Papers by Heung-Yeung Shum

3 papers
PaCoRe: Learning to Scale Test-Time Compute with Parallel Coordinated Reasoning (2026.acl-long)

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Challenge: Parallel Coordinated Reasoning (PaCoRe) overcomes a central limitation of contemporary language models: their inability to scale test-time compute (TTC) far beyond sequential reasoning under a fixed context window.
Approach: They propose a training-and-inference framework to overcome a central limitation of language models: their inability to scale test-time compute (TTC) under a fixed context window.
Outcome: The proposed model scales to multi-million-token effective TTC without exceeding context limits.
Multi-matrix Factorization Attention (2025.findings-acl)

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Challenge: Existing variants for Multi-Head Attention (MHA) fail to maintain strong performance under stringent Key-Value cache (KV cache) constraints.
Approach: They propose to use multi-matrix factorization attention and MFA-Key-reuse attention architectures to increase model capacity under tight KV cache constraints.
Outcome: The proposed architecture outperforms existing methods while reducing KV cache usage by 56% and 93.7% in large-scale experiments.
Alpha-GPT: Human-AI Interactive Alpha Mining for Quantitative Investment (2025.emnlp-demos)

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Challenge: Traditional methods of alpha mining have inherent limitations, especially in implementing the ideas of quant researchers.
Approach: They propose a new alpha mining paradigm by introducing human-AI interaction and a prompt engineering algorithmic framework to implement this paradigm by using large language models.
Outcome: The proposed framework is based on human-AI interaction and large language models and is comparable to human participants in the WorldQuant International Quant Championship.

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