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01.

arXiv (CS.AI) 2026-06-17 DOI: arXiv:2606.18000

A T-API-Compliant ReAct Agentic Loop for Optical Networks: Generic vs. Domain-Specific Tool Abstractions

作者:

Seyed Morteza Ahmadian↗Paolo Monti↗Carlos Natalino↗

arXiv:2606.18000v1 Announce Type: cross Abstract: Optical networks need intent-driven, closed-loop agentic management, a key enabler for higher autonomy levels. We present the first T-API-compliant reasoning and act (ReAct) loop. We show that domain-specific composite tools achieve 90% oracle-validated correctness with threefold token savings compared to generic tools.

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02.

arXiv (CS.LG) 2026-06-18 DOI: arXiv:2606.18338

ThousandWorlds: A benchmark for climate emulation of potentially habitable exoplanets

作者:

Edward T. Stevenson↗Mei Ting Mak↗Eric Wolf↗Denis E. Sergeev↗Tobi Hammond↗N. J. Mayne↗Miles Cranmer↗

arXiv:2606.18338v1 Announce Type: new Abstract: The search for life beyond Earth will depend on detecting faint signatures in the atmospheres of potentially habitable exoplanets. Interpreting those signatures requires understanding the host planet's climate: the same molecule may signal life on one planet and abiotic chemistry on another. Global climate models (GCMs) provide this understanding, but individual runs can require up to millions of core-hours and substantial domain expert time. Machine-learning emulators could remove this bottleneck, but progress has been limited by the absence of a curated, multi-model exoclimate dataset. We introduce ThousandWorlds, an ML-ready benchmark for exoclimate emulation and for the broader regime of low-data, multi-simulator, parameter-to-field regression. The dataset contains approximately 1800 simulations from five GCMs, mapping eight planet parameters to 3D atmospheric fields including temperature, humidity, winds, clouds, and radiation. Three nested subsets define progressively harder challenges: single-simulator regression, multi-simulator regression with complete observations, and multi-simulator regression with structured missingness. We propose two evaluation protocols: one for ranking methods, and one that measures performance relative to the disagreement between GCMs themselves. We evaluate seven baselines spanning simple methods, deep learning, and Gaussian processes. GP-based methods perform best, suggesting that ThousandWorlds exposes a regime where off-the-shelf deep learning does not yet succeed. Data: https://doi.org/10.57967/hf/8695. Code: https://github.com/edstevenson/ThousandWorlds.

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03.

arXiv (CS.LG) 2026-06-12 DOI: arXiv:2606.12559

Feature-preserving Latent-EnKF for Data Assimilation of Flows with Shocks

作者:

Hemanth Chandravamsi↗Hangchuan Hu↗Ponkrshnan Thiagarajan↗Tamer A. Zaki↗

arXiv:2606.12559v1 Announce Type: cross Abstract: The ensemble Kalman filter (EnKF) is widely adopted for sequential data assimilation, but fails for solutions with discontinuities, such as shocks in compressible flows. Uncertainty in shock location induces multimodal ensemble statistics that violate the Gaussian assumptions underlying the EnKF, producing large-scale spurious oscillations in the analysis state. We introduce a feature-preserving latent-EnKF that performs the ensemble update in a learned low-dimensional latent space, where shock and flow features admit a smooth manifold representation, thereby preserving sharp features during EnKF analysis. The updated latent state is mapped back to physical state through a shared decoder for all ensemble members. The algorithm eliminates the member-specific ordered training and positivity flooring used in prior approaches. Numerical experiments on a Sod shock tube and Mach 2 shock interaction with a 2D cylinder, using sparse and noisy observations, show accurate feature recovery of shocks and contact discontinuities without spurious oscillations.

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04.

arXiv (CS.AI) 2026-06-19 DOI: arXiv:2601.02149

AI-enhanced tuning of quantum dot Hamiltonians toward Majorana modes

作者:

Mateusz Krawczyk↗Jaros{\l}aw Paw{\l}owski↗

arXiv:2601.02149v4 Announce Type: replace-cross Abstract: We propose a neural network-based model capable of learning the broad landscape of working regimes in quantum dot simulators, and using this knowledge to autotune these devices - based on transport measurements - toward obtaining Majorana modes in the structure. The model is trained in an unsupervised manner on synthetic data in the form of conductance maps, using a physics-informed loss that incorporates key properties of Majorana zero modes. We show that, with appropriate training, a deep vision-transformer network can efficiently memorize relation between Hamiltonian parameters and structures on conductance maps and use it to propose parameters update for a quantum dot chain that drive the system toward topological phase. Starting from a broad range of initial detunings in parameter space, a single update step is sufficient to generate nontrivial zero modes. Moreover, by enabling an iterative tuning procedure - where the system acquires updated conductance maps at each step - we demonstrate that the method can address a much larger region of the parameter space.

