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Amplitude Gating improves LLM structured output without retraining

By PulseAugur Editorial · [2 sources] ·

Researchers have developed a new method called Amplitude Gating (AG) to improve the structured output of large language models during inference without retraining. This technique modulates activation magnitudes within feed-forward networks (FFNs), preserving pretrained weights. AG showed particular promise on tool-structured tasks, improving performance on models like Qwen3.5-9B and Qwen3-8B, with notable gains in function-call and JSON mode tasks. AI

IMPACT This method could lead to more reliable and accurate structured outputs from LLMs in tool-use scenarios, reducing errors in function calls and data formatting.

RANK_REASON The cluster contains an academic paper detailing a new method for LLM inference.

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

Amplitude Gating improves LLM structured output without retraining

COVERAGE [2]

  1. arXiv cs.CL TIER_1 English(EN) · Sheng Xu, Boyuan Huang, Ke Jia, Jiadun Zhu, Zhen Chen ·

    Amplitude-Only FFN Intervention for Tool-Structured LLM Inference Method: Gated Evaluation Protocol, and Cross-Model Empirical Results

    arXiv:2607.11183v1 Announce Type: new Abstract: Large language models increasingly operate as tool-using agents, where small format, argument, or function-call errors can invalidate otherwise plausible responses. We study inference-time feed-forward network (FFN) intervention for…

  2. arXiv cs.CL TIER_1 English(EN) · Zhen Chen ·

    Amplitude-Only FFN Intervention for Tool-Structured LLM Inference Method: Gated Evaluation Protocol, and Cross-Model Empirical Results

    Large language models increasingly operate as tool-using agents, where small format, argument, or function-call errors can invalidate otherwise plausible responses. We study inference-time feed-forward network (FFN) intervention for improving structured outputs without retraining…

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