Paper Plaza - AcademicHub

01.

arXiv (CS.LG) 2026-06-18 DOI: arXiv:2606.18457

Task-Restricted Symmetries in Recurrent Weight Space

Authors:

arXiv:2606.18457v1 Announce Type: new Abstract: Recurrent networks can contain substantial functional redundancy in weight space: changing a recurrent matrix may leave the input-output rollout nearly unchanged on a task distribution, while similar-scale changes can destroy the same behavior. We study this redundancy in one-layer tanh RNNs using ordered real Schur coordinates. The Schur form separates spectral blocks from directed nonnormal couplings, giving a diagnostic basis for structured ablations that keep the input and readout maps fixed. In a fixed-length copy task, selected nonnormal Schur couplings can be removed with little loss in some trained solutions, whereas other couplings are necessary for accurate autonomous replay. Across flip-flop, sine generation, and context-dependent integration, the loss-preserving ablation profile varies across tasks and trained solutions. These results identify candidate approximate functional invariances, not universal symmetries of recurrent weight space. Schur-coordinate ablations provide a practical diagnostic for which structured perturbations preserve a trained recurrent solution and which ones disrupt its computation.

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02.

arXiv (CS.AI) 2026-06-15 DOI: arXiv:2606.14466

The Perceived Fragility of Explanations in Audio Models: Manipulation of Attribution with Unchanged Predictions

Authors:

Piotr Kit{\l}owski ↗Dominik Wi\k{a}cek ↗Mateusz Modrzejewski ↗

arXiv:2606.14466v1 Announce Type: cross Abstract: This paper investigates the fragility of post-hoc explanation methods in audio deepfake detection. While previous work on explanation manipulation focused on images using standard $L_p$ metrics, we introduce a psychoacoustic framework that optimizes inaudible perturbations to decouple model attributions from final classifications. We evaluate this vulnerability across state-of-the-art architectures under strict prediction-preserving constraints. By evaluating the manipulation cost through domain-specific perceptual audio quality metrics alongside explanation alignment criteria, our framework demonstrates that an adversary can systematically distort automated explanation heatmaps while preserving the predicted deepfake label. Full code available at: https://github.com/cncPomper/Audio-XAI

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03.

arXiv (CS.LG) 2026-06-18 DOI: arXiv:2606.19023

Lifecycle-Aware Dynamic Analysis for Secure ML Model Execution

Authors:

Gabriele Digregorio ↗Marco Di Gennaro ↗Francesco Pastore ↗Stefano Zanero ↗Stefano Longari ↗Michele Carminati ↗

arXiv:2606.19023v1 Announce Type: cross Abstract: The growing reliance on pre-trained Machine Learning (ML) models has introduced new attack surfaces. Recent vulnerabilities demonstrate that malicious behavior can be embedded within model artifacts, often bypassing existing defenses. Current model-scanning solutions primarily rely on static, format-specific rules or known attack signatures, which limit their ability to generalize across frameworks and to detect novel exploitation paths. In contrast, we propose a solution that focuses on the effects an attack has on the host system executing the model and builds on foundational intuitions about ML model execution. In particular, we observe that ML models operate within well-defined lifecycle phases and that, within each phase, interactions with the host system are highly structured and predictable. We translate these intuitions into Moat, a dynamic lifecycle-aware approach for securing ML model execution, and instantiate this design in Re-Moat, our reference implementation. We evaluate Re-Moat across multiple ML frameworks using 77,974 real-world model artifacts from the Hugging Face Hub, 31 Proofs-of-Concept (PoCs) from CVEs, and 334 models from a state-of-the-art dataset, and compare it against state-of-the-art model-scanning solutions. Our results show that our approach detects all evaluated attack classes while maintaining a close-to-zero false-positive rate, validating our intuitions and motivating dynamic analysis for securing ML model execution.

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04.

arXiv (CS.LG) 2026-06-16 DOI: arXiv:2606.16926

Functional Gradient Descent with Adaptive Representations

Authors:

Daniel Csillag ↗Rodrigo Schuller ↗Pedro Dall'Antonia ↗Leonidas Guibas ↗Luiz Velho ↗Tiago Novello ↗

arXiv:2606.16926v1 Announce Type: cross Abstract: Functional optimization problems are typically solved by optimizing the parameters of a fixed representation, such as a neural network, resulting in highly nonconvex losses that complicate both training and theoretical analysis. An interesting alternative is functional gradient descent (FGD), that is, gradient descent directly in function space, which benefits from strong convergence results and admits a clean theory. However, FGD is difficult to implement in practice because functional gradients are infinite-dimensional, and thus cannot be fully computed nor stored in memory. Existing implementations therefore rely on fixed approximations, which introduce approximation error. We propose a new, theoretically-grounded FGD algorithm that adapts the representation of the functional gradients over the course of optimization. By explicitly incorporating this approximation into the analysis, we establish convergence to a stationary point (for smooth losses) and to a global minimizer (under smoothness + a Polyak-Lojasiewicz-type condition) regardless of our approximations. To the best of our knowledge, this is the first implementable FGD method with such guarantees in a general setting. We demonstrate the effectiveness of our method on regression, numerical solution of PDEs, and modern computer vision. Across settings, our method consistently outperforms both FGD with fixed approximations and neural network baselines in efficiency and accuracy.