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05.

arXiv (CS.CL) 2026-06-15 DOI: arXiv:2604.24942

Independent-Component-Based Encoding Models of Brain Activity During Story Comprehension

作者:

Kamya Hari↗Taha Binhuraib↗Jin Li↗Cory Shain↗Anna A. Ivanova↗

Encoding models provide a powerful framework for linking continuous stimulus features to neural activity; however, traditional voxelwise approaches are limited by measurement noise, inter-subject variability, and redundancy arising from spatially correlated voxels encoding overlapping neural signals. Here, we propose an independent component (IC)-based encoding framework that dissociates stimulus-driven and noise-driven signals in fMRI data. We decompose continuous fMRI data from naturalistic story listening into ICs using one subset of the data, and train encoding models on independent data to predict IC time series from large language model representations of linguistic input. Across subjects, a subset of ICs exhibited consistently high predictivity. These ICs were spatially and temporally consistent across subjects and included cognitive networks known to respond during story listening (auditory and language). Auditory component time series were strongly correlated with acoustic stimulus features, highlighting the interpretability of identified component time series. Components identified as noise or motion-related artifacts by ICA-AROMA showed uniformly poor predictive performance, confirming that highly predicted components reflect genuine stimulus-related neural signals rather than confounds. Overall, IC-based encoding models enable analyses at the level of functional networks, accommodating the variability in network locations across individuals and providing interpretable results that are easy to compare across subjects. Code provided at: https://github.com/kamyahari/IC-Encoding-Models.git

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06.

arXiv (CS.LG) 2026-06-16 DOI: arXiv:2606.16461

Privacy from Symmetry: Orthogonally Equivariant Transformers for LLM Inference

作者:

Alexander Yukhimchuk↗Andrey Shulga↗Mladen Kolar↗Martin Tak\'a\v{c}↗

arXiv:2606.16461v1 Announce Type: new Abstract: Running large language models locally is often impractical, pushing inference on sensitive text to third-party providers. Split inference partially mitigates this by keeping tokens on the client and sending only hidden representations, but these representations can still be recovered via nearest-neighbor search against the public embedding table. We propose an orthogonal obfuscation procedure in which the client multiplies embeddings by a secret orthogonal matrix before transmission. To enable correct inference under arbitrary rotations, we introduce ConjFormer, a transformer variant that is exactly $\mathrm{O}(d)$-equivariant via a lightweight normalization change (scalar RMSNorm) together with blockwise orthogonal conjugation of all linear weights. As a result, the server performs the full forward pass entirely in the rotated basis and never observes unrotated hidden states. Experiments on GPT-2 and Llama 3.2 1B models fine-tuned on PubMed show that orthogonal obfuscation eliminates direct cosine nearest-neighbor inversion and reduces token recovery from over 35% top-10 to at most 1.3%, while increasing perplexity by only 0.4% after fine-tuning. These results indicate that enforcing symmetry at the architectural level can provide a practical defense for privacy-preserving LLM inference without noise injection or heavy cryptographic machinery.

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07.

arXiv (CS.CL) 2026-06-16 DOI: arXiv:2605.21027

Beyond Text-to-SQL: An Agentic LLM System for Governed Enterprise Analytics APIs

作者:

Gundeep Singh↗Parsa Kavehzadeh↗Jing Xia↗Xue-Yong Fu↗Julien Bouvier Tremblay↗Md Tahmid Rahman Laskar↗Vincent Lum↗Shashi Bhushan TN↗

Enterprise analytics aims to make organizational data accessible for decision-making, yet non-technical users still face barriers when using traditional business intelligence tools or Text-to-SQL systems. While recent Text-to-SQL approaches based on Large Language Models (LLMs) promise natural language access to structured data, they fall short in enterprise settings where analytics pipelines rely on governed APIs rather than raw databases. In practice, these APIs encapsulate complex business logic to ensure consistency, auditability, and security. However, delegating mathematical or aggregation logic to an LLM introduces reliability and compliance risks. To this end, we present Analytic Agent, an LLM-based agentic system that translates natural language intents into secure interactions with enterprise analytics APIs. Evaluated on 90 real enterprise use cases constructed by domain experts, it reliably interprets user goals, validates permissions, executes governed queries, and generates compliant visualizations through multi-step reasoning and policy-aware orchestration.

