Text-to-image generation models have achieved strong performance in culturally homogeneous settings, yet their ability to generate multicultural scenes—where people and landmarks originate from different cultures—remains largely unexplored. We introduce multicultural text-to-image generation as a new task and present the first benchmark designed to study this setting. Our dataset contains 9,000 images spanning five countries, three age groups, two genders, 25 historical landmarks, and five languages. Using this benchmark, we analyze the behavior of state-of-the-art text-to-image models across multiple dimensions, including alignment, image quality, aesthetics, knowledge, and fairness. As one strategy for composing cultural and demographic information, we explore MosAIG, a Multi-Agent framework that enhances multicultural image generation by leveraging large language models with distinct cultural personas. Our analysis shows that richer prompt composition can improve image quality and cultural grounding compared to simple prompts, while also revealing substantial disparities across languages and demographic groups. We release our dataset and code at https://github.com/AIM-SCU/MosAIG
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Diffusion large language models (dLLMs) offer a promising alternative to autoregressive models, but their practical utility is severely hampered by slow, iterative sampling. We present *SchED*, a training-free, model-agnostic early-exit algorithm that terminates diffusion decoding using a progress-aware confidence threshold. We evaluate *SchED* across multiple diffusion model families and a diverse set of benchmarks spanning multiple-choice, math, long-form QA, and translation. *SchED* delivers substantial acceleration: on instruction-tuned models, it achieves approximately 4\times speedups while retaining baseline performance on average. On base models, *SchED* yields consistent speedup gains with 99.1–100% performance retention, with up to 2.34\times under more aggressive settings. Under a conservative quality–penalized speed metric, *SchED* consistently outperforms prior confidence-based early-exit methods, including on long-form generation where existing approaches tend to break down. An entropy analysis of the model’s token predictions reveals that instruction tuning speeds up the decay of predictive entropy. By leveraging inherent confidence stabilization as a signal for computational efficiency, *SchED* provides a robust framework for efficient dLLM inference.
LLM-KT: Enhancing Large Language Models with Knowledge Tracing via Multi-Level Plug-and-Play Alignment
PDF ↗Knowledge Tracing (KT) is a pivotal task in personalized education, aiming to predict students’ future performance based on their historical interactions. While prior work has focused on learning behavioral sequences using question IDs or surface-level textual features, these methods often fail to capture complex behavioral patterns due to a lack of deep reasoning capabilities and world knowledge. To address this, we propose LLM-KT, a novel framework that integrates the reasoning power of Large Language Models (LLMs) with the sequential modeling strengths of traditional KT methods via multi-level plug-and-play alignment. Specifically, for task-level alignment, we design a plug-and-play instruction to leverage the rich knowledge and reasoning capacity of LLMs for the KT objective. For modality-level alignment, we introduce two mechanisms to integrate representations learned by traditional methods: (1) a Semantic History Projector that flexibly inserts compressed context embeddings into LLMs using question- and concept-specific tokens to capture long-term history; and (2) a Behavioral Dynamics Projector that enhances LLMs with sequential interaction patterns via a sequence adapter. Extensive experiments on four standard datasets demonstrate that LLM-KT achieves state-of-the-art performance, significantly outperforming over 20 competitive baselines.
Large language models (LLMs) are transforming natural language processing across diverse linguistic communities. However, they can reproduce and amplify toxic content, including hate speech, harassment, and bias, posing significant risks to multilingual applications. We provide the first comprehensive survey of the many detoxification methods specifically tailored to multilingual LLMs. First, we define toxicity its measurement, then we provide a brief review of monolingual mitigation strategies, including data filtering, style transfer, expert-based logit steering, retrieval augmentation, and alignment with human feedback. We then present an in-depth taxonomy of multilingual approaches spanning (1) training methods, (2) post-hoc editing and decoding strategies, (3) alignment and reinforcement-learning techniques, and (4) data-centric innovations, such as parallel detox corpora and synthetic data generation. Finally, we discuss open challenges in multilingual detoxification, including data scarcity, evaluation inconsistencies, cultural nuances and biases. Overall, we produce a needed overview of the state of multi-lingual toxicity detection and mitigation on which the community can ground to build globally safe and equitable LLMs.
