Artificial Intelligence · Cross-listed from cs.CV
EvTexture++: Event-Driven Texture Enhancement for Video Super-Resolution
Dachun Kai, Jiayao Lu, Yueyi Zhang et al. (+1 more)
Event-based
vision has drawn increasing
attention owing to its distinctive properties, including ultra-high temporal resolution and extreme dynamic range. Recent works have introduced it to
video super-resolution (VSR) to enhance flow estimation and temporal
alignment. In contrast, this paper shifts the focus of event signals from motion refinement to texture enhancement in VSR. We propose EvTexture++, the first event-driven framework dedicated to texture enhancement in VSR. It leverages high-frequency spatiotemporal details from events to improve texture recovery. EvTexture++ incorporates a customized texture enhancement branch, along with an iterative texture enhancement module that progressively exploits high-temporal-resolution event information for texture restoration. This enables gradual refinement of texture regions across iterations, yielding more accurate and detailed high-resolution outputs. Besides intra-frame texture recovery, large motions could degrade inter-frame temporal consistency, particularly in texture regions, leading to texture flickering. To mitigate this, we further exploit the continuous-time motion cues of events to enhance temporal consistency, introducing a temporal texture
alignment module that estimates event-guided texture-aware flow for precise inter-frame texture
alignment. Moreover, EvTexture++ is designed as a plug-and-play tool to flexibly boost the performance of existing VSR models. Experiments on five
datasets demonstrate that EvTexture++ achieves state-of-the-art performance. When integrated into recent VSR models, it yields significant improvements, with gains of up to 1.55 dB in PSNR on the texture-rich Vid4
dataset. Code: https://github.com/DachunKai/EvTexture.
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All authors Dachun Kai, Jiayao Lu, Yueyi Zhang, Xiaoyan Sun
Artificial Intelligence · Cross-listed from cs.CL
LabVLA: Grounding Vision-Language-Action Models in Scientific Laboratories
Baochang Ren, Xinjie Liu, Xi Chen et al. (+15 more)
Scientific laboratories increasingly rely on AI systems to reason about experiments, but the physical act of doing science remains largely outside their reach. AI can help read literature, generate hypotheses, and plan protocols, yet the execution of those protocols at the bench still requires a human operator.
Vision-
Language-Action (VLA) models provide one possible interface between written protocols and
robot execution, but existing policies are trained mostly on household and tabletop demonstrations and rarely encounter the instruments, transparent
liquids, or fixed protocol workflows found in scientific laboratories. Closing this gap requires both laboratory-specific supervision and a unified learning framework that can accommodate the diverse
robot embodiments used to execute experimental protocols. We therefore identify data and embodiment as central bottlenecks alongside model design. To address the data side, we build RoboGenesis, a simulation-based workflow and data engine that composes configured laboratory workflows from atomic skills, validates and filters rollouts, and exports structured demonstrations across supported
robot profiles. On the policy side, we present LabVLA, trained with a two-stage recipe: FAST action
token pretraining first makes the Qwen3-VL-4B-Instruct backbone action aware before any continuous control is learned, and flow matching posttraining then attaches a DiT action expert under knowledge insulation. On the LabUtopia
benchmark, LabVLA achieves the highest average success rate among all evaluated baselines under both in-distribution and out-of-distribution settings.
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All authors Baochang Ren, Xinjie Liu, Xi Chen, Yanshuo Liu, Chenxi Li, Daqi Gao, Zeqin Su, Jintao Xing, Zirui Xue, Rui Li, Xiangyu Zhao, Shuofei Qiao, Minting Pan, Wangmeng Zuo, Lei Bai, Dongzhan Zhou, Ningyu Zhang, Huajun Chen
Artificial Intelligence · Cross-listed from cs.CL
Tanmoy Kanti Halder, Akash Ghosh, Subhadip Baidya et al. (+2 more)
Multimodal Large
Language Models (MLLMs) have shown promising
reasoning capabilities in general domains, yet their performance remains limited in specialized settings such as healthcare, especially in
multilingual and low-resource scenarios. This gap is critical in regions like rural India, where patients often express complex medical queries in native Indic
languages and rely on
multimodal inputs such as medical images. Existing English-centric MLLMs struggle to support such use cases, limiting equitable access to AI-driven healthcare assistance. To address this challenge, we introduce ArogyaBodha, a large-scale
multilingual multimodal medical question-answer
dataset constructed from eight heterogeneous sources, covering 31 body systems, six imaging modalities, and 21 clinical domains across English and seven major Indian
languages. We further propose ArogyaSutra, an actor-critic-based multi-
agent framework that integrates tool grounding with dual-
memory mechanisms for step-wise,
reasoning-aware decision making, and uses stored actor-critic simulation trajectories for
distillation. Experiments show that our
dataset and framework improve
multilingual medical
reasoning accuracy across all Indic
languages, with ablations validating the contribution of each component. The source code and
dataset are available at: https://iitp-cse.github.io/ArogyaSutra/
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All authors Tanmoy Kanti Halder, Akash Ghosh, Subhadip Baidya, Arijit Roy, Sriparna Saha
Artificial Intelligence · Cross-listed from cs.LG
Existence Precedes Value: Joint Modeling of Observational Existence and Evolving States in Time Series Forecasting
Yifan Hu, Hongzhou Chen, Peiyuan Liu et al. (+3 more)
Real-world time series are often highly incomplete and irregular due to sensor dormancy, transmission delays, and event-driven sampling, making reliable
forecasting fundamentally challenging. Existing methods have evolved from impute-then-forecast pipelines to continuous-time models such as Neural ODEs and continuous-time graph networks. While these approaches improve the modeling of historical irregularity, they still rely on an implicit oracle assumption at inference time: the timestamps of future valid observations are presumed to be known in advance. This assumption limits practical relevance, since in many real systems the more fundamental question is not only what the future value will be, but also whether a valid observation will occur at all. In this paper, we propose Timeflies, a unified framework that reformulates
forecasting as a joint problem of future observability inference and value estimation. To explicitly model the interaction between observation dynamics and state
evolution, Timeflies adopts an observation stream and a value stream, coupled through three dedicated modules for reliability-aware
embedding, observation-guided dependency modeling, and joint prediction. We further construct Shadow, a
benchmark that combines natural missingness from public
datasets with real-world industrial data, and introduce the Observation-Value Joint
Entropy (OVJE) metric to comprehensively evaluate this coupled predictability. Extensive experiments show that Timeflies consistently outperforms existing methods, highlighting the importance of explicitly modeling future observability in time series
forecasting with missing values. Code and
dataset are available in https://github.com/ant-intl/Timeflies.
