Academic Intelligence · Curated Daily

Explore the Frontier of Global Academia

AcademicHub aggregates real-time literature from top journals and preprint platforms. Build your personal research radar and let large language models compile cross-disciplinary analysis briefings automatically.

01.
arXiv (CS.CL) 2026-06-15

Indirect Computing Model with Indirect Formal Method

Authors:

This paper,from the perspective of a collaborative intelligent computing system formed by combining human-computer interface and collaborative computing programs, discusses the principles of optimized cloud computing technology supported by the combination of an indirect computing model and an indirect formal method. On the basis of systematically reviewing the influence of previous theoretical achievements Turing's computability theory,Kleene's formal theory of small strings,von Neumann's digital computer architecture and Turing's hypothesis on AI judgment on the mainstream general-purpose digital computer paradigm,the author focuses on introducing an indirect computing model and an indirect formal theory compatible with both large and small strings. Using Chinese information data as an example,the design concept of a collaborative intelligent computing system prototype is presented. The significance is that this achievement facilitates optimization of cloud computing from data centers to knowledge centers.

02.
arXiv (CS.AI) 2026-06-24

Zero-Shot Test-Time Canonicalization using Out-of-Distribution Scoring

arXiv:2606.24178v1 Announce Type: cross Abstract: Pretrained vision models often misclassify inputs that are rotated, scaled, or sheared, even though these affine transformations leave the object class unchanged. Robustness is usually restored either by building equivariance into the architecture or by retraining with augmentation, both of which require changing or retraining the model. Test-time canonicalization instead leaves the classifier untouched. It undoes the transformation of each input, mapping it to a canonical form near the training distribution before classification. Existing canonicalizers, however, rely on a narrow set of logit-based energy scores and bespoke search procedures, leaving the design space of scoring functions and optimizers unexplored. We reframe canonicalization as out-of-distribution (OOD) detection, which lets any OOD score serve as the energy minimized over transformations. Across benchmarks ranging from handwritten characters and sketches to natural images and 3D point clouds, we systematically evaluate around twenty OOD scores and nine search algorithms, finding that distance-based scores paired with random search and local refinement perform best overall. Because canonicalizing an already-aligned input can hurt accuracy, we add a gated mechanism that transforms an input only when its OOD score indicates this is needed, preserving most in-distribution accuracy while retaining the robustness gains on transformed inputs. Code is available at github.com/johschm/its.

03.
arXiv (CS.LG) 2026-06-16

Fast Non-Episodic Finite-Horizon RL with K-Step Lookahead Thresholding

arXiv:2602.00781v2 Announce Type: replace Abstract: Online reinforcement learning in non-episodic, finite-horizon MDPs remains underexplored and is challenged by the need to estimate returns to a fixed terminal time. Existing infinite-horizon methods, which often rely on discounted contraction, do not naturally account for this fixed-horizon structure. We introduce a modified Q-function: rather than targeting the full-horizon, we learn a K-step lookahead Q-function that truncates planning to the next K steps. To further improve sample efficiency, we introduce a thresholding mechanism: actions are selected only when their estimated K-step lookahead value exceeds a time-varying threshold. We provide an efficient tabular learning algorithm for this novel objective, proving it achieves fast finite-sample convergence: it achieves minimax optimal constant regret for $K=1$ and $\mathcal{O}(\max((K-1),C_{K-1})\sqrt{SAT\log(T)})$ regret for any $K \geq 2$. We numerically evaluate the performance of our algorithm under the objective of maximizing reward. Our implementation adaptively increases K over time, balancing lookahead depth against estimation variance. Empirical results demonstrate superior cumulative rewards over state-of-the-art tabular RL methods across synthetic MDPs and RL environments: JumpRiverswim, FrozenLake and AnyTrading. Code is provided on \href{https://github.com/jamie01713/K-Step-Lookahead}{github}.

04.
Science (Express) 2026-05-07

Induction of broadly neutralizing HIV antibodies by a two-step mechanism informs vaccine design | Science

Authors: Unknown Author

A major obstacle confronting HIV-1 vaccine and cure research is the lack of an outbred animal model for rapid and consistent induction of broadly neutralizing antibodies (bNAbs). We designed an epitope-focused simian-human immunodeficiency virus (SHIV.5MUT) that elicited broad and potent V3-glycan-targeted antibodies within a year of infection in 14 of 22 macaques compared with 0 of 14 control animals. SHIV.5MUT elicited bNAbs by a two-step mechanism, inducing an initial wave of V1-directed antibodies that selected for Envs with shortened, hypoglycosylated V1 loops, which in turn primed V3-glycan bNAb precursors. Rhesus bNAbs were immunogenetically and structurally diverse, closely resembling human V3-glycan bNAbs. Env-bNAb coevolution revealed a diverse repertoire of bNAb precursors and the Env variants that matured them, yielding a molecular blueprint for vaccine design.

