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新论文 · v1arXiv:2611.07100

Stacking Stars: Minimum-Area Packing with and without Rotation

Mikkel Abrahamsen, Jacobus Conradi, Arindam Khan, Jack Stade, Sampson Wong, Shuyi Yan, Hanwen Zhang

原文摘要

We study the problem of packing a given set of polygons into a minimum-area convex container, that is, placing them without overlap so as to minimize the area of their convex hull. Every previously known polynomial-time algorithm with a nontrivial approximation guarantee for packing polygons with arbitrary edge orientations required the polygons to be convex. In fact, an algorithm was not even known when every input polygon was a quadrilateral. We obtain such algorithms for the entire class of star-shaped polygons with unrestricted combinatorial complexity. The stars may be just translated or also rotated. In either case, we give an $O(\log n)$-approximation algorithm, where $n$ is the number of stars. Likewise, we get $O(\log n)$-approximation algorithms for bin and strip packing, assuming that the diameter of the stars is at most some (small) constant. Previous approximation algorithms for polygon packing focused on convex objects, and the analysis always relied on a density argument, i.e., if the area of the packed objects is at least a fraction $ρ$ of the area of the container, then we have a $(1/ρ)$-approximation. However, star-shaped polygons may need an arbitrarily large area to be packed, even when the total area of the stars approaches $0$ and each star has bounded diameter. This phenomenon prevents the use of density arguments. We instead establish a strong structural property about any packing: Even though the stars can fit together in complicated ways, we can reorganize them into simple stacks, replace these stacks by rectangles, and consider a packing of these rectangles. The rearrangement costs only a factor of $O(\log n)$, which stems from the number of size groups. We complement our algorithms by showing that both the translational and the rotational versions are APX-hard, even for constant complexity stars.

cs.CG · cs.DS

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.06006

Familiar letters, not related languages, let unseen languages into a shared space of meaning

Yuliang Liu, Xinyu Wang, Xiang Bai

原文摘要

Hundreds of languages have written text but almost no translations, and whether a model learning one from untranslated text builds on a related language or familiar letters is unclear: they usually coincide. Here we show that familiar letters, not a related language, let an unseen language into a shared space of meaning. GlyphMind, a tokenizer-free vision transformer that reads text as pixels and lets any sentence be shown in any script, was pretrained to align translations across 27 writing systems. Adapted on untranslated Lao, a language and script it had never seen, it retrieved English translations seven times as often as the strongest simple baseline. Rewritten in unfamiliar letters, Lao did not enter although its close relative Thai was in pretraining; Santali, which had none, entered in Devanagari, familiar from Hindi, but not in its own script. Transliteration into familiar scripts may thus open multilingual models to languages without translations.

cs.CL

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.06961

Operationalizing Responsible AI in MLOps and ML Production: A Systematic Literature Review

Hinduja Nirujan, Siddhartha Paudel, Gouri Ginde

原文摘要

Artificial intelligence (AI)-enabled systems are increasingly deployed in operational environments where machine learning (ML) models interact with changing data, users, infrastructure, and organizational processes. While Responsible AI principles are widely discussed, less is known about how these principles are translated into concrete mechanisms within Machine Learning Operations (MLOps) and ML production workflows. This paper reports a systematic literature review of studies that operationalize Responsible AI in ML production. The review synthesizes evidence across monitoring and runtime assurance, audit and compliance, ML lifecycle integration, and traceability/provenance/documentation. Across the final study set, the literature shows a shift from abstract ethical principles toward engineering mechanisms such as drift monitoring, runtime checks, audit logs, provenance records, evidence bundles, governance gates, policy-as-code, model passports, and compliance-oriented documentation. However, the evidence base remains uneven: many studies propose architectures or prototypes, while fewer evaluate the mechanisms in realistic production settings, longitudinal operations, or organizational governance workflows. The review identifies a central gap around integrated, evidence-producing Responsible MLOps: current mechanisms often detect, document, or govern isolated parts of the ML lifecycle, but rarely connect runtime signals, ML lifecycle provenance, human accountability, and compliance evidence into closed operational loops. These findings motivate future software engineering research on traceable, evaluable, and operationally integrated Responsible AI support for ML production.

cs.SE

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2611.00935

Labadain-MedQA: A Tetun Medical QA Dataset for Fine-Tuning and Evaluating Large Language Models

Gabriel de Jesus, Sérgio Nunes

原文摘要

Advances in large language models (LLMs) have created opportunities for cross-lingual transfer of medical knowledge from high-resource to low-resource languages to support access to healthcare information. However, medical question-answering (QA) datasets for fine-tuning and evaluating LLMs remain scarce for Tetun. To address this gap, we translated MedAlpaca's Medical Flashcards and WikiDoc Patient Information collections into Tetun using Gemini 2.5 Flash, producing 39,077 medical QA pairs. Translation quality was assessed on 50 randomly sampled QA pairs (100 documents) using automatic metrics against human reference translations and evaluations by two native Tetun-speaking healthcare professionals, focusing on medical accuracy and Tetun fluency. Human translations achieved higher mean ratings than LLM-generated translations for both criteria (4.43/5 vs. 4.02/5), with statistically significant differences (Wilcoxon signed-rank test, p < 0.001). To assess downstream utility, we fine-tuned Gemma and Llama variants on the translated MedAlpaca dataset using parameter-efficient fine-tuning (PEFT) and evaluated them on 325 Tetun-translated United States Medical Licensing Examination multiple-choice questions. Gemma 4 12B fine-tuned using QLoRA achieved relative improvements in mean accuracy of 10.79% with 4-bit NormalFloat (NF4) inference and 10.42% after merging the LoRA adapters into the BF16 base model, compared with the corresponding base models. These results highlight the viability of automatic dataset translation for Tetun medical QA and the effectiveness of PEFT in enhancing model performance. The datasets, models, and evaluation resources are accessible at https://github.com/labadain/labadain-medqa to support reproducible research on Tetun medical QA and facilitate machine translation, cross lingual, and multilingual research.

