Computation and Language
Artificial Rigidities vs. Biological Noise: A Comparative Analysis of Multisensory Integration in AV-HuBERT and Human Observers
Francisco Portillo López
This study evaluates AV-HuBERT's perceptual bio-fidelity by benchmarking its response to incongruent audiovisual stimuli (McGurk effect) against human observers (N=44). Results reveal a striking quantitative isomorphism: AI and humans exhibited nearly identical auditory dominance rates (32.0% vs. 31.8%), suggesting the model captures biological thresholds for auditory resistance. However, AV-HuBERT showed a deterministic bias toward phonetic fusion (68.0%), significantly exceeding human rates (47.7%). While humans displayed perceptual stochasticity and diverse error profiles, the model remained strictly categorical. Findings suggest that current self-supervised architectures mimic multisensory outcomes but lack the neural variability inherent to human
speech perception.
Read the full abstract ▾
Computation and Language
Determinants of Training Corpus Size for Clinical Text Classification
Jaya Chaturvedi, Saniya Deshpande, Chenkai Ma et al. (+5 more)
Introduction: Clinical text classification using natural
language processing (NLP) models requires adequate training data to achieve optimal performance. For that, 200-500 documents are typically annotated. The number is constrained by time and costs and lacks justification of the sample size requirements and their relationship to text vocabulary properties. Methods: Using the publicly available MIMIC-III
dataset containing hospital discharge notes with ICD-9 diagnoses as labels, we employed pre-trained BERT
embeddings followed by Random Forest classifiers to identify 10 randomly selected diagnoses, varying training corpus sizes from 100 to 10,000 documents, and analyzed vocabulary properties by identifying strong and noisy predictive words through Lasso logistic regression on bag-of-words
embeddings. Results: Learning curves varied significantly across the 10 classification tasks despite identical preprocessing and
algorithms, with 600 documents sufficient to achieve 95% of the performance attainable with 10,000 documents for all tasks. Vocabulary analysis revealed that more strong predictors and fewer noisy predictors were associated with steeper learning curves, where every 100 additional noisy words decreased accuracy by approximately 0.02 while 100 additional strong predictors increased maximum accuracy by approximately 0.04.
Read the full abstract ▾
All authors Jaya Chaturvedi, Saniya Deshpande, Chenkai Ma, Robert Cobb, Angus Roberts, Robert Stewart, Daniel Stahl, Diana Shamsutdinova
Computation and Language
Can professional translators identify machine-generated text?
Michael Farrell
This study investigates whether professional translators without prior specialized training can reliably identify short stories generated in Italian by artificial intelligence (AI). Sixty-nine translators took part in an in-person experiment, where they assessed three anonymized short stories - two written by ChatGPT-4o and one by a human author. For each story, participants rated the likelihood of AI authorship and provided justifications for their choices. While average results were inconclusive, a statistically significant subset (16.2%) successfully distinguished the synthetic texts from the human text, suggesting that their judgements were informed by analytical skill rather than chance. However, a nearly equal number misclassified the texts in the opposite direction, often relying on subjective impressions rather than objective markers, possibly reflecting a reader preference for AI-generated texts. Low burstiness and narrative contradiction emerged as the most reliable indicators of synthetic authorship, with unexpected calques, semantic loans and syntactic transfer from English also reported. In contrast, features such as grammatical accuracy and emotional tone frequently led to misclassification. These findings raise questions about the role and scope of synthetic-text editing in professional contexts.
Read the full abstract ▾
Computation and Language
ExDR: Explanation-driven Dynamic Retrieval Enhancement for Multimodal Fake News Detection
Guoxuan Ding, Yuqing Li, Ziyan Zhou et al. (+3 more)
The rapid spread of
multimodal fake news poses a serious societal threat, as its evolving nature and reliance on timely factual details challenge existing detection methods. Dynamic
Retrieval-Augmented Generation provides a promising solution by triggering keyword-based
retrieval and incorporating external knowledge, thus enabling both efficient and accurate evidence selection. However, it still faces challenges in addressing issues such as redundant
retrieval, coarse similarity, and irrelevant evidence when applied to deceptive content. In this paper, we propose ExDR, an Explanation-driven Dynamic
Retrieval-Augmented Generation framework for
Multimodal Fake News Detection. Our framework systematically leverages model-generated explanations in both the
retrieval triggering and evidence
retrieval modules. It assesses triggering confidence from three complementary dimensions, constructs entity-aware indices by fusing deceptive entities, and retrieves contrastive evidence based on deception-specific features to challenge the initial claim and enhance the final prediction. Experiments on two
benchmark datasets, AMG and MR2, demonstrate that ExDR consistently outperforms previous methods in
retrieval triggering accuracy,
retrieval quality, and overall detection performance, highlighting its effectiveness and generalization capability.