05.
arXiv (math.PR) 2026-06-24

Sparsity-adaptive concentration inequalities for random polynomials

arXiv:2606.24090v1 Announce Type: new Abstract: We prove concentration inequalities for polynomials of independent, sparse $\alpha$-sub-exponential random variables. Specifically, we consider $X_i=\delta_i\xi_i$, where the Bernoulli selectors $\delta_i$ are independent with parameters $p_i$, and the variables $\xi_i$ are independent \(\alpha\)-sub-exponential random variables (not necessarily centered). For any polynomial $f:\mathbb R^n\to\mathbb R $ of degree at most $D$ and any $0

06.
arXiv (CS.LG) 2026-06-16

Dual-Network PINNs for Optimal Control: A Reproducible Benchmark on the Mass-Spring-Damper System

arXiv:2606.15271v1 Announce Type: cross Abstract: This work presents a transparent and reproducible benchmark study of a direct dual-network Physics-Informed Neural Network (PINN) formulation for the optimal control of a mass-spring-damper system. The classical linear-quadratic optimal control problem is solved by two independent classical methods – Pontryagin's Minimum Principle with single shooting, and direct transcription through trapezoidal collocation – and recast as a constrained optimization problem solved by two feedforward neural networks: a state network whose boundary conditions are enforced exactly through a composite cubic-and-mask ansatz, and an unconstrained control network. The composite loss combines the physics residual at the collocation points with a trapezoidal approximation of the cost functional, weighted by a single scalar hyperparameter. On the benchmark considered, the PINN reproduces the classical optimal cost to four significant digits, satisfies the terminal state constraints exactly by construction, and produces pointwise state and control errors that fall within the spread of the two classical references. Training is approximately two orders of magnitude slower than classical shooting on this benchmark, which is honestly reported. The contribution is methodological clarity rather than methodological novelty: the formulation and the accompanying Google Colab implementation are intended to lower the barrier to entry for practitioners exploring PINN-based optimal control without prior exposure to adjoint methods or two-point boundary value problems.

07.
arXiv (CS.AI) 2026-06-17

Functional Equivalence in Attention: A Comprehensive Study with Applications to Linear Mode Connectivity

arXiv:2606.17830v1 Announce Type: cross Abstract: Neural network parameter spaces are inherently non-injective, as distinct parameter configurations can realize identical functions through functional equivalence. While this symmetry is well understood in classical fully connected and convolutional models, it becomes substantially more intricate in modern attention-based architectures. Existing analyses of multihead attention have largely focused on the vanilla formulation, overlooking positional encodings that fundamentally reshape architectural symmetries. In this work, we provide a formal study of functional equivalence in Transformers with positional encodings. Focusing on the two most widely used variants–sinusoidal and rotary positional encodings (RoPE)–we show that sinusoidal encodings preserve the equivalence structure of vanilla attention, whereas rotary encodings significantly reduce the symmetry group, thereby enhancing expressivity. This offers a principled explanation for the growing prominence of RoPE in practice. We further examine how positional encodings affect linear mode connectivity, and through an alignment algorithm, empirically demonstrate that the presence and variability of connectivity across Transformer settings crucially depend on the positional encoding.

08.
arXiv (quant-ph) 2026-06-24

Exact log-depth preparation of highly entangled matrix product states

arXiv:2606.24475v1 Announce Type: new Abstract: Preparing matrix product states (MPS) on a quantum device is a key subroutine in many quantum algorithms. The most competitive methods, based on the renormalisation group, prepare translationally invariant MPS of size $L$ and bond dimension $\chi$, up to an error $\varepsilon$, in circuit depth $\tilde O(\chi^{4}\log(L/\varepsilon))$ or $\tilde O(\chi^{6}\log\log(L/\varepsilon))$. We improve multiple aspects of these methods. First, using block-encoded correction maps, whose post-selection succeeds with constant probability, we render the preparation exact without sacrificing the scaling in $L$. Second, through a generalisation of oblivious amplitude amplification to isometries, we reduce the bond-dimension dependence, improving the depth to $\tilde O(\chi^{2}\log L + \chi^{4})$ or $\tilde O(\chi^{2}\log\log L + \chi^{4})$, and even to $\tilde O(\chi^{3}\log L)$ for incoherent preparations. Finally, we extend the framework to non-translationally invariant MPS and prove logarithmic-depth exact preparation for independent and identically distributed random tensor sequences. Confirmed by numerical studies, these results constitute, to the best of our knowledge, the most efficient exact MPS preparation protocols in the relevant parameter regimes.

