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01.

medRxiv (Medicine) 2026-06-15 DOI: HASH:8b9ad26ce23316e6b98de95e57b45447

Modelling the public-health impact of indoor air quality interventions on respiratory virus transmission

作者:

Howes↗Jeyapragasan↗Williamson↗Carel↗Koos↗Swett↗J. L↗Montavon↗Belenky↗Lietar↗Fitzjohn↗Charles↗…

Respiratory virus transmission occurs in indoor settings where ventilation, occupancy, and dwell time determine exposure levels. Improving indoor air quality (IAQ) therefore could help reduce disease burden associated with respiratory viruses, yet its population-level impact remains poorly quantified. Here, we develop an individual-based transmission modelling framework that links within-location airborne dynamics to individual infection risk and population-level spread, whilst explicitly incorporating heterogeneity in ventilation and baseline indoor air quality across locations. We use this modelling approach to evaluate IAQ-improving interventions (air-quality interventions or AQIs), using hypothetical endemic and pandemic pathogen archetypes with properties similar to SARS-CoV-2 and influenza, and evaluate how effects on key epidemiological metrics (such as annualized incidence and epidemic final size) depend on AQI coverage, efficacy and allocation strategy. At 20% AQI intervention coverage and 80% efficacy, annualized incidence was reduced by approximately 7.2% for an endemic 'SARS-CoV-2-like' respiratory virus, and 17.0% for an endemic 'influenza-like' virus; at 60% coverage (80% efficacy) the reductions were 26.3% and 56.4%, respectively. Targeting AQI installation to the highest-risk locations outperformed random allocation: for SARS-CoV-2-like transmission, 20% coverage at 80% efficacy cut absolute incidence by 10.8% when targeted versus 7.2% when random; for influenza-like transmission, this comparison was 28.9% versus 17.0%. In epidemic scenarios, random installation at 40% coverage and 60% efficacy reduced final size by 23.7% (influenza-like) versus 6.3% (SARS-CoV-2-like). These results support treating clean indoor air as core public-health infrastructure and prioritising risk-based deployment of IAQ-improving interventions to maximise population-level benefit within budgetary and operational constraints.

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02.

arXiv (CS.CL) 2026-06-19 DOI: arXiv:2504.02885

Med-R2: Perception and Reflection-driven Complex Reasoning for Medical Report Generation

作者:

Hao Wang↗Shuchang Ye↗Jinghao Lin↗Usman Naseem↗Jinman Kim↗

Automated medical report generation (MRG) is increasingly used to reduce the burden of manual reporting and for decision support. Large vision-language models (LVLMs) hold great promise for automated MRG due to their fine-grained image-text alignment and advanced text-generation capabilities. Currently, state-of-the-art MRGs primarily focus on adapting pre-trained LVLMs with direct supervised fine-tuning (SFT), a fine-tuning strategy with medical image-report pairs. However, several factors limit the performance of these LVLMs. Firstly, direct SFT enables LVLMs to generate medical reports directly without an intermediate thinking process of pathological feature perception and diagnostic reasoning. This causes a potential failure to perceive pathological features and thus leads to misdiagnosis. Secondly, direct SFT lacks the incorporation of radiology-specific knowledge guidance, causing LVLMs to misinterpret perceived pathological features and make incorrect diagnoses. To address these gaps, we propose a novel fine-tuning strategy named Med-R2. We introduce a perception-driven long reasoning process that precedes report generation and incorporates radiology-specific knowledge as guidance. Additionally, to alleviate potential perceptual errors in complex reasoning, a reflection mechanism is introduced to refine the perception of pathological features and the generated report. Our experiments demonstrate that Med-R2 effectively enhances the capability of pathological features perception and diagnosis accuracy for MRG via fine-tuned LVLMs.

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03.

arXiv (CS.AI) 2026-06-15 DOI: arXiv:2512.02318

COGNITION: From Evaluation to Defense against Multimodal LLM CAPTCHA Solvers

作者:

Junyu Wang↗Changjia Zhu↗Yuanbo Zhou↗Lingyao Li↗Xu He↗Mingkui Wei↗Junjie Xiong↗

arXiv:2512.02318v4 Announce Type: replace-cross Abstract: This paper studies how multimodal large language models (MLLMs) undermine the security guarantees of visual CAPTCHA. We identify the attack surface where an adversary can cheaply automate CAPTCHA solving using off-the-shelf models. We evaluate 7 representative MLLMs on 18 real-world CAPTCHA task types, measuring single-shot accuracy, success under limited retries, end-to-end latency, and per-solve cost. We further validate our findings through a supplemental external dataset and an adaptive-attacker setting with session memory, while also analyzing the impact of task-specific prompt engineering and few-shot demonstrations on solver effectiveness. We reveal that MLLMs can reliably solve recognition-oriented and low-interaction CAPTCHA tasks at human-like cost and latency, whereas tasks requiring fine-grained localization, multi-step spatial reasoning, or cross-frame consistency remain significantly harder for current models. By examining the reasoning traces of such MLLMs, we investigate the underlying mechanisms of why models succeed/fail on specific CAPTCHA puzzles and use these insights to derive defense-oriented guidelines for selecting and strengthening CAPTCHA tasks. To validate these principles, we present a proof-of-concept by hardening a vulnerable CAPTCHA type using our guidelines. We demonstrate that incorporating fine-grained localization and implicit counting reduces the success rate of state-of-the-art MLLMs from over 95\% to 0\%, confirming that structural changes can effectively mitigate the threat. We conclude by emphasizing the urgent need for CAPTCHA redesign as MLLM capabilities increasingly threaten existing defenses. Code Availability (https://doi.org/10.5281/zenodo.20406852).