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All authors Yifan Hu, Hongzhou Chen, Peiyuan Liu, Yiding Liu, Zewei Dong, Jiang-Ming Yang
Artificial Intelligence
A Three-Layer Framework for AI in Scientific Discovery
Guojun Liao
Current discussions of AI in scientific discovery are often dominated by two visible capabilities: search over existing knowledge and execution through optimization, simulation, and automation. Both are important, but neither fully captures the central act of discovery: the formation and
evolution of models. This paper proposes a three-layer view of AI in discovery. Layer 1 is search and
retrieval by large
language models. Layer 2, as the main innovation of this paper, is model formation through qualitative
reasoning: the capacity to recognize when a current framework is structurally inadequate and to understand the problem within a broader representational space, not through trial and error, but through structural insight into what is missing and where it can be found. Layer 3 is execution, optimization, and refinement. The main claim is that Layer 2 is both the most important and the least developed. Search without model formation remains confined to inherited frameworks, while execution without conceptual revision only amplifies an existing formulation. We illustrate Layer 2
reasoning through three case studies: S. S. Chern's intrinsic proof of the Gauss-Bonnet
theorem, the resolution of the Nesterov Accelerated Gradient convergence problem via Lyapunov functions, and the autonomous disproof of the Erdos unit distance
conjecture by OpenAI in 2026. Each case exhibits the same structural signature: a framework that had become inadequate, a missing conceptual object, and a resolution found in an unexpected neighboring field.
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Artificial Intelligence · Cross-listed from cs.CV
Contrast-Informed Augmentation and Domain-Adversarial Training for Adult-to-Neonatal MR Reconstruction Generalization
Stephen Moore, Lara Leijser, Richard Frayne et al. (+1 more)
Purpose: To investigate whether contrast-informed data augmentation and domain-
adversarial training improve the adult-to-neonatal generalization of the E2E-VarNet. Methods: Three training regimes were investigated: (1) adult-only training with unaugmented adult data, (2) mixed training with paired unaugmented and neonatal-informed augmented adult data, and (3) mixed training with a domain-
adversarial objective. Models were trained on retrospectively undersampled multi-coil adult T2-weighted
brain MR data and evaluated on neonatal and adult test data at acceleration factors $R=4$ and $R=8$ using quantitative metrics and qualitative evaluation. Feature analyses assessed whether domain-
adversarial training altered the latent representations of unaugmented adult, augmented adult, and neonatal test samples. Results: Mixed training (Mixed) and mixed domain-
adversarial training (Mixed-DAT) outperformed unaugmented adult-only training (Unaug-Only) when evaluated on neonatal data. At R=4, Mixed-DAT achieved the best performance (SSIM = 0.924 +/- 0.027, PSNR = 33.98 +/- 1.15 dB). At R=8, Mixed-DAT performed best when measured using SSIM (0.848 +/- 0.031 vs. 0.766 +/- 0.037 for Unaug-Only and 0.814 +/- 0.035 for Mixed) and Mixed performed best when measured using PSNR (29.56 +/- 0.83 dB vs. 26.26 +/- 0.78 dB for Unaug-Only and 29.43 +/- 0.83 dB for Mixed-DAT). Qualitative assessment of t-SNE plots suggested that Mixed-DAT increased the overlap among the latent representations of the unaugmented adult, augmented adult, and neonatal test data. Conclusion: Contrast-informed augmentation and domain-
adversarial training improved adult-to-neonatal generalization of deep learning-based MR reconstruction. These findings suggest that contrast-informed data augmentation combined with
adversarial training may improve robustness to domain shift in undersampled neonatal MR reconstruction.
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All authors Stephen Moore, Lara Leijser, Richard Frayne, Roberto Souza
Artificial Intelligence
Is It You or Your Environment? A Bayesian Inference Framework for Genomically-Anchored Personalized Physiological Interpretation
Aruna Dey and Suraj Biswas
Personalized health AI systems face a fundamental cold-start problem: machine learning models for physiological interpretation require weeks of individual behavioral data before they can distinguish constitutional variation from environmentally driven deviation. We propose a solution grounded in causal inference and
Bayesian prior design. An individual's genomic profile serves as an exogenous genetic anchor – a domain-informed, personalized prior that is fixed at conception, immune to reverse causation, and available before a single behavioral observation is collected. The anchor initializes a
Bayesian belief state over an individual's physiological set point G-hat = mu + sum(beta_i * g_i), where beta_i are GWAS-derived effect sizes and g_i are risk-allele counts. Each incoming physiological measurement P produces a non-constitutional deviation delta = P - G-hat that separates the signal attributable to environment and state from the constitutionally fixed baseline. As behavioral data accrue, the prior decays according to G-hat_t = w(t)*G-hat_genomic + [1-w(t)]*P-bar_t, transitioning from genome-dominated to empirical-baseline-dominated inference. The same observed HRV of 55 ms generates a suppression hypothesis for a person whose prior predicts 80 ms, and an enhancement hypothesis for a person whose prior predicts 30 ms – a reversal impossible without a personalized anchor. We develop this architecture across six physiological domains, grading genomic priors by evidence strength, distinguishing robustly replicated anchors (FTO, FADS1/2, FKBP5) from contested candidate genes (SLC6A4, MAOA, DRD2). We address the inference boundary between association, Mendelian randomization, and individual
token causation, and define four constraints for deployment: evidence-graded priors, dynamic decay, ancestry-matched effect sizes, and attribution rather than deterministic output.