09.
arXiv (CS.CL) 2026-06-16

Cloze: An Open Research Platform for Studying Human-AI Conversations in Mental Health Contexts

Cloze is an open-source web platform for conducting controlled, monitored studies of human-AI conversation in mental health research contexts. Consumer large language model (LLM) products such as ChatGPT, Claude, and Gemini are built for individual productivity, and offer researchers little experimental control, inconsistent data export, and no shared safety scaffolding that holds across providers. Cloze gives research teams a single environment in which they configure which models participants converse with, how the AI is instructed, how conversations are scheduled over time, and which safety constraints apply unconditionally, while every message is captured with full provenance (model version, prompt configuration, timing). The platform currently supports OpenAI, Anthropic, Google, and locally hosted open-weight models served through Ollama behind a unified interface, and runs in the cloud or fully on premises so that participant data need never leave an institution. Cloze is research infrastructure for building an evidence base on human-AI interaction in mental health contexts. It is not a therapeutic product.

10.
arXiv (CS.CV) 2026-06-24

Do Foundation Models See Biology? Evaluating Attention Coherence with Spatial Transcriptomics in Glioblastoma

Whether attention maps from pathology foundation models capture genuine biology remains unknown, yet this question is critical for clinical trust and regulatory approval. We propose a spatial transcriptomics-based framework for orthogonal, hypothesis-free evaluation of attention and apply it to five pathology foundation models (CONCH v1.5, UNI v2, Virchow2, GigaPath, H-Optimus-1) and a ResNet50 baseline. Using attention-based multiple instance learning, we train single-task and multi-task models to predict five molecular alterations in glioblastoma on the CPTAC cohort, validate on an independent TCGA cohort, and evaluate biological coherence of attention maps against 87 transcriptional signatures using co-registered Visium spatial transcriptomics data from 18 samples. Internally, no single encoder dominates across all tasks, and external validation inverts internal performance rankings. Attention maps show a five-fold enrichment gradient from pathways (Cohen's d=0.329) to individual genes (d=0.055), indicating that attention captures emergent multi-gene transcriptional programs rather than individual molecular events. Spatially smooth attention maps do not imply biological coherence, and different encoders attend to distinct biological compartments. Our framework provides objective, quantitative assessment of what foundation models learn from histopathology, moving the field beyond qualitative saliency map review.

11.
arXiv (CS.CL) 2026-06-16

Speaking the Language of Science: Toward a General-Purpose Generative Foundation Model for the Natural Sciences

In this report, we present LOGOS (Language Of Generative Objects in Science), a scientific generative language model that unifies heterogeneous tasks across the natural sciences within a single autoregressive framework based on a shared scientific grammar. It encodes diverse scientific objects and their spatial interactions as token sequences over a common vocabulary. By representing spatial contact and constraint patterns as discrete tokens, the model captures complex structural interactions in a purely sequential manner, without relying on explicit coordinates or geometric neural networks. This unified representation enables a wide range of downstream tasks to be formulated consistently as next-token prediction in the same grammar space, creating strong alignment between continued multi-domain pre-training and downstream objectives. Across diverse tasks, LOGOS consistently matches or outperforms domain-specific baselines, providing preliminary evidence for the feasibility of "one model fits all" in the natural sciences. We train LOGOS models at different scales (1B, 3B, and 8B parameters) and find a consistent positive correlation between model size and performance. This suggests that the future of AI for Science (AI4S) may not lie in building an independent technical stack that is separated from large language models (LLMs). Instead, it may depend on deeply aligning scientific foundation models with LLMs through shared architectures, shared training paradigms, and shared inference infrastructure, so that LLMs can truly become a new entry point for AI4S. We release the model weights and associated resources to facilitate further research.