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05.

arXiv (CS.AI) 2026-06-17 DOI: arXiv:2606.17514

Unlocking LLM Code Correction with Iterative Feedback Loops

Authors:

Le Zhang ↗Suresh Kothari ↗

arXiv:2606.17514v1 Announce Type: cross Abstract: Large Language Models have shown remarkable capabilities in code generation. However, most existing evaluations focus only on single-attempt accuracy and overlook the iterative refinement process that is central to real-world programming. This study presents a systematic investigation of LLMs' ability to rectify their own code through execution feedback. Using real-world programming problems across four models and two major programming languages, this study evaluates performance using iterative refinement framework where LLMs receive compiler error messages and testcase feedback after each attempt. This study introduces metrics to evaluate code failures, analyze rectification patterns, and compare the effectiveness of reasoning and non-reasoning models, offering actionable insights into both the understanding and practical application of feedback loops in LLM-driven code generation systems. Results show that reasoning models consistently improve over iterations, substantially outperforming non-reasoning models in leveraging feedback, while syntactic and runtime errors are far more tractable than logical or algorithmic failures.

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06.

arXiv (CS.CV) 2026-06-16 DOI: arXiv:2606.14780

YTClickbait21K: Human-Annotated Multimodal Dataset for YouTube Clickbait Detection Across Diverse Channels and Content Categories

Authors:

Md. Minhazul Islam ↗Md. Tanbeer Jubaer ↗Amith Khandakar ↗Shovon Sarker ↗Sumaiya Rahman ↗Md. Masum Mia ↗Mohamed Arselene Ayari ↗Hamed Noori ↗

Clickbait content on video-sharing platforms poses a significant challenge to information reliability, yet progress in automated detection has been constrained by the lack of large-scale, high-quality multimodal datasets. We present YTClickbait21K, a human-annotated YouTube clickbait dataset comprising 21,238 videos collected from 40 channels across 29 countries, covering diverse content categories such as news, entertainment, education, and gaming. Each sample includes structured metadata (title, description, engagement statistics) along with associated thumbnail images, enabling comprehensive multimodal analysis. To ensure annotation quality, every video was independently labeled by three annotators using a standardized decision framework that incorporates textual, visual, and cross-modal consistency cues, with final labels determined through majority voting. The dataset exhibits substantial inter-annotator agreement (k=0.65), confirming reliable labeling despite the inherent subjectivity of clickbait detection. By combining scale, annotation rigor, and multimodal richness, this dataset provides a robust benchmark for developing and evaluating machine learning models, facilitating research in cross-modal semantic understanding, and advancing automated content moderation systems.

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07.

arXiv (CS.AI) 2026-06-18 DOI: arXiv:2606.18976

CAPRA: Scaling Feedback on Software Architecture Deliverables with a Multi-Agent LLM System

Authors:

Marco Becattini ↗Niccol\`o Caselli ↗Matteo Minin ↗Roberto Verdecchia ↗Enrico Vicario ↗

arXiv:2606.18976v1 Announce Type: cross Abstract: Automated assessment in software engineering education has advanced significantly for code grading and essay scoring. However, reviewing software architecture deliverables, which requires analyzing structural completeness and requirements traceability, has not yet been fully automated. Applying Large Language Models (LLMs) to this task requires robust architectures to ensure technical feedback is accurate and reliable for students. This paper presents CAPRA (Configurable Architecture Proficiency Report Assessment), a multi-agent LLM system that analyzes software architecture deliverables to generate personalized, template-compliant LaTeX feedback. As a core design choice, CAPRA coordinates multiple specialized agents and employs a Python-based microservice for multi-modal document extraction, utilizing PyMuPDF and vision-enabled LLMs (specifically gpt-4o) to parse text and UML diagrams. To ensure educational reliability and mitigate hallucinations, CAPRA introduces a deterministic Evidence Anchoring step using fuzzy matching via normalized Levenshtein distance, along with a ConsistencyManager agent that cross-verifies, deduplicates, and merges findings. System performance is assessed using a structured eight-criterion binary evaluation taxonomy covering: (i) extraction completeness, (ii) feature validation, (iii) issue grounding and severity detection, (iv) recommendation specificity and traceability, and (v) template and tone compliance. A preliminary empirical evaluation on 10 student reports shows that CAPRA satisfied 88.8% of the evaluated criteria under a strict two-rater aggregation rule, achieved moderate inter-rater agreement with human evaluators (kappa = 0.582), and processed each report in slightly over 4 minutes. While these results support the viability of LLM-supported architectural feedback, human oversight remains essential for subjective assessment dimensions.