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Artificial Intelligence
Uncertainty-Aware Hybrid Retrieval for Long-Document RAG
Hoin Jung and Xiaoqian Wang
Retrieval augmented generation (RAG) depends critically on the quality and granularity of retrieved evidence. Large
retrieval units preserve context but often introduce irrelevant content, which can dilute answer bearing evidence and worsen long context utilization. Fine-grained units are more compact, but they may be difficult to retrieve reliably because short chunks can lack semantic, lexical, or bridging cues needed to match the query. We propose Uncertainty-aware Multi-Granularity RAG (UMG-RAG), a training-free hybrid
retrieval framework that treats chunk granularity as query-specific reliability estimation. Instead of training a new retriever or modifying the generator, UMG-RAG uses existing dense and sparse retrievers as complementary experts across multiple chunk granularities. For each query, it converts each expert-granularity score list into an evidence distribution, estimates reliability from distribution
entropy, and fuses candidates according to query-specific semantic, lexical, and granularity confidence. We further introduce UMGP-RAG, a parent promotion variant that uses fine-grained hits to locate relevant evidence while returning broader non-redundant parent chunks for local coherence. Experiments on question answering
benchmarks show that uncertainty-aware fusion and parent promotion improve generation quality while maintaining a lightweight, plug-and-play
retrieval pipeline.
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Artificial Intelligence · Cross-listed from eess.AS
Adaptive Turn-Taking for Real-time Multi-Party Voice Agents
Soumyajit Mitra, Prabhat Pandey, Abhinav Jain et al. (+2 more)
Turn-taking in multi-party spoken conversations remains a fundamental challenge for voice-based
agents, particularly under dynamic floor competition and varying user expectations. We propose ModeratorLM, a role-playing voice
agent that conditions turn-taking behavior on an explicitly assigned role in multi-party settings. The system is built on a
speech large
language model operating in chunk-wise streaming manner. We further introduce a
reasoning-augmented variant that incorporates chain-of-thought
reasoning over conversational context and the assigned role. We construct RolePlayConv, a large-scale synthetic
dataset of spoken multi-party conversations with diverse assistant roles. Experiments on real-world meeting data and RolePlayConv show improved turn-taking precision by over 40% and recall by more than 70%, while substantially reducing false-positive interruptions compared to non-role-conditioned baselines.
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All authors Soumyajit Mitra, Prabhat Pandey, Abhinav Jain, Shanmukha Sahith, K V Vijay Girish
Artificial Intelligence · Cross-listed from hep-ex
AgentRivet: an automated system for producing Rivet routines from journal publications
Antonio J. Costa, Caterina Doglioni, Christian Gütschow et al. (+2 more)
Particle physics
collider experiments provide Rivet routines as part of the analysis preservation strategy for model-independent measurements. Rivet is a C++ toolkit that allow new theoretical models to be compared to the measurements, thus aiding the development and tuning of Monte Carlo event generators as well as searches for physics beyond the Standard Model. However, analysis coverage is known to be incomplete, with only 39% of measurements having documented and publicly available Rivet routines. In this article, we design and implement an automated workflow based on Large
Language Models with the goal of providing the missing routines. This multi-step workflow, referred to as AgentRivet, extracts the physics analysis information from published papers and writes the missing Rivet routines, with intermediate code- and physics- reviews as part of an autonomous quality control. We report the results obtained using commercial Large
Language Models, provided by OpenAI, Anthropic, and Google, for two recent measurements from the ATLAS and CMS experiments. We find that AgentRivet produces competent Rivet routines with few syntax errors. The physics fidelity of the routines is reasonable and follows the explanations given in the relevant publications. Nevertheless, physics-implementation issues do arise and are investigated using the artefacts produced by AgentRivet. The majority of physics implementation issues arise from subtle-but-ambiguous definitions in the given publication, although some models struggle to implement complex observables even when clear definitions are given.
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All authors Antonio J. Costa, Caterina Doglioni, Christian Gütschow, Andrew D. Pilkington, Sukanya Sinha
Artificial Intelligence
CloudCons: A Comprehensive End-to-End Benchmark for Cloud Resource Consolidation
Xiaobin Zhang, Lefei Shen, Mouxiang Chen et al. (+6 more)
Driven by conservative over-provisioning to guarantee service reliability, resource utilization in cloud data centers remains at low levels. To mitigate this, the forecast-then-optimize paradigm has emerged to optimize consolidation by anticipating future demands. While emerging time series foundation models promise to enhance this paradigm through zero-shot generalization, existing
benchmarks focus solely on prediction error metrics. The actual decision utility of these advanced models remains unverified, rendering their practical value for downstream tasks uncertain. To bridge this gap, we propose CloudCons, a comprehensive end-to-end
benchmark designed to evaluate
forecasting models within the specific context of cloud resource consolidation. We build high-quality
datasets that cover diverse workloads from Huawei Cloud, Microsoft Azure, and Google Borg, capturing distinct service characteristics ranging from synchronized diurnal rhythms to
stochastic, pulse-like bursts and high-frequency noise. We conduct an extensive evaluation of statistical, deep learning, and foundation models. Our experiments reveal a pivotal finding: while foundation models demonstrate superior zero-shot
forecasting accuracy, this advantage does not inherently translate into better decision utility. Of practical significance, we systematically analyze how the selection of predictive
quantiles acts as a critical lever. We provide actionable guidelines for calibrating these selections to balance the trade-off between resource efficiency and service reliability, offering vital insights for real-world deployment decisions.