Read the full abstract ▾
All authors Guoxuan Ding, Yuqing Li, Ziyan Zhou, Zheng Lin, Daren Zha, Jiangnan Li
Computation and Language · Cross-listed from cs.AI
ErrorMap and ErrorAtlas: Charting the Failure Landscape of Large Language Models
Shir Ashury-Tahan, Yifan Mai, Elron Bandel et al. (+2 more)
Large
Language Models (LLM)
benchmarks tell us when models fail, but not why they fail. A wrong answer on a
reasoning dataset may stem from formatting issues, calculation errors, or
dataset noise rather than weak
reasoning. Without disentangling such causes,
benchmarks remain incomplete and cannot reliably guide model improvement. We introduce ErrorMap, the first method to chart the sources of LLM failure. It extracts a model's unique "failure signature", clarifies what
benchmarks measure, and broadens error identification to reduce blind spots. This helps developers debug models, aligns
benchmark goals with outcomes, and supports informed model selection. ErrorMap works on any model or
dataset with the same logic. Applying our method to 35
datasets and 83 models we generate ErrorAtlas, a taxonomy of model errors, revealing recurring failure patterns. ErrorAtlas highlights error types that are currently underexplored in LLM research, such as omissions of required details in the output and question misinterpretation. By shifting focus from where models succeed to why they fail, ErrorMap and ErrorAtlas enable advanced evaluation - one that exposes hidden weaknesses and directs progress. Unlike success, typically measured by task-level metrics, our approach introduces a deeper evaluation layer that can be applied globally across models and tasks, offering richer insights into model behavior and limitations. We make the taxonomy and code publicly available with plans to periodically update ErrorAtlas as new
benchmarks and models emerge.
Read the full abstract ▾
All authors Shir Ashury-Tahan, Yifan Mai, Elron Bandel, Michal Shmueli-Scheuer, Leshem Choshen
Computation and Language
SteerEval: Inference-time Interventions Strengthen Multilingual Generalization in Neural Summarization Metrics
Silvia Casola, Ryan Soh-Eun Shim, Felicia Körner et al. (+2 more)
An increasing body of work has leveraged
multilingual language models for Natural
Language Generation tasks such as summarization. A major empirical bottleneck in this area is the shortage of accurate and robust evaluation metrics for many
languages, which hinders progress. Recent studies suggest that
multilingual language models often use English as an internal pivot
language, and that misalignment with this pivot can lead to degraded downstream performance. Motivated by the hypothesis that this mismatch could also apply to
multilingual neural metrics, we ask whether steering their activations toward an English pivot can improve correlation with human judgments. We experiment with encoder- and decoder-based metrics and find that test-time intervention methods are effective across the board, increasing metric effectiveness for diverse
languages.
Read the full abstract ▾
All authors Silvia Casola, Ryan Soh-Eun Shim, Felicia Körner, Yuchen Mao, Barbara Plank
Computation and Language
HumanLLM: Towards Personalized Understanding and Simulation of Human Nature
Yuxuan Lei, Tianfu Wang, Jianxun Lian et al. (+3 more)
Motivated by the remarkable progress of large
language models (LLMs) in objective tasks like mathematics and coding, there is growing interest in their potential to simulate human behavior–a capability with profound implications for transforming social science research and customer-centric business insights. However, LLMs often lack a nuanced understanding of human cognition and behavior, limiting their effectiveness in social simulation and personalized applications. We posit that this limitation stems from a fundamental misalignment: standard LLM pretraining on vast, uncontextualized web data does not capture the continuous, situated context of an individual's decisions, thoughts, and behaviors over time. To bridge this gap, we introduce HumanLLM, a foundation model designed for personalized understanding and simulation of individuals. We first construct the Cognitive Genome
Dataset, a large-scale corpus curated from real-world user data on platforms like Reddit, Twitter, Blogger, and Amazon. Through a rigorous, multi-stage pipeline involving data filtering, synthesis, and quality control, we automatically extract over 5.5 million user logs to distill rich profiles, behaviors, and thinking patterns. We then formulate diverse learning tasks and perform supervised fine-tuning to empower the model to predict a wide range of individualized human behaviors, thoughts, and experiences. Comprehensive evaluations demonstrate that HumanLLM achieves superior performance in predicting user actions and inner thoughts, more accurately mimics user writing styles and preferences, and generates more authentic user profiles compared to base models. Furthermore, HumanLLM shows significant gains on out-of-domain social intelligence
benchmarks, indicating enhanced generalization.