cs.CL · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.04949

HERIT: Enabling Global Access to Hanja-to-English Historical Translation by Mitigating Temporal Bias

Dayeon Jeong, Kyeongpil Kang

原文摘要

Historical archives are invaluable for multidisciplinary research but remain inaccessible globally due to severe language barriers. While manual translation is prohibitively expensive and time-consuming, simply applying existing machine translation models is inadequate due to unique linguistic nuances and the temporal bias inherent in scarce human-labeled corpora. To address these challenges, we propose HERIT, a systematic domain-adaptation pipeline that leverages retrieval-augmented generation (RAG) to generate high-quality pseudo-labeled data from abundant Hanja-Korean corpora. This approach substantially expands the training dataset, mitigating both data scarcity and temporal overfitting across different historical periods. Extensive automated and human evaluations demonstrate that HERIT consistently outperforms baselines. Finally, we translate previously untranslated portions of the archives using our model, thereby enabling global access to these resources for researchers.

cs.CL

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.04829

Geometric Bin Packing Beyond Convexity: Approximation Schemes without Augmentation

Lucas de Oliveira Silva, Elisa Dell'Arriva, Flávio Keidi Miyazawa

原文摘要

We study the geometric bin packing problem for simple polygonal regions, i.e., non-degenerate, connected, rational regions, in the minimum number of unit-square bins, allowing only translations. We even consider packing general non-convex objects, possibly with holes, where we can then pack smaller objects. Previous schemes cover squares (MOR'06), circles and other $L_p$-balls (Algorithmica'16), sufficiently skewed rectangles (Algorithmica'23), and convex fat objects with a constant number of distinct shapes under resource augmentation (arXiv'24, WAOA'23). The result for circles and other $L_p$-balls needs bin augmentation to obtain rational packing realizations. For restricted classes of $L$-shaped polygons, Gerlach et al. (SoCG'26) give constant worst-case guarantees, while for general non-convex objects, bounding-box reductions give just approximation factors that depend on fatness. Against this background, we present a general framework that provides asymptotic polynomial-time approximation schemes (APTAS) for bin packing of any fixed class of simple polygonal regions satisfying just two assumptions. First, covering any object by the grid cells intersecting its interior in any sufficiently fine square grid, relative to the object's bounding box, increases its area by at most a small fraction. Second, the class can be partitioned into a constant number of sequences such that each object in a sequence can be translated to fit inside every preceding object. In particular, certain classes of star-shaped polygons and more general polygonal regions, including some with simple holes, satisfy both assumptions. In addition, our framework does not require a bound on the objects' boundary complexity. We emphasize that our results require no bin augmentation whatsoever and yield explicit, exact rational packings of polynomial encoding length for all input objects.

cs.CG · cs.DS

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2608.24707

Lost in Speech: Trilingual Spoken Hallucination Detection Across Audio and Transcripts

Meruyert Aristombayeva, Jason S. Lucas, Chaewan Chun, Dongwon Lee

原文摘要

While text-based hallucination detection is well studied, reference-free detection of factual alterations in speech remains underexplored, especially for low-resource languages. Our spoken benchmark comprises 12,013 English, Russian, and Kazakh news samples with three synthetic alteration types and three severity levels, pairing source articles with rewrites as text, synthesized audio, and ASR transcripts. We add 290 fact-checked misinformation items collected in Russian (225) and Kazakh (65), translated into the other language and rendered through the same TTS-ASR pipeline. We evaluate fine-tuned multilingual encoders and zero-shot multimodal decoders on text, transcripts, and audio. Detectors receive only target inputs without source articles or external evidence; the task evaluates reference-free classification rather than evidence-grounded verification. Encoder degradation from source text to transcripts generally tracks per-language ASR error on the binary task. Among decoders, only Gemma-3n exceeds the binary majority-class baseline in macro-F1, on transcripts only; the other four fall below their respective baselines. Comparisons between audio and transcripts are confounded by differences in evaluation coverage and class balance. Synthetic-trained detectors achieve 0.82--0.88 macro-F1 on real-world misinformation source text; Russian provenance analysis reveals veracity-related and model-dependent machine-style signals, a key confound in synthetic hallucination benchmarks.

cs.CL

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.04570

Setting the Stage: Enhancing Music Recommendations with Spoken Introductions to Stimulate Serendipity

Brett Binst

原文摘要

People like to keep their music listening fresh and interesting, but recommender systems often struggle to support this need. Even when a recommended song is potentially refreshing and enriching, listeners may not perceive it as such. Building on Silvia's appraisal theory of interest, this paper studies whether enhancing recommendations with spoken introductions, immersive or informative, can stimulate two distinct flavors of experienced serendipity: taste-broadening (sparking interest in music outside one's typical preferences) and taste-deepening (deepening interest in music within one's typical preferences). Results from a large-scale user study show that both introduction types stimulate taste-broadening and taste-deepening serendipity. Taste-broadening is most effectively stimulated through transportation (absorption in a narrative world), whereas taste-deepening is most effectively stimulated through cognitive elaboration (learning something new about the artist or the social context in which the music emerged). We discuss how these findings could be translated into practice through curated taste-broadening playlists, where both introduction types appear promising, and taste-deepening playlists, where informative introductions seem particularly effective.