12.
arXiv (math.PR) 2026-06-18

On a class of reflected McKean-Vlasov Stochastic Differential Equations with jumps

arXiv:2606.18433v1 Announce Type: new Abstract: This paper investigates a class of reflected McKean-Vlasov Stochastic Differential Equations driven by both Brownian motion and a compensated Poisson random measure. We establish the existence and uniqueness of solutions and provide moments estimates for the state processes.

13.
bioRxiv (Bioinfo) 2026-06-14

Robust integration of weakly anchored spatial multi-omics

Spatial multi-omics holds great promise for dissecting complex biological processes, though inherent technical constraints continue to limit its widespread adoption. Currently, most studies therefore measure distinct omics features on separate tissue sections, necessitating spatial diagonal integration. An emerging practical solution is to leverage hematoxylin and eosin (H&E) images as an integration anchor, given their ubiquity, low cost, and compatibility across tissue preparations. However, this anchor is frequently compromised in real-world settings by variations in H&E staining style, absence of reliable histological landmarks, and mismatches in spatial resolutions across omics modalities. To address this, we introduce SpaWeaver, a computational framework that couples a pathology foundation model with a graph Transformer and a latent feature aligner module, providing a highly robust solution for weakly anchored spatial omics data diagonal integration. Extensive experiments demonstrate that SpaWeaver exhibits superior robustness against isolated or synergistic weak-anchoring factors. The spatial multi-omics profiles generated by SpaWeaver link molecular features originally separated on two sections, unlocking diverse downstream analyses once exclusive to co-assayed spatial multi-omics data, including niche-aware cell-cell communication inference and multi-omics resolved cell state. In this study, it unveils tumor-distance-dependent fibroblast-CD4+ T-cell signaling in human colon adenocarcinoma and identifies a hypoxic glycolytic tumor state with pyknotic nuclei in human ovarian cancer. Overall, our approach bridges readily accessible single-omics measurements across weakly anchored tissue sections, enabling unified spatial multi-omics characterization and system-level tissue analysis.

14.
arXiv (CS.AI) 2026-06-18

The Long Delay to Arithmetic Generalization: When Learned Representations Outrun Behavior

arXiv:2604.13082v2 Announce Type: replace-cross Abstract: Grokking in transformers trained on algorithmic tasks is characterized by a long delay between training-set fit and abrupt generalization, but the source of that delay remains poorly understood. In encoder-decoder arithmetic models, we argue that this delay reflects limited access to already learned structure rather than failure to acquire that structure in the first place. We study one-step Collatz prediction and find that the encoder organizes parity and residue structure within the first few thousand training steps, while output accuracy remains near chance for tens of thousands more. Causal interventions support the decoder bottleneck hypothesis. Transplanting a trained encoder into a fresh model accelerates grokking by 2.75 times, while transplanting a trained decoder actively hurts. Freezing a converged encoder and retraining only the decoder eliminates the plateau entirely and yields 97.6% accuracy, compared to 86.1% for joint training. What makes the decoder's job harder or easier depends on numeral representation. Across 15 bases, those whose factorization aligns with the Collatz map's arithmetic (e.g., base 24) reach 99.8% accuracy, while binary fails completely because its representations collapse and never recover. The choice of base acts as an inductive bias that controls how much local digit structure the decoder can exploit, producing large differences in learnability from the same underlying task.

15.
medRxiv (Medicine) 2026-06-11

Impact of Out-Migration and Remittances on Food Consumption Outcomes among Rural Households in Tigray, Ethiopia

Authors:

