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cs.AI2026

The Deterministic Horizon: When Extended Reasoning Fails and Tool Delegation Becomes Necessary

Dongxin Guo, Jikun Wu, Siu Ming Yiu

Extended chain-of-thought reasoning can degrade performance on deterministic state-tracking tasks, not solely because of preference biases but, on the evidence we present, because…

cs.AI2026

The Deterministic Horizon: Impossibility Results as Design Specifications for Trustworthy AI Systems

Dongxin Guo

Large language models now write software, draft legal documents, and produce clinical notes, yet fundamental limits, from Turing and Arrow to the No Free Lunch theorems, shape what…

cs.AI2026

Bias by Necessity: Impossibility Theorems for Sequential Processing with Convergent AI and Human Validation

Jikun Wu, Dongxin Guo, Siu-Ming Yiu

Are certain cognitive biases mathematically inevitable consequences of sequential information processing? We prove that primacy effects, anchoring, and order-dependence are archite…

cs.AI2026

When Can Human-AI Teams Outperform Individuals? Tight Bounds with Impossibility Guarantees

Dongxin Guo, Jikun Wu, Siu-Ming Yiu

Human-AI teams fail to outperform their best member in 70% of studies, yet no theory specifies when complementarity is achievable. We derive tight bounds for the broad class of con…

cs.AI2026

Geometric Metrics for MoE Specialization: From Fisher Information to Early Failure Detection

Dongxin Guo, Jikun Wu, Siu Ming Yiu

Expert specialization is fundamental to Mixture-of-Experts (MoE) model success, yet existing metrics (cosine similarity, routing entropy) lack theoretical grounding and yield incon…