Read the full abstract ▾
All authors Yuxuan Lei, Tianfu Wang, Jianxun Lian, Zhengyu Hu, Defu Lian, Xing Xie
Computation and Language · Cross-listed from cs.AI
Agentic Confidence Calibration
Jiaxin Zhang, Caiming Xiong and Chien-Sheng Wu
AI
agents are rapidly advancing from passive
language models to autonomous systems executing complex, multi-step tasks. Yet their overconfidence in failure remains a fundamental barrier to deployment in high-stakes settings. Existing calibration methods, built for static single-turn outputs, cannot address the unique challenges of agentic systems, such as compounding errors along trajectories, uncertainty from external tools, and opaque failure modes. To address these challenges, we introduce, for the first time, the problem of Agentic Confidence Calibration and propose Holistic Trajectory Calibration (HTC), a novel diagnostic framework that extracts rich process-level features ranging from macro dynamics to micro stability across an
agent's entire trajectory. Powered by a simple, interpretable model, HTC consistently surpasses strong baselines in both calibration and discrimination, across eight
benchmarks, multiple LLMs, and diverse
agent frameworks. Beyond performance, HTC delivers three essential advances: it provides interpretability by revealing the signals behind failure, enables transferability by applying across domains without retraining, and achieves generalization through a General
Agent Calibrator (GAC) that achieves the best calibration (lowest ECE) on the out-of-domain
GAIA benchmark. Together, these contributions establish a new process-centric paradigm for confidence calibration, providing a framework for diagnosing and enhancing the reliability of AI
agents.
Read the full abstract ▾
Computation and Language
Beyond Marginal Distributions: A Framework to Evaluate the Representativeness of Demographic-Aligned LLMs
Tristan Williams, Franziska Weeber, Sebastian Padó et al. (+1 more)
Large
language models are increasingly used to represent human opinions, values, or beliefs, and their steerability towards these ideals is an active area of research. Existing work focuses predominantly on aligning marginal response distributions, treating each
alignment evaluation example independently. While essential, this may overlook deeper latent structures that characterise real populations and underpin cultural values theories. We propose a framework for evaluating the representativeness of aligned models through multivariate correlation patterns in addition to marginal distributions. We show the value of our evaluation scheme by comparing two model steering techniques (persona prompting and demographic fine-tuning) and evaluating them against human responses from the World Values Survey. While the demographic fine-tuned model better approximates marginal response distributions, persona prompting performs marginally better at reproducing the empirical correlation structure between survey items. Despite this reversal, neither technique aligns with human correlation patterns. We conclude that representativeness is a distinct aspect of value
alignment and an evaluation focused on marginals can mask structural failures, leading to overly optimistic conclusions about model representativeness.
Read the full abstract ▾
All authors Tristan Williams, Franziska Weeber, Sebastian Padó, Alan Akbik
Computation and Language
Hallucination Mitigating for Medical Report Generation
Ruoqing Zhao, Runze Xia and Piji Li
In the realm of medical report generation (MRG), the integration of natural
language processing has emerged as a vital tool to alleviate the workload of radiologists. Despite the impressive capabilities demonstrated by large
vision language models (LVLMs) in understanding natural
language, their susceptibility to generating plausible yet inaccurate claims, known as “
hallucinations”, raises concerns-especially in the nuanced and critical field of medical. In this work, we introduce a framework, Knowledge-Enhanced with Fine-Grained Reinforced Rewards Medical Report Generation (KERM), to tackle the issue. Our approach refines the input to the LVLM by first utilizing MedCLIP for knowledge
retrieval, incorporating relevant lesion fact sentences from a curated knowledge corpus. We then introduce a novel purification module to ensure the retrieved knowledge is contextually relevant to the patient's clinical context. Subsequently, we employ fine-grained rewards to guide these models in generating highly supportive and clinically relevant descriptions, ensuring the
alignment of model's outputs with desired behaviors. Experimental results on IU-Xray and MIMIC-CXR
datasets validate the effectiveness of our approach in mitigating
hallucinations and enhancing report quality.