cs.HC

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.04303

FlowTranslator: Training-Free In-Image Machine Translation with Flow-Based Editing

Zhixian Li, Xingsong Ye, Yongkun Du, Yunyan Xiao, Yanglin Huang, Pinxuan Chen, Jiyu Zhuang, Cong Xu 等 9 位作者

原文摘要

In-image machine translation (IIMT) replaces text in an image with its translation while preserving the surrounding visual context. Existing task-specific models achieve convincing performance but require massive paired training images before and after translation. As advanced text-to-image (T2I) models have reached a practically usable level of multilingual text generation, we argue that this capability can be transferred to IIMT and present FlowTranslator. It is a training-free IIMT framework that first employs an OCR model and an LLM to generate the translation prompt, which is injected into the T2I-based diffusion model. To optimize the accurate and natural generation of translated text, we introduce two trajectory controls: Region Coupling Release (RCR) partially decorrelates target-branch noise inside the text region early in the trajectory, while Coupling Expansion (CE) amplifies the emerging edit displacement mid-trajectory. Under the EasyOCR read-back protocol, FlowTranslator achieves average PRIM BLEU and COMET scores of 20.6 and 57.2, respectively, substantially outperforming the strong training-based baseline UniTranslator. On the more challenging and realistic whole-image ScenePair photographs translated into Chinese, it reaches 40.1\% exact-match accuracy and surpasses existing editing systems, some of which only address the text-rendering stage rather than the complete IIMT task. These results establish flow-based editing as a strong, fully training-free baseline for IIMT.

cs.CV · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.04091

Same Rule, Different Verdicts: Rubric Language and Clause Structure in LLM Judges for Korean Support Dialogues

Sangdo Han, Moonsoo Kim

原文摘要

Services that operate in Korean often write their evaluation rubrics in Korean and hand them to an LLM judge. We test whether a judge's verdict depends on the language of the rubric and on how a rubric item is phrased. We write 12 criteria for support dialogues in four clause structures (affirmative, negated, conditional, exception) such that two pairs of structures have the same meaning, and we give each item in Korean and in English. Six low-cost models from three providers, plus one reference model, judge 60 translated ESConv dialogues and 144 constructed boundary cases under every combination (51,744 requests across the seven models). The analysis plan was fixed and committed before data collection. Cross-provider agreement is lower with Korean rubrics than with English rubrics (Cohen's kappa 0.687 vs. 0.745; difference -0.058, 95% CI [-0.072,-0.045]). Disagreement concentrates in negated, conditional, and exception items (odds ratio 5.6 relative to affirmative items). A single model changes its verdict between two items with the same meaning in 11-15% of cases. Two hypotheses are not supported: models from the same provider do not agree more than models from different providers, and adding paired examples does not reduce the language gap. We measure consistency only; no human labels are used, and all materials were drafted by an LLM.

cs.CL

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2603.20406

Thinking in Different Spaces: Domain-Specific Latent Geometry Survives Cross-Architecture Translation

Marcus Armstrong, Navid Ayoobi, Arjun Mukherjee

原文摘要

We investigate whether independently trained language models converge to geometrically compatible latent representations, and whether this compatibility can be exploited to correct model behavior at inference time without any weight updates. We learn a linear projection matrix that maps activation vectors from a large teacher model into the coordinate system of a smaller student model, then intervene on the student's residual stream during generation by substituting its internal state with the translated teacher representation. Across a fully crossed experimental matrix of 20 heterogeneous teacher-student pairings spanning mixture-of-experts, dense, code-specialized, and synthetically trained architectures, the Ridge projection consistently achieves R^2 = 0.50 on verbal reasoning and R^2 = 0.40 on mathematical reasoning, collapsing to R^2 = -0.22 under permutation control and R^2 = 0.01 under L_1 regularization. Behavioral correction rates range from 14.0% to 50.0% on TruthfulQA (mean 25.2%) and from 8.5% to 43.3% on GSM8K arithmetic reasoning (mean 25.5%), demonstrating that the method generalizes across fundamentally different reasoning domains. We report a near-zero correlation between geometric alignment quality and behavioral correction rate (r = -0.07), revealing a dissociation between representation space fidelity and output space impact. Intervention strength is architecture-specific: student models exhibit characteristic sensitivity profiles that invert across domains, with the most steerable verbal student becoming the least steerable mathematical student. Finally, a double dissociation experiment conducted across all 20 model pairings confirms without exception that projection matrices collapse catastrophically when transferred across reasoning domains (mean R^2 = -3.83 in both transfer directions), establishing domain-specific subspace geometry as a universal property of LMs.

cs.LG · cs.AI

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2603.22146

Reusable Neural Reachability via Local Refinement

Prashant Solanki, Jasper Van Beers, Coen De Visser

原文摘要

This paper studies obstacle avoidance under translation invariant dynamics using an avoid-side travel cost Hamilton Jacobi formulation. For running costs that are zero outside an obstacle and strictly negative inside it, we prove that the value function is non-positive everywhere, equals zero exactly outside the avoid set, and is strictly negative exactly on it. Under translation invariance, this yields a reuse principle: the value of any translated obstacle is obtained by translating a single template value function. We show that the pointwise minimum of translated template values exactly characterizes the union of the translated single-obstacle avoid sets and provides a conservative inner certificate of unavoidable collision in clutter. To reduce conservatism, we introduce a blockwise composition framework in which subsets of obstacles are merged and solved jointly. This yields a hierarchy of conservative certificates from singleton reuse to the exact clutter value, together with monotonicity under block merging and an exactness criterion based on the existence of a common clutter avoiding control. The framework is illustrated on a Dubins car example in a repeated clutter field.