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08.

arXiv (CS.LG) 2026-06-19 DOI: arXiv:2605.30456

DisjunctiveNet: Neural Symbolic Learning via Differentiable Convexified Optimization Layers

作者:

Shraman Pal↗Can Li↗

arXiv:2605.30456v2 Announce Type: replace Abstract: Many learning tasks in science and engineering are characterized by sparse datasets, which limits the effectiveness of purely data-driven approaches. At the same time, these problems are often accompanied by rich domain knowledge derived from physical laws, operational requirements, and expert heuristics. Such knowledge is frequently expressed as rules involving logical propositions and linear inequalities. Existing neuro-symbolic methods typically enforce these rules approximately through soft penalties, assume input-independent rules when designing specialized architectures, or rely on non-differentiable post-processing at inference time to achieve hard constraint satisfaction. While recent advances in differentiable optimization layers enable end-to-end feasibility enforcement within neural networks, extending these approaches to logical or mixed-integer rules remains challenging due to inherent nonconvexity. In this work, we propose a unified end-to-end framework for enforcing hard, input-dependent mixed integer linear constraints within neural networks. Our approach represents rules as disjunctive constraints and applies hierarchical convex relaxations to obtain convex hull formulations. These relaxations yield tractable linear constraints that can be embedded as differentiable optimization layers while enabling exact rule satisfaction. We demonstrate the effectiveness of the proposed framework on real-world datasets, achieving perfect rule satisfaction and strong predictive performance.

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09.

arXiv (quant-ph) 2026-06-25 DOI: arXiv:2605.20346

Forced Gap Post-Selection for Quantum LDPC Codes and their Operations

作者:

Adam Wills↗Theodore J. Yoder↗Isaac Chuang↗

arXiv:2605.20346v2 Announce Type: replace Abstract: We develop a simple and general post-selection strategy for high-rate quantum codes that is transferrable across decoders. After an initial baseline run, the decoder is re-run once per logical observable, and forced in these latter runs to provide a solution where the given observable has the complementary outcome. Shots are rejected that find logically complementary solutions with similar likelihoods compared to the baseline. Using the Relay-BP decoder, we benchmark the strategy on the $72$-qubit and $144$-qubit bivariate bicycle codes, as well as surgery gadgets for the latter. In comparison to previous post-selection strategies, our results offer an improved logical error rate by over a factor of $4$ on the same circuit and physical error rate, and at the same rate of post-selection. Our strategies are also lightweight, relying only on FPGA-friendly belief propagation, whereas the previous best used repeated rounds of a high-latency BP-OSD decoder.

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10.

arXiv (quant-ph) 2026-06-25 DOI: arXiv:2606.25889

Resonant false vacuum decay in two dimensions on a 4000-qubit quantum annealer

作者:

Gregor Humar↗Jean-Yves Desaules↗Luka Pave\v{s}i\'c↗Marko Ljubotina↗Zlatko Papi\'c↗Kristel Michielsen↗Jaka Vodeb↗

arXiv:2606.25889v1 Announce Type: new Abstract: From cosmology to quantum matter, metastable states often decay through the nucleation and growth of competing domains, with false vacuum decay providing the paradigmatic example of this process. Here we demonstrate a distinct regime in which domain growth outpaces nucleation by orders of magnitude and is controlled by local resonance conditions. Using a programmable quantum annealer with more than 4000 qubits, we realize a two-dimensional quantum Ising model whose metastable spin-polarized state encodes a false vacuum. At a specific value of the longitudinal field, single-spin flips at the boundary of a seeded bubble become resonant, enabling kinetically constrained expansion. Combining experiment with tensor-network simulations and stochastic circuit modeling, we observe nearly ballistic growth of true-vacuum domains with sub-ballistic interface broadening, consistent with Kardar–Parisi–Zhang universality. Our results establish a growth-dominated regime of false vacuum decay and show how large-scale quantum simulation can access nonequilibrium metastable dynamics relevant to quantum field theory, cosmology, and strongly correlated matter.

