Applied Research & Systems Papers
Our lab investigates fundamental questions in low-latency machine learning inference, verifiable multi-agent consensus, and memory safety in distributed systems.
Reasoning Distillation in Constrained Foundation Models
Investigating parameter-efficient distillation of multi-step chain-of-thought capabilities into sub-8B parameter models to enable high-accuracy autonomous decision-making in edge and offline environments.
- Synthetic Dataset Curations & Verification
- Reinforcement Learning with Verified Feedback (RLVF)
- Formal Mathematical Reasoning Proof Trees
Asynchronous Byzantine Consensus for Tool-Using Agent Swarms
Developing fault-tolerant coordination protocols that allow heterogeneous language model agents to achieve distributed agreement on external state mutations under partial network partition conditions.
- Quorum-Sensing Decision Boundaries
- Cryptographically Signed State Transitions
- Fault Injection & Chaos Engineering Simulations
Zero-Copy Tensor Quantization in SIMD Vector Environments
Analyzing hardware cache behavior and memory bandwidth constraints when executing mixed-precision 4-bit neural weights across modern x86_64 AVX-512 and ARM Neon processor architectures.
- Non-Uniform Memory Access (NUMA) Awareness
- Lock-Free Atomic Buffer Allocations
- Sub-Millisecond Inference Tail Latency
Deterministic Verification of Neural Tool Invocations
Establishing mathematical bounds and runtime monitors that prevent autonomous agents from breaching operational sandboxes, executing unauthorized API calls, or triggering unintended data modifications.
- Finite-State Machine (FSM) Output Encoders
- Sandboxed Syscall Interception
- Dynamic Threat Surface Reduction
Published Technical Briefs & Whitepapers
Sub-Millisecond Coordination Protocol for Autonomous Agent Swarms
Distributed Systems Research Group • BEMS Labs LLC
Adaptive Quantization Techniques for Low-Footprint Vector Embeddings
Applied Machine Intelligence Core • BEMS Labs LLC
Academic & Research Inquiries
For academic collaboration, research partnerships, or peer paper exchanges, reach out directly to info@bems.work.
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