eess.SY · cs.RO

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2607.27428

Rethinking AI in Medical Imaging: Assumptions, Reality, and Reframing; Towards Physician-Centered Agentic Reasoning

Arman Rahmim, Nourhan Bayasi, Xiaoxiao Li, Babak Saboury, Fereshteh Yousefirizi

原文摘要

Medical imaging has served as primary proving ground for clinical AI, yet a decade of intense research has not translated into proportionate bedside impact. We argue that this gap is not primarily a product of insufficient algorithmic performance, inadequate regulation, or limited explainability. Rather, it reflects a structural misalignment, between how AI systems are designed and evaluated, and how clinical decisions are made. This work identifies 8 interconnected dimensions of this misalignment: 1) the dominance of pixel-only models in a multimodal clinical world; 2) the erosion of physician trust through opaque and inflexible systems; 3) the unfulfilled promise of foundation models in data-sparse medical domains; 4) the persistent bottleneck of non-shareable under-curated datasets; 5) the gap between validated algorithms and deployable clinical platforms; 6) the slow, unmonitored translation of evidence into routine practice; 7) the risk of AI displacing rather than extending physician judgment; and 8) the failure of prediction-centric AI to generate actionable, agentic clinical guidance. For each dimension, we reframe the problem and propose a path forward, culminating in a vision of physician-centered AI solutions with agentic reasoning that extend, rather than replace, clinical judgment.

physics.med-ph · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.03140

When Optimization Meets Human Intent: Human-Centered Diffusion Model Optimization

Renye Yan, Jikang Cheng

原文摘要

Diffusion models are evolving from general tools into personalized systems that adapt to user needs through fine-tuning and guidance. However, diffusion optimization mainly targets measurable objectives such as aesthetics, rarely examining whether they align with human intent. Human needs are multidimensional, contextual, and dynamic, whereas existing methods approximate them with fixed metrics. Because these metrics capture human needs only partially and may amplify deviations from true intent during optimization, higher scores need not yield results better matching human expectations. To understand how human needs enter and influence optimization, we systematically analyze how human intent is expressed, translated into optimizable objectives, applied to model behavior or generation, and evaluated through outputs. We further discuss trade-offs affecting human-intent alignment in personalized image generation and gaps between automated metrics and real user judgments. This human-centered synthesis reorganizes diffusion optimization and provides a systematic reference for personalized generation systems designed for human preferences.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02815

Predictive Safety Guardrails Do Not Always Make LLM Agents Safer

Weixiang Zhao, Zhihao Liu, Guancheng Li, Yanyan Zhao, Bing Qin, Ting Liu

原文摘要

Large language model agents increasingly take consequential actions in digital and physical environments, where safety risks may emerge only after multiple interaction steps. This has motivated world-model-driven predictive safety guardrails, which anticipate future consequences before execution. However, existing evaluations largely focus on end-to-end safety outcomes, leaving unclear whether safety gains arise from reliable prediction and faithful use of predicted risks. In this work, we systematically diagnose the prediction-to-action pipeline along three dimensions: how world-model predictions are integrated into decision making, whether the predictions themselves are reliable, and whether their content is faithfully used. Across seven policy models and two safety environments, we find that stronger prediction involvement does not yield monotonic safety gains and can reduce task completion while substantially increasing inference cost. We further find that world-model predictions remain insufficiently reliable, especially over longer horizons, where safety prediction degrades substantially relative to near-term forecasting. Finally, we find that safety gains can persist even under corrupted predictions, while locally verified predictions still leave subsequent safety violations, showing that predictive content is not faithfully incorporated into the decision process. Together, these findings expose a fundamental prediction-to-action gap in current predictive guardrails: producing more or seemingly accurate foresight is insufficient unless that foresight is both reliable and faithfully translated into safer decisions.

cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02585

From Language to Country to Valence: A Multilingual Audit of Stereotype Bias Across LLMs

Zihao Sun

原文摘要

Large language models (LLMs) are increasingly considered for education, recruitment, workplace decision support, and other consequential applications, making pre-deployment social-bias detection essential. Yet major benchmarks are English-centred or translated, focus mainly on negative stereotypes, and may miss linguistic and cultural variation. We investigate gender, racial, and religious bias using 12,000 locally written Reddit posts in English, Spanish, French, and German. Only search terms are translated, preserving local context. The dataset is balanced by language and category; English gender is additionally balanced by stereotype valence. Three open-weight and three proprietary API-based models are evaluated with a two-blank design that reduces grammatical-gender cues. Religion has the highest absolute choice imbalance in every model-language condition. Spanish is highest for gender, whereas German is highest for race and religion. The evaluated API models score below the open-weight models throughout. Negative English-gender items exceed positive items; Australian and Mexican contexts exceed their Canadian and Spanish counterparts. These descriptive differences measure choice concentration, not stereotype endorsement or causal country effects. The crossed patterns show how aggregate scores can conceal category- and context-specific behaviour, motivating closer inspection before deployment.