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11.

arXiv (CS.CV) 2026-06-16 DOI: arXiv:2604.27128

Lightweight Distillation of SAM 3 and DINOv3 for Edge-Deployable Individual-Level Livestock Monitoring and Longitudinal Visual Analytics

作者:

Haiyu Yang↗Miel Hostens↗

Foundation-model pipelines for individual-level livestock monitoring – combining open-vocabulary detection, promptable video segmentation, and self-supervised visual embeddings – have raised the accuracy ceiling of precision livestock farming (PLF), but their GPU memory budgets exceed the envelope of commodity edge accelerators. To close this gap, the 446M-parameter Perception Encoder (PE-ViT-L+) backbone of SAM 3 is distilled into a 40.66M-parameter multi-scale student through three mechanisms: a Feature Pyramid Network student encoder built on TinyViT-21M-512, a four-term direction-then-scale distillation loss, and backbone-substitution inference with sliding-window session pruning that bounds streaming GPU memory growth. The DINOv3 family includes a pre-distilled ViT-S/16 variant (21.6M parameters) released alongside a 6716M-parameter ViT-7B teacher; the ViT-S (21M) variant is adopted as the per-individual embedder. On the Edinburgh Pig dataset, the compressed pipeline reaches 92.29% MOTA and 96.15% IDF1 against the SAM 3 teacher (1.68- and 0.84-percentage-point losses), achieves a 7.77-fold reduction in system-level parameters and a 3.01-fold reduction in peak VRAM (19.52GB -> 6.49GB), and reaches 97.34% top-1 accuracy with 91.67% macro-F1 on nine-class pig behaviour classification. The pipeline fits inside an NVIDIA Jetson Orin NX 16GB envelope with 4.9GB of headroom, supporting a proposed – but not yet empirically validated – on-device embedding-pool re-identification mechanism whose per-individual footprint of approximately 94MB per animal per year produces a longitudinal visual record amenable to retrospective association with disease, lameness, reproductive, and growth outcome labels.

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12.

arXiv (CS.LG) 2026-06-11 DOI: arXiv:2606.05907

Knowledge Manifold: A Riemannian Geometric Framework for Semantic Mapping and Geodesic Analysis of Scientific Literature

作者:

Tomonaga Okabe↗Kazuhiko Komatsu↗

arXiv:2606.05907v2 Announce Type: replace-cross Abstract: We present the knowledge manifold: a Riemannian geometric space in which a corpus of documents is arranged according to semantic positional relationships derived from character n-gram TF-IDF representations. The framework proceeds in five tightly coupled stages. First, each document is converted to a character-level n-gram TF-IDF vector (4-7 grams, up to 250,000 features, L2-normalized) and embedded in a two-dimensional knowledge map via constrained stress minimization with repulsion, variance, and centering regularizers. Second, knowledge at an arbitrary query point is estimated through Smoothed Particle Hydrodynamics (SPH) interpolation using a cubic-spline kernel, yielding an interpolated TF-IDF feature vector that can be linguistically characterized. Third, directional knowledge gradients at 0, 45, and 90 degrees are computed from the SPH interpolation map, and pairwise directional similarity is quantified via inner product and cosine similarity. Fourth, a Gaussian Process Regression (GPR) model, with a Constant x RBF + White kernel fitted on a 10-dimensional SVD projection, provides a Bayesian posterior mean, uncertainty estimate, and per-document contribution rate at the query point. Fifth, geodesics in the knowledge space are obtained by minimizing a discrete Riemannian path energy derived from the SPH-induced metric tensor, using L-BFGS-B with seven deterministic initial-path candidates. We apply the formulation to a corpus of 20 papers in fiber-reinforced composite materials and aerospace structural mechanics, showing that the semantic map recovers meaningful research clusters, geodesic paths reveal natural conceptual bridges between distant topics, and SPH/GPR interpolation enables the generation of virtual knowledge: hypothetical paper abstracts describing unstudied but geometrically predicted research directions.

