Papers by Tianchen Huang

2 papers
Hallucinations as Orthogonal Noise: Inference-Time Manifold Alignment via Dynamic Contextual Orthogonalization (2026.findings-acl)

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Challenge: Hallucinations in Large Language Models persist in critical domains where generated content diverges from contextual facts or logical constraints.
Approach: They propose to generate hallucinations as orthogonal noise relative to the semantic manifold of the residual stream.
Outcome: The proposed method achieves superior contextual faithfulness compared to state-of-the-art methods.
Resonant Context Anchoring: Decoupling Attention Routing and Signal Gain at Inference Time (2026.findings-acl)

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Challenge: Existing mitigation strategies rely on suppressing specific neuron activations or employing computationally expensive contrastive decoding mechanisms, which often result in increased perplexity or significantly elevated inference latency.
Approach: They propose a lightweight inference-time intervention method grounded in the perspective of residual stream signal dynamics to resolve the signal attenuation of external evidence during its propagation through deep networks.
Outcome: The proposed method improves contextual faithfulness across multiple factual consistency and strong knowledge-conflict tasks while maintaining the model’s general language understanding capabilities.

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