cs.CL

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02018

CULTURE-CMT: Benchmarking Contextualized Social Media Translation

Linjuan Wu, Dingming Li, Yiwen Qiu, Xinze Lyu, Ye Guo, Daoxin Zhang, Zhe Xu, Weiming Lu

原文摘要

Social-media comments are interactions, yet they are often translated and evaluated as isolated text. This mismatch is especially problematic when meaning depends on surrounding discussion, community-specific slang, or culturally situated expressions. We introduce CULTURE-CMT, a Chinese-English benchmark for contextualized social-media comment translation that restores the post and conversational context naturally available to users. Its 572 manually verified examples are organized into Slang, Context, and Both, enabling diagnostic analysis of distinct and interacting sources of translation difficulty. To support scalable evaluation, we further develop a retrieval-augmented cultural-effectiveness Judger that combines a calibrated scoring model with dynamically retrieved cultural knowledge. CULTURE-CMT reveals several non-trivial patterns. Providing interaction context improves most model configurations, but does not eliminate culturally ineffective translations. Examples requiring both contextual grounding and culturally situated language remain substantially more difficult than either source of difficulty alone. Moreover, larger models are not consistently better, and thinking produces highly model-dependent gains, including occasional degradation. In contrast, a 27B model adapted on benchmark-disjoint social-media data achieves the lowest observed invalidity rate among the evaluated configurations under thinking inference with context. Together, these results suggest that realistic social-media translation is not simply a matter of scaling models or reasoning longer, but of learning to translate meaning as it is constructed within evolving online interactions.

cs.CL

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.01633

BanglishDepYT: Depression Detection in Native Bengali Social Media with Emotion-Preserving Preprocessing and Domain-Adaptive Pretraining

Ayon Roy, Sadman Sharif, Syed Shams Islam

原文摘要

Depression affects millions of Bengali speakers across South Asia, yet automated detection systems have relied almost entirely on datasets translated from English. Such datasets cannot capture the emoticons, romanized Bengali words, and elongated spellings that characterize real expressions of distress. We introduce BanglishDepYT, a Bengali depression dataset collected natively from YouTube, comprising 191,000 unlabeled comments and 1,000 samples verified through dual bilingual annotation. We propose EPH (Emotion-Preserving Hybrid preprocessing), which converts emoticons to semantic tags, preserves Banglish tokens, protects negation words, and normalizes emotional elongation. A key finding is that preprocessing choice substantially affects performance on informal text: standard cleaning reduces the Banglish Survival Rate to zero (SVM F1: 0.7062), while EPH preserves signals (CESS: 0.9996 vs. 0.6694) and raises SVM F1 to 0.8570, an improvement of 0.1508. We further apply domain-adaptive pretraining (DAPT), continuing SahajBERT pretraining on unlabeled in-domain comments. DAPT-SahajBERT achieves the highest mean F1 (0.8161) and MCC (0.6272), significantly outperforming the baseline (F1: 0.7805, p = 0.0013, d = 1.450) and matching MuRIL (p = 0.589). These results suggest that targeted domain pretraining is a competitive alternative to large-scale multilingual pretraining. LLM-assisted annotation using Qwen2.5-7B achieves substantial human agreement (kappa = 0.611), enabling pseudo-labeling of 96,071 comments. Semi-supervised training improves AUC from 0.8499 to 0.8731 (p = 0.044), offering a scalable path to expanding annotation. All code, models, and datasets are publicly released to support reproducible research.

cs.CL · cs.CY · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.40516

如何对齐你的模型：用模板扩展多语言安全训练

How I Aligned Your Model: Scaling Up Multilingual Safety Training with Templates

Max Neuwinger, Hanna Yukhymenko, Imanol Schlag

AI 速读 基于摘要

提出基于模板的可扩展增强流程，构建覆盖60种语言的MSafeTemp安全语料，并展示多语言安全对齐的改进。

cs.CL · cs.AI · cs.LG

arXiv ↗PDF ↗
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Safety alignment for Large Language Models is heavily focused on English with limited multilingual representation, particularly damaging low-resource language performance. Related open training datasets are often limited in size or do not follow region-localized harmful topics to broaden the scope of alignment beyond cultures. We propose a scalable template-based augmentation pipeline to produce massive multilingual refusal and benign corpora, extracting safe templates from harmful prompts, translating them and then filling in the slots with culturally authentic entities, both harmful and benign. As a result, we release MSafeTemp, a validated corpus spanning 60 languages useful for multilingual safety alignment and evaluation. Using the dataset, we show that English-only evaluation misjudges multilingual safety: for several models, attacks that fail in English succeed once translated, while culturally localized variants expose further failures that translation alone misses. We also confirm that augmenting safety training data through templates allows for higher diversity and quality of alignment, resulting in improved safety performance beyond English. MSafeTemp and the respective pipeline are aimed at facilitating research and development of safer and more responsible models for less-represented cultures.