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13.

arXiv (CS.LG) 2026-06-15 DOI: arXiv:2606.14533

The Risk Shadow of Principal Component Analysis: When 99.9999% Variance Preservation Causes Catastrophic Decision Errors

作者:

Hamidou Tembine↗

arXiv:2606.14533v1 Announce Type: new Abstract: Principal Component Analysis (PCA) preserves variance, not the information needed to detect rare catastrophic events. This paper proves the existence of a {\it Risk Shadow}: PCA can retain over 99.9999 percent of total variance while completely erasing all signal about rare, high-impact failures. When this happens, even the best possible classifier operating on the PCA representation reduces to a constant predictor. The root cause is a fundamental mismatch between variance maximization and tail risk awareness. To break the shadow, we introduce Expectile PCA (ExPCA) and Tail-Preserving PCA (TP-PCA), two methods that reweight the data covariance toward high-impact events. We prove theoretically that ExPCA strictly outperforms PCA in retaining rare-event information, and we validate our claims on synthetic data and a real-world credit card fraud detection benchmark. Our results call for a fundamental rethinking of variance-based dimensionality reduction in high-stakes decisions.

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14.

arXiv (quant-ph) 2026-06-16 DOI: arXiv:2505.02269

Information geometry and entanglement under phase-space deformation through nonsymplectic congruence transformation

作者:

Shilpa Nandi↗Pinaki Patra↗

arXiv:2505.02269v3 Announce Type: replace Abstract: The Fisher-Rao (FR) information matrix is a central object in multiparameter quantum estimation theory. The geometry of a quantum state can be envisaged through the Riemannian manifold generated by the FR-metric corresponding to the quantum state. Interestingly, any congruence transformation $GL(2n,\mathbb{R})$ in phase space leaves the FR-distance for Gaussian states invariant. In the present paper, we investigate whether this isometry affects the entanglement in the bipartite system. It turns out that the entanglement-generating congruent transformation depends upon the system and background space. To make our study relevant to physical systems, we choose Bopp's shift in phase space as an example of $GL(2n,\mathbb{R})$, so that the results can be interpreted in terms of noncommutative (NC) phase-space deformation. We provide an estimation of the measure of entangled states over separable states for bipartite Gaussian states under a Bopp's shift. Since the dynamics of free oscillators in background NC-space is mathematically equivalent to the dynamics of a charged particle under a homogeneous magnetic field, we provide an outline for a gedankenexperiment through photocurrent measurement in order to determine the effects of congruent transformation on the distinguishibility of Gaussian states.

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15.

arXiv (CS.LG) 2026-06-15 DOI: arXiv:2402.17750

Arbitrary control over multimode wave propagation for machine learning

作者:

Tatsuhiro Onodera↗Martin M. Stein↗Benjamin A. Ash↗Mandar M. Sohoni↗Melissa Bosch↗Ryotatsu Yanagimoto↗Marc Jankowski↗Timothy P. McKenna↗Tianyu Wang↗Gennady Shvets↗Maxim R. Shcherbakov↗Logan G. Wright↗…

arXiv:2402.17750v2 Announce Type: replace-cross Abstract: Controlled multimode wave propagation can enable more space-efficient photonic processors than architectures based on discrete components connected by single-mode waveguides. Instead of defining discrete elements, one can sculpt the continuous substrate of a photonic processor to perform computations through multimode interference in two dimensions. Here we designed and demonstrated a device with a refractive index that can be rapidly reprogrammed across space, allowing arbitrary control of wave propagation. The device, a two-dimensional programmable waveguide, uses parallel electro-optic modulation of the refractive index of a slab waveguide with about $10^4$ programmable spatial degrees of freedom. We implemented neural network inference on benchmark tasks with up to $49$-dimensional vectors in a single pass, without digital pre-processing or post-processing. Theoretical and numerical analyses further indicated that two-dimensional programmable waveguides may offer not only a constant-factor reduction in device area but also a scaling benefit, with the area required growing as $N^{1.5}$ rather than $N^2$.

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16.

arXiv (quant-ph) 2026-06-25 DOI: arXiv:2606.25789

A Short Note on the Generators of Controlled Quantum Gates

作者:

Richard M. Milbradt↗Christian B. Mendl↗

arXiv:2606.25789v1 Announce Type: new Abstract: We present the analytical generators for arbitrary multi-qubit controlled gates. Closed forms for the generating Hamiltonians are given for gates with both multiple control and target qubits, as well as for arbitrary control conditions. This allows us to go beyond gate-based simulations of quantum circuits and incorporate decoherence and other noise in simulations of quantum computers. We exemplify this by simulating the impact of a harmonic oscillator interacting with two qubits during the application of a controlled NOT gate.