Read the full abstract ▾
Computation and Language · Cross-listed from cs.AI
PhysProver: Advancing Automatic Theorem Proving for Physics
Hanning Zhang, Ruida Wang, Rui Pan et al. (+3 more)
The combination of verifiable
languages and LLMs has significantly influenced both the mathematical and computer science communities because it provides a rigorous foundation for
theorem proving. Recent advancements in the field provide foundation models and sophisticated agentic systems pushing the boundaries of formal mathematical
reasoning to approach the natural
language capability of LLMs. However, little
attention has been given to the formal physics
reasoning, which also heavily relies on similar problem-solving and
theorem-proving frameworks. To solve this problem, this paper presents, to the best of our knowledge, the first approach to enhance formal
theorem proving in the physics domain. We compose a dedicated
dataset PhysLeanData for the task. It is composed of
theorems sampled from PhysLean and data generated by a
conjecture-based formal data generation pipeline. In the training pipeline, we leverage DeepSeek-Prover-V2-7B, a strong open-source mathematical
theorem prover, and apply Reinforcement Learning with Verifiable Rewards (RLVR) to train our model PhysProver. Comprehensive experiments demonstrate that, using only $\sim$5K training samples, PhysProver achieves an overall 2.4% improvement in multiple sub-domains. Furthermore, after formal physics training, we observe 1.3% gains on the MiniF2F-Test
benchmark, which indicates non-trivial generalization beyond physics domains and enhancement for formal math capability as well. The results highlight the effectiveness and efficiency of our approach, which provides a paradigm for extending formal provers outside mathematical domains. To foster further research, we will release both our
dataset and model to the community.
Read the full abstract ▾
All authors Hanning Zhang, Ruida Wang, Rui Pan, Wenyuan Wang, Bingxu Meng, Tong Zhang
Computation and Language · Cross-listed from cs.LG
Towards Automated Kernel Generation in the Era of LLMs
Yang Yu, Peiyu Zang, Chi Hsu Tsai et al. (+11 more)
The performance of modern AI systems is fundamentally constrained by the quality of their underlying GPU kernels, which translate high-level algorithmic semantics into low-level hardware operations. Achieving near-optimal kernels requires expert-level understanding of hardware architectures and programming models, making kernel engineering a critical but notoriously time-consuming and non-scalable process. Recent advances in large
language models and LLM-based
agents have opened new possibilities for automating kernel generation and optimization. LLMs are well-suited to compress expert-level kernel knowledge that is difficult to formalize, while agentic systems further enable scalable optimization by casting kernel development as an iterative, feedback-driven loop. Rapid progress has been made in this area. However, the field remains fragmented and lacks a systematic perspective for LLM-driven kernel generation. This survey addresses this gap by providing a structured overview of existing approaches, spanning LLM-based approaches and agentic optimization workflows, and systematically organizing the
datasets and
benchmarks that underpin learning and evaluation in this domain. Moreover, key open challenges and future research directions are further outlined, aiming to establish a comprehensive reference for the next generation of automated kernel optimization. To keep track of this field, we maintain an open-source GitHub repository at https://github.com/flagos-ai/awesome-LLM-driven-kernel-generation.
Read the full abstract ▾
All authors Yang Yu, Peiyu Zang, Chi Hsu Tsai, Haiming Wu, Yixin Shen, Jialing Zhang, Haoyu Wang, Zhiyou Xiao, Jingze Shi, Yuyu Luo, Wentao Zhang, Chunlei Men, Guang Liu, Yonghua Lin
Computation and Language
RebuttalAgent: Strategic Persuasion in Academic Rebuttal via Theory of Mind
Zhitao He, Zongwei Lyu and Yi R Fung
Although artificial intelligence (AI) has become deeply integrated into various stages of the research workflow and achieved remarkable advancements, academic rebuttal remains a significant and underexplored challenge. This is because rebuttal is a complex process of strategic communication under severe information asymmetry rather than a simple technical debate. Consequently, current approaches struggle as they largely imitate surface-level linguistics, missing the essential element of perspective-taking required for effective persuasion. In this paper, we introduce RebuttalAgent, the first framework to ground academic rebuttal in Theory of Mind (ToM), operationalized through a ToM-Strategy-Response (TSR) framework that models reviewer mental state, formulates persuasion strategy, and generates evidence-based response. To train our
agent, we construct RebuttalBench, a large-scale
dataset synthesized via a novel critique-and-refine approach. Our training process consists of two stages, beginning with a supervised fine-tuning phase to equip the
agent with ToM-based analysis and strategic planning capabilities, followed by a reinforcement learning phase leveraging the self-reward mechanism for scalable self-improvement. For reliable and efficient automated evaluation, we further develop Rebuttal-RM, a specialized evaluator trained on over 100K samples of multi-source rebuttal data, which achieves scoring consistency with human preferences surpassing powerful judge GPT-4.1. Extensive experiments show RebuttalAgent significantly outperforms the base model by an average of 18.3% on automated metrics, while also outperforming advanced proprietary models across both automated and human evaluations.