This paper offers a call to action. We urge our colleagues in the research community to play a greater role in the articulation of our findings to the public. To illustrate the stakes we present a case study on the initial stages of an LLM-based machine translation application’s deployment in a real-world context: a text-2-911 system advertising capabilities in 55 languages for use in emergencies in which it may be difficult to call operators directly. We identify a number of common misconceptions about technologies such as these, concluding with a set of concrete recommendations and best practices for stakeholders at every stage of the development and deployment pipeline. While the advancement of scientific research often lies in solving the "hard" problems, we argue it is often the "easy" ones— problems for which the latest technology is often unnecessary— that are most overlooked.
Idea First, Code Later: Disentangling Problem Solving from Code Generation in Evaluating LLMs for Competitive Programming
PDF ↗Large Language Models (LLMs) increasingly succeed on competitive programming problems, yet existing evaluations conflate algorithmic reasoning with code-level implementation. We argue that competitive programming is fundamentally a problem-solving task and propose centering natural-language editorials in both solution generation and evaluation. Generating an editorial prior to code improves solve rates for some LLMs, with substantially larger gains when using expertly written gold editorials. However, even with gold editorials, models continue to struggle with implementation, while the gap between generated and gold editorials reveals a persistent problem-solving bottleneck in specifying correct and complete algorithms. Beyond pass/fail metrics, we diagnose reasoning errors by comparing model-generated editorials to gold standards using expert annotations and validate an LLM-as-a-judge protocol for scalable evaluation. We introduce a dataset of 83 ICPC-style problems with gold editorials and full test suites, and evaluate 19 LLMs, arguing that future benchmarks should explicitly separate problem solving from implementation.
As large language models (LLMs) are increasingly deployed in real-world applications, ensuring their fair responses across demographics has become crucial. Despite many efforts, an ongoing challenge is hidden bias: LLMs appear fair under standard evaluations, but can produce biased responses outside those evaluation settings. In this paper, we identify framing – differences in how semantically equivalent prompts are expressed (e.g., “A is better than B” vs. “B is worse than A”) – as an underexplored contributor to this gap. We first introduce the concept of “framing disparity” to quantify the impact of framing on fairness evaluation. By augmenting fairness evaluation benchmarks with alternative framings, we find that (1) fairness scores vary significantly with framing and (2) existing debiasing methods improve overall (i.e., frame-averaged) fairness, but often fail to reduce framing-induced disparities. To address this, we propose a framing-aware debiasing method that encourages LLMs to be more consistent across framings. Experiments demonstrate that our approach reduces overall bias and improves robustness against framing disparities, enabling LLMs to produce fairer and more consistent responses.
Listen, Pause, and Reason: Toward Perception-Grounded Hybrid Reasoning for Audio Understanding
PDF ↗Recent Large Audio Language Models (LALMs) have shown strong capabilities in audio understanding, yet their reasoning remains vulnerable to perceptual errors, especially in noisy and multi-speaker environments. We argue that reliable audio reasoning requires first grounding model’s perception in structured auditory scenes. Motivated by Auditory Scene Analysis, we introduce **PAQA**, a large-scale dataset for **Perception-Aware Question Answering** covering over 300 categories. PAQA adopts a hierarchical decoupling strategy that separates speech from environmental sounds and distinguishes among multiple speakers, providing explicit perceptual supervision for audio reasoning. Building on this, we propose **HyPeR**, a two-stage **Hybrid Perception-Reasoning** framework for perception-grounded audio understanding. In Stage I, the model is fine-tuned on PAQA for cold start to improve perception of acoustic attributes in complex auditory scenes. In Stage II, we further refine its internal reasoning via **Group Relative Policy Optimization (GRPO)**. To support deliberation under acoustic ambiguity, we introduce **PAUSE tokens** for latent computation and a **Perceptual Consistency Reward** to align reasoning rationales with the underlying audio evidence. Extensive ablation studies isolate the effects of the perception-attention mechanism, self-correction module, and pause-based reasoning strategy. Experiments on multiple benchmarks show that HyPeR consistently improves over the base model, including on MMAU-mini (+13.1%), MMAR (+25.5%), and PAQA (+28.2%), while achieving performance comparable to much larger models. Additional analyses of inference latency and computational overhead show that these gains come with acceptable efficiency trade-offs. Overall, our results demonstrate the effectiveness of hybrid perception-grounded reasoning for robust audio understanding.