版本更新 · v2arXiv:1504.06203

A Behavioural Thesis of Synchronous Parallel Algorithms -- Correction of the Simplified Parallel ASM Thesis

Flavio Ferrarotti, Klaus-Dieter Schewe, Loredana Tec, Qing Wang

原文摘要

A behavioural theory consists of machine-independent postulates characterizing a particular class of algorithms or systems, an abstract machine model that provably satisfies these postulates, and a rigorous proof that any algorithm or system stipulated by the postulates is captured by the abstract machine model. The class of interest in this article is that of synchronous parallel algorithms. For this class a behavioural theory has already been developed by Blass and Gurevich, which, though mathematically correct, fails to be convincing, as it is not intuitively clear that the postulates really capture the essence of (synchronous) parallel algorithms. We present a much simpler (and presumably more convincing) set of four postulates for (synchronous) parallel algorithms, which are close to those of Gurevich's sequential ASM thesis. The key difference is an extension of the bounded exploration postulate using multiset comprehension terms instead of ground terms formulated over the signature of the states. In addition, all implicit assumptions are made explicit, which amounts to representing states of a parallel algorithm by meta-finite first-order structures. The article first provides evidence that the axiomatization characterizes the whole class of deterministic, synchronous, parallel algorithms, then formally proves that parallel algorithms are captured by Abstract State Machines (ASMs). The proof uses the requirement of the bounded exploration postulate that update sets be uniformly definable, by equality-free first-order formulae, in the finite critical structures determined by the witness terms; these formulae are translated into the guards of a parallel ASM rule. This version corrects the characterization proof of the first version, whose key lemma was false; the bounded exploration postulate has been amended accordingly.

cs.LO

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新论文 · v1arXiv:2610.39746

Step Recursion: Rational Strides and Exact Inclusion Orders over the Reals

Kirill Osipov

原文摘要

We study finite-terminating, address-only step recursion on the nonnegative reals, from zero, unit successor and projections. For reduced positive rational strides $s=a/q$ and $t=b/v$, we prove $\mathcal{C}s\subseteq\mathcal{C}t$ if and only if $q$ divides $v$, for both the bounded and unbounded algebras $\mathcal{C}\in{\mathcal{B},\mathcal{U}}$. The bounded compiler preserves earlier global majorants on every auxiliary input. Mixed rational stride sets are classified by least common denominators and supernatural denominators. Primitive-recursive translated-coordinate grid codes characterize the unbounded algebras and yield $\mathcal{U}{1/q}\cap\mathcal{U}{1/v}=\mathcal{U}{1/\gcd(q,v)}$. For bounded intersections, we prove an exact reduction: all corresponding identities hold if and only if the single identity $\mathcal{B}{1/2}\cap\mathcal{B}_{1/3}=\mathcal{B}_1$ holds, equivalently if the Boolean parts of the integer successor and addition algebras under scalar bounded recursion coincide. A Boolean reconstruction reduces general functions to predicates; a clipped fine-grid simulation with a frozen global resource bound identifies the union of predicate precision classes with the addition level. These reductions do not decide the resulting predicate equality, so literal bounded intersections remain unresolved. We also prove that unbounded-program equality is $Π^0_1$-complete and extensional bounded-algebra membership is $Σ^0_2$-complete. The model has no free real arithmetic or Zeno limits; semantic majorant validity is distinguished from syntax transformation.

cs.LO · math.LO

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新论文 · v1arXiv:2610.38788

Gold vs.~Translation: Are We Measuring Language, Knowledge, or Locality in Translated Benchmarks?

Klaudia-Doris Thellmann, Bernhard Stadler, Dan Saattrup Nielsen, Michael Färber, Jens Lehmann

原文摘要

Translated versions of English-origin benchmarks such as MMLU are widely used to evaluate large language models in non-English languages, yet it remains unclear whether they measure the same constructs as natively created benchmarks. We frame this as a proxy validity question and compare DeepL-translated MMLU proxies against six native benchmarks in seven pairings across five Nordic and Baltic languages, evaluating 29 models on each pairing. Beyond ranking agreement, we test incremental proxy validity: does the translated score add predictive value for native performance after controlling for model size and language proficiency? Proxy validity varies substantially across benchmark pairings. Applying the same robustness criteria to all seven pairings, we find four robust effects, two marginal effects, and one null result. The strongest evidence occurs for curriculum-overlap benchmarks such as Skolprov ($ΔR^2$=.128, 95% CI [.038,.253]), whereas translated MMLU provides no significant incremental value for Sweden-specific factual knowledge in SwedishFacts (p=.242). Because SwedishFacts is also the only open-ended benchmark, we cannot fully separate the effects of local content and response format. Overall, the results indicate a clear pattern: translated benchmarks provide a better proxy when their source content overlaps with the native evaluation target, while locally anchored knowledge is less reliably captured.

cs.CL

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.38664

Upper Bounds for CSP Non-redundancy via Locally Decodable Codes

Joshua Brakensiek, David Conlon, Venkatesan Guruswami, Xiaoyu He, Jiaxi Nie, Maya Sankar, Hung-Hsun Hans Yu

原文摘要

In the study of constraint satisfaction problems (CSPs), the non-redundancy of a relation measures the maximum number of clauses in a CSP instance which can be logically independent. In the last few years, CSP non-redundancy has found a number of applications including in query complexity, sparsification, kernelization and streaming. In this paper, we present a novel technique for upper-bounding the non-redundancy of some relations using locally decodable codes (LDCs) and locally correctable codes (LCCs). More precisely, known limitations on the structure of 2-query LDCs and LCCs can be translated into limitations on the size of certain non-redundant CSP instances. This technique builds on a technique used by Brakensiek and Guruswami [STOC 2025] to bound non-redundancy via techniques from matching vector codes. Using LDCs and LCCs, we give improved upper bounds on non-redundancy of the BCK predicate pioneered by Bessiere, Carbonnel, and Katsirelos [AAAI 2020] as well as its well-studied Boolean variant. We also give the first example of a Boolean predicate whose non-redundancy is provably not of the form $n^{k\pm o(1)}$ for some positive integer $k$.

