1bit-LLM: The BitNet Frontier
Domain: Quantization
Status: In-Development
Abstract
Conventional LLMs rely on 16-bit or 8-bit floating point weights, leading to massive memory and
compute overhead. This experiment implements the BitNet b1.58 architecture, which uses ternary
weights (-1, 0, 1), effectively eliminating the need for multiplication operations during inference.
The Core Thesis
By forcing weights into a 1.58-bit representation, we can leverage addition-only kernels. This
reduces energy consumption by up to 70% while maintaining perplexity competitive with FP16 models at
scale.
Implementation Plan
1. Develop custom CUDA kernels for ternary matrix multiplication.
2. Quantization-aware training
(QAT) on Llama-3-8B base.
3. Benchmark energy efficiency on mobile-grade ARM processors.
CUDA
PyTorch
Quantization
BitNet
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Q-Logic: Quantum Hybrid Reasoning
Domain: Quantum Computing
Status: Theoretical Blueprint
Abstract
Q-Logic explores the integration of Variational Quantum Circuits (VQC) as specialized reasoning
layers within classical Transformer architectures. The goal is to solve combinatorial optimization
problems that are NP-hard for traditional neural networks.
Architecture
A hybrid system where the LLM acts as a controller, encoding problems into quantum circuits, while
the QPU (Quantum Processing Unit) performs the high-dimensional state search. The result is returned
as a collapsed state token back into the transformer's latent space.
Qiskit
TensorFlow Quantum
VQC
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OmniSync: The Unified Latent Architecture
Domain: Multimodal Fusion
Status: Active Prototype
Abstract
OmniSync is a universal encoding framework designed to dissolve the boundaries between data types.
Instead of separate encoders for text, vision, and audio, OmniSync uses a single high-dimensional
manifold where all tokens exist as part of a continuous signal.
Mechanism
The system leverages a "Latent Synchronizer" that maps heterogeneous inputs into a shared geometric
space. This allows for direct cross-modal operations (e.g., "subtracting" a visual style from a text
prompt via vector arithmetic in the core manifold).
Cross-Attention
Unified Latent Space
OmniSync
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Sparse-X: Infinite Context Attention
Domain: Attention Efficiency
Status: Benchmark Stage
Abstract
Sparse-X addresses the quadratic complexity of traditional self-attention. By implementing sparse
attention kernels and Flash-Attention-3, we can process million-token contexts in linear time with
minimal memory overhead.
Architecture
A multi-stage sparse-attention kernel that identifies high-impact token relationships and ignores
unimportant Noise. This allows for long-range dependency modeling without the O(N²) cost.
FlashAttention
Sparse Kernel
Million-Token
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Neuro-Symbolic Reasoning: The Logic Bridge
Domain: Reasoning & Logic
Status: Research
Abstract
This experiment bridges the gap between neural network intuition and symbolic logic. By integrating a
formal reasoning engine (like Z3 or Lean) into the LLM's decoding loop, we can verify mathematical
proofs in real-time.
The Logic Loop
The neural network generates a hypothesis, which is then parsed by a symbolic engine. If the logic
fails, the engine provides a counter-example, forcing the network to refine its reasoning
recursively.
Z3 Solver
Lean 4
Zero-Shot Proofs
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Agent-Zero: Recursive Self-Evolution
Domain: Autonomous Agents
Status: Alpha Lab
Abstract
Agent-Zero is an autonomous framework designed for recursive self-improvement. Unlike traditional
agents, Agent-Zero is capable of writing, testing, and deploying its own code to optimize its
internal logic for specific tasks.
Feedback Loop
The agent operates in a sandbox, attempting tasks and identifying bottlenecks. It then generates a
"Code-Expansion" patch to update its own instruction set, effectively evolving its capabilities over
time.
Self-Coding
Recursive Loops
Agentic AI
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Real-Time Latent Diffusion: Instant Vision
Domain: Generative Media
Status: Optimized
Abstract
Accelerating high-fidelity video generation to 60fps. This experiment leverages TensorRT and custom
CUDA kernels to perform latent space denoising in sub-millisecond intervals.
Hardware Optimization
By bypassing standard high-level libraries and interacting directly with GPU registers, we achieve
massive throughput for real-time interactive AI environments.
TensorRT
CUDA
60fps Video
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Bio-Synthetic Synapses: Learning in Silicon
Domain: Neuromorphic Computing
Status: Simulation Stage
Abstract
Simulating biological synaptic plasticity (Hebbian learning) within standard backpropagation models.
This initiative explores learning efficiency that approaches biological speed by mimicking synaptic
strengthening and weakening.
Thesis
Traditional neural networks are static after training. Bio-Synthetic Synapses allow the model to
adapt its weights locally in response to new data without retraining the entire model.
Hebbian Learning
Plasticity
Synaptic Simulation
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Differentiable Search Index (DSI): Weight as Memory
Domain: Information Retrieval
Status: Prototype
Abstract
Transforming Retrieval Augmented Generation (RAG) into internal model weight memory. Instead of
querying a vector database, the model is trained to generate document IDs directly from its own
parameter space.
The Neural Index
We eliminate the retrieval-latency bottleneck by teaching the model to navigate its own weights as a
search index, unifying knowledge storage and generation into a single neural process.
Neural Search
Weight-Memory
RAG Evolution
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