Recent automated transcription systems have focused on end-to-end orthographic approaches driven by deep neural networks and sequence-to-sequence transformers. Growing public interest in transcription at the phonemic or phonetic level has led to re-purposing these systems to segment and identify phones, the basic sounds which comprise human speech. However, they miss the mark on a fundamental component of time-series analysis, namely time. For linguistic applications which require high fidelity in the temporal domain, the loss of timing information is untenable. Our work proposes a deadline-bounded expectation maximization (EM) algorithm with a novel initialization method to estimate formants, i.e., salient speech frequencies, for enhanced phonetic segmentation. Based on the concept of spectral gravity, i.e., treating spectral energy as mass attenuated by the square of frequency distance across the spectrum, our technique outperforms the recent state of the art on key clustering metrics, generating reasonable alignments across multiple languages with no a priori training.
Caregivers seeking AI-mediated support express complex needs—information-seeking, emotional validation, and distress cues—that warrant careful evaluation of response safety and appropriateness. Existing AI evaluation frameworks, primarily focused on general risks (toxicity, hallucinations, policy violations, etc) may not adequately capture the nuanced risks of LLM-responses in caregiving-contexts. We introduce RubRIX (Rubric-based Risk Index), a theory-driven, clinician-validated framework for evaluating risks in LLM caregiving responses. Grounded in the Elements of an Ethic of Care, RubRIX operationalizes five empirically-derived risk dimensions: Inattention, Bias Stigma, Information Inaccuracy, Uncritical Affirmation, and Epistemic Arrogance. We evaluate six state-of-the-art LLMs on over 20,000 caregiver queries from Reddit and ALZConnected. Rubric-guided refinement consistently reduced risk-components by 45-98% after one iteration across models. This work contributes a methodological approach for developing domain-sensitive, user-centered evaluation frameworks for high-burden contexts. Our findings highlight the importance of domain-sensitive, interactional risk evaluation for the responsible deployment of LLMs in caregiving support contexts. We release benchmark datasets to enable future research on contextual risk evaluation in AI-mediated support.
In this paper, we introduce the Polish Massive Text Embedding Benchmark (PL-MTEB), a comprehensive benchmark for text embeddings in the Polish language. PL-MTEB comprises 30 diverse NLP tasks across five categories: classification, clustering, pair classification, information retrieval, and semantic text similarity. Within the scope of this work, we added 12 new Polish-language tasks to MTEB based on existing datasets and prepared two new datasets used to create four clustering tasks. We evaluated 30 publicly available text embedding models, including Polish and multilingual models. We analyzed the results in detail for specific task types and model sizes. We made the prepared datasets, the source code for evaluation, and the obtained results available to the public at https://github.com/rafalposwiata/pl-mteb.
Can unified vision–language models (VLMs) perform forward dynamics prediction (FDP), i.e., predicting the future state (in image form) given the previous observation and an action (in language form)? We find that VLMs struggle to generate physically plausible transitions between frames from instructions. Nevertheless, we identify a crucial asymmetry in multimodal grounding: fine-tuning a VLM to learn inverse dynamics prediction (IDP)—effectively captioning the action between frames—is significantly easier than learning FDP. In turn, IDP can be used to bootstrap FDP through two main strategies: 1) weakly supervised learning from synthetic data and 2) inference time verification. Firstly, IDP can annotate actions for unlabelled pairs of video frame observations to expand the training data scale for FDP. Secondly, IDP can assign rewards to multiple samples of FDP to score them, effectively guiding search at inference time. We evaluate the FDP resulting from both strategies through the task of action-centric image editing on Aurora-Bench with two families of VLMs. Despite remaining general-purpose, our best model achieves a performance competitive with state-of-the-art image editing models, improving on them by a margin of 15% on real-world subsets according to GPT4o-as-judge, and achieving the best average human evaluation across all subsets of Aurora-Bench.