cs.DM · cs.CC · cs.IT · math.CO

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新论文 · v1arXiv:2610.38312

Robust LLM-Generated Text Detection through Matched Conditional-Response Probing

Junfeng Hao, Wenxian Wang, Tao Leng, Xingshu Chen, Hao Ren, Haizhou Wang

原文摘要

Reliable detection of large language model (LLM)-generated text must remain effective under meaning-preserving text revisions, such as rewriting, polishing, and expansion. In practice, LLM outputs are often rewritten, polished, expanded, translated, or lightly edited before publication, weakening the lexical, syntactic, and local discourse cues used by many existing detectors. We introduce \textbf{M}atched \textbf{C}onditional-\textbf{R}esponse \textbf{P}robing (\textbf{MCRP}), a frozen-LLM probing framework that measures how a candidate text responds when a semantically matched condition is introduced, removed, or replaced. For each candidate, the frozen probe LLM first generates a short semantic condition and then compares token-level losses and prompt--target attention measurements under matched, null, and control conditions. It aggregates bidirectional conditioned alignment, counterfactual sensitivity, and internal attention coupling into a probe representation, on which it trains a lightweight supervised random-forest classifier while keeping the probe LLM frozen. Across standard detection, robustness to meaning-preserving text revisions, multilingual, and multidomain evaluations, MCRP achieves consistently strong performance. Under rewriting, polishing, and expansion, it reaches 93.97\% F1 and 96.24\% ROC-AUC, outperforming the strongest evaluated baseline by 19.50 and 25.77 percentage points, respectively. Component ablations show that the response feature groups make complementary contributions to predictive performance. In the condition-control study, matched conditions consistently outperform random, mismatched, and corrupted alternatives, demonstrating a measurable incremental benefit from semantic matching.

cs.CL

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.38181

Component Translation and Judge-Labelled Refusal: A Multilingual Safety Evaluation

Yongxi Zhou, Junwei Yao, Wenbo Ye, Jiahui Ge

原文摘要

Whole-prompt translation evaluates a request in different languages but does not compare which component carries a language. We introduce a paired reversal: translate the complete request component or the complete operation component into the same target language, holding behaviour, English frame and translated-span count fixed. Component identity and non-English text quantity change together. Assigning English, Chinese and Spanish independently to the two components yields nine configurations on 190 of 300 sampled harmful behaviours retained after consensus annotation and automatic gating. Refusal scored by language-model judges is the primary outcome; unsafe compliance is reported separately. On Gemini 2.5 Flash and DeepSeek, three refusal swaps are about 5.3 percentage points and the fourth is -0.5; none survives the pre-specified six-test Holm correction. In an exploratory extension, Mistral Small 4 has a 13.7-point Spanish swap (95% interval [8.4, 18.9]), which survives the twelve-test Holm sensitivity correction under the primary judge, and 12.1 points under a second judge. GPT-5.4-mini estimates are within 2 points of zero. Component assignment supplies an additional evaluation dimension. Evaluations should record component languages and translated text quantity, comparing matched behaviours at equal translated-span count while interpreting refusal and unsafe compliance separately.

cs.CL · cs.CR

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.24764

BanglaBBQ: A Bias Benchmark for Question Answering in the Bangladeshi Context

Sumit Anthony Gomes, Yasin Rahman, Taeeba Tasnia, Faiza Nooren Suhita

原文摘要

This paper presents BanglaBBQ, a question-answering bias benchmark designed to capture stereotypes grounded in the Bangladeshi context, in both Bangla and English. Following KoBBQ and PakBBQ, BanglaBBQ adapts the original Bias Benchmark for Question Answering (BBQ) by classifying its templates into three groups: directly translated (DT), modified to align target groups or contexts with Bangladesh (TM), and removed when deemed unsuitable for the Bangladeshi context (SR). This process retains 140 templates across six categories, comprising 76 DT and 64 TM templates. Five newly authored (NA) categories, based on stereotypes common in Bangladeshi society, are then added: marital status, age and political perception, regional origin, educational background, and mental-health status. Native Bangladeshi annotators judged all 466 newly authored templates, and 372 were retained because a strict majority of English annotators identified the intended target and rated the attitude as common enough. Each newly authored template is linked to a documented source and a verbatim quote from it. Each evaluated template also has a counter-stereotypical version of its disambiguated context. The dataset was evaluated using two open-weight models (Qwen3-4B and Qwen3-8B) and two closed models (GPT-4.1-mini and Gemini 2.5 Flash). All four lean towards the stereotyped person when the context is ambiguous, in both languages, and the effect of language differs by model, so English results do not predict behaviour in Bangla. On the adapted BBQ templates, both Qwen3 models are also less accurate when the context contradicts a stereotype than when it confirms it. On the newly authored templates, the closed models follow counter-stereotypical evidence almost perfectly, and all four models are more biased on the templates that annotators validated than on those they rejected.

cs.CL

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新论文 · v1arXiv:2610.37245

Aligning Large Language Models for Safety in African Languages: A Case Study of Hausa

Valdini Douglace Lemofouet, Blessing Ngozi Uzor, Paula Chikaodinaka Anyanwu, Danielle Blanche Kapsa, Saminu Mohammad Aliyu, Seid Muhie Yimam, Shamsuddeen Hassan Muhammad