Read the full abstract ▾
Computation and Language · Cross-listed from cs.LG
Even GPT-5.2 Can't Count to Five: The Case for Zero-Error Horizons in Trustworthy LLMs
Ryoma Sato
We propose Zero-Error Horizon (ZEH) for trustworthy LLMs, which represents the maximum range that a model can solve without any errors. While ZEH itself is simple, we demonstrate that evaluating the ZEH of state-of-the-art LLMs yields abundant insights. For example, by evaluating the ZEH of GPT-5.2, we found that GPT-5.2 cannot even compute the parity of a short string like 11000, and GPT-5.2 cannot determine whether the parentheses in ((((()))))) are balanced. This is surprising given the excellent capabilities of GPT-5.2. The fact that LLMs make mistakes on such simple problems serves as an important lesson when applying LLMs to safety-critical domains. By applying ZEH to Qwen2.5 and conducting detailed analysis, we found that while ZEH correlates with accuracy, the detailed behaviors differ, and ZEH provides clues about the emergence of algorithmic capabilities. Finally, while computing ZEH incurs significant computational cost, we discuss how to mitigate this cost by achieving up to one order of magnitude speedup using tree structures and online softmax.
Read the full abstract ▾
Computation and Language
Persona Switch: Mixing Distinct Perspectives in Decoding Time
Junseok Kim, Nakyeong Yang and Kyomin Jung
Role-play prompting is known to steer the behavior of
language models by injecting a persona into the
prompt, improving their zero-shot
reasoning capabilities. However, such improvements are inconsistent across different tasks or instances. This inconsistency suggests that zero-shot and role-play prompting may offer complementary strengths rather than one being universally superior. Building on this insight, we propose Persona Switch, a novel decoding method that dynamically combines the benefits of both prompting strategies. Our method proceeds step-by-step, selecting the better output between zero-shot and role-play prompting at each step by comparing their output confidence, as measured by the logit gap. Experiments with widely-used LLMs demonstrate that Persona Switch consistently outperforms competitive baselines, achieving up to 5.13% accuracy improvement. Furthermore, we show that output confidence serves as an informative measure for selecting the more reliable output.
Read the full abstract ▾
Computation and Language · Cross-listed from cs.AI
Agentic Uncertainty Quantification
Jiaxin Zhang, Prafulla Kumar Choubey, Kung-Hsiang Huang et al. (+2 more)
Although AI
agents have demonstrated impressive capabilities in long-horizon
reasoning, their reliability is severely hampered by the “Spiral of
Hallucination,” where early epistemic errors propagate irreversibly. Existing methods face a dilemma: uncertainty quantification (UQ) methods typically act as passive sensors, only diagnosing risks without addressing them, while self-reflection mechanisms suffer from continuous or aimless corrections. To bridge this gap, we propose a unified Dual-Process Agentic UQ (AUQ) framework that transforms verbalized uncertainty into active, bi-directional control signals. Our architecture comprises two complementary mechanisms: System 1 (Uncertainty-Aware
Memory, UAM), which implicitly propagates verbalized confidence and semantic explanations to prevent blind decision-making; and System 2 (Uncertainty-Aware Reflection, UAR), which utilizes these explanations as rational cues to trigger targeted inference-time resolution only when necessary. This enables the
agent to balance efficient execution and deep deliberation dynamically. Extensive experiments on closed-loop
benchmarks and open-ended deep research tasks demonstrate that our training-free approach achieves superior performance and trajectory-level calibration. We believe this principled framework AUQ represents a significant step towards reliable
agents.
Read the full abstract ▾
All authors Jiaxin Zhang, Prafulla Kumar Choubey, Kung-Hsiang Huang, Caiming Xiong, Chien-Sheng Wu
Computation and Language
What Patients Really Ask: Exploring the Effect of False Assumptions in Patient Information Seeking
Raymond Xiong, Furong Jia, Lionel Wong et al. (+1 more)
Patients are increasingly using large
language models (LLMs) to seek answers to their healthcare-related questions. However, benchmarking efforts in LLMs for question answering often focus on medical exam questions, which differ significantly in style and content from the questions patients actually raise in real life. To bridge this gap, we sourced data from Google's People Also Ask feature by querying the top 200 prescribed medications in the United States, curating a
dataset of medical questions people commonly ask. A considerable portion of the collected questions contains incorrect assumptions and dangerous intentions. We demonstrate that the emergence of these corrupted questions is not uniformly random and depends heavily on the degree of incorrectness in the history of questions that led to their appearance. Current LLMs that perform strongly on other
benchmarks struggle to identify incorrect assumptions in everyday questions.