This study examines the effects of rural out-migration and remittance inflows on food consumption outcomes among rural households in the Tigray region of Ethiopia. Utilizing household survey data collected from 521 rural households across three distinct Weredas (districts) (Tahtay Maichew, Kola Tembien, and Kilte-awlaelo). A Binary Probit model was employed to identify factors influencing migration decisions, while an Endogenous Switching Regression (ESR) model was used to estimate the impact of migration on food consumption outcomes while controlling for selection bias and unobserved heterogeneity. Food security was measured using the Food Consumption Score (FCS) and dietary diversity indicators. The empirical results reveal that severe food insecurity is widespread, with over 60% of all surveyed households falling into the "Poor" food consumption category. Descriptive baseline comparisons show that migration and remittance transfers marginally shift the raw average FCS upward from 23.86 to 25.48. However, this impact is profoundly nuanced: remittances serve as an immediate consumption-smoothing safety net but run parallel to a "labor-lost" constraint that reduces own-production capacities, forcing households to rely increasingly on market purchases for staple foods. The findings reveal that migration creates short-term labor shortages in agricultural production; however, remittance inflows substantially improve household food consumption frequencies, particularly for pulses, vegetables, and other nutrient-rich foods. After accounting for self-selection bias and unobserved traits, the rigorous ESR estimates indicate that migration increases the Food Consumption Score of participating households by an average Treatment Effect on the Treated (ATT) of 10.75 points, shifting them into more secure dietary tiers. Moreover, remittances help households mitigate the adverse effects of drought and other shocks by relaxing liquidity constraints and supporting both food purchases and agricultural investments. The study recommends establishing target food security safety nets for non-remittance households, promoting scale-appropriate labor-saving agricultural technologies, expanding traditional communal labor-sharing innovations, and boosting irrigation and agricultural input support programs to enhance rural food security and livelihood resilience.

16.
bioRxiv (Bioinfo) 2026-06-12

Computational Design of Optimal Sequences for Targeted Hypermutagenesis Using Recombination-Coupled Diversity-Generating Retroelements

Diversity-generating retroelements (DGRs) are natural systems that accelerate evolution via targeted hypermutation at adenines. We previously developed DGRec, a system combining DGRs and recombineering for programmable mutagenesis in Escherichia coli. We here address two important issues with DGRec: the dependence of mutagenesis efficiency on the dgrRNA secondary structure and the variability of the reverse-transcription biases with sequence context and position. First, we introduce and validate a method to recode non-functional templates, i.e. with low mutagenesis efficiency, into highly functional ones through synonymous mutations. Second, we develop a Long Short-Term Memory (LSTM) model to predict DGRec mutational profiles for any given template sequence. By integrating this LSTM model with our recoding method, we establish a comprehensive workflow for customized directed evolution, enabling researchers to precisely fine-tune DGRec in vivo mutagenesis to their engineering needs.

17.
arXiv (CS.LG) 2026-06-15

Beyond task performance: Decoding bioacoustic embeddings with speech features

arXiv:2606.14662v1 Announce Type: new Abstract: Pretrained audio embeddings are standard in bioacoustics, yet little is known about which acoustic features these models encode, nor which are useful for a given task. This hinders transparency and limits extension to rare species or data-scarce domains. Here we reveal which speech-like features are encoded in bioacoustic representations. Using the 88~eGeMAPS features across six taxonomic groups, we apply linear and nonlinear regression probes to quantify which acoustic properties each model captures. Results confirm a ``no free lunch'' pattern: no single model captures the full feature space. A concatenated embedding achieves the highest performance, suggesting complementary acoustic space coverage across models. Loudness features are best encoded ($R^2 = 0.76$) while F0 is hardest to recover ($R^2 = 0.33$). By cross-referencing recoverability with per-species feature salience (NMI), we derive data-driven model selection guidance for bioacoustics.

18.
arXiv (CS.LG) 2026-06-24

SkyJEPA: Learning Long-Horizon World Models for Zero-Shot Sim-to-Real Control of Quadrotors

arXiv:2606.23444v2 Announce Type: replace-cross Abstract: Accurate dynamics models are critical for informed decision-making in robotic systems, particularly for agile aerial vehicles operating under uncertainty. Neural network dynamics models are attractive for capturing complex nonlinear effects, but existing predictive approaches struggle with long-horizon forecasting because their autoregressive rollout mechanism amplifies errors over time. Joint Embedding Predictive Architectures (JEPAs) offer a compelling alternative by modeling dynamics in latent space, yet prior JEPA-style methods for robot navigation have been studied primarily for kinematic-level planning, with limited investigation in high-frequency control. In this work, we introduce the JEPA-style model for real-time quadrotor control. The proposed approach combines a latent dynamics model with a novel physics-inspired prober that maps frozen latents to interpretable state, enabling physically grounded long-horizon prediction. Additionally, we combine the learned model with a sampling-based optimal control solution to take advantage of its predictive capabilities for real-time control on embedded hardware. Finally, to reduce the dependence on expensive and unsafe real-world data collection, we develop a structured pipeline for automated dataset generation. Extensive open-loop and outdoor closed-loop experiments demonstrate accurate prediction, robust zero-shot sim-to-real transfer, and strong generalization across diverse operating conditions.

