Politeness vs performance in LLM prompts is the latest AI skirmish. A wave of posts wonders whether a courteous prompt nudges accuracy or if politeness is cosmetic [1].
Politeness and accuracy - The 2510.04950 study says politeness shouldn’t predict problem-solving ability [1]. Yet many researchers push back, noting that measuring true problem solving in language tasks is messy and highly context-dependent.
Reasoning models: emergent or engineered? - The piece Reasoning Models Reason Well, Until They Don't argues LRMs improve through longer prompts and self-verification, not magical leaps in reasoning; critics say more tokens = more computation, not new understanding [2].
Open-ended benchmarking - GDPVal evaluates agent performance on economically valuable tasks; labs experiment with persistent agents across stacks in the AI Village to test goals like raising charity money, organizing events, or selling T-shirts online [3]. Gemini 2.5 Pro’s rudimentary podcast/documentary attempts highlight the action gap between ambition and real-world work [3].
Context-Bench - The Context-Bench benchmark from LeTTA tests LLMs on agentic context engineering, a key way prompts shape behavior [4].
Meanwhile, Quanta Magazine reports AI models analyze language as well as a human expert, hinting at broader language-understanding benchmarks [5].
As prompts tighten, benchmarking like GDPVal and Context-Bench will keep redefining what we call “accuracy” in real-world use.
References
Investigating How Prompt Politeness Affects LLM Accuracy
Explores whether prompt politeness changes LLM performance; questions if politeness affects problem-solving and language-based evaluation in model accuracy metrics studies.
View sourceReasoning Models Reason Well, Until They Don't
Critiques of LRMs vs LLMs; argues 'reasoning' is prompt-driven, more tokens equals better; disputes true reasoning emergence, claims reliability issues.
View source[D] How to benchmark open-ended, real-world goal achievement by computer-using LLMs?
Discusses benchmarking frontier LLMs on real-world tasks with goals, tools, and evaluation challenges, noting hallucinations and action gaps.
View sourceContext-Bench: Benchmarking LLMs on Agentic Context Engineering
Discusses benchmarking LLMs on agentic context engineering, evaluating capabilities and limitations, with evaluation methods and comparisons.
View sourceIn a First, AI Models Analyze Language as Well as a Human Expert
Quanta Magazine reports AI models matching human expert language analysis, sparking discussion on capabilities and limits of LLMs
View source