{"slug": "grounded-compute-efficient-llm-policy-agents-for-energy-poverty-equity-in-peer", "title": "Grounded, Compute-Efficient LLM Policy Agents for Energy-Poverty Equity in Physically-Constrained Peer-to-Peer Energy Markets", "summary": "Researchers introduced EqGrid, a closed-loop simulation where a low-frequency, open-weight LLM policy agent sets price, carbon bounds, and targeted subsidies for peer-to-peer energy markets, reducing the Gini of energy burden to 0.305 from 0.351 and mean burden by 28% while cutting costs. The decoupled-safety design achieved zero grid-constraint violations versus 55 under direct LLM control, and a 3B-active model retained 95% of the equity benefit at roughly 9x lower inference energy than a 235B teacher, with a 0.8B on-device model retaining 92% at roughly 24x lower energy.", "body_md": "arXiv:2609.01918v1 Announce Type: new\nAbstract: Energy poverty is nearly absent from NLP-for-social-good, and the little existing work is either static retrieval/QA or relies on carbon-intensive cloud LLMs, a self-defeating \"computational irony\" for a humanitarian setting. We present EqGrid, a closed-loop simulation in which a low-frequency, open-weight LLM policy agent sets price and carbon bounds and targeted subsidies over a community of empirically-grounded household personas, while high-frequency multi-agent RL traders clear a continuous double auction constrained by a physical distribution grid (IEEE-33-bus with Dynamic Operating Envelopes). Our contribution is threefold and directly addresses how to measure the social impact of AI: (i) grounded personas (region-matched socio-demographics) whose load curves are checked for shape and level realism against real smart-meter data; (ii) formal energy-poverty equity metrics (Energy Burden, Gini of EB, LIHC) showing the intervention reduces burden inequality without raising net grid cost; and (iii) a compute-efficiency frontier that measures how much equity performance survives compressing the policy agent from a 235B teacher down to a sub-1B model deployable on a laptop, in estimated energy/carbon per decision. A decoupled-safety design (the LLM sets bounds; a validate-and-project grid gate executes) yields zero grid-constraint violations versus 55 under direct LLM control. On energy-poverty equity, the LLM policy lowers the Gini of energy burden to 0.305 (from 0.351) and mean burden by 28% while cutting cost (outperforming a tuned rule baseline), and a 3B-active model retains 95% of the benefit at roughly 9x lower inference energy than the teacher, with even a 0.8B on-device model retaining 92% at roughly 24x lower energy. We will release code and configs.", "url": "https://wpnews.pro/news/grounded-compute-efficient-llm-policy-agents-for-energy-poverty-equity-in-peer", "canonical_source": "https://arxiv.org/abs/2609.01918", "published_at": "2026-09-03 04:00:00+00:00", "updated_at": "2026-09-03 04:25:47.179774+00:00", "lang": "en", "topics": ["artificial-intelligence", "large-language-models", "ai-research", "ai-safety"], "entities": ["EqGrid", "arXiv", "IEEE-33-bus"], "alternates": {"html": "https://wpnews.pro/news/grounded-compute-efficient-llm-policy-agents-for-energy-poverty-equity-in-peer", "markdown": "https://wpnews.pro/news/grounded-compute-efficient-llm-policy-agents-for-energy-poverty-equity-in-peer.md", "text": "https://wpnews.pro/news/grounded-compute-efficient-llm-policy-agents-for-energy-poverty-equity-in-peer.txt", "jsonld": "https://wpnews.pro/news/grounded-compute-efficient-llm-policy-agents-for-energy-poverty-equity-in-peer.jsonld"}}