Read the full abstract ▾
All authors Raymond Xiong, Furong Jia, Lionel Wong, Monica Agrawal
Computation and Language
Towards Reliable Medical LLMs: Benchmarking and Enhancing Confidence Estimation of Large Language Models in Medical Consultation
Zhiyao Ren, Yibing Zhan, Siyuan Liang et al. (+3 more)
Large-scale
language models (LLMs) often offer clinical judgments based on incomplete information, increasing the risk of misdiagnosis. Existing studies have primarily evaluated confidence in single-turn, static settings, overlooking the coupling between confidence and correctness as clinical evidence accumulates during real consultations, which limits their support for reliable decision-making. We propose the first
benchmark for assessing confidence in multi-turn interaction during realistic medical consultations. Our
benchmark unifies three types of medical data for open-ended diagnostic generation and introduces an information sufficiency gradient to characterize the confidence-correctness dynamics as evidence increases. We implement and compare 27 representative methods on this
benchmark; two key insights emerge: (1) medical data amplifies the inherent limitations of
token-level and consistency-level confidence methods, and (2) medical
reasoning must be evaluated for both diagnostic accuracy and information completeness. Based on these insights, we present MedConf, an evidence-grounded linguistic self-assessment framework that constructs symptom profiles via
retrieval-augmented generation, aligns patient information with supporting, missing, and contradictory relations, and aggregates them into an interpretable confidence estimate through weighted integration. Across two LLMs and three medical
datasets, MedConf consistently outperforms state-of-the-art methods on both AUROC and Pearson correlation coefficient metrics, maintaining stable performance under conditions of information insufficiency and multimorbidity. These results demonstrate that information adequacy is a key determinant of credible medical confidence modeling, providing a new pathway toward building more reliable and interpretable large medical models.
Read the full abstract ▾
All authors Zhiyao Ren, Yibing Zhan, Siyuan Liang, Guozheng Ma, Baosheng Yu, Dacheng Tao
Computation and Language · Cross-listed from cs.SD
Qwen3-TTS Technical Report
Hangrui Hu, Xinfa Zhu, Ting He et al. (+13 more)
In this report, we present the Qwen3-TTS series, a family of advanced
multilingual, controllable, robust, and streaming text-to-
speech models. Qwen3-TTS supports state-of-the-art 3-second voice cloning and description-based control, allowing both the creation of entirely novel voices and fine-grained manipulation over the output
speech. Trained on over 5 million hours of
speech data spanning 10
languages, Qwen3-TTS adopts a dual-track LM architecture for real-time synthesis, coupled with two
speech tokenizers: 1) Qwen-TTS-Tokenizer-25Hz is a single-codebook codec emphasizing semantic content, which offers seamlessly integration with Qwen-Audio and enables streaming waveform reconstruction via a block-wise DiT. 2) Qwen-TTS-Tokenizer-12Hz achieves extreme bitrate reduction and ultra-low-latency streaming, enabling immediate first-packet emission ($97\,\mathrm{ms}$) through its 12.5 Hz, 16-layer multi-codebook design and a lightweight causal ConvNet. Extensive experiments indicate state-of-the-art performance across diverse objective and subjective
benchmark (e.g., TTS
multilingual test set, InstructTTSEval, and our long
speech test set). To facilitate community research and development, we release both tokenizers and models under the Apache 2.0 license.
Read the full abstract ▾
All authors Hangrui Hu, Xinfa Zhu, Ting He, Dake Guo, Bin Zhang, Xiong Wang, Zhifang Guo, Ziyue Jiang, Hongkun Hao, Zishan Guo, Xinyu Zhang, Pei Zhang, Baosong Yang, Jin Xu, Jingren Zhou, Junyang Lin
Computation and Language · Cross-listed from cs.LG
When Sharpening Becomes Collapse: Sampling Bias and Semantic Coupling in RL with Verifiable Rewards
Mingyuan Fan, Weiguang Han, Daixin Wang et al. (+3 more)
Reinforcement Learning with Verifiable Rewards (RLVR) is a central paradigm for turning large
language models (LLMs) into reliable problem solvers, especially in logic-heavy domains. Despite its empirical success, it remains unclear whether RLVR elicits novel capabilities or merely sharpens the distribution over existing knowledge. We study this by formalizing over-sharpening, a phenomenon where the policy collapses onto limited modes, suppressing valid alternatives. At a high level, we discover finite-batch updates intrinsically bias learning toward sampled modes, triggering a collapse that propagates globally via semantic coupling. To mitigate this, we propose inverse-success advantage calibration to prioritize difficult queries and distribution-level calibration to diversify sampling via a
memory network. Empirical evaluations validate that our strategies can effectively improve generalization.