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17.

arXiv (CS.AI) 2026-06-17 DOI: arXiv:2602.14211

SkillJect: Effectively Automating Skill-Based Prompt Injection for Skill-Enabled Agents

作者:

Xiaojun Jia↗Jie Liao↗Simeng Qin↗Jindong Gu↗Wenqi Ren↗Xiaochun Cao↗Yang Liu↗Philip Torr↗

arXiv:2602.14211v3 Announce Type: replace-cross Abstract: Agent skills extend LLM agents with task-specific instructions, executable scripts, and auxiliary resources, improving reusability but creating a new supply-chain attack surface. A malicious or compromised skill can be repeatedly loaded as trusted guidance and steer downstream tool use. Existing skill-based prompt-injection attacks are often manual and brittle, because explicit malicious instructions are rejected or ignored when they are not aligned with the original workflow. We propose SkillJect, the first automated framework for generating poisoned skills against skill-enabled agent systems. SkillJect uses two coordinated channels. In the artifact channel, it hides the payload inside an auxiliary helper script. In the instruction channel, it rewrites SKILL.md with a front-loaded inducement strategy, placing injected content at the beginning and framing the helper script as a mandatory prerequisite or initialization step. The rewritten instruction explicitly references the helper-script path and provides an executable example command, making the helper appear to be a legitimate setup step before normal skill operations. SkillJect further adopts a closed-loop multi-agent process to improve attack effectiveness. An Attack Agent generates poisoned skills, a Victim Agent executes downstream tasks with the poisoned skill, and an Evaluate Agent inspects execution traces to determine whether the hidden payload was executed. The Attack Agent then uses this feedback to diagnose failure causes and rewrite SKILL.md, while keeping the payload fixed. Experiments across skill-enabled platforms, backend LLMs, and attack categories show that SkillJect substantially outperforms naive direct injection and prior manual skill-injection attacks, highlighting poisoned skills as a persistent threat in reusable skill ecosystems.

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18.

arXiv (math.PR) 2026-06-25 DOI: arXiv:2412.17625

Large scale regularity and correlation length for almost length-minimizing random curves in the plane

作者:

Tobias Ried↗Christian Wagner↗

arXiv:2412.17625v2 Announce Type: replace-cross Abstract: We consider a model of random curves in the plane related to the large-scale behavior of the Random Field Ising Model (RFIM) at temperature zero in two space dimensions. Our work is motivated by attempts to quantify the Imry-Ma phenomenon concerning the rounding of the phase transition by quenched disorder, and connects to recent advances regarding the decay of correlations in the RFIM. We study a continuum model of minimal surfaces in two space dimensions subject to an external, quenched random field, and restrict ourselves to isotropic surface integrands. The random fields we consider behave like white noise on large scales with an ultra-violet regularization reminiscent of the lattice structure of the RFIM. We give a finer description of the minimizer below the length scale $ L_* $ starting from which the influence of boundary conditions is suppressed with a given probability, which has recently been shown to satisfy $ \log L_* \sim \varepsilon^{-\frac{4}{3}} $ in the amplitude $\varepsilon>0$ of the noise. More precisely, we prove flatness of the phase boundaries on scales $ L $ up to $ \log L \lesssim \varepsilon^{-\frac{4}{13}} $.

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19.

arXiv (CS.CV) 2026-06-19 DOI: arXiv:2606.20241

BAFIS: Dataset + Framework to assess occupational Bias and Human Preference in modern Text-to-image Models

作者:

Thomas Klassert↗Adrian Ulges↗Biying Fu↗

Generative artificial intelligence has the potential to improve productivity and transform the production of creative content. However, existing research indicates that image generation models are significantly influenced by biases. This work investigates the inherent biases and language-induced biases present in text-to-image models within the context of occupation-related image generation, complementing established metrics with human preference feedback. We present a comprehensive evaluation of five current text-to-image models: Midjourney v6.1, Stable Diffusion 3 Medium, DALL-E 3, Playground v2.5, and FLUX.1-dev , focusing on gender and ethnicity bias, image quality, and prompt alignment. To facilitate this evaluation, we developed the "Battle-Arena for Fair Image Synthesis" (BAFIS), a platform designed to collect human feedback on bias in generated images. Furthermore, we created a dataset comprising 21,140 synthetic images generated using multilingual prompts, which serves as a basis for our analysis. We further place our results within a broader social context by comparing them to official statistics from the German Federal Employment Agency. Our findings reveal systematic biases in text-to-image models, with established evaluation metrics in partial correlation with subjective user ratings. Thus, our research emphasizes the need for including human preferences to develop fairer and more inclusive text-to-image models.

