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 answers the workshop’s special theme of 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 measured 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. We will release code and configs. 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 ≈9× lower inference energy than the teacher, with even a 0.8B on-device model retaining 92% at ≈24× lower energy.

Literature-grounded question answering requires a system to retrieve the right paper, locate the right evidence within it, and generate an answer faithful to that evidence. These are three separate stages, each with its own failure mode. We present a stage-wise diagnostic study of this pipeline on LitTraceQA, a shared task requiring paper retrieval, coarse evidence localization (table/figure/page), and structured answer generation. Our pipeline combines cross-model hypothesis diversity for retrieval, a filter-then-rank evidence localizer (caption-pattern match, embedding similarity, evidence-type gate), and cross-model semantic entropy for generation-time answer aggregation and confidence estimation. We use an oracle-substitution ablation ladder, sequentially handing the pipeline the gold paper and then the gold evidence value, and reading off the resulting change in each stage’s own metric. These are sequential oracle-intervention gains, not an additive error decomposition: the stages are measured with different quantities and are not independent. Read that way, the ladder points at evidence localization as the dominant remediable bottleneck — multiple-choice accuracy rises from 0.42 with predicted evidence to 0.76 with the gold evidence value, and end-to-end accuracy stays near 0.29 even on the questions where retrieval already returned every gold paper. We also test whether cheap uncertainty signals computed at retrieval time predict which stage will fail before it does. Along the way we surface two infrastructure-level findings with direct implications for anyone building literature-grounded QA systems. First, evidence page numbers in this and similar benchmarks are keyed to a paper’s canonical published version rather than its arXiv preprint; a real failure mode when a paper has been substantively revised, though a systematic paired ablation across 45 questions finds it is an occasional rather than pervasive hazard in this pool. Second, a substantial fraction of papers hosted on OpenReview (ICLR/ICML/NeurIPS) are unreachable by ordinary scripted HTTP requests due to a JS bot-challenge, a venue-correlated failure mode unrelated to any modeling choice. We report our diagnostic protocol, quantitative results with bootstrap confidence intervals given the benchmark’s small public dev set, and a qualitative failure taxonomy, framing this as a resource and lessons-learned contribution rather than a leaderboard claim. All multiple-choice numbers are a best-case diagnostic condition: they assume access to the gold option text, which a genuine blind submission would not have.