Read the full abstract ▾
All authors Mingyuan Fan, Weiguang Han, Daixin Wang, Cen Chen, Zhiqiang Zhang, Jun Zhou
Computation and Language
ToxiTwitch: Toward Emote-Aware Hybrid Moderation for Live Streaming Platforms
Baktash Ansari, Elias Martin and Afra Mashhadi
The rapid growth of live-streaming platforms such as Twitch has introduced complex challenges in moderating toxic behavior. Traditional moderation approaches, such as human annotation and keyword-based filtering, have demonstrated utility, but human moderators on Twitch constantly struggle to scale effectively in the fast-paced, high-volume, and context-rich chat environment of the platform while also facing harassment themselves. Recent advances in large
language models (LLMs), such as DeepSeek-R1-Distill and Llama-3-8B-Instruct, offer new opportunities for toxicity detection, especially in understanding nuanced,
multimodal communication involving emotes. In this work, we present an exploratory comparison of toxicity detection approaches tailored to Twitch. Our analysis reveals that incorporating emotes improves the detection of toxic behavior. To this end, we introduce ToxiTwitch, a hybrid model that combines LLM-generated
embeddings of text and emotes with traditional machine learning classifiers, including Random Forest and SVM. In our case study, the proposed hybrid approach reaches up to 80 percent accuracy under channel-specific training (with 13 percent improvement over BERT and F1-score of 76 percent). This work is an exploratory study intended to surface challenges and limits of emote-aware toxicity detection on Twitch.
Read the full abstract ▾
Computation and Language
Parallelism and Generation Order in Masked Diffusion Language Models: Limits Today, Potential Tomorrow
Yangyang Zhong, Yanmei Gu, Zhengqing Zang et al. (+14 more)
Masked
Diffusion Language Models (MDLMs) promise parallel
token generation and arbitrary-order decoding, yet it remains unclear to what extent current models truly realize these capabilities. We characterize MDLM behavior along two dimensions – parallelism strength and generation order – using Average Finalization Parallelism (AFP) and Kendall's tau. We evaluate eight mainstream MDLMs (up to 100B parameters) on 58
benchmarks spanning knowledge,
reasoning, and programming. The results show that MDLMs still lag behind comparably sized autoregressive models, mainly because parallel probabilistic modeling weakens inter-
token dependencies. Meanwhile, MDLMs exhibit adaptive decoding behavior: their parallelism and generation order vary significantly with the task domain, the stage of
reasoning, and whether the output is correct. On tasks that require "backward information" (e.g., Sudoku), MDLMs adopt a solution order that tends to fill easier Sudoku blanks first, highlighting their advantages. Finally, we provide theoretical motivation and design insights supporting a Generate-then-Edit paradigm, which mitigates dependency loss while retaining the efficiency of parallel decoding.
Read the full abstract ▾
All authors Yangyang Zhong, Yanmei Gu, Zhengqing Zang, Xiaomeng Li, Yuqi Ding, Xibei Jia, Yuting Shen, Zhenzhong Lan, Liwang Zhu, Weiping Liu, Junlin Zhou, Haisheng Liu, Zhong Xin Yu, Pengxin Luo, Donglian Qi, Yunfeng Yan, Junbo Zhao
Computation and Language
YuFeng-XGuard: A Reasoning-Centric, Interpretable, and Flexible Guardrail Model for Large Language Models
Junyu Lin, Meizhen Liu, Xiufeng Huang et al. (+12 more)
As large
language models (LLMs) are increasingly deployed in real-world applications, safety guardrails are required to go beyond coarse-grained filtering and support fine-grained, interpretable, and adaptable risk assessment. However, existing solutions often rely on rapid classification schemes or post-hoc rules, resulting in limited transparency, inflexible policies, or prohibitive inference costs. To this end, we present YuFeng-XGuard, a
reasoning-centric guardrail model family designed to perform multi-dimensional risk perception for LLM interactions. Instead of producing opaque binary judgments, YuFeng-XGuard generates structured risk predictions, including explicit risk categories and configurable confidence scores, accompanied by natural
language explanations that expose the underlying
reasoning process. This formulation enables safety decisions that are both actionable and interpretable. To balance decision latency and explanatory depth, we adopt a tiered inference paradigm that performs an initial risk decision based on the first decoded
token, while preserving ondemand explanatory
reasoning when required. In addition, we introduce a dynamic policy mechanism that decouples risk perception from policy enforcement, allowing safety policies to be adjusted without model retraining. Extensive experiments on a diverse set of public safety
benchmarks demonstrate that YuFeng-XGuard achieves stateof-the-art performance while maintaining strong efficiency-efficacy trade-offs. We release YuFeng-XGuard as an open model family, including both a full-capacity variant and a lightweight version, to support a wide range of deployment scenarios.