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08.

arXiv (quant-ph) 2026-06-24 DOI: arXiv:2506.13724

Logical qubits with erasure conversion using metastable neutral atoms

Authors:

Bichen Zhang ↗Genyue Liu ↗Guillaume Bornet ↗Sebastian P. Horvath ↗Pai Peng ↗Shuo Ma ↗Shilin Huang ↗Shruti Puri ↗Jeff D. Thompson ↗

arXiv:2506.13724v2 Announce Type: replace Abstract: Implementing large-scale quantum algorithms with practical advantage will require fault-tolerance achieved through quantum error correction, but the associated overhead is prohibitive. This overhead can be reduced by engineering physical qubits with fewer errors, and by shaping the residual errors to be more easily correctable. In this work, we demonstrate quantum error correcting codes and logical qubit circuits in a metastable ytterbium-171 nuclear spin qubit with a noise bias towards erasure errors. These errors can be located separately from any syndrome information diagnosing the error, and we demonstrate adaptive circuit execution based on erasure information. We show that dephasing errors on the qubit during coherent transport can be strongly suppressed, and implement entangling gates that maintain a high fidelity in the presence of gate beam inhomogeneity or pointing errors. Furthermore, we demonstrate logical qubit encoding in the [[4, 2, 2]] code, with error correction during decoding based on mid-circuit erasure measurements despite the fact that the code is too small to correct any Pauli errors. Finally, we demonstrate logical qubit teleportation between multiple code blocks with conditionally selected ancillas based on mid-circuit erasure checks, a key part of leakage-robust error correction schemes using neutral atoms.

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09.

arXiv (CS.AI) 2026-06-17 DOI: arXiv:2606.17459

Can LLMs Be CEOs? Benchmarking Strategic Resource Reallocation with Multi-Role Agent Simulation

Authors:

Yuyang Dai ↗Xueqing Peng ↗Lingfei Qian ↗Zhuohan Xie ↗

arXiv:2606.17459v1 Announce Type: new Abstract: Evaluating the decision-making capabilities of large language models (LLMs) is a growing research priority, yet existing benchmarks focus on isolated cognitive tasks such as reasoning, knowledge retrieval, and economic rationality in stylized settings. These evaluations overlook the defining challenge of real executive decision-making: integrating conflicting recommendations from specialized stakeholders under information asymmetry, organizational constraints, and temporal dependencies. We introduce \textsc{CEO-Bench}, a multi-agent benchmark that evaluates LLMs on CEO-level strategic resource reallocation – the process of redirecting capital across business units in a multi-round, constraint-rich organizational environment. In \textsc{CEO-Bench}, LLM agents receive conflicting advice from four role-conditioned C-suite advisors (CFO, CTO, COO, CMO), each with private signals and distinct priorities, and must synthesize these into a concrete allocation plan evaluated along four dimensions: role integration, conditional boldness, history-sensitive judgment, and plan validity. Experiments across five frontier models on 13 scenarios reveal that all models achieve high structural validity but diverge sharply on strategic calibration – the hardest capability layer. We identify systematic failure modes including single-advisor capture, conservative default under ambiguity, and historical amnesia, and uncover a structural integration-boldness tradeoff: models that engage more deeply with conflicting perspectives tend to produce less decisive action. These findings delineate the current capability boundary of LLMs as organizational decision-makers and inform the design of future AI-assisted executive systems.

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10.

arXiv (quant-ph) 2026-06-16 DOI: arXiv:2511.01518

Quantum Energy Teleportation under Equilibrium and Nonequilibrium Environments

Authors:

Xiaokun Yan ↗Kun Zhang ↗Jin Wang ↗

arXiv:2511.01518v3 Announce Type: replace Abstract: Quantum energy teleportation (QET), implemented via local operations and classical communication, enables carrier-free energy transfer by exploiting quantum resources. While QET has been extensively studied theoretically and validated experimentally in various quantum platforms, enhancing energy output for mixed initial states, as the system inevitably interacts with environments, remains a significant challenge. In this work, we study QET performance in a two-qubit system coupled to equilibrium or nonequilibrium reservoirs. We derive an analytical expression for the energy output in terms of the system Hamiltonian eigenstates, enabling analysis of energy output for mixed states. Using the Redfield master equation, we systematically examine the effects of qubit detuning, nonequilibrium temperature difference, and nonequilibrium chemical potential difference on the energy output. We find that the energy output for mixed states often follows that of the eigenstate with the highest population, and that nonequilibrium environments can enhance the energy output in certain parameter regimes.

