Humanities & Social Sciences

AI safety policy needs diverse input, yet the public cannot join every discussion. The work builds pluralistic agents: each AI agent first interviews the person it represents, then speaks for them in a multi-agent deliberation, while a policy-writer agent drafts and revises a budget after each round. In a preregistered study with 746 participants, people rated this process above giving input to a single reasoning model, recovering 68% of the satisfaction gap and 82% of the procedural-justice gap between that baseline and direct participation.

AI safety policy needs diverse input, yet the public cannot join every discussion. The work builds pluralistic agents: each AI agent first interviews the person it represents, then speaks for them in a multi-agent deliberation, while a policy-writer agent drafts and revises a budget after each round. In a preregistered study with 746 participants, people rated this process above giving input to a single reasoning model, recovering 68% of the satisfaction gap and 82% of the procedural-justice gap between that baseline and direct participation.

Ottoman Divan poetry is written in aruz, a quantitative meter that counts each syllable as long or short, and modern Turkish spelling drops the marks that record long vowels, so length has to be inferred from context. A lattice-based hidden Markov model lays out candidate scholarly spellings of each word together with linking and lengthening choices as paths, is trained by EM from each poem's printed meter, and scans 77.6% of held-out lines fully correctly, against 26.8% for a rule-based scanner, 55.0% for a neural tagger trained with the same supervision and 63.6% for Claude Opus 5.5 few-shot on the same 500 lines. The code is open-sourced and the tool is released on the web.

Ottoman Divan poetry is written in aruz, a quantitative meter that counts each syllable as long or short, and modern Turkish spelling drops the marks that record long vowels, so length has to be inferred from context. A lattice-based hidden Markov model lays out candidate scholarly spellings of each word together with linking and lengthening choices as paths, is trained by EM from each poem's printed meter, and scans 77.6% of held-out lines fully correctly, against 26.8% for a rule-based scanner, 55.0% for a neural tagger trained with the same supervision and 63.6% for Claude Opus 5.5 few-shot on the same 500 lines. The code is open-sourced and the tool is released on the web.

Ancient-book pages often carry paper ageing, water-induced distortion, insect holes and mold at once, while existing public benchmarks are limited in scale and stack isolated degradations, which weakens supervised restoration. AncBench supplies 100,379 pixel-aligned clean–degraded pairs in which chemical, physical and biological damage are composed in sequence; trained on it, AncRestore first estimates page-wide and local degradation, then dynamically selects and distributes visual priors according to those cues. On the AncBench test set AncRestore reaches 26.70 dB PSNR, 0.9191 SSIM and 0.1346 LPIPS, with 89.58% character-recognition accuracy.

Ancient-book pages often carry paper ageing, water-induced distortion, insect holes and mold at once, while existing public benchmarks are limited in scale and stack isolated degradations, which weakens supervised restoration. AncBench supplies 100,379 pixel-aligned clean–degraded pairs in which chemical, physical and biological damage are composed in sequence; trained on it, AncRestore first estimates page-wide and local degradation, then dynamically selects and distributes visual priors according to those cues. On the AncBench test set AncRestore reaches 26.70 dB PSNR, 0.9191 SSIM and 0.1346 LPIPS, with 89.58% character-recognition accuracy.

An occupational task catalogue for the US economy now has language models interview workers directly instead of relying only on fixed questionnaires. Standardized surveys like O*NET are slow to catch tasks that have just appeared, and human-led interviews are expensive to run at scale. The interviewer asks follow-up questions to clarify vague descriptions and confirms the resulting task bundles with the worker; on a holdout of 1,184 task reports from 57 workers, TaskNet's lists covered 89.3% of reported tasks on average across six occupations, against 78.6% for O*NET.

An occupational task catalogue for the US economy now has language models interview workers directly instead of relying only on fixed questionnaires. Standardized surveys like O*NET are slow to catch tasks that have just appeared, and human-led interviews are expensive to run at scale. The interviewer asks follow-up questions to clarify vague descriptions and confirms the resulting task bundles with the worker; on a holdout of 1,184 task reports from 57 workers, TaskNet's lists covered 89.3% of reported tasks on average across six occupations, against 78.6% for O*NET.