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All authors Xiaobin Zhang, Lefei Shen, Mouxiang Chen, Zhuo Li, Hongkai Li, Han Fu, Jianling Sun, Xiaoxue Ren, Chenghao Liu
Artificial Intelligence · Cross-listed from cs.CV
Measurement-Calibrated Multi-Camera Fusion for Vision-Based Indoor Localization
Mateo Toro Diz, Jonathan Hoss and Noah Klarmann
Indoor
vision-based localization systems are affected by detection noise, occlusions, and limited camera coverage, leading to uncertainty at multiple stages of the pipeline. While multi-camera data fusion is widely used to mitigate these issues, it is typically treated as a black-box component and evaluated solely end-to-end, obscuring its mechanistic contributions. To address this gap, this work investigates whether explicitly characterizing single-camera localization errors can be leveraged to calibrate and optimize multi-camera data fusion. We introduce a measurement-calibrated fusion approach that integrates component-wise error quantification, specifically isolating homography calibration, human detection, and motion tracking. A component-wise evaluation is conducted to quantify error contributions from homography calibration, human detection, and motion tracking. Experimental results show that data fusion improves localization accuracy compared to single-camera baselines. While measurement-calibrated fusion provides only limited improvement in absolute accuracy over standard fusion, it substantially reduces trajectory variance and improves motion smoothness, which are critical for applications requiring stable and continuous motion estimates. These results highlight the value of explicit error characterization when designing data fusion strategies for
vision-based indoor positioning systems.
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Artificial Intelligence · Cross-listed from cs.CV
Heterogeneous LiDAR Early Fusion and Learned Re-Ranking Strategy for Robust Long-Term Place Recognition in Unstructured Environments
Judith Vilella-Cantos, Juan José Cabrera, Mónica Ballesta et al. (+2 more)
Robust localization in unstructured environments, such as agricultural fields, is a critical challenge for autonomous systems.
LiDAR sensors provide detailed 3D information about the environment and are invariant to lighting conditions. For this reason,
LiDAR-based place recognition methods have gained significant
attention. In this paper, we propose MinkUNeXt-VINE++, a novel approach that combines early fusion of heterogeneous
LiDAR data from two sensors (Livox Mid-360 and Velodyne VLP-16) and a learned re-ranking strategy in inference time. This fusion leverages the strengths of each sensor to provide a more comprehensive representation of the environment. Additionally, the re-ranking approach is particularly important in repetitive environments, such as vineyards, as finding true positives is a major challenge. We evaluated our approach using the TEMPO-VINE
dataset, which provides heterogeneous
LiDAR data in vineyard environments across different phenological stages. Our results demonstrate that MinkUNeXt-VINE++ significantly improves place recognition performance compared to single-sensor approaches and state-of-the-art methods. MinkUNeXt-VINE++ achieves a 20% improvement in the Recall@1 metric compared to single-sensor approaches, and +30% including re-ranking. The code of our method is publicly available for reproduction.
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All authors Judith Vilella-Cantos, Juan José Cabrera, Mónica Ballesta, David Valiente, Luis Payá
Artificial Intelligence · Cross-listed from cs.LG
CRAFTIIF: Cross-Resolution Analytic Four-Type Interpretable Isolation Forest for Multivariate Time Series Anomaly Detection
William Smits
Anomaly detection in multivariate time series is challenged by four structurally distinct anomaly types – point (isolated spikes), distributional (level shifts), temporal (rhythm changes), and collective (inter-sensor correlation breakdowns) – each requiring different feature representations. Most unsupervised methods target only one or two types and provide limited interpretability. We present CRAFTIIF (Cross-Resolution Analytic Four-Type Interpretable Isolation Forest), a fully unsupervised framework targeting all four types without
dataset-specific tuning. CRAFTIIF generates K=500 random analytic wavelet feature draws across four families (Morlet, DOG, Haar, Coiflet), each targeting a specific anomaly type, feeding five structured Isolation Forests – one per type plus a meta-IF for compound anomalies. An adaptive Otsu/MAD threshold calibrates detection automatically across anomaly rates from 0.1% to 69.2%. Because each IF is trained exclusively on type-specific features, branch firing provides direct anomaly-type attribution by construction, without post-hoc explanation. Evaluated on all 19
datasets of the mTSBench
benchmark (Zhou et al., TMLR 2026), CRAFTIIF achieves mean F1=0.228 (all 19
datasets) and F1=0.322 (13 detectable
datasets), ranking first among all 25 evaluated methods on VUS-PR (0.463 vs. previous best 0.329, +40.7%). A diagnostic framework – oracle F1, detectability limits, and branch separation ratios – identifies 6 of 19
datasets as fundamentally undetectable by any unsupervised method. Ablation over 11 conditions confirms adaptive thresholding (+38% F1), four-branch structure (+20%), and meta-IF (+23%) are each essential. Code: https://github.com/smitswil/craftiif
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Artificial Intelligence · Cross-listed from cs.LG
SupraBench: A Benchmark for Supramolecular Chemistry
Tianyi Ma, Yijun Ma, Zehong Wang et al. (+6 more)
Supramolecular chemistry, which includes the study of non-covalent host-guest assemblies, has advanced various applications. However, designing host-guest systems remains time-consuming, requiring days of dry-lab verification per candidate pair. Although LLMs have emerged as a fast alternative with strong performance on molecular binding tasks, no
benchmark currently systematically evaluates LLMs for host-guest
reasoning across fundamental supramolecular chemistry tasks, e.g., binding affinity prediction. To this end, we collaborate with domain experts to release the first Supramolecular
Benchmark, called SupraBench, to evaluate LLMs in chemistry
reasoning. Specifically, we design four fundamental tasks, i.e., binding affinity prediction, top-binder selection, solvent identification, and host-guest description, plus an auxiliary
vision-based task for molecular identification. We also release SupraPMC, a curated 16M-
token corpus of Supramolecular chemistry articles distilled from Europe PMC, to support the adaptation to the supramolecular domain. We
benchmark a broad range of open and proprietary LLMs and find that LLMs leave substantial headroom across all tasks. Domain adaptation pretraining over SupraPMC transfers cleanly to in-distribution regression but trades off against strict letter-format output. Moreover, the difficulty profile differs sharply across task families, revealing distinct failure modes that indicate specific gaps in current supramolecular chemistry
reasoning. Our source codes and
benchmark datasets are available at https://github.com/Tianyi-Billy-Ma/SupraBench.
