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This paper proposes a qubit-efficient quantum framework for hyperdimensional computing decomposition that reduces representation cost from O(D) to O(log D) qubits while preserving the quadratic search advantage, achieving up to 2000x fewer qubits.
Mitchell Hashimoto shares a heuristic for using AI agents: any diff over 1500 lines indicates a need for decomposition, and outlines a pattern for iterative development with agent-generated code.
This paper presents an information-theoretic analysis of multimodal learning, revealing the need to capture sample-specific interactions, and proposes DMIL, a paradigm that explicitly models and learns from these interactions via variational decomposition and fine-tuning, achieving superior performance.
Proposes TD-Grokking, a training-time decomposition framework that recursively breaks down intractable zero-reward problems into verifiable subproblems, enabling LLMs to learn from failed trajectories. Outperforms vanilla GRPO and baselines on mathematical and medical reasoning tasks.
DeSQ is a decomposition-based framework for generating SPARQL queries from natural language questions. It breaks complex questions into atomic constraints, maps them to SPARQL fragments, and assembles them into complete queries, outperforming state-of-the-art on four out of five benchmarks.
The paper decomposes the attention interaction matrix into routing (skew-symmetric) and filtering (symmetric) components, introducing S-D attention to disentangle them. It reveals a spectral cascade in routing that predicts where attention can be simplified, achieving significant parameter reduction with minimal perplexity loss.
Proposes PESD-TSF, a physics-inspired structured decomposition framework for long-term time series forecasting that addresses periodic perception degradation, trend-noise entanglement, and loss of cross-variable dependencies via multiplicative periodic gating, multi-scale structured encoder, and cross-scale collaborative attention.