Splitting the Enron email network into 44 thirty-day snapshots and matching maximal cliques one-to-one by Jaccard similarity leaves about 60% of cliques without a successor, so next-month tension is undefined for most rows. Over 24 rolling origins, residual GCN, GraphSAGE-style and GCN+GRU models that start at persistence are equivalent to carrying the current value forward on surviving cliques, a plain GCN does worse, and neighbourhood and recurrent memory add nothing measurable. In the deployable setting, multiplying persistence by a simple hazard-model survival probability lowers error by about 17%.

Splitting the Enron email network into 44 thirty-day snapshots and matching maximal cliques one-to-one by Jaccard similarity leaves about 60% of cliques without a successor, so next-month tension is undefined for most rows. Over 24 rolling origins, residual GCN, GraphSAGE-style and GCN+GRU models that start at persistence are equivalent to carrying the current value forward on surviving cliques, a plain GCN does worse, and neighbourhood and recurrent memory add nothing measurable. In the deployable setting, multiplying persistence by a simple hazard-model survival probability lowers error by about 17%.

Grammar assessment for international Chinese education is recast as proof-carrying constrained inference: retrieved rules, error spans, minimal repairs and ordinal grades are coupled through one latent evidence graph, and every score-affecting judgment must carry a rule-level support certificate. On public corpora including MuCGEC, PC2G reaches an error-detection F0.5 of 71.4 against 67.6 for the strongest listed retrieval baseline, CRAG-style, while five-level grade MAE falls from 0.57 to 0.42 and evidence faithfulness reaches 91.8%. Those grades are measured against a silver rubric derived from reference edits, not independent teacher ratings.

Grammar assessment for international Chinese education is recast as proof-carrying constrained inference: retrieved rules, error spans, minimal repairs and ordinal grades are coupled through one latent evidence graph, and every score-affecting judgment must carry a rule-level support certificate. On public corpora including MuCGEC, PC2G reaches an error-detection F0.5 of 71.4 against 67.6 for the strongest listed retrieval baseline, CRAG-style, while five-level grade MAE falls from 0.57 to 0.42 and evidence faithfulness reaches 91.8%. Those grades are measured against a silver rubric derived from reference edits, not independent teacher ratings.

Chinese regions use separate support policies to compete for one-person companies, solo founders who work through digital platforms and AI agents and can compare locations, and the two sides used to act on their own separate estimates. PolicyMediator turns that into a cognition-constrained leader-multi-follower Stackelberg framework: a mediator agent explains verified policies to the companies to build awareness and trust, then reports their needs and unresolved concerns to governments for policy learning. Across six-region simulations in China with five OPC types, five scenarios and three simulator backbones, it ranks first in all 15 combinations, with the aggregate government objective 42.4% above the eight-baseline mean.

Chinese regions use separate support policies to compete for one-person companies, solo founders who work through digital platforms and AI agents and can compare locations, and the two sides used to act on their own separate estimates. PolicyMediator turns that into a cognition-constrained leader-multi-follower Stackelberg framework: a mediator agent explains verified policies to the companies to build awareness and trust, then reports their needs and unresolved concerns to governments for policy learning. Across six-region simulations in China with five OPC types, five scenarios and three simulator backbones, it ranks first in all 15 combinations, with the aggregate government objective 42.4% above the eight-baseline mean.

When large language models apply a codebook, a structured annotation guide that maps theoretical concepts onto labels, high aggregate scores can hide label-level failures. Across four datasets and five models, controlled prompt perturbations, repeated runs and a domain shift left worst-label F1 about 26 to 30 points below micro-F1, and models leaned on label names more than on definitions. These failures stayed invisible in accuracy and micro-F1 and persisted after prompt optimization.

When large language models apply a codebook, a structured annotation guide that maps theoretical concepts onto labels, high aggregate scores can hide label-level failures. Across four datasets and five models, controlled prompt perturbations, repeated runs and a domain shift left worst-label F1 about 26 to 30 points below micro-F1, and models leaned on label names more than on definitions. These failures stayed invisible in accuracy and micro-F1 and persisted after prompt optimization.