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All authors Tianyi Ma, Yijun Ma, Zehong Wang, Weixiang Sun, Ziming Li, Connor R. Schmidt, Chuxu Zhang, Matthew J. Webber, Yanfang Ye
Artificial Intelligence · Cross-listed from cs.LG
MaxProof: Scaling Mathematical Proof with Generative-Verifier RL and Population-Level Test-Time Scaling
Jiacheng Chen, Xinyu Zhang, Shunkai Zhang et al. (+20 more)
We present MaxProof, a population-level test-time scaling framework for competition-level mathematical proof in the MiniMax-M3 series. M3 first trains three proof-oriented capabilities – proof generation, proof verification, and critique-conditioned proof repair – using a defense-in-depth generative verifier engineered for low false-positive rate. These capabilities are merged into a single released M3 model. At test time, MaxProof treats the model as a generator, verifier, refiner, and ranker, searches over a population of candidate proofs, and returns one final proof through tournament selection. With MaxProof test-time scaling, the M3 model reaches 35/42 on IMO 2025 and 36/42 on USAMO 2026, exceeding the human gold-medal threshold on both.
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All authors Jiacheng Chen, Xinyu Zhang, Shunkai Zhang, Yanmohan Wang, Lin Li, Tiancheng Qin, Qin Wang, Zhengmao Zhu, Tianle Li, Jingyang Li, Zehan Li, Binyang Jiang, Jin Zhu, Han Ding, Fei Yu, Chenyu Du, Zijian Song, Jiayuan Song, Zhi Zhang, Yunan Huang, Weiyu Cheng, Pengyu Zhao, Yu Cheng
Artificial Intelligence · Cross-listed from cs.SE
Understanding the Rejection of Fixes Generated by Agentic Pull Requests – Insights from the AIDev Dataset
Mahmoud Abujadallah, Ali Arabat and Mohammed Sayagh
AI coding
agents are increasingly used to generate pull requests (PRs) that propose code fixes in software projects. From a first exploration of the AIDev
dataset, we find that 46.41% of the fixes proposed by the
agents Copilot, Devin, Cursor, and Claude are rejected. This represents a significant amount of wasted resources that require human reviews, verifications, and running tests and validations for fixes that are merely discarded. Our goal in this paper is to understand the failure modes of AI-
agents, an understanding that is crucial for better integrating AI-
agents as efficient teammates. In this paper, we conduct a qualitative study on a representative sample of 306 non-merged pull requests created or co-authored by the
agents mentioned earlier, followed by a quantitative analysis of the reasons for rejection. Our qualitative findings identify 14 reasons divided into four high-level categories for rejecting AI-
agent fixes. We observe that developers can reject fixes due to fixes whose implementation is incorrect (e.g., incomplete, wrong approach), fixes that do not pass the continuous integration (CI) pipelines and fail tests, fixes for which the
agent is unable to perform the implementation (e.g., no code generated, sessions lost), and fixes whose priority is low. Our results shed light on the importance of better guiding the model at these levels: (1) proposing hints about the approach to follow for fixing an issue, (2) outlining constraints or limitations regarding the approaches that should not be taken, and (3) instructing the
agent on how to validate the implementation through CI pipelines and without introducing a breaking change. Our results suggest the need for good prioritization of tasks so that generated fixes do not lead to wasted human review efforts or wasted
agent resources (e.g.,
tokens, compute, or allowed number of requests).
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Artificial Intelligence · Cross-listed from cs.CL
Ontology Memory-Augmented ASR Correction for Long Text-Speech Interleaved Conversations
Xinxin Li, Huiyao Chen, Meishan Zhang et al. (+6 more)
Automatic
speech recognition (ASR) correction has traditionally focused on isolated utterances or short local contexts. However, as text and
speech become increasingly interleaved in long interactions, ASR correction requires conversation-level contextual evidence. Existing ASR correction methods often rely on the current hypothesis or concatenate raw dialogue history. In such contexts, sparse correction evidence can be difficult to locate amid redundancy and noise. Addressing these challenges, we propose an ontology
memory-augmented ASR correction framework for long text-
speech interleaved conversations. The framework organizes preceding interaction history into a dynamically updatable ontology
memory, where entities, terminology, surface variants, potential ASR confusions, and semantic relations are stored as retrievable nodes for context-grounded correction. To evaluate this setting, we construct RAMC-Corr, a
dataset derived from MAGIC-RAMC for long-range ASR correction with grounded context. Experiments on RAMC-Corr show that our method improves over direct correction in 9 out of 10 paired backbone-setting combinations and encourages more selective and evidence-grounded corrections for context-dependent ASR errors.