19.
arXiv (CS.AI) 2026-06-11

Offline Diffusion Policy for Multi-User Delay-Constrained Scheduling

arXiv:2501.12942v2 Announce Type: replace Abstract: Effective multi-user delay-constrained scheduling is crucial in various real-world applications, including embodied AI, instant messaging, live streaming, and data center management, where efficient resource allocation is required among users with diverse delay sensitivities. In these scenarios, schedulers must make real-time decisions to satisfy both delay and resource constraints without prior knowledge of system dynamics, which are often time-varying and challenging to estimate. {Current learning-based methods typically require online interactions with actual systems during the training stage. Therefore, these approaches are often difficult or impractical, as they can significantly degrade system performance and incur substantial service costs.} To address these challenges, we propose a novel offline reinforcement learning-based algorithm, named \underline{S}cheduling By \underline{O}ffline Learning with \underline{C}ritic Guidance and \underline{D}iffusion Model (SOCD), to learn efficient scheduling policies purely from pre-collected offline data. SOCD innovatively employs a diffusion policy, complemented by a sampling-free critic network for policy guidance. By integrating the Lagrangian multiplier optimization into the offline reinforcement learning, SOCD efficiently trains high-quality constraint-aware policies exclusively from available datasets, eliminating the need for online interactions with the system. Experimental results demonstrate that SOCD is resilient to various system dynamics, including partially observable and large-scale environments, and delivers superior performance compared to existing methods.

20.
arXiv (math.PR) 2026-06-12

Averaging principles for nonautonomous multiscale McKean-Vlasov stochastic systems

arXiv:2606.12820v1 Announce Type: new Abstract: This paper investigates a class of nonautonomous multiscale McKean-Vlasov stochastic systems. By leveraging the nonautonomous Poisson equation, we rigorously establish both strong and weak averaging principles, accompanied by explicit convergence rates. Notably, the coefficients of the averaging equations derived in the general case retain dependence on the scaling parameter $\varepsilon$. However, under the additional assumptions that the fast-scale coefficients are either asymptotically convergent or time-periodic, we demonstrate that the slow component converges, in the strong or weak sense, to averaging equations with coefficients independent of $\varepsilon$.

21.
arXiv (math.PR) 2026-06-18

A random recursive tree model with doubling events

arXiv:2501.18466v3 Announce Type: replace Abstract: We introduce a new model of random tree that grows like a random recursive tree, except at some exceptional "doubling events" when the tree is replaced by two copies of itself attached to a new root. We prove asymptotic results for the size of this tree at large times, its degree distribution, and its height profile. We also prove a lower bound for its height. Because of the doubling events that affect the tree globally, the proofs are all much more intricate than in the case of the random recursive tree in which the growing operation is always local.

22.
arXiv (CS.CV) 2026-06-12

CPAM: Context-Preserving Adaptive Manipulation for Zero-Shot Real Image Editing

Editing natural images using textual descriptions in text-to-image diffusion models remains a significant challenge, particularly in achieving consistent generation and handling complex, non-rigid objects. Existing methods often struggle to preserve textures and identity, require extensive fine-tuning, and exhibit limitations in editing specific spatial regions or objects while retaining background details. This paper proposes Context-Preserving Adaptive Manipulation (CPAM), a novel zero-shot framework for complicated, non-rigid real image editing. Specifically, we propose a preservation adaptation module that adjusts self-attention mechanisms to preserve and independently control the object and background effectively. This ensures that the objects' shapes, textures, and identities are maintained while keeping the background undistorted during the editing process using the mask guidance technique. Additionally, we develop a localized extraction module to mitigate the interference with the non-desired modified regions during conditioning in cross-attention mechanisms. We also introduce various mask-guidance strategies to facilitate diverse image manipulation tasks in a simple manner. CPAM can be seamlessly integrated with multiple diffusion backbones, including SD1.5, SD2.1, and SDXL, demonstrating strong generalization across different model architectures. Extensive experiments on our newly constructed Image Manipulation BenchmArk (IMBA), a robust benchmark dataset specifically designed for real image editing, demonstrate that our proposed method is the preferred choice among human raters, outperforming existing state-of-the-art editing techniques. The source code and data will be publicly released at the project page: https://vdkhoi20.github.io/CPAM

23.
bioRxiv (Bioinfo) 2026-06-22

Few-Shot Classification of C. elegans Developmental Stages via Explainable Hierarchical Hyperbolic Graph Embeddings