Read the full abstract ▾
All authors Junyu Lin, Meizhen Liu, Xiufeng Huang, Jinfeng Li, Haiwen Hong, Xiaohan Yuan, Yuefeng Chen, Longtao Huang, Hui Xue, Ranjie Duan, Zhikai Chen, Yuchuan Fu, Defeng Li, Lingyao Gao, Yitong Yang
Computation and Language
From Generation to Collaboration: Using LLMs to Edit for Empathy in Healthcare
Man Luo, Bahareh Harandizadeh, Amara Tariq et al. (+5 more)
Clinical empathy is essential for patient care, but physicians need continually balance emotional warmth with factual precision under the cognitive and emotional constraints of clinical practice. This study investigates how large
language models (LLMs) can function as empathy editors, refining physicians' written responses to enhance empathetic tone while preserving underlying medical information. More importantly, we introduce novel quantitative metrics, an Empathy Ranking Score and a MedFactChecking Score to systematically assess both emotional and factual quality of the responses. Experimental results show that LLM edited responses significantly increase perceived empathy while preserving factual accuracy compared with fully LLM generated outputs. These findings suggest that using LLMs as editorial assistants, rather than autonomous generators, offers a safer, more effective pathway to empathetic and trustworthy AI-assisted healthcare communication.
Read the full abstract ▾
All authors Man Luo, Bahareh Harandizadeh, Amara Tariq, Halim Abbas, Umar Ghaffar, Christopher J Warren, Segun O. Kolade, Haidar M. Abdul-Muhsin
Computation and Language · Cross-listed from cs.CY
LLM or Human? Perceptions of Trust and Information Quality in Research Summaries
Nil-Jana Akpinar, Sandeep Avula, CJ Lee et al. (+3 more)
Large
Language Models (LLMs) are increasingly used to generate and edit scientific abstracts, yet their integration into academic writing raises questions about trust, quality, and disclosure. Despite growing adoption, little is known about how readers perceive LLM-generated summaries and how these perceptions influence evaluations of scientific work. This paper presents a mixed-methods survey experiment investigating whether readers with ML expertise can distinguish between human- and LLM-generated abstracts, how actual and perceived LLM involvement affects judgments of quality and trustworthiness, and what orientations readers adopt toward AI-assisted writing. Our findings show that participants struggle to reliably identify LLM-generated content, yet their beliefs about LLM involvement significantly shape their evaluations. Notably, abstracts edited by LLMs are rated more favorably than those written solely by humans or LLMs. We also identify three distinct reader orientations toward LLM-assisted writing, offering insights into evolving norms and informing policy around disclosure and acceptable use in scientific communication.
Read the full abstract ▾
All authors Nil-Jana Akpinar, Sandeep Avula, CJ Lee, Brandon Dang, Kaza Razat, Vanessa Murdock
Computation and Language
Common to Whom? Regional Cultural Commonsense and LLM Bias in India
Sangmitra Madhusudan, Trush Shashank More, Steph Buongiorno et al. (+3 more)
Existing cultural commonsense
benchmarks treat nations as monolithic, assuming uniform practices within national boundaries. But does cultural commonsense hold uniformly within a nation, or does it vary at the sub-national level? We introduce Indica, the first
benchmark designed to test LLMs' ability to address this question, focusing on India - a nation of 28 states, 8 union territories, and 22 official
languages. We collect human-annotated answers from five Indian regions (North, South, East, West, and Central) across 515 questions spanning 8 domains of everyday life, yielding 1,630 region-specific question-answer pairs. Strikingly, only 39.4% of questions elicit agreement across all five regions, demonstrating that cultural commonsense in India is predominantly regional, not national. We evaluate eight state-of-the-art LLMs and find two critical gaps: models achieve only 13.4%-20.9% accuracy on region-specific questions, and they exhibit geographic bias, over-selecting Central and North India as the "default" (selected 30-40% more often than expected) while under-representing East and West. Beyond India, our methodology provides a generalizable framework for evaluating cultural commonsense in any culturally heterogeneous nation, from question design grounded in anthropological taxonomy, to regional data collection, to bias measurement.
Read the full abstract ▾
All authors Sangmitra Madhusudan, Trush Shashank More, Steph Buongiorno, Renata Dividino, Jad Kabbara, Ali Emami