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All authors Xinxin Li, Huiyao Chen, Meishan Zhang, Yunxin Li, Zulong Chen, Zhibo Ren, Xiaoqing Dong, Baotian Hu, Min Zhang
Artificial Intelligence · Cross-listed from cs.SE
Toward Instructions-as-Code: Understanding the Impact of Instruction Files on Agentic Pull Requests
Ali Arabat and Mohammed Sayagh
AI-
agents (e.g., GitHub Copilot) collaborate as teammates in different software engineering tasks, including code generation proposed through pull requests (Agentic-PRs). For better
agent efficiency, developers create instruction files that guide the AI-
agents, including how to navigate the project, locate the right components, run tests, respect best practices, and more. In this paper, we investigate the relationship between the creation of these instructions and the performance of AI-
agents in creating better pull requests, which have a higher chance of success (i.e., the merge rate), address more complex tasks (e.g., code churn), and require less effort to be merged (e.g., time to merge). To this end, we analyze 15,549 agentic PRs from 148 projects in the AIDev
dataset. Using the three dimensions, we compare each project before and after the creation of the instruction files. We find that specifying instructions for AI-
agents does not necessarily lead to better results. With the instruction files, 27.7% of the projects increased their merge rate by at least 20%, while 26.35% decreased it. The same observation is seen with the amount of changes (e.g., code churn, number of modified files) and with the efforts to merge an agentic PR (e.g., merge time and number of comments). From a first exploration, we find that projects that managed to increase their merge rate have substantially longer instruction files, which are also well structured into a higher number of sections and sub-sections. Our results motivate the need for research to assist practitioners in framing the development of instruction files as a software engineering activity (aka, Instructions-as-Code).
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Artificial Intelligence
Why Sampling Is Not Choosing: Intentionality, Agency, and Moral Responsibility in Large Language Models
Joseph Keshet
Recent advances in large
language models (LLMs) have prompted claims that such systems exhibit agency or qualify as moral
agents. This paper argues that these attributions are misguided. We maintain that moral responsibility requires commitment-bearing agency grounded in intrinsic intentionality and self-attributed action, and that such agency constitutes the form of free will relevant to responsibility. Although LLMs generate coherent and normatively evaluable outputs, their operation is fully characterized by probabilistic input-output mappings learned from data. Their apparent intentionality is derived rather than intrinsic, and their outputs are neither owned as commitments nor guided by reasons. Variability introduced by
stochastic sampling does not amount to choice or authorship. We address objections from the intentional stance, functionalism, compatibilism, and the presence of moral
reasoning in model outputs, arguing that none suffice to establish genuine agency.
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Artificial Intelligence
Evaluation Sovereignty in Metadata-Driven Classification: A Multi-Track Framework for Weakly Supervised Information Systems
Raymond Vasquez
Evaluation in machine learning is typically treated as a neutral measurement process. However, in operational information systems, evaluation outcomes are often conditioned by the processes used to generate labels. This paper does not seek to improve classification performance. Instead, it examines the validity of performance measurement under differing label-authority regimes. This issue is particularly relevant in large-scale metadata-driven systems, where labels are often incomplete, inconsistent, or weakly supervised. We introduce evaluation sovereignty, defined as the degree to which performance metrics are independent of label authority and supervision regime, and propose a multi-track evaluation framework that systematically varies training and evaluation label sources. Using hierarchical multi-label classification on large-scale scientific metadata, we demonstrate that models exhibiting strong performance under operational ("silver") evaluation degrade substantially under independent ("gold") evaluation, particularly for fine-grained classification. For example, Micro-F1 decreases from approximately 0.54 to 0.03. Notably, ranking-based metrics remain above baseline, revealing a divergence between latent model signal and classification validity. These findings suggest that commonly reported performance metrics may reflect
alignment with labeling processes rather than true predictive capability. We therefore reconceptualize evaluation validity as a system-level property shaped by label governance and provide a practical methodology for
auditing intelligent systems operating under weak supervision.
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Artificial Intelligence · Cross-listed from cs.CV
OmniDirector: General Multi-Shot Camera Cloning without Cross-Paired Data
Jiwen Liu, Shujuan Li, Zhixue Fang et al. (+8 more)
Cloning camera motion from reference
videos is an important task in
video generation, as
videos provide intuitive and precise control. Existing methods either directly use parametric representations that fail to handle multi-shot generation or synthesize cross-paired data, which suffer from data scarcity, resulting in poor performance in complicated camera motion cloning. To address these issues, we introduce a general camera motion representation that encodes cameras as grid motion
videos. This camera grid represents the camera parameters visually and supports the integration of diverse trajectories for multi-shot
video generation. Building upon this, we propose OmniDirector, a unified framework trained on a million-scale camera grid-
video pairs that coordinates characters, actions, and cameras to provide director-level control for
multimodal diffusion transformers. Furthermore, we design a novel hierarchical
prompt expansion
agent that harmoniously integrates different control signals by systematically describing camera motion and visual content through understanding signal relationships. Extensive experiments demonstrate the superior performance and outstanding controllability of our framework. Project page: https://ymlinfeng.github.io/OmniDirector.github.io/
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All authors Jiwen Liu, Shujuan Li, Zhixue Fang, Xiaohan Li, Yan Zhou, Zijie Meng, Zhimin Zhang, Yawen Luo, Guoxin Zhang, Yu-Shen Liu, Pengfei Wan
Artificial Intelligence
Optimizing Appliance Scheduling for Solar Energy Management Using Metaheuristic Algorithms
Hiba Ahmed, Alexander E.I. Brownlee, Jason Adair et al. (+1 more)
Renewable energy is essential for meeting future energy demands; however,
solar energy generation, which occurs only during daylight hours often does not align with household consumption patterns. Appliances such as cookers, washing machines, and dryers are typically operated according to user preferred schedules rather than
solar energy availability, creating a scheduling optimization problem. The objective is to determine optimal appliance start times to maximize renewable energy utilization while minimizing user inconvenience and adhering to system constraints. This paper presents a metaheuristic approach using Iterated Local Search (ILS) and Simulated Annealing (SA) to optimize appliance start times, while considering appliance operating durations, power consumption, inverter limit, battery state of charge constraints, and
solar generation forecasts. Unlike most existing work, the scheduling is extended beyond a single day to accommodate unfinished tasks from previous days (spillover), ensuring operational continuity and enabling sequential operation across multiple days. Experimental results show that the sequential multi-day scheduling framework effectively manages system constraints while ensuring user convenience under exclusive
solar generation. These findings also open opportunities for future research on multi-objective trade-offs between investment in equipment of various sizes, return on that investment, and user satisfaction.