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11.

arXiv (CS.AI) 2026-06-16 DOI: arXiv:2508.05287

FlowState: Sampling-Rate-Equivariant Time-Series Forecasting

Authors:

Lars Graf ↗Thomas Ortner ↗Stanis{\l}aw Wo\'zniak ↗Angeliki Pantazi ↗

arXiv:2508.05287v3 Announce Type: replace-cross Abstract: Existing time series foundation models (TSFMs), often based on transformer variants, lack adaptability to different sampling rates, struggle with generalization across varying context and target lengths, and are computationally inefficient. We introduce FlowState, a novel TSFM architecture that achieves sampling-rate-equivariant forecasting through a unified design that pairs a state space model (SSM) encoder with a functional basis decoder (FBD). This design enables continuous-time modeling and dynamic time-scale adjustment, allowing FlowState to inherently generalize across all possible temporal resolutions, and dynamically adjust the forecasting horizons without retraining. We further propose an efficient pretraining strategy that improves robustness and accelerates training. Despite being one of the smallest TSFMs, FlowState achieves state-of-the-art results on the widely used GIFT-Eval benchmark, while demonstrating superior adaptability to unseen sampling rates. Our detailed analyses confirm the effectiveness of its components, and we demonstrate its unique ability to adapt to varying input sampling rates.

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12.

arXiv (CS.LG) 2026-06-18 DOI: arXiv:2605.04267

QUIVER: Cost-Aware Adaptive Preference Querying in Surrogate-Assisted Evolutionary Multi-Objective Optimization

Authors:

Florian A. D. Burnat ↗

arXiv:2605.04267v2 Announce Type: replace Abstract: Interactive multi-objective optimization systems face a budget allocation dilemma: one can spend resources on expensive objective evaluations or on eliciting decision-maker preferences that identify the relevant region of the Pareto set. Moreover, preference elicitation itself spans modalities with different information content and cognitive burden, ranging from cheap, noisy pairwise preference statements (PS) to richer but costlier indifference adjustments (IA). We study cost-aware optimization under an unknown scalarization and introduce QUIVER (Query-Informed Value Estimation for Regret), a surrogate-assisted evolutionary multi-objective optimizer that adaptively chooses between objective evaluations and heterogeneous preference queries. At each step, QUIVER selects the next action by maximizing the expected decision-quality improvement per unit total cost. Across DTLZ and WFG benchmarks under synthetic decision-maker models, QUIVER achieves the lowest final utility regret on challenging WFG problems (utility regret of 2.14 on WFG4, 2.82 on WFG9: a 25% improvement over baselines), outperforming all single-modality baselines. We analyze how the optimal mix of PS and IA adapts to problem difficulty: on easy problems (DTLZ2), QUIVER selects 80\% PS queries; on hard problems (WFG9), it shifts to 35% IA queries. This adaptive modality selection demonstrates cost-aware preference learning in action.

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13.

arXiv (quant-ph) 2026-06-16 DOI: arXiv:2606.16784

Quantum Nonlocal Games on Graph Ensembles

Authors:

arXiv:2606.16784v1 Announce Type: new Abstract: Quantum entanglement is one of the most striking discoveries in all of science. This effect allows, for instance, two spatially separated agents to coordinate their actions, without communication, to an extent that is both counter-intuitive, and provably impossible by any other physical means. A recently discovered example is that of mobile agents (players) performing spatial coordination tasks such as rendezvous, where the agents aim to meet on a network without communication. Until now, demonstrations of this advantage have relied on highly idealized conditions: agents are assumed to have complete knowledge of the topography, and experiments have been restricted to simulations using data generated by qubits within a single quantum processor. Here we address both limitations by developing a theory for graph ensembles that capture topographical uncertainty and by experimentally demonstrating the advantage in rendezvous scenarios between physically separated ion-trap systems with access to remote entanglement. Moreover, we simulate a broader set of problems on superconducting hardware. Surprisingly, when players are given the ability to gather more local information the quantum advantage increases – a feat impossible by classical means. Our findings establish a concrete route toward practical quantum advantages in motion coordination problems. More broadly, they point to a new way of using portable quantum devices to enhance collective decision-making in uncertain environments.