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04.

arXiv (CS.LG) 2026-06-16 DOI: arXiv:2606.14999

Unlocking Latent Dimensions: Exploring Representations of Large-Scale X-ray Scattering Data using Variational Autoencoders

作者:

Monika Choudhary↗Xiaoya Chong↗Runbo Jiang↗Wiebke Koepp↗Petrus H. Zwart↗Damon English↗Gregory M. Su↗Eric Schaible↗Chenhui Zhu↗Mostafa Nassr↗Noah P. Wamble↗Kelvin Kam-Yun Li↗…

arXiv:2606.14999v1 Announce Type: new Abstract: Scientific user facilities generate X-ray scattering data faster than traditional workflows can process them. We address this challenge across two settings, offline dataset exploration and live on-the-fly analysis. We train a domain-specific attention-based Convolutional Variational Autoencoder (C-VAE) on 1.5 million X-ray scattering images to learn low-dimensional representations capturing structural variation across diverse experimental conditions. The learned latent space reveals well-organized clusters and smooth trajectories reflecting experimental progression. It further supports controlled synthetic scattering image generation across diverse structural states. When deployed without retraining, the model organizes time-resolved film formation experiments at two synchrotron facilities into interpretable latent structures. Benchmarking against DINOv3 (ViT-7B), a general-purpose vision foundation model, demonstrates that domain-specific training yields more interpretable latent organization for scattering data. Both workflows are integrated within Latent Space Explorer, a component of the MLExchange platform, supporting interactive structural exploration across archived datasets and live experiments.

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05.

arXiv (CS.CL) 2026-06-16 DOI: arXiv:2512.21577

A Unified Definition of Hallucination: It's The World Model, Stupid!

作者:

Emmy Liu↗Varun Gangal↗Chelsea Zou↗Michael Yu↗Xiaoqi Huang↗Alex Chang↗Zhuofu Tao↗Karan Singh↗Sachin Kumar↗Steven Y. Feng↗

Despite numerous attempts at mitigation since the inception of language models, hallucinations remain a persistent problem even in today's frontier LLMs. Why is this? We review existing definitions of hallucination and fold them into a single, unified definition wherein prior definitions are subsumed. We argue that hallucination can be unified by defining it as simply inaccurate (internal) world modeling, in a form where it is observable to the user. For example, stating a fact which contradicts a knowledge base OR producing a summary which contradicts the source. By varying the reference world model and conflict policy, our framework unifies prior definitions. We argue that this unified view is useful because it forces evaluations to clarify their assumed reference "world", distinguishes true hallucinations from planning or reward errors, and provides a common language for comparison across benchmarks and discussion of mitigation strategies. Building on this definition, we also connect our framework to HalluWorld, a complementary benchmark that instantiates fully specified reference world models for stress-testing model hallucinations.

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06.

arXiv (CS.CL) 2026-06-19 DOI: arXiv:2606.20482

Your Mouse and Eyes Secretly Leak Your Preference: LLM Alignment using Implicit Feedback from Users

作者:

Haw-Shiuan Chang↗Jeffrey Gomez↗Mehul Patwari↗Aryan Sajith↗Hamed Zamani↗

To align a Large Language Model (LLM), most existing methods collect explicit human feedback and train a reward model to predict the human preference based on the response text. These existing methods have two key limitations. First, the users rarely provide explicit feedback for LLM responses, which makes the high-quality preference annotation expensive to collect. Second, the methods do not leverage implicit human feedback, which has proven vital to the economic moats of Internet giants. To quantify the value of implicit feedback, we build a new dataset called IFLLM, which collects 1336 multi-turn questions from the 59 Mechanical Turk workers, their mouse trajectories, and eye gazing points to the LLMs' responses from their webcams. IFLLM shows that the users have very diverse types of gazing behavior and mouse trajectories. Our reward model based on the implicit user feedback boosts the accuracy of the text-based reward model from 55% to 64% and nearly triples the relative response quality improvements after applying the DPO to eight LLMs, demonstrating the value of implicit feedback in the wild. Our data collection website, dataset, and codes can be found at https://github.com/themehulpatwari/llm-implicit-feedback/.