Automated, accurate, and fast developmental-stage classification of C. elegans from microscopy-based morphological images is essential for aging research, drug screening, and disease modeling. However, it remains challenging due to morphological similarities between stages and the limited annotated data. In this work, we propose HyperDev, a hyperbolic few-shot learning framework that addresses these limitations by directly encoding developmental hierarchies in the embedding space, unlike conventional Euclidean approaches that treat stages as independent classes. HyperDev uses Poincare ball geometry, combined with a biologically informed developmental prior, to naturally represent stage relationships. We introduce our selfcurated C. elegans dataset spanning seven developmental stages (Egg, L1-L4, Adult, Dauer) with extreme class imbalance (6-8 samples per minority class). HyperDev achieves competitive classification accuracy (76.9-88.3%) while providing intrinsic explainability across nine 7-way few-shot evaluation settings. The learned embeddings exhibited strong biological alignment (Pearson r = 0.669, p < 0.001), while significantly outperforming ProtoNet (r = 0.187), MatchingNet (r = 0.235), and RelationNet (r = 0.464). These results establish hyperbolic geometry as a principled approach to explainable few-shot learning in biological imaging, where understanding learned representations is as critical as predictive performance. Clinical Relevance–By enabling explainable, data-efficient developmental staging from scarce samples, HyperDev supports improved phenotype quantification for aging research, disease modeling, and drug screening. Index Terms–Hyperbolic learning, few-shot classification, developmental staging, Caenorhabditis elegans, interpretability, explainability.

24.
arXiv (CS.AI) 2026-06-17

LineageMark: Multi-user White-box Watermarking for Contribution Tracing in Model Derivation Chains

arXiv:2606.17123v1 Announce Type: cross Abstract: In open large language model (LLM) ecosystems, models are frequently adapted across multiple domains and applications, forming multi-stage derivation chains. Consequently, tracking and verifying historical contributions is essential for model provenance and intellectual property protection. However, existing watermarking methods are mainly designed for single-user, one-time embeddings, often fail under repeated model derivation and incremental updates. To address this problem, we propose LineageMark, a multi-user white-box watermarking framework for model derivation chains. The framework encodes watermarks in model parameters using a projection-based approach. Stable carriers are first selected to reduce sensitivity to model changes, each watermark bit is then represented as a projection statistic over these carriers. Additional watermark insertions introduce only bounded perturbations in the projection space, and margin constraints are used to maintain signal integrity. We evaluate the effectiveness of LineageMark in multi-stage model derivation chains. Experimental results show that LineageMark preserves contributor watermarks across multi-stage derivation and supports incremental multi-user watermark insertion. Furthermore, it exhibits robustness against perturbations such as re-watermarking, fine-tuning, quantization, and pruning.

25.
arXiv (quant-ph) 2026-06-17

Demonstration of Exponential Quantum Speedup with Constant-Depth Compiled Circuits for Simon's Problem

arXiv:2604.27457v2 Announce Type: replace Abstract: We demonstrate exponential algorithmic quantum speedup for a restricted-Hamming-weight version of Simon's problem, in which the hidden string $b$ is promised to satisfy $HW(b)\le w$ for a Hamming-weight cutoff $w$, on present-day superconducting quantum processors. We introduce a hardware-aware compilation strategy that reduces the quantum part of each Simon query circuit to constant depth. The resulting compiled circuits have $O(1)$ depth, require only linear nearest-neighbor connectivity, map directly onto common device layouts, and avoid additional routing and SWAP overhead. Implemented on IBM's $156$-qubit Boston and $120$-qubit Miami processors, these circuits achieve sufficient fidelity to exhibit algorithmic quantum speedup without error suppression. Using the number-of-queries-to-solution (NTS) metric, we observe exponential speedup over the classical lower-bound benchmark for all restricted-Hamming-weight cutoffs $w\ge 4$ on Boston and across low-to-intermediate Hamming-weight cutoffs on Miami; at higher Hamming-weight cutoffs on Miami, we still observe polynomial speedup. The same construction also enables unrestricted instances of Simon's problem, corresponding to $w=n$ for problem size $n$, over the finite problem-size ranges for which our NTS computation is feasible; in this regime, the observed scaling advantage is not limited to the restricted-Hamming-weight setting. These results show that careful hardware-aware compilation can make quantum speedup experimentally accessible for a canonical hidden-subgroup problem in the NISQ regime.