems.
Safe Reinforcement Learning via Shielding
Published Third-party DarcStar commentary AAAI·February 2, 2018By Mohammed Alshiekh, Roderick Bloem, Ruediger Ehlers, Bettina Könighofer, Scott Niekum, Ufuk Topcu
This is third-party work — not authored by or affiliated with DarcStar Technologies.
Reinforcement learning algorithms discover policies that maximize reward, but do not necessarily guarantee safety during learning or execution phases. We introduce a new approach to learn optimal policies while enforcing properties expressed in temporal logic. To this end, given the temporal logic specification that is to be obeyed by the…