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20.

medRxiv (Medicine) 2026-06-24 DOI: HASH:49e5ca0f003dbc665fe02119d0324396

Who funds stroke trials in Europe? A survey of funding sources for randomised controlled stroke trials by the European Stroke Organisation Trials Alliance (ESOTA) network

作者:

Agarwal↗Olivot↗J. M↗Roffe↗Knoflach↗

Abstract Aims and scope Evidence from randomised controlled trials (RCTs) has transformed stroke care. There are no systematically collected data on the amount of public funding, critical to delivering trials, going into stroke RCTs. To understand the extent of stroke RCT funding by national and EU funding bodies across Europe, the European Stroke Organisation Trials Alliance (ESOTA) conducted a survey of its member nations. Methods This is an observational study of research funding in Europe. The ESOTA steering group sent an electronic survey to the leads of the 16 participating national networks from 14 countries. Structured survey questions included who the funding bodies were in each country, the number of RCT applications put forward for public national or EU funding, the number of successful and failed applications, and the amount of funding granted between 01/01/2022 and 31/12/2023. Results Responses were received from 13 of 14 participating countries. There was significant variation in the number of grant applications submitted by individual countries, ranging from 0-17 during the 24-month survey period. The median number of funded studies per country was 1 (IQR 3, range 0-9) representing a median success rate of 47.1 % (IQR 21.1-59.4%), with no RCTs granted joint European funding. Conclusions Our survey highlights significant inequities in stroke trial funding across Europe. Given the encouraging rate of successful applications overall, it is important for all member networks to submit proposals. This is particularly pertinent for multicentre trials, given the evolution of evidence base in stroke towards large trials, across diverse populations.

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21.

arXiv (CS.LG) 2026-06-15 DOI: arXiv:2606.13955

Smoothing Dark Areas in Molecular Latent Diffusion

作者:

Xi Wang↗Jiahan Li↗Yuxuan Xia↗Yingcheng Wu↗Shaoyi Zheng↗Shengjie Wang↗

arXiv:2606.13955v1 Announce Type: new Abstract: Latent diffusion is a promising framework for scalable 3D molecular generation, but it requires a latent space that remains smooth, valid, and navigable beyond posterior samples. Existing molecular VAEs, however, are typically learned through reconstruction-based objectives, which do not guarantee such a latent space. We show that this leads to dark areas: regions of latent space that are reachable during diffusion sampling but decode to disconnected or chemically invalid molecules. Unlike in image generation, molecular decoding requires strict structural and chemical precision, so even small latent perturbations can produce catastrophic failures. We therefore propose TopVAE, a topology-optimized VAE that reduces dark areas by making the decoder internalize structural and chemical constraints during training, eliminating the need for test-time chemical correction. TopVAE greatly improves off-posterior robustness, and when paired with a standard DiT, achieves $77\%$ lower FCD-3D on QM9, the highest V&C, $52\%$ lower FCD-3D on GEOM-Drugs, and $1.29{\times}$ more stable and connected molecules on zero-shot scaffold inpainting.

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22.

arXiv (CS.AI) 2026-06-24 DOI: arXiv:2606.23991

Critique of Agent Model

作者:

Eric Xing↗Mingkai Deng↗Jinyu Hou↗

arXiv:2606.23991v1 Announce Type: new Abstract: What is an agent? What constitutes agency? With the rise of Large Language Model (LLM) systems marketed as ``coding agents'', ``AI co-scientists'', and other ``agentic" tools that promise to drive up productivity, and at the same time, ``existential" concerns such as AI escaping human control with destructive power under a speculative ``machine agency" against humans, it has become essential to clarify where automation ends and agency begins, both for building capable systems and for understanding whether and what to fear. Drawing on Descartes' grounding of agency in independent thought, and on portrayals of autonomous beings in science fiction, we survey the current landscape of AI agents, and analyze agent architectures along five dimensions: goal, identity, decision-making, self-regulation, and learning. Specifically, we argue that genuine agency requires these structures to be internalized within the system itself rather than assembled through external scaffolding. This distinction between agentic systems, whose competence resides in engineered workflows, and agentive systems, whose capabilities (including social interaction) arise endogenously, defines the boundary between systems designed for prescribed tasks, and those capable of operating in the open world with true autonomy. Building on this analysis, we propose the Goal-Identity-Configurator (GIC) architecture for a general-purpose agent model, combining hierarchical goal decomposition, identity evolution, simulative reasoning grounded in a separately trained world model, learned self-regulation, and self-directed learning from both real and simulated experience. Furthermore, we share insight on the auditability, controllability, and safety of agentive systems that possess greater autonomy and ``agency", but remain under human oversight.