原文摘要

Safety alignment research for large language models (LLMs) remains concentrated on high-resource languages, leaving open how existing alignment methods perform in low-resource, culturally distinct settings. We study this question for Hausa, a language spoken by more than 200 million people across West Africa. We introduce HausaSafeLM, one of the first Hausa safety preference dataset, built from 10,000 prompts derived from PKU-SafeRLHF: 3,000 human-corrected (gold) and 7,000 machine-translated (silver) examples spanning six harm macro-categories. Using Qwen2.5-7B-Instruct, we compare supervised fine-tuning (SFT) and SFT followed by Direct Preference Optimization (DPO) under both data regimes, evaluating on 500 harmful and 200 benign Hausa prompts.We find that the untuned base model appears highly safe by refusal-rate metrics alone, but this reflects limited Hausa generation ability rather than genuine alignment: its refusals are almost uniformly low quality. SFT improves both refusal fluency and precision and benefits substantially from the larger, noisier silver-tier data, while DPO sharpens refusal precision but comes at a steep, largely data-invariant cost to linguistic quality. No single configuration jointly maximizes safety, helpfulness, and fluency. Our results suggest that current alignment techniques teach models when to refuse more readily than how to do so fluently in underrepresented languages, and that refusal-rate metrics alone are an unreliable signal of safety in low-resource settings.

cs.CL · cs.AI

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新论文 · v1arXiv:2610.36990

The State and Fate of Multilingual, Contextual Evaluation in the NLP World

Manan Uppadhyay, Himanshu Beniwal, Prashant Kodali, Sunayana Sitaram

原文摘要

Multilingual evaluation benchmarks are the primary instrument for assessing whether large language models generalize beyond English, yet the adequacy of these benchmarks has received little systematic scrutiny. We present a data-driven audit of 51 recent multilingual benchmarks spanning 242 datasets and 219 languages, organized around three pillars: coverage, representativeness, and rigor. Our analysis reveals that coverage is wide but thin with 36% of evaluated languages appearing in only a single benchmark, entire regions (Oceania, the Americas, Central Asia) are near-zero, and a stark task equity gap leaves low-resource languages evaluated on only 1-3 task categories versus 14 for high-resource languages. Representativeness is structurally compromised: translation from English remains the dominant construction strategy where 56% of all dataset-language instances are translated introducing artifacts and English-centric framing, while culturally grounded content is concentrated in a handful of community-driven benchmarks with narrow language scope. The ecosystem thus forces a trade-off between breadth and validity. Rigor is undermined by benchmark contamination, including translated benchmark leakage and parallel corpus overlap that evade surface-form detection. We synthesize these findings into concrete recommendations for building evaluation frameworks that are natively constructed, culturally grounded, contamination-aware, and designed to serve the communities whose languages they claim to evaluate. The dashboard with all the metadata collected can be found at https://aka.ms/multilingual-evals.

cs.CL

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.26062

Procedural Exposure Bias in Skill-Augmented Language Model Agents

Qing Yang, Yuqi Xiong, Pengcheng Huang, Zhipeng Xu, Zhenghao Liu, Zulong Chen, Yu Gu, Ge Yu

原文摘要

External skills provide language-model agents with reusable procedural knowledge, yet how models use such knowledge after it is supplied remains poorly understood. We identify Procedural Exposure Bias (PEB), the tendency of language models to increasingly favor an action as its associated procedural pattern is repeatedly exposed in a skill, even when the interaction state and action relevance remain unchanged. We find that PEB strongly depends on how procedural knowledge is represented: presenting the same content visually substantially attenuates exposure-induced shifts in action preference relative to textual presentation. Further analysis shows that this attenuation primarily arises from visual encoding rather than structured layout. Although repeated procedural signals remain recoverable from Visual representations, they exert a weaker influence on downstream action prediction, distinguishing signal accessibility from its use as action evidence. This effect also extends to executable action selection, where increasing exposure more strongly favors the corresponding action under Text than Visual. Layer-wise analyses show that exposure-dependent action evidence becomes more pronounced toward the model output, with stronger evidence under Text, while activation interventions reveal architecture-specific roles of internal components in transmitting or counteracting the preference shift. Complete-agent evaluations across ALFWorld, ScienceWorld, and WebShop further show that Visual skills achieve higher macro-average task success than Text skills for all four evaluated models. These findings establish skill representation as an important factor governing how procedural knowledge is translated into agent decisions. All code is available at https://github.com/NEUIR/Vis-Skill.

cs.CV

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新论文 · v1arXiv:2610.36911

Developing a Speech Language Model for Basque

Andoni Sudupe, Inma Hernaez, Ander Arriandiaga, Christoforos Souganidis, Ibon Saratxaga, Eva Navas

原文摘要

Although the recent development of LLMs has paved the way for multi-modal interaction, minority languages face difficulties keeping pace. This work presents the first open Speech Language Model for Basque, designed for low-latency, end-to-end spoken interaction. Based on the LLaMA-Omni architecture, the system integrates a Latxa model as its backbone, transferring its Basque capabilities to the spoken modality by fine-tuning several modules. To address data scarcity, the English VoiceAssistant-400K corpus was translated into Basque, and the audio was synthesized using a Basque TTS. Evaluation results confirm the system's viability, achieving 3.86/5 in content quality, a 9.94% WER for spoken intelligibility, and a 3.07 UTMOS for audio quality. This work demonstrates that advanced spoken interaction systems can be developed using modular architectures and synthetic data even in low-resource languages.

cs.CL · cs.AI · eess.AS

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