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This paper proposes a randomized anchoring strategy to mitigate anchoring bias in LLM-based agents for energy-efficient 6G autonomous networks, achieving up to 25% energy savings using a lightweight 1B-parameter model.
This paper investigates how irrelevant numbers in prompts cause anchoring effects in language models and localizes the internal pathways carrying this signal using attribution-based circuit methods on Qwen and Llama models.