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01.

medRxiv (Medicine) 2026-06-17 DOI: HASH:53ff6afa91deea4280552d9ef6b18054

Investigating shared genetic overlap of immune-mediated inflammatory diseases and cardiometabolic diseases

作者:

Alduhayhi↗S. S↗Morris↗A. P↗Zhao↗Bowes↗

Abstract Background: Immune-mediated inflammatory diseases (IMIDs) are associated with increased risk of cardiometabolic diseases. Investigating genetic overlap among these conditions can provide insights into their clinical management. Methods: Genetic correlation was assessed using linkage disequilibrium score regression (LDSC). Then, a meta-analysis was conducted using Association Analysis Based on SubSETs (ASSET) to pinpoint independent single nucleotide polymorphisms (SNPs) shared across the diseases. Each independent SNP was then used to define a genomic window (+/-500KB) for colocalisation analysis and Local Analysis of [co]Variant Association (LAVA) to offer multiple layers of regional pleiotropic evidence. Over-representation analysis was then run to identify enriched biological pathways, which then were used for drug target analysis. Results: The LDSC analysis showed a significant global genetic correlation for rheumatoid arthritis (RA) and cardiometabolic diseases including hypertension, coronary artery disease (CAD), heart failure (HF), stroke, atrial fibrillation (AF), and type two diabetes mellitus (T2DM) ranging from rg = 0.09 to 0.24. ASSET meta-analysis identified 164 independent SNPs shared across RA and the cardiometabolic diseases with P < 5 x 10- in the overall one-sided meta-analysis P-value, FDR < 0.05 in both individual GWASs, and TRUE phenotype matrix. Colocalisation analysis revealed multiple loci with strong evidence (Posterior probabilities [&ge;] 80) of single causal SNPs between the trait pairs. LAVA analysis was then used as an additional layer of confirmation for the findings generated by ASSET and colocalisation and thus several loci were highlighted. Over-representation analysis showed significant enriched immune-related pathways across RA-hypertension, RA-CAD, RA-AF, and RA-T2DM trait pairs. Drug target analysis highlighted several drugs which could be further tested for their effectiveness in RA and its common comorbidities. Conclusion: The findings revealed a shared genetic architecture and key immune-related biological pathways underlying RA and its associated cardiometabolic comorbidities. The identified genes and drugs provide opportunities for further therapeutic assessment which could improve clinical management strategies.

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02.

arXiv (CS.CV) 2026-06-16 DOI: arXiv:2606.16690

PATCH: Action-Chunk-Conditioned Latent Patch Innovation Monitoring for Robot Manipulation

作者:

Yanan Zhou↗Ranpeng Qiu↗Yincong Chen↗Jiajie Cui↗Weiming Zhi↗

Learning-based manipulation policies have made substantial progress in real-world robot manipulation, particularly for short-horizon action generation. However, deployment in open workspaces remains fragile under unexpected local scene dynamics, such as moving objects, transient occlusions, or disturbances near the intended motion. Existing runtime monitors often rely on global observation anomalies, policy uncertainty, or frame-level visual changes, and struggle to distinguish task-relevant execution risk from benign visual variation. We introduce PATCH, an action-chunk-conditioned latent patch innovation monitor for deployment-time intervention. Given the active action chunk, PATCH defines a projected execution corridor, predicts latent patch evolution inside it, and accumulates persistent residuals unexplained by the robot's own motion. These residuals form a localized intervention signal that allows PATCH-Router to pause execution, select an available recovery source, and resume the original policy once localized innovation subsides. Experiments on real robot rollout data show that PATCH produces more stable and context-relevant triggers than competing runtime monitors. Real-robot deployment further demonstrates monitor-driven intervention and policy resumption for disturbance-aware manipulation. Project Page: https://yananzhou5555.github.io/PATCH/.

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03.

arXiv (quant-ph) 2026-06-15 DOI: arXiv:2606.13778

Resurgence of the Thermal Transition between Bounce and Sphaleron

作者:

Shaun D. Hampton↗Kyungsun