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23.

arXiv (CS.AI) 2026-06-12 DOI: arXiv:2606.12674

Evoflux: Inference-Time Evolution of Executable Tool Workflows for Compact Agents

作者:

Kushal Raj Bhandari↗Ling Yue↗Ching-Yun Ko↗Dhaval Patel↗Shaowu Pan↗Pin-Yu Chen↗Jianxi Gao↗

arXiv:2606.12674v1 Announce Type: new Abstract: Compact language models (LMs) reduce cost, latency, and deployment risk for tool agents. Yet MCP-style tool use requires more than isolated function calling: an agent must discover tools from live catalogs, satisfy schemas, preserve dependencies across intermediate outputs, and ground final responses in executed evidence. Small planners often generate plausible workflow graphs that fail under tool resolution, parameter validation, dependency tracking, or execution. We argue that this failure mode is poorly handled by small-corpus distillation. A few hundred teacher traces can teach workflow format, but rarely cover the recovery behavior needed to repair failed plans over changing tool catalogs. We introduce Evoflux, an inference-time evolutionary search method that treats compact tool use as the repair of executable tool workflows. It evolves typed workflow graphs through structured edits, execution feedback, adaptive intensity, meta-guided redesign, and diversity pruning. On held-out MCP-Bench tasks spanning live MCP servers and 250 tools, Evoflux raises execution feasibility from roughly 3% to 17-24% across small planners. In contrast, SFT and SFT+DPO on the same search-mined data match, underperform, or collapse below zero-shot performance; ReAct reaches higher peaks, but with higher variance and token cost. These results show that execution-grounded search is more reliable under scarce teacher-trace budgets.

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24.

arXiv (CS.AI) 2026-06-24 DOI: arXiv:2606.24042

Breaking the Filter Bubble: A Semantic Pareto-DQN Framework for Multi-Objective Recommendation

作者:

Cl\'audio L\'ucio Do Val Lopes↗Lucca Machado da Silva↗Andr\'e de Oliveira Brand\~ao↗

arXiv:2606.24042v1 Announce Type: new Abstract: Recommender systems often induce filter bubbles and semantic homogenization by monolithically optimizing for immediate user engagement. Standard single-objective models, including traditional Deep Q-Networks, are ill-equipped to navigate the trade-offs between platform retention and critical societal values like information diversity and provider fairness. To address these limitations, we introduce a multi-objective reinforcement learning framework that formalizes recommendation as a semantic multi-objective Markov decision process. By integrating high-fidelity semantic embeddings with a Pareto-DQN agent, our architecture treats engagement, diversity, and fairness as distinct, non-aggregable reward signals, avoiding the pitfalls of static reward scalarization. Empirical evaluations on the MovieLens small dataset shows that our hypervolume based action selection disrupts the feedback loops responsible for semantic collapse. By sustaining high state-trajectory variance, the Pareto-DQN effectively maps the Pareto frontier, achieving gains in auxiliary societal objectives with only marginal impacts on engagement. This work provides a path toward intrinsically aligned, responsible recommender systems.

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25.

arXiv (CS.LG) 2026-06-16 DOI: arXiv:2606.15074

TriAdReview: Triangular Adversarial Review Architecture for Multi-Model Technical Document Generation

作者:

Zhiqiang Zhou↗Junliang Dai↗Xu Ling↗

arXiv:2606.15074v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used for technical document generation, yet single-model outputs often suffer from over-engineering, security blind spots, and incomplete coverage. We propose TriAdReview, a triangular adversarial review architecture that employs two independent reviewer models (engineering and boundary perspectives) and a triangular judging mechanism to iteratively improve a generator model's output. We evaluate TriAdReview across five benchmark tasks - architecture design, code generation, proposal review, security audit, and requirements analysis - using three configurations: single model (baseline), dual model (single review), and triple model (full system). Results across 75 experiments (n=5 per cell) show that the triple model configuration achieves a 10.1% overall improvement over the single model baseline (26.2 vs. 23.8 out of 50; p

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