Real-world health questions from patients often unintentionally embed false assumptions or premises. In such cases, safe medical communication typically involves redirection: addressing the implicit misconception and then responding to the underlying patient context, rather than the original question. While large language models (LLMs) are increasingly being used by lay users for medical advice, they have not yet been tested for this crucial competency. Therefore, in this work, we investigate how LLMs react to false premises embedded within real-world health questions. We develop a semi-automated pipeline to curate MedRedFlag, a dataset of 1100+ questions sourced from Reddit that require redirection. We then systematically compare responses from state-of-the-art LLMs to those from clinicians. Our analysis reveals that LLMs often fail to redirect problematic questions, even when the problematic premise is detected, and provide answers that could lead to suboptimal medical decision making. Our benchmark and results reveal a novel and substantial gap in how LLMs perform under the conditions of real-world health communication, highlighting critical safety concerns for patient-facing medical AI systems. Code and data are available at https://github.com/srsambara-1/MedRedFlag.
The generation of high-fidelity synthetic data is a cornerstone of modern machine learning, yet Large Language Models (LLMs) frequently suffer from hallucinations, logical inconsistencies, and mode collapse when tasked with structured generation. Existing approaches, such s prompting or retrieval-augmented generaon, lack the mechanisms to balance linguistic expressivity with formal guarantees regarding validity and coverage. To address this, we propose CircuitSynth, a novel neuro-symbolic framework that decouples semantic reasoning from surface realization. By distilling the reasoning capabilities of a Teacher LLM into a Probabilistic Sentential Decision Diagram (PSDD), CircuitSynth creates a tractable semantic prior that structurally enforces hard logical constraints. Furthermore, we introduce a convex optimization mechanism to rigorously satisfy soft distributional goals. Empirical evaluations across diverse benchmarks demonstrate that CircuitSynth achieves 100% Schema Validity even in complex logic puzzles where unconstrained baselines fail (12.4%) while significantly outperforming state-of-the-art methods in rare-combination coverage.
Language model users often embed personal and social context in their questions. Theasker’s role—implicit in how the question is framed—creates specific needs for an appropriate response. However, most evaluations, while capturing the model’s capability to respond, often ignore who is asking. This gap is especially critical in stigmatized domains such as opioid use disorder (OUD), where accounting for users’ contexts is essential to provide accessible, stigma-free responses. We propose CORUS (COmmunity-driven Roles for User-centric Question Simulation), a framework for simulating role-based questions. Drawing on role theory and posts from an online OUD recovery community (r/OpiatesRecovery), we first build a taxonomy of asker roles—patients, caregivers, practitioners. Next, we use it to simulate 15,321 questions that embed each role’s goals, behaviors, and experiences. Our evaluations show that these questions are both highly believable and comparable to real-world data. When used to evaluate five LLMs, for the same question but differing roles, we find systematic differences: vulnerable roles, such as patients and caregivers, elicit more supportive responses (+17%) and reduced knowledge content (−19%) in comparison to practitioners. Our work demonstrates how implicitly signaling a user’s role shapes model responses, and provides a methodology for role-informed evaluation of conversational AI.
Formal postconditions precisely characterize program behavior and support debugging, testing, and verification, but writing them requires substantial expertise and effort. This has motivated recent work on automatically generating postconditions from code and natural-language artifacts using large language models (LLMs). However, evaluation remains a key bottleneck. Existing benchmarks primarily emphasize correctness under limited evaluation settings, often relying on surface-form matching or manual assessment on small or synthetic datasets.We introduce POSTCONDBENCH, a multilingual benchmark for evaluating method-level postcondition generation from real-world software. POSTCONDBENCH comprises 420 Python and Java tasks drawn from 121 open-source projects, each paired with a high-quality ground-truth postcondition set constructed with expert involvement. To enable automatic evaluation, POSTCONDBENCH provides a runnable execution environment and operationalizes completeness via defect discrimination: a postcondition set is more complete if it is violated by more defective implementations while remaining satisfied on correct executions. Using POSTCONDBENCH, we formulate three generation settings and evaluate five SOTA LLMs. Our results reveal a substantial gap between correctness and completeness, and show that repository-level dependencies and method complexity exacerbate this gap.