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All authors Hiba Ahmed, Alexander E.I. Brownlee, Jason Adair, Simon T. Powers
Artificial Intelligence
Neuro-Symbolic Agents for Regulated Process Automation: Challenges and Research Agenda
Alexander Rombach, Chantale Lauer and Nijat Mehdiyev
LLM-based
agents are entering regulated industries where they automate judgment intensive quality management processes. We argue that symbolic structures already embedded in these domains, including regulations, typed process models, and compliance constraints, should be treated not merely as external monitoring mechanisms but as core architectural components that shape the
agent's decision-making and behavior. We propose compliance-by-construction as a complementary paradigm to guardrail-based monitoring: a structural foundation that prevents control-flow violations, while guardrails remain essential for catching semantic errors. We identify a structured set of neuro-symbolic research challenges on foundational and capability level and show that addressing them jointly enables compliance-by-construction. We call on the neuro-symbolic community to engage with regulated process automation as a high impact research domain.
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Artificial Intelligence · Cross-listed from cs.LG
PolyFlow: Safe and Efficient Polytope-Constrained Flow Matching with Constraint Embedding and Projection-free Update
Jianming Ma, Qiyue Yang, Yang Zhang et al. (+4 more)
While flow-based generative models have demonstrated strong performance across a wide range of domains, deploying them in safety-critical physical systems remains challenging due to strict constraint requirements. Existing approaches typically enforce safety through post-hoc corrections, which incur substantial computational overhead and may distort the learned distribution. We propose PolyFlow, a polytope-constrained flow matching framework that embeds constraints directly into the model and flow dynamics. PolyFlow introduces a discrete-time flow formulation and a projection-free architecture, which eliminate the discretization error and guarantee strict satisfaction of arbitrary polyhedral constraints, without the need for expensive iterative solvers. Experimental results show that PolyFlow achieves zero constraint violation while maintaining high distributional fidelity across a range of planning and control tasks. Compared to state-of-the-art constrained generation baselines, PolyFlow significantly reduces inference latency and demonstrates a favorable trade-off between safety, efficiency, and generative quality. Code is available on https://github.com/MJianM/PolyFlow.
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All authors Jianming Ma, Qiyue Yang, Yang Zhang, Liyun Yan, Zhanxiang Cao, Yazhou Zhang, Yue Gao
Artificial Intelligence · Cross-listed from cs.HC
Mod-Guide: An LLM-based Content Moderation Feedback System to Address Insensitive Speech toward Indigenous Ethnic and Religious Minority Communities
Dipto Das, Achhiya Sultana, Ankit Singh Chauhan et al. (+5 more)
Language operates as a mechanism of both marginalization and resistance, especially for minority communities navigating insensitive and harmful
speech online. As content moderation increasingly depends on large
language models (LLMs), concerns arise about whether these systems can recognize culturally insensitive
speech-
language that disregards or marginalizes the cultural and religious perspectives of historically underrepresented communities, often through implicit erasure, misrepresentation, or normative framing, rather than overt hostility. Focusing on Bangladesh's Hindu and Chakma communities – the country's largest religious and Indigenous ethnic minorities, respectively – this paper investigates the epistemic limits of LLM-based moderation systems and explores methods for incorporating minority perspectives. We co-created a culturally grounded corpus of insensitive
speech with community members and integrated their narratives into moderation pipelines using
retrieval augmented generation (RAG). Our tool, Mod-Guide, improves LLM sensitivity to minority viewpoints by leveraging contextual cues derived from lived experience. Through mixed-method evaluations involving both minority and majority participants, we demonstrate that RAG-enhanced moderation responses are more contextually accurate and perceived differently across ethnic lines. This work advances research in human-computer interaction, AI ethics, and social computing by foregrounding restorative justice and hermeneutical inclusion in the design of content moderation systems.