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14.

arXiv (CS.LG) 2026-06-24 DOI: arXiv:2606.24025

Information-Theoretic Classifier-Free Guidance with Adaptive Schedule Optimization

Authors:

Haobo Chen ↗Xiangxiang Xu ↗Yuheng Bu ↗

arXiv:2606.24025v1 Announce Type: new Abstract: Diffusion models have achieved strong performance in image, text-to-image, and video generation, where conditional generation is often controlled by classifier-free guidance (CFG). CFG improves condition consistency by increasing a guidance weight, but stronger guidance typically reduces diversity and distributional coverage. It remains unclear how this consistency-coverage trade-off should be controlled across the reverse trajectory, since the distribution induced by CFG is not simply the fixed-time tilted distribution given by the guided score field. To address this issue, we propose an information-theoretic framework for CFG schedule optimization. Our approach uses a clean endpoint reference to specify the desired consistency-coverage trade-off, while optimizing the actual distribution induced by the guided sampler toward this reference. We derive trajectory-level formulas to estimate the objective from samples and score evaluations, avoiding explicit density estimation. On ImageNet-512 with EDM-XXL and COCO with SD-XL, the learned schedules achieve competitive or improved trade-offs over constant guidance and allocate guidance selectively across noise levels.

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15.

arXiv (quant-ph) 2026-06-16 DOI: arXiv:2603.05627

Classical Explanations in (and of) General Probabilistic Theories

Authors:

John Harding ↗Alex Wilce ↗

arXiv:2603.05627v2 Announce Type: replace Abstract: We introduce a notion of the ``explanation" of one (generalized) probabilistic model by another as particular kind of span in the category $\Prob$ of probabilistic models and morphisms. We show that explanations compose under a standard pullback construction (notwithstanding that $\Prob$ does not support arbitrary pullbacks). We then show that every locally-finite probabilistic model has a canonical, sharp classical explanation. The construction is functorial, so every locally-finite probabilistic theory has a canonical, sharp classical (though of course, usually non-local) representation.

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16.

arXiv (CS.CL) 2026-06-19 DOI: arXiv:2606.20477

Scalable Training of Spatially Grounded 2D Vision-Language Models for Radiology

Authors:

Yusuf Salcan ↗Simon Ging ↗Robin Schirrmeister ↗Philipp Arnold ↗Elmar Kotter ↗Behzad Bozorgtabar ↗Thomas Brox ↗

We study how to train visually grounded vision-language models (VLMs) for radiology without manual spatial annotations. We introduce RefRad2D, a large-scale bilingual (German/English) dataset of 1.2M CT and MR image-text pairs derived from clinical practice, with task-specific VQA and spatial grounding subsets generated automatically via LLM-based curation and automated segmentation. Trained on this data, our model RadGrounder jointly performs report generation, visual question answering, and spatial grounding via bounding-box detection or segmentation. On external VQA benchmarks (Slake, VQA-RAD), RadGrounder achieves competitive results with specialized medical VLMs. Adding our clinical data to the training mixture improves open-ended VQA over fine-tuning on the downstream datasets alone, showing the transferability of our dataset. Crucially, adding grounding supervision does not degrade language quality, enabling spatially verifiable outputs at no cost to VQA performance.

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17.

medRxiv (Medicine) 2026-06-18 DOI: HASH:3a8ed4243032beead4e375cadf260517

Human Intuition vs. Computational Precision: Neurologists, Feature-based Models, and Deep Learning for Stroke Prognosis

Authors:

Herzog ↗Blindenbacher ↗Globas ↗Haeberlin ↗M. I ↗Baumgartner ↗Capecchi ↗Inauen ↗Sick ↗Majoie ↗C. B ↗van Zwam ↗…

Background: Prognostication in large vessel occlusion (LVO) stroke remains challenging. Although several prognostic models exist, their comparison to clinician performance, human-model interaction, and specific sources of human bias remain poorly understood. Methods: Using pre-treatment clinical and CT data from the MR CLEAN trial (n=500), six neurologists predicted three-month modified Rankin Scale (mRS) scores for 40 patients, both unaided and assisted by a validated feature-based model (MR PREDICTS). Human performance was benchmarked against MR PREDICTS and a multimodal, interpretable deep learning (DL) approach using raw imaging data. We explicitly assessed neurologists? ability to estimate model-required imaging features and identified systematic human biases. Models were additionally validated in a larger MR CLEAN trial cohort (n=404). Results: For predicting the full mRS distribution, standalone models achieved good ordinal agreement (MR PREDICTS quadratic weighted kappa (QWK) 0.51 [0.24 to 0.70]; DL model 0.49 [0.25 to 0.67]), significantly outperforming unaided neurologists (QWK 0.27 [0.10, 0.42]). Neurologists showed systematic overoptimism, predicting lower mRS scores than observed. Furthermore, there was poor accuracy in extracting imaging features. Raters? ASPECTS predictions deviated by 3.4 points from the confirmed scores, and collateral score accuracy was 44.6%. However, for predicting binary mRS (0-2 vs. 3-6), accuracy was comparable between unaided neurologists (64.17% [55.42% to 72.92%]) and models (MR PREDICTS 67.50% [52.50% to 82.50%]; DL model 63.16% [47.37% to 78.95%]). Model-assistance modestly improved and harmonized neurologists? predictions (QWK 0.41 [0.22 to 0.55]; binary accuracy 68.75% [58.33% to 78.34%]. Model performance remained robust in the larger cohort. Conclusions: Multimodal prognostic models outperform clinicians in predicting the full range of mRS outcomes, while human error in imaging assessment and systematic optimism bias are primary drivers of prognostic inaccuracy. End-to-end DL models eliminate human-input variability and hold strong potential as an automated second opinion to support prognostication and decision-making in acute LVO stroke.

