Summarize Before You Speak with ARACH: A Training-Free Inference-Time Plug-In for Enhancing LLMs via Global Attention Reallocation

2026-03-13 · Source: cs.CL updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Natural Language Processing · Depth: Advanced, extended

Summary

ARACH (Attention Reallocation via an Adaptive Context Hub) is a training-free, inference-time plug-in designed to enhance Large Language Models (LLMs) by intervening in their internal attention computation. Unlike most training-free methods that operate on input/output, ARACH augments decoder-only Transformers with an adaptive context hub that dynamically summarizes the causally available prefix. This hub operates as a parallel stream of tokens, providing an explicit, compact representation of long-range context for next-token prediction without modifying any pre-trained model weights. ARACH also incorporates a tunable logit offset, typically negative, to regulate hub-related attention strength, preventing over-concentration and mitigating the "attention sink" phenomenon. Experiments on GPT-2 small across datasets like LAMBADA, PG-19, and StoryCloze show consistent performance improvements with minimal inference overhead.

Key takeaway

For AI Engineers seeking to improve LLM performance without costly retraining or complex prompt engineering, ARACH offers a compelling, training-free inference-time solution. By integrating this plug-in, you can enhance next-token prediction and mitigate attention sink issues in decoder-only Transformers like GPT-2 small, particularly for tasks demanding robust long-range context integration. Consider deploying ARACH to achieve consistent gains with minimal computational overhead and no parameter updates.

Key insights

ARACH enhances LLMs by reallocating internal attention via a training-free context hub and logit offset at inference time.

Principles

• Internal computation intervention improves LLMs.
• Attention reallocation mitigates attention sink.
• Training-free methods can yield consistent gains.

Method

ARACH introduces a context hub as a parallel token stream with specific causal visibility constraints and a logit offset to regulate hub-mediated attention, all without parameter updates.

In practice

• Apply ARACH to GPT-2 small for improved performance.
• Use a negative logit offset (e.g., b=-0.5) for stable gains.
• Integrate ARACH for tasks requiring broad prefix context.

Topics

• Large Language Models
• Attention Reallocation
• Training-Free Inference
• Context Hub
• Attention Sink Mitigation

Best for: AI Engineer, NLP Engineer, AI Scientist, AI Researcher, Machine Learning Engineer, Research Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.CL updates on arXiv.org.