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All authors Dipto Das, Achhiya Sultana, Ankit Singh Chauhan, Saadia Binte Alam, Mohammad Shidujaman, Shion Guha, Sunandan Chakraborty, Syed Ishtiaque Ahmed
Artificial Intelligence
Xunhao Lai, Weiqi Xu, Yufeng Yang et al. (+14 more)
Ultra-long-context capability is becoming indispensable for frontier LLMs: agentic workflows, repository-scale code
reasoning, and persistent
memory all require the model to jointly attend over hundreds of thousands to millions of
tokens, yet the quadratic cost of softmax
attention makes this untenable at deployment scale. We introduce MiniMax Sparse
Attention (MSA), a blockwise sparse
attention built upon Grouped Query
Attention (GQA). A lightweight Index Branch scores key-value blocks and independently selects a Top-k subset for each GQA group, enabling group-specific sparse
retrieval while maintaining efficient block-level execution; the Main Branch then performs exact block-sparse
attention over only the selected blocks. Designed around a principle of simplicity and scalability, MSA is deliberately streamlined, making it straightforward to deploy efficiently across a broad range of GPUs. To translate sparsity into practical speedups, we co-design MSA with a GPU execution path that uses exp-free Top-k selection and KV-outer sparse
attention to improve tensor-core utilization under block-granular access. On a 109B-parameter model with native
multimodal training, MSA performs on par with GQA while reducing per-
token attention compute by 28.4x at 1M context. Paired with our co-designed kernel, MSA achieves 14.2x prefill and 7.6x decoding wall-clock speedups on H800. Our inference kernel is available at: https://github.com/MiniMax-AI/MSA. A production-grade natively
multimodal model powered by MSA has been publicly released at: https://huggingface.co/MiniMaxAI/MiniMax-M3.
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All authors Xunhao Lai, Weiqi Xu, Yufeng Yang, Qiaorui Chen, Yang Xu, Lunbin Zeng, Xiaolong Li, Haohai Sun, Haichao Zhu, Vito Zhang, Jinkai Hu, Jiayao Li, Rui Gao, Zekun Li, Songquan Zhu, Jingkai Zhou, Pengyu Zhao
Artificial Intelligence · Cross-listed from cs.CR
Who Pays the Price? Stakeholder-Centric Prompt Injection Benchmarking for Real-world Web Agents
Zihao Wang, Yiming Li, Yutong Wu et al. (+8 more)
LLM-based web
agents are increasingly deployed in real-world settings such as e-commerce, where they interact extensively with untrusted web content while executing actions that carry direct financial consequences. This makes them vulnerable to
prompt-injection attacks, in which seemingly benign web content conceals
adversarial instructions that manipulate the
agent's behavior. Existing security
benchmarks adopt an attack-centric perspective, focusing on the technical feasibility of injections while overlooking the nuanced distribution of resulting harms. In practice, however,
prompt-injection risk is victim-dependent: a single exploit can produce asymmetric consequences for different stakeholders, and the same attack pattern may exhibit substantially different effectiveness depending on whom it targets. To capture these properties, we introduce StakeBench, a stakeholder-centric
benchmark that systematically categorizes and attributes harm in real-world web
agent systems for online shopping. In general, StakeBench decomposes
prompt-injection risk into 12 concrete attack objectives across three stakeholder classes, realized by 22 reusable templates and instantiated into 264 executable
adversarial cases spanning 12 product categories, with each case evaluated along complementary outcome- and process-level metrics. Evaluating four deployable
agent-backbone configurations across 3,168 attacked runs, we find substantial and heterogeneous vulnerabilities: no attack objective is reliably resisted by current LLM-based web
agents, and outcomes span four qualitatively distinct modes. These patterns are missed by conventional attack-centric, single-metric evaluation, underscoring the need for stakeholder-aware assessment of LLM-based
agents in real-world deployments.
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All authors Zihao Wang, Yiming Li, Yutong Wu, Kangjie Chen, Zheyu Liu, Fok Kar Wai, Pin-Yu Chen, Vrizlynn L. L. Thing, Bo Li, Dacheng Tao, Tianwei Zhang
Artificial Intelligence · Cross-listed from cs.CV
SmartFont: Dynamic Condition Allocation for Few-Shot Font Generation
Zian Yang and Zixin Wang
Few-shot font generation simultaneously requires global structural completeness and fine-grained local style fidelity. Existing methods usually either rely on global content-style modeling, which is robust but imperfectly disentangled, or emphasize component/local modeling, which captures fine details but relies heavily on local priors and reference coverage. We argue that the key challenge is not merely to learn purer conditions, but to organize complementary yet biased global and local conditions through multi-level allocation during generation. To this end, we propose SmartFont, a
diffusion-based few-shot font generation framework that combines global content-style generation with weakly supervised local corrective experts. The local branch performs semantic-spatial allocation by learning expert-wise local concepts and semantically meaningful spatial maps under weak component supervision, enabling fine-grained correction without requiring explicit component-conditioned inference. On top of this, a denoising-state condition allocation module adaptively weights global content, global style, and local corrective feature across timesteps and injection blocks. Extensive experiments show that SmartFont achieves better global-local balance, improves glyph quality and local detail fidelity.
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Artificial Intelligence · Cross-listed from quant-ph
An LLM System for Autonomous Variational Quantum Circuit Design
Kenya Sakka, Wataru Mizukami and Kosuke Mitarai
The design of high performing
quantum circuits remains largely dependent on human expertise. We introduce an autonomous agentic framework that employs large
language models (LLMs) to conduct iterative
quantum circuit designs under explicit design constraints. Our system integrates seven components: Exploration, Generation, Discussion, Validation, Storage, Evaluation, and Review. These components form a closed-loop workflow that combines web-based knowledge acquisition, literature-grounded critique, executable code generation, and experimental feedback. We evaluate the framework on two tasks:
quantum feature map construction for
quantum machine learning and ansatz generation for variational
quantum eigensolver applications in
quantum chemistry. In image classification
benchmarks, the best generated feature map outperforms representative
quantum feature maps and, when scaled to larger
qubit counts, surpasses the classical radial basis function kernel. In molecular ground state estimation across seven molecules, the generated ansatz attains competitive accuracy with widely used chemically inspired and hardware-efficient constructions while satisfying the imposed scaling constraints. These results establish LLM driven agentic system as a viable paradigm for automated
quantum circuit design and illustrate how AI systems can participate in iterative scientific optimization workflows across scientific domains.
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