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18.

arXiv (quant-ph) 2026-06-24 DOI: arXiv:2512.19413

Clifford Volume and Free Fermion Volume: Complementary Scalable Benchmarks for Quantum Computers

Authors:

Attila Portik ↗Orsolya K\'alm\'an ↗Thomas Monz ↗Zolt\'an Zimbor\'as ↗

arXiv:2512.19413v2 Announce Type: replace Abstract: As quantum computing advances toward the late-NISQ and early fault-tolerant eras, scalable and platform-independent benchmarks are essential for quantifying computational capacity in a classically verifiable manner. We introduce two volumetric benchmarks, Clifford Volume and Free Fermion Volume, that assess quantum hardware by testing the execution of random Clifford and free fermion operations. These two groups of unitaries possess a combination of properties that make them ideal for benchmarking: (i) each is individually efficient to simulate classically, enabling verification at scale; (ii) together they form a universal gate set; (iii) they serve as essential algorithmic primitives in practical applications (including shadow tomography and quantum chemistry); and (iv) their definitions are formulated abstractly, without explicit reference to hardware-specific features such as qubit connectivity or native gate sets. This framework thus enables scalable and fair cross-platform comparisons and tracks meaningful computational advancement. We demonstrate the practical feasibility of these benchmarks through extensive numerical simulations across realistic noise parameters and through experimental validation on Quantinuum's H2-1 trapped-ion quantum computer, which achieves a Clifford Volume of 34.

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19.

arXiv (CS.CV) 2026-06-25 DOI: arXiv:2606.25375

Beyond Visual Forensics: Auditing Multimodal Robustness for Synthetic Medical Image Detection

Authors:

Ching-Hao Chiu ↗Hao-Wei Chung ↗Gelei Xu ↗Xueyang Li ↗Pin-Yu Chen ↗John Kheir ↗Meysam Ghaffari ↗Carlos Morato ↗Ahmed Abbasi ↗Yiyu Shi ↗

With the rapid adoption of generative AI, synthetic medical images pose growing risks, including diagnostic deception and insurance fraud. Although prior work has explored vision-language model (VLM)-based synthetic image detection, these evaluations typically consider images in isolation. In clinical practice, however, images are interpreted alongside structured records and metadata, and VLMs are increasingly deployed under joint image-record inputs. We uncover a previously underexamined multimodal vulnerability: when given both modalities, VLMs may overweight record context in authenticity judgments, such that the same image receives different predictions solely due to changes in its accompanying text. This raises concerns about robustness in real-world deployment. To systematically characterize this effect, we reformulate synthetic medical image detection as an audit of multimodal robustness at the image-record interface and introduce a paired benchmark that holds the image fixed while swapping controlled metadata variants. Across multiple imaging modalities, we evaluate diverse open-weight and frontier API VLMs and quantify how metadata alone shifts authenticity predictions. Our benchmark provides a standardized tool for assessing and improving multimodal robustness beyond image-only settings. The code is available at https://github.com/chiuhaohao/Beyond-Visual-Forensics.

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20.

arXiv (quant-ph) 2026-06-15 DOI: arXiv:2605.11895

Universal Speed Limit in a Far-from-Equilibrium Bose Gas: Symmetry and Dynamical Decoherence

Authors:

Jun-Cheng Liang ↗Bo Chen ↗

arXiv:2605.11895v2 Announce Type: replace-cross Abstract: Predicting universal transport coefficients in far-from-equilibrium quantum systems remains a fundamental challenge. A paradigmatic example is the non-thermal fixed point (NTFP) of isolated Bose gases, where coherence spreads as $\ell^2(t) = C\hbar t/m$ with a universal constant $C$. While the scaling exponent $z=2$ is well established, the amplitude $C$ has remained elusive because the underlying particle cascade $n(k)\sim k^{-4}$ leads to a divergent kinetic energy, threatening the very existence of a constant speed limit. Here we resolve this paradox and present the first analytical, parameter-free prediction of a universal amplitude $C$. A deep interplay between symmetry and dissipation is uncovered. The emergent weak U(1) symmetry at the NTFP enforces a conserved total current, forcing the low-energy phase dynamics to obey a diffusive Langevin equation with noise entering as the divergence of a stochastic current. This structure, combined with dynamical decoherence of high-momentum modes, yields a universal power-law momentum distribution $\tilde{f}(v)\sim(1+v^2)^{-3}$ (with $v=k\ell$) that naturally regularizes the ultraviolet divergence. From this, a parameter-free geometric baseline $C=3$ is obtained, independent of microscopic details. The experimental value $C=3.4(3)$ [Martirosyan et al., Nature 647, 608 (2025)] is then shown to be quantitatively consistent with universal logarithmic corrections arising from a marginally irrelevant coupling at the fixed point. A new paradigm is thus established for predicting transport coefficients in strongly correlated non-equilibrium systems: symmetry constraints determine the low-energy effective theory, dynamical decoherence provides a natural ultraviolet completion, and scaling analysis delivers testable predictions moving beyond scaling exponents to quantitative amplitude prediction.

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21.

arXiv (CS.AI) 2026-06-18 DOI: arXiv:2606.18256

Dynamic In-Group Persona Generation for Enhancing Human-AI Rapport

Authors:

Yoonseok Oh ↗Inseo Jung ↗Jinkyu Kim ↗Jungbeom Lee ↗Minwoo Kang ↗Suhong Moon ↗

arXiv:2606.18256v1 Announce Type: cross Abstract: LLM-based chatbots are increasingly applied in interpersonal domains such as counseling and peer support, where establishing human-AI rapport is crucial yet remains challenging. In this work, we introduce a novel approach for conditioning LLMs with in-group personas, which (i) first identifies a user's primary concern and brief personal context (e.g., a computer science undergraduate worried about future career prospects), and (ii) generates a synthetic in-group persona that shares a similar primary concern while differing in background and narrative details, such as age or profession (e.g., a junior researcher at an AI startup). Furthermore, we conduct a human-subject study to systematically evaluate the effectiveness of in-group persona agents in enhancing human-AI rapport. We compare our approach against two baseline conditions: a conventional agent without persona conditioning and an agent exhibiting minimal self-disclosure (e.g., "I've felt that too"). Results from post-task questionnaires assessing rapport and user experience indicate that the in-group persona agent significantly improves perceived rapport and personal relevance compared to the baselines, and also yields more positive user experience-most notably higher engagement.

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22.

arXiv (CS.LG) 2026-06-16 DOI: arXiv:2505.08774

Generative Molecular Design with Steerable and Granular Synthesizability Control

Authors:

arXiv:2505.08774v2 Announce Type: replace-cross Abstract: Designing molecules that are both property-optimal and readily synthesizable is a central challenge in drug discovery. Existing works that do consider synthesizability can jointly output predicted synthesis routes for generated molecules. However, there has been minimal attention in addressing the ease of synthesis and with flexibility to incorporate desired reaction constraints. On the other hand, virtual screening searches for commercially available compounds, but imposes challenges when scaling to ultra-large (billion-size and beyond) chemical spaces. Here, we propose a generative design framework that unifies synthesis-constrained molecular design and ultra-large-scale virtual screening through steerable and granular synthesizability control. Generated molecules satisfy arbitrary multi-parameter optimization objectives with predicted synthesis routes satisfying mix-and-match constraints: including or avoiding certain reactions, incorporating specific building blocks, and minimizing synthesis route length. In an end-to-end in-house campaign targeting BRD4, we designed molecules synthesizable with specific selected reactions and building blocks, synthesized all six selected compounds, and identified two micromolar binders. We further demonstrate that reaction control enables efficient navigation of ultra-large make-on-demand chemical spaces to identify property-optimal candidates. By applying our framework to Chemspace's Freedom 4.0 make-on-demand space (142 billion molecules), we generated ~320k molecules (0.00023% of the library) on a single consumer-grade GPU (with only 8 GB GPU memory) and identified a micromolar Wee1 binder amongst 60 synthesized candidates. The single unified framework thus enables generating novel synthesizable molecules and retrieving catalogue-ready candidates, offering a flexible solution to mitigating the synthesizability bottleneck.