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07.

arXiv (CS.AI) 2026-06-11 DOI: arXiv:2606.12279

Mathematical perspective on genetic algorithms with optimization guided operators

作者:

Anna Brandenberger↗Ilan Doron-Arad↗Elchanan Mossel↗

arXiv:2606.12279v1 Announce Type: cross Abstract: Recent work in ML applies genetic algorithms at inference time to iteratively improve solutions to optimization problems. The basic mutation and recombination operators involved are qualitatively different from those studied classically. Mutations are no longer random; an ML algorithm mutates a solution with the goal of improving an objective. Similarly, recombination is not based on random collages of parent solutions. Instead, it is an ML optimization-based operator whose goal is to synthesize improved solutions from its inputs. Thus, these mutation and recombination operators are more likely to improve the objective, but their computational cost is much higher. We introduce a general model of genetic algorithms and formulating optimization in this model as a query-complexity problem, using the language of reinforcement learning. We then study specialized models. We show that some optimization problems require generation, mutation, and recombination to be solved. We then obtain qualitatively tight algorithms for a family of problems within this framework that captures the nontrivial role of diversity in the solution pool, a key feature of practical ML genetic algorithms.

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08.

arXiv (CS.CL) 2026-06-25 DOI: arXiv:2606.24957

Dustin: Draft-Augmented Sparse Verification for Efficient Long-Context Generation with Speculative Decoding

作者:

WenHung Lee↗Jian-Jia Chen↗Xiaolin Lin↗Pei-Shuo Wang↗Chi-Chih Chang↗Chun-Che Yang↗Ning-Chi Huang↗Grace Li Zhang↗Kai-Chiang Wu↗

While speculative decoding improves inference throughput for multi-batch long-context Large Language Models (LLMs), its efficiency is often limited by a verification bottleneck where Key-Value (KV) cache loading dominates latency. Existing compression methods fail in this regime: static eviction incurs accuracy loss due to saliency shift, while dynamic selection introduces prohibitive computational overhead during the verification path. We propose Dustin, a sparse verification framework designed for long-context speculative decoding. Dustin integrates lookahead signals from the draft model with historical attention from the target model to identify critical tokens with high fidelity across multi-step verification windows. To reduce recomputation latency, this approach further employs a sparse estimation scheme that restricts importance scoring to a minimal subset of attention heads. Evaluations on PG-19 and LongBench with Qwen2.5-72B demonstrate that Dustin achieves a 27.85x speedup in self-attention and a 9.17x end-to-end decoding speedup at a 32k sequence length, all with negligible accuracy degradation.

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09.

medRxiv (Medicine) 2026-06-12 DOI: HASH:e66e822f6b6db35445cfeebd14933584

Immunologically Optimized Zmp1 Peptides Reveal a Translational Serological Biomarker Platform for Tuberculosis Diagnosis Across Disease Manifestations

作者:

Zade↗O. S↗Yandrapally↗Choudhari↗Gaikwad↗A. V↗Panda↗Neela↗V. S. K↗Devalraju↗K. P↗Eedara↗…

Tuberculosis (TB) diagnosis remains challenging, particularly for extrapulmonary TB (EPTB), where invasive sampling, low bacillary burden, and suboptimal sensitivity of nucleic acid-based tests in peripheral specimens hinder timely detection. Here, we report an immunology-driven strategy for biomarker discovery and development of a peptide-based serological assay targeting Mycobacterium tuberculosis zinc metalloprotease-1 (Zmp1). Leveraging fundamental principles of adaptive immunity that antigenic regions containing overlapping B-cell and CD4 T-helper cell epitopes would preferentially generate high antibody titers through linked recognition and cognate T-cell help, we used an immunoinformatics pipeline to identify two nested immunodominant peptide regions within Zmp1 (Mtb-Zp-NT and Mtb-Zp-CT) enriched for overlapping B- and T-cell epitopes. The diagnostic potential of these peptides was evaluated through ELISA-based serological assays. A blinded pilot study (N=137) demonstrated a clear discrimination between active TB and TB-recovered individuals. The assay was subsequently validated in an expanded cohort (N=875) by screening 6,086 individuals, which identified 457 TB-positive cases. The cohort included pulmonary TB (PTB), EPTB, TB-recovered individuals, household contacts, non-specific infections, and healthy controls. Receiver operating characteristic analyses, supported by DeLong and bootstrap comparisons, revealed superior diagnostic performance of the peptide-based assays relative to full-length Zmp1. Mtb-Zp-CT exhibited the highest accuracy (AUC=0.93; specificity >90%), while Mtb-Zp-NT also demonstrated strong discriminatory power (AUC{approx}0.89). These findings establish that the immunologically optimized Zmp1 peptides are highly promising serological biomarkers for TB and EPTB. More broadly, they demonstrate how mechanistically informed epitope selection can accelerate translation of pathogen-specific immune signatures into sensitive, minimally invasive, and potentially point-of-care diagnostic platforms for resource-limited settings.

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10.

arXiv (CS.AI) 2026-06-25 DOI: arXiv:2510.04773

Distribution Preference Optimization: A Fine-grained Perspective for LLM Unlearning

作者:

Kai Qin↗Jiaqi Wu↗Jianxiang He↗Haoyuan Sun↗Yifei Zhao↗Xu Wang↗Bin Liang↗Yongzhe Chang↗Cheng Li↗Tiantian Zhang↗Houde Liu↗

arXiv:2510.04773v2 Announce Type: replace-cross Abstract: As Large Language Models (LLMs) demonstrate remarkable capabilities learned from vast corpora, concerns regarding data privacy and safety are receiving increasing attention. LLM unlearning, which aims to remove the influence of specific data while preserving overall model utility, is becoming an important research area. One of the mainstream unlearning classes is optimization-based methods, which achieve forgetting directly through fine-tuning, exemplified by Negative Preference Optimization (NPO). However, NPO's effectiveness is limited by its inherent lack of explicit positive preference signals. Attempts to introduce such signals by constructing preferred responses often necessitate domain-specific knowledge or well-designed prompts, fundamentally restricting their generalizability. In this paper, we shift the focus to the distribution-level, directly targeting the next-token probability distribution instead of entire responses, and derive a novel unlearning algorithm termed Distribution Preference Optimization (DiPO). We show that the requisite preference distribution pairs for DiPO, which are distributions over the model's output tokens, can be constructed by selectively amplifying or suppressing the model's high-confidence output logits, thereby effectively overcoming NPO's limitations. We theoretically prove the consistency of DiPO's loss function with the desired unlearning direction. Extensive experiments demonstrate that DiPO achieves a strong trade-off between model utility and forget quality. Notably, DiPO attains the highest forget quality on the TOFU benchmark, and maintains leading scalability and sustainability in utility preservation on the MUSE benchmark.

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11.

arXiv (CS.CL) 2026-06-18 DOI: arXiv:2602.06470

Improve Large Language Model Systems with User Logs

作者:

Changyue Wang↗Weihang Su↗Qingyao Ai↗Xingzhao Yue↗Rui Zhang↗Xiaojia Chang↗Yiqun Liu↗

Scaling training data and model parameters has long driven progress in large language models (LLMs), but this paradigm is increasingly constrained by the scarcity of high-quality data and diminishing returns from rising computational costs. As a result, recent work is increasing the focus on continual learning from real-world deployment, where user interaction logs provide a rich source of authentic human feedback and procedural knowledge. However, learning from user logs is challenging due to their unstructured and noisy nature. Vanilla LLM systems often struggle to distinguish useful feedback signals from noisy user behavior, and the disparity between user log collection and model optimization (e.g., the off-policy optimization problem) further strengthens the problem. To this end, we propose UNO (User log-driveN Optimization), a unified framework for improving LLM systems (LLMsys) with user logs. UNO first distills logs into semi-structured rules and preference pairs, then employs query-and-feedback-driven clustering to manage data heterogeneity, and finally quantifies the cognitive gap between the model's prior knowledge and the log data. This assessment guides the LLMsys to adaptively filter out noisy feedback and construct different modules for primary and reflective experiences extracted from user logs, thereby improving future responses. Extensive experiments show that UNO achieves state-of-the-art effectiveness and efficiency, significantly outperforming Retrieval Augmented Generation (RAG) and memory-based baselines. We have open-sourced our code at https://github.com/bebr2/UNO .

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12.

arXiv (CS.AI) 2026-06-11 DOI: arXiv:2606.12251

Reinforcement Learning Disrupts Gradient-Based Adversarial Optimization

作者:

Xinhai Zou↗Chang Zhao↗Alireza Aghabagherloo↗Dave Singel\'ee↗Robin Degraeve↗Bart Preneel↗

arXiv:2606.12251v1 Announce Type: cross Abstract: Gradient-based adversarial attacks remain a dominant threat to deep neural networks (DNNs), as they exploit gradient information to efficiently optimize adversarial perturbations. To address this, we investigate whether reinforcement learning (RL) training can disrupt the gradient structure used by attackers by training image classifiers with policy-gradient objectives and epsilon-greedy exploration. Through systematic experiments across CIFAR-10, CIFAR-100, and ImageNet-100 with multiple architectures, we find that RL-trained classifiers significantly disrupt gradient-based adversarial optimization. To explain this, we conduct a comprehensive mechanism analysis using loss landscape visualization, static and dynamic gradient indicators, and predictive entropy. Our analysis reveals that RL acts as an implicit regularizer, producing models with highly unstable gradient directions and smaller gradient magnitudes. This combination makes each PGD step both unreliable in direction and limited in magnitude, causing gradient-based attacks to fail within practical iteration budgets. We further show that combining RL with adversarial training (RL-adv) provides a dual-layer defense operating at two complementary levels: RL degrades gradient information available to attackers (gradient-level defense), while adversarial training strengthens decision boundaries (boundary-level defense). RL-adv achieves the highest robustness across all major attack types evaluated, including gradient-based (PGD, AutoAttack), transfer-based, and query-based attacks, outperforming SL-adv by a significant margin. These findings identify RL-induced gradient disruption as a complementary robustness mechanism and motivate future research on hybrid SL-RL training schedules that combine SL's efficiency with RL's gradient-regularization properties.

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13.

arXiv (CS.CL) 2026-06-11 DOI: arXiv:2606.11762

Automated Creativity Evaluation of Language Models Across Open-Ended Tasks

作者:

Min Sen Tan↗Zachary Kit Chun Choy↗Syed Ali Redha Alsagoff↗Nadya Yuki Wangsajaya↗Mohor Banerjee↗Swaagat Bikash Saikia↗Alvin Chan↗

Large language models (LLMs) have achieved remarkable progress in language understanding, reasoning, and generation, sparking growing interest in their creative potential. Realizing this potential requires systematic and scalable methods for evaluating creativity across diverse tasks. However, most existing creativity metrics are tightly coupled to specific tasks, embedding domain assumptions into the evaluation process, and limiting scalability and generality. To address this gap, we introduce an automated, domain-agnostic framework for quantifying LLM creativity across open-ended tasks. Our approach separates the measurement apparatus from the creative task itself, enabling scalable, task-agnostic assessment. Divergent creativity is measured using semantic entropy, a reference-free and robust metric for novelty and diversity, validated against human annotations, LLM-based novelty judgments and baseline diversity measures. Convergent creativity is assessed via a novel retrieval-based multi-agent judge framework that delivers context-sensitive evaluation of task fulfilment with over 60% improved efficiency. We validate our framework in three qualitatively distinct domains: problem-solving (MacGyver), research ideation (HypoGen), and creative writing (BookMIA), using a broad suite of LLMs. Empirical results show that our framework reliably captures key facets of creativity, including novelty, diversity, and task fulfilment, and reveal how model properties, such as size, temperature, recency, and reasoning, impact creative performance. Our work establishes a reproducible and generalizable standard for automated LLM creativity evaluation, paving the way for scalable benchmarking and accelerating progress in creative AI.

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14.

arXiv (CS.AI) 2026-06-16 DOI: arXiv:2606.15377

Learning Earthquake Wave Arrival Time Picking from Labels with Inaccuracies

作者:

Sen Li↗Xu Yang↗S. Mostafa Mousavi↗Anye Cao↗Keting Fan↗Yaoqi Liu↗Changbin Wang↗Qiang Niu↗

arXiv:2606.15377v1 Announce Type: cross Abstract: Inaccurately labeled training data, or "label noise", poses a significant threat to the integrity of supervised machine learning models. This corruption directly degrades performance by teaching the model erroneous mappings between features and labels, which leads to poor generalization and reduced accuracy on properly labeled validation and test data. Current seismological applications mainly rely on large-scale training sets or data augmentation to reduce the label-noise impact, which can be labor-intensive and costly. Here, we introduce a Label Noise-Contrastive Robust Learning (LaNCoR) approach that can effectively handle noisy labels in seismic signal processing tasks, without requiring large-scale training datasets. In this approach, the input waveform feature and label representation distributions are aligned in the feature space to correct mislabeling and reduce its impact on the training process. We present LaNCoR's performance on the task of P-phase arrival-time picking of real microseismic data using two baseline models and training approaches. Our results indicate that LaNCoR can improve performance by up to 28.8% across performance metrics. This approach holds great promise for model training in seismology and geosciences.

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15.

arXiv (CS.AI) 2026-06-19 DOI: arXiv:2402.14035

Wisdom of Committee: Diverse Distillation from Large Foundation Models and Domain Experts

作者:

Zichang Liu↗Qingyun Liu↗Yuening Li↗Liang Liu↗Anshumali Shrivastava↗Shuchao Bi↗Lichan Hong↗Ed H. Chi↗Zhe Zhao↗

arXiv:2402.14035v4 Announce Type: replace-cross Abstract: Knowledge distillation from foundation models to compact domain models is challenging due to substantial gaps in capacity, architecture, and modality. For example, in our experiments, distilling from a 76M-parameter language model to a 2M-parameter recommender closes less than 40% of the performance gap between the undistilled student and the teacher. We show that introducing domain-specific experts – which share the student's architectural characteristics – alongside the foundation model as a diverse teacher committee significantly improves transfer. However, standard multi-teacher methods fail to exploit this diversity: naively combining heterogeneous teachers can degrade performance below single-teacher distillation. To address this, we propose DiverseDistill, an interactive distillation framework that employs a learnable Question-Answer mechanism to generate teacher-conditioned queries and align heterogeneous teacher outputs into the student's representation space. Unlike methods requiring gradient-based co-optimization or architectural modification of teachers, DiverseDistill operates with frozen teachers using only forward-pass inference through their intermediate layers: no parameter updates, no co-training, and no architectural surgery. A dynamic teacher importance mechanism further reduces training cost by filtering low-relevance teachers per sample (e.g., ~30% fewer forward passes with no quality loss for recommendation tasks), while the entire Distillation Module is discarded after training, adding zero inference overhead. Evaluations on recommendation (38x compression) and vision (3.6x compression) tasks demonstrate that DiverseDistill recovers 73-114% of the teacher-student performance gap, consistently outperforming all single- and multi-teacher baselines.

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16.

arXiv (CS.AI) 2026-06-19 DOI: arXiv:2606.19803

Policy-aware Vector Search: A Vision for Fine Grained Access Control in Vector Databases

作者:

Lakshmi Sahithi Yalamarthi↗Primal Pappachan↗

arXiv:2606.19803v1 Announce Type: cross Abstract: Vector databases are increasingly used in security sensitive contexts with Retrieval Augmented Generation and organizational AI pipelines; however, their security capabilities remain limited. Specifically, Fine-grained Access Control (FGAC) which is required to ensure that data access adheres to user-specific policies is not fully supported in modern vector databases. Unlike relational databases, vector databases combine structured and unstructured attributes to provide semantic, approximate query results, which complicates FGAC implementation. This creates an inherent tension between enforcing FGAC policies correctly, achieving high ANN search recall and maintaining low query latency. In this paper, we present a vision for Policy-aware Vector Search by formalizing the FGAC policy model in vector databases as well as the enforcement problem. We compare various enforcement strategies, present preliminary findings, and identify key open challenges for future research in policy-aware vector search.

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17.

arXiv (CS.CV) 2026-06-19 DOI: arXiv:2606.20556

Thinking in Boxes: 3D Editing in Real Images Made Easy

作者:

Pradhaan S Bhat↗Naveen Chandra R↗Rishubh Parihar↗Vaibhav Vavilala↗R. Venkatesh Babu↗D. A. Forsyth↗Anand Bhattad↗

Text and 2D-conditioning interfaces provide weak, ambiguous control over spatial transformations in image editing – particularly under large object motions and camera changes. Prior work has used 3D primitives such as boxes, but only as loose conditioning signals indicating approximate object location rather than specifying the transformation. We instead use 3D boxes as structured specifications: the user provides the input and output boxes of the edit, casting editing as a well-posed geometry problem. This ``thinking in boxes'' interface, where each box face is color-coded to convey 3D orientation, gives precise control over translation, rotation, scaling, and viewpoint changes in real images while preserving scene and object identity, and recovering previously unseen object regions. To ground transformations in scene appearance, we introduce a depth-aligned planar floor as a global reference frame, shaded with depth-aware cues. Conditioned on this structure, an image generator produces consistent results under large transformations. Trained in two stages – on synthetic multi-object scenes and a small set of real-world videos from Objectron – the system generalizes to complex, in-the-wild real images. Our method operates directly on real photographs and substantially outperforms recent state-of-the-art methods on large 3D edits.

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18.

arXiv (CS.CV) 2026-06-16 DOI: arXiv:2606.14786

MatchLM2Lite: A Scalable MLLM-to-Lite Framework for Reproduced Content Identification

作者:

Xiaotian Fan↗Hiok Hian Ong↗David Yuchen Wang↗Zirui Zhu↗Kanchan Sarkar↗Kun Xu↗

Content moderation is critical for online video platforms to ensure content safety, protect creators, and sustain positive user experiences. Beyond filtering harmful content, platforms must guarantee content authenticity at scale so that users are exposed to diverse, original videos rather than low-value reproductions. We present MatchLM2Lite, a real-time, production-grade reproduced content identification (RCI) system that leverages the powerful understanding of a multimodal large language model (MLLM) distilled into a small and fast-inference model. Our system jointly models video, audio, and text signals, operating on pairs of videos to produce fine-grained reproduction scores. The system comprises two modules, MatchLM and MatchLite, and a two-stage training recipe. First, our high-capacity MLLM, MatchLM, serves as a teacher model to define the upper bound of RCI performance. Its capabilities are then distilled into a compact student model, MatchLite. This design allows MatchLite to deliver low-latency, high-throughput inference on video pairs while preserving much of MatchLM's accuracy, making it suitable for integration into real-time recommendation systems. MatchLM achieves an F1-score improvement of +8.57 compared to our previous production model. After knowledge distillation, MatchLite retains a +6.55 gain in F1-score while reducing computational cost by 35x. Deployed at scale, MatchLM2Lite enables efficient, pairwise multimodal RCI, stably serving online traffic at high queries per second (QPS) with an end-to-end latency below 30 seconds. This system has reduced the reproduced video view rate on our platform by 2.5% without degrading user engagement, demonstrating its effectiveness in a large-scale production environment.

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19.

arXiv (CS.AI) 2026-06-12 DOI: arXiv:2601.00921

Geometric and Quantum Kernel Methods for Predicting Skeletal Muscle Outcomes in chronic obstructive pulmonary disease

作者:

Azadeh Alavi↗Hamidreza Khalili↗Stanley H. Chan↗Fatemeh Kouchmeshki↗Muhammad Usman↗Ross Vlahos↗

arXiv:2601.00921v3 Announce Type: replace-cross Abstract: Chronic obstructive pulmonary disease (COPD) affects hundreds of millions of people worldwide, and skeletal-muscle dysfunction is clinically important. Quantum machine learning is increasingly explored for biomedical prediction, but its value in small biomarker cohorts requires benchmarking against strong classical baselines. We analysed a cigarette-smoke COPD cohort of 213 animals with blood and bronchoalveolar-lavage biomarkers to predict tibialis anterior muscle weight, muscle quality, and force. We developed a kernel-geometric quantum hybrid method in which synthetic symmetric positive definite (SPD) references are mapped through a reproducing kernel Hilbert space, compressed using train-only random projection, normalised, and supplied to low-dimensional quantum regression circuits. We benchmarked this approach against classical ridge/kernel models, SPD relational representations, and quantum-kernel regression (QKR). All methods were evaluated using condition-stratified repeated cross-validation. The largest numerical improvement was observed for muscle weight, where the proposed method had the numerically lowest mean root mean squared error (RMSE), approximately 1.8% below the best classical comparator; paired fold-level testing did not establish statistically significant superiority after Holm adjustment, but the endpoint is biologically meaningful. The method also had the numerically lowest mean RMSE for muscle quality. For force, biomarker-only Ridge performed best, suggesting a more linear endpoint structure.

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20.

arXiv (CS.AI) 2026-06-19 DOI: arXiv:2604.13416

DF3DV-1K: A Large-Scale Dataset and Benchmark for Distractor-Free Novel View Synthesis

作者:

Cheng-You Lu↗Yi-Shan Hung↗Wei-Ling Chi↗Hao-Ping Wang↗Charlie Li-Ting Tsai↗Yu-Cheng Chang↗Yu-Lun Liu↗Thomas Do↗Chin-Teng Lin↗

arXiv:2604.13416v2 Announce Type: replace-cross Abstract: Advances in radiance fields have enabled photorealistic novel view synthesis. In several domains, large-scale real-world datasets have been developed to support comprehensive benchmarking and to facilitate progress beyond scene-specific reconstruction. However, for distractor-free radiance fields, a large-scale dataset with clean and cluttered images per scene remains lacking, limiting the development. To address this gap, we introduce DF3DV-1K, a large-scale real-world dataset comprising 1,048 scenes, each providing clean and cluttered image sets for benchmarking. In total, the dataset contains 89,924 images captured using consumer cameras to mimic casual capture, spanning 128 distractor types and 161 scene themes across indoor and outdoor environments. A curated subset of 41 scenes, DF3DV-41, is systematically designed to evaluate the robustness of distractor-free radiance field methods under challenging scenarios. Using DF3DV-1K, we benchmark nine recent distractor-free radiance field methods and 3D Gaussian Splatting, identifying the most robust methods and the most challenging scenarios. Beyond benchmarking, we demonstrate an application of DF3DV-1K by fine-tuning a diffusion-based 2D enhancer to improve radiance field methods, achieving average improvements of 0.96 dB PSNR and 0.057 LPIPS on the held-out set (e.g., DF3DV-41) and the On-the-go dataset. We hope DF3DV-1K facilitates the development of distractor-free vision and promotes progress beyond scene-specific approaches. The dataset and leaderboard are available at https://johnnylu305.github.io/df3dv1k_web/.

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21.

arXiv (CS.CV) 2026-06-11 DOI: arXiv:2605.31219

Latent Geometric Chords for Query-Efficient Decision-Based Adversarial Attacks

作者:

Ei Hmue Khine↗Yao Li↗Jiebao Sun↗Shengzhu Shi↗Zhichang Guo↗Boying Wu↗

While decision-based black-box adversarial attacks present a severe security threat, current methodologies suffer from fundamental limitations. Pixel-wise attacks frequently introduce unnatural, high-frequency visual artifacts, while latent-space frameworks are confined by the limited search space of low-dimensional manifolds and inherent reconstruction flaws. To resolve these limitations, we propose Latent Geometric Chords (LGC) for Query-Efficient Decision-Based Adversarial Attacks alongside a variant, LGC-H. At its core, LGC navigates decision boundaries by executing a curvature-aware geometric search within a compressed semantic manifold. To guarantee high visual fidelity and circumvent dimensionality bottlenecks, we introduce a Residual-based Adversarial Generation (RAG) mechanism. RAG isolates semantic perturbations as geometric chords and superimposes them directly onto the original source image. RAG substantially resolves baseline reconstruction flaws and effectively doubles the permissible search space dimensions. Experimental results demonstrate that LGC achieves robust cross-dataset transferability and substantially outperforms state-of-the-art baselines. Notably, our method, LGC, minimizes perturbation magnitudes while achieving state-of-the-art visual fidelity–with a Structural Similarity Index Measure (SSIM) exceeding 0.99 and a Learned Perceptual Image Patch Similarity (LPIPS) below 0.01 at 5000 queries–and sustaining high attack success rates under stringent perceptual constraints, successfully compromising adversarially trained robust models. The source code is available at: https://github.com/eihmuekhine/Latent-Geometric-Chords.

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22.

arXiv (CS.LG) 2026-06-11 DOI: arXiv:2606.11382

GLACIER: A Multimodal Student-Teacher Foundation Model for Molecular Property Prediction

作者:

Emily Nguyen↗Yongchan Hong↗Harsh Toshniwal↗Yan Liu↗Andreas Luttens↗

arXiv:2606.11382v1 Announce Type: new Abstract: Deep learning models facilitate the discovery of molecules with tailored properties among billions of candidate compounds. However, the computational burden to develop and deploy state-of-the-art models continuously increases, limiting their scalability. Most large-scale models are unimodal in nature and overlook the potential to leverage complementary molecular data modalities. To address these shortcomings, this paper introduces the Graph-Language Alignment for Chemical Inference and Exploration using Representations (GLACIER) model, a student-teacher framework that integrates molecular graphs, SMILES strings, and physicochemical descriptors to learn rich molecular embeddings. Our framework consists of three stages: (1) we pretrain three student encoders on 100,000 drug-like molecules: a message-passing neural network for molecular graphs, a transformer-based encoder for SMILES strings, and a multilayer perceptron for physicochemical descriptors, (2) we fuse these student modalities using a novel Finsler geometry-aware module, and (3) distill complementary knowledge from large teacher models, including MiniMol and MolFormer, into a single lightweight model via contrastive learning. We demonstrate that GLACIER is a robust framework that delivers high predictive performance and computational efficiency in complex molecular property prediction tasks. Our code is publicly available at https://github.com/eemokey/glacier.

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23.

arXiv (CS.CL) 2026-06-12 DOI: arXiv:2606.12807

Detect, Remask, Repair: Diffusion Editing for Faithful Summarization of Evolving Contexts

作者:

Hao Zou↗Zachary Horvitz↗Chandhru Karthick↗Zhou Yu↗Kathleen McKeown↗

Summaries of real-world events can become outdated as contexts evolve and new information arrives. A common response is to generate a new summary from the updated context, but full regeneration discards the previous draft, can obscure what changed, and may be unnecessary when only a few claims are unsupported. We study localized faithfulness repair: updating outdated spans in an existing summary while preserving supported content. We propose DETECT-REMASK-REPAIR, a diffusion-based framework that identifies, remasks, and repairs outdated regions with masked diffusion language models. To evaluate evolving-context summarization, we introduce StreamSum, a benchmark of synthetic event timelines. Experiments on DialogSum and StreamSum show that localized diffusion repair provides a controllable alternative to full rewriting: faithfulness-steered repair improves early drafts, one-step repair reduces repair cost to under half a second, with the framework enabling faithfulness-speed-preservation tradeoffs across datasets. We also find that the framework can provide a post-hoc correction step that improves faithfulness for autoregressive systems.

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24.

arXiv (CS.AI) 2026-06-16 DOI: arXiv:2602.12670

SkillsBench: Benchmarking How Well Agent Skills Work Across Diverse Tasks

作者:

Xiangyi Li↗Yimin Liu↗Wenbo Chen↗Bingran You↗Zonglin Di↗Yifeng He↗Shenghan Zheng↗Kyoung Whan Choe↗Jiankai Sun↗Shuyi Wang↗Chujun Tao↗Binxu Li↗…

arXiv:2602.12670v4 Announce Type: replace Abstract: Agent Skills are structured packages of procedural knowledge that augment large language model (LLM) agents at inference time. Despite rapid adoption, there is no standard way to measure whether they actually help. We present SkillsBench, a benchmark whose current inventory contains 87 tasks across 8 domains paired with curated Skills and deterministic verifiers. Our latest aggregate evaluation runs the 87-task benchmark under matched no-Skills and curated-Skills conditions for 18 model-harness configurations. Curated Skills raise the average pass rate from 33.9% to 50.5% (+16.6 percentage points; 25.5% normalized gain), with configuration-level gains ranging from +4.1 to +25.7 pp. Focused Skills with at most three modules outperform larger or exhaustive bundles, and smaller models with Skills can match larger models without them. SkillsBench establishes paired evaluation as the foundation for rigorous measurement of Skill efficacy on agentic, expertise-heavy work.

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25.

arXiv (quant-ph) 2026-06-24 DOI: arXiv:2606.23722

Quantum-enhanced estimation of stimulated Raman optical activity

作者:

Mahadeva Chanda Durjoy↗Girish S. Agarwal↗

arXiv:2606.23722v1 Announce Type: new Abstract: In recent times there has been growing interest in Raman optical activity (ROA) for its label free detection of absolute configuration, conformation, and stereochemical structure in chiral biosamples and drug molecules. Since ROA signals are generally small, techniques such as stimulation by a probe beam can be used to enhance the signal strength. However, with a classical probe, the measurement precision is still fundamentally limited by its shot noise. To solve this problem we propose the use of two-mode squeezed vacuum and show that it can achieve sub-shot noise limited measurement sensitivity. Using quantum estimation theory, we derived the quantum Fisher information and the quantum Cramér-Rao bound (QCRB) for stimulated ROA measurement to quantify the precision enhancement. This improvement comes from photon-number correlations which suppress the intensity fluctuation common to both modes. We further show that balanced detection of the output intensity difference is a practical measurement scheme that approaches the QCRB and becomes optimal in the small-chirality limit. This opens a promising path toward more sensitive Raman chiroptical spectroscopy of weak and photosensitive samples.

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