Save the Good Prefix: Precise Error Penalization via Process-Supervised RL to Enhance LLM Reasoning
PDF ↗Reinforcement learning (RL) has emerged as a powerful framework for improving the reasoning capabilities of large language models (LLMs). However, most existing RL approaches rely on sparse outcome rewards, which fail to credit correct intermediate steps in partially successful solutions. Process reward models (PRMs) offer fine-grained step-level supervision, but their scores are often noisy and difficult to evaluate. As a result, recent PRM benchmarks focus on a more objective capability: detecting the first incorrect step in a reasoning path. However, this evaluation target is misaligned with how PRMs are typically used in RL, where their step-wise scores are treated as raw rewards to maximize. To bridge this gap, we propose Verifiable Prefix Policy Optimization (VPPO), which uses PRMs only to localize the first error during RL. Given an incorrect rollout, VPPO partitions the trajectory into a verified correct prefix and an erroneous suffix based on the first error, rewarding the former while applying targeted penalties only after the detected mistake. This design yields stable, interpretable learning signals and improves credit assignment. Across multiple reasoning benchmarks, VPPO consistently outperforms sparse-reward RL and prior PRM-guided baselines on both Pass@1 and Pass@K.
Scaling training compute, measured in FLOPs, has long been shown to improve the accuracy of large language models, yet training remains resource-intensive. Prior work shows that increasing test-time compute (TTC)—for example through iterative sampling—can allow smaller models to rival or surpass much larger ones at lower overall cost. We introduce TTC-aware training, where an intermediate checkpoint and a corresponding TTC configuration can together match or exceed the accuracy of a fully trained model while requiring substantially fewer training FLOPs. Building on this insight, we propose an early stopping algorithm that jointly selects a checkpoint and TTC configuration to minimize training compute without sacrificing accuracy. To make this practical, we develop an efficient TTC evaluation method that avoids exhaustive search, and we formalize a break-even bound that identifies when increased inference compute compensates for reduced training compute. Experiments demonstrate up to 92% reductions in training FLOPs while maintaining and sometimes remarkably improving accuracy. These results highlight a new perspective for balancing training and inference compute in model development, enabling faster deployment cycles and more frequent model refreshes.
Chart-to-table translation converts chart images into structured tabular data. Accurate translation is crucial for Multimodal Language Modal (MLM) to answer complex queries. We observe imbalances in the number of images across different aspects of the y-axis information in public chart datasets. Such imbalances can introduce unintended biases, causing uneven MLM performance. Previous works have not systematically examined these biases. To address this gap, we propose a new framework, FairChart2Table, for analyzing y-axis-related bias on five state-of-the-art models.Key Findings: (1) There are significant y-axis biases related to the digit length of the major tick values, the number of major ticks, the range of values, and the tick value format (e.g., abbreviation or scientific format). (2) The number of legends/entities in chart images impacts MLM performance. (3) Prompting MLM with y-axis information can significantly enhance the performance for some MLMs.
Charts are widely used to present complex information. Deriving meaningful insights in real-world contexts often requires interpreting multiple related charts together. Research on understanding multi-chart images has not been extensively explored. We introduce PolyChartQA, a mid-scale dataset specifically designed for question answering over multi-chart images. PolyChartQA comprises 534 multi-chart images (with a total of 2,297 sub-charts) sourced from peer-reviewed computer science research publications and 2,694 QA pairs. We evaluate the performance of nine state-of-the-art Multimodal Language Models (MLMs) on PolyChartQA across question type, difficulty, question source, and key structural characteristics of multi-charts. Our results show a 27.4% LLM-based accuracy (L-Accuracy) drop on human-authored questions compared to MLM-generated questions, and a 5.39% L-accuracy gain with our proposed prompting method.