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23.

arXiv (math.PR) 2026-06-24 DOI: arXiv:2509.24950

On domains of elliptic operators with distributional coefficients

Authors:

Immanuel Zachhuber ↗

arXiv:2509.24950v2 Announce Type: replace-cross Abstract: We show how one can use recently gained insights from the study of singular SPDEs, more particularly the study of singular operators via the theory of Paracontrolled Distributions, to construct domains for (singular) elliptic operators. Formally we consider \[ A (u) = (1 - \Delta) u + \nabla V \cdot \nabla u + \xi u + {{div} (\rho u)}, \] where $V \in \mathcal{C}^{\delta}$, $\xi \in \mathcal{C}^{- 2 + \delta}$, $\rho \in \mathcal{C}^{- 1 + \delta}, {div} \rho = 0$} and which satisfy a structural assumption that is notably satisfied when $\xi$ is a sub-critical noise, see {[MvZ22]}. We also show that under this assumption, one can construct a continuous change of variables $\Theta$ which satisfies \[ A \Theta - (1 - \Delta) \in \mathcal{L} (H^{2 - \delta''} ; H^{\delta'}) \] which allows us to define $A$ rigorously and parametrise a domain. Moreover, for suitably regularised operators \[ A_{\varepsilon} (u) := (1 - \Delta) u + \nabla V_{\varepsilon} \cdot \nabla u + (\xi_{\varepsilon} + c_{\varepsilon}) \cdot u + {{div} (\rho_{\varepsilon} \cdot u)}, \] we show that for a strongly converging regularised change of variables $\Theta_{\varepsilon} \rightarrow \Theta$ we have \[ A_{\varepsilon} \Theta_{\varepsilon} \rightarrow A \Theta in \mathcal{L} (H^2 ; L^2) \] which in particular implies norm resolvent convergence to a limiting closed operator. Finally, we give a class of examples and show how to apply these results to prove strong analytical local well-posedness for a singular Schrödinger equation formally given by \[ i \partial_t u + (1 - \Delta) u + \nabla V \cdot \nabla u + \xi \cdot u = - | u |^2 u \] for singular $V, \xi$ and that its solution is the limit of the solution of the classical solutions of a regularised equation

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24.

arXiv (CS.AI) 2026-06-16 DOI: arXiv:2606.08151

Decision-Aware Memory Cards: Counterfactual-Inspired Context Selection and Compression for Tool-Using LLM Agents

Authors:

Xinyu Guan ↗Qianyang Zhao ↗Yuming Deng ↗

arXiv:2606.08151v2 Announce Type: replace Abstract: Modern large language model (LLM) agents do not simply need longer contexts; they need decision-relevant evidence at the moment of action. We study decision-aware context selection: ranking retrieved files, tests, traces, rules, and memories by their expected effect on an agent's next action rather than by semantic similarity alone. We present the Counterfactual-Inspired Context Layer (CICL), which builds an instance context graph, estimates decision-oriented utility for candidate units, and compresses selected evidence into typed memory cards. The same schema can be instantiated with hosted LLM judges, local surrogates, or lightweight rankers, making the selection protocol auditable across model choices. On 50 SWE-bench Verified file-retrieval instances, Qwen3.6-Plus reranking of BM25 top-50 candidates improves hit@1 from 0.58 to 0.78 and MRR@10 from 0.634 to 0.790, with all 2,500 judgments parseable. Controlled diagnostics show that CICL identifies action-critical evidence: removing the top-utility semantic unit reduces F1 from 0.245 to 0.000. In selected-then-compressed mode, memory cards save 44.93 tokens per query while preserving selected evidence. CICL provides a practical layer for measuring, ranking, and compressing decision-critical context for tool-using agents. Code is available at https://github.com/stephen-guan-researcher/CICL.

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25.

bioRxiv (Bioinfo) 2026-06-19 DOI: HASH:8ee6f57fc4b0f83a17958c557471c99d

ContinuumCellAgent: A Framework-Guided Agent for Long-Horizon Scientific Research

Authors:

Li ↗Lu ↗Fang ↗Xu ↗

AI-scientist systems are beginning to automate parts of scientific research. We present ContinuumCellAgent, an autonomous agent that executes literature review, hypothesis formation, computational experimentation, manuscript drafting, and adversarial peer review as a single unattended run. Existing AI scientist systems remain difficult to diagnose because they lack modularity, systematic prompt grounding, and observability into long-running behavior. ContinuumCellAgent addresses these gaps with a modular supernode architecture for stage-wise backend swapping, protocols grounded in curated research-method checklists that also define reviewer rubrics, and a diagnostics layer that records file-based artifacts, message traces, and state transitions. We evaluate the system on open-domain QA benchmarks and biomedical/longevity case studies, showing that it can produce checkable research artifacts while exposing pipeline dynamics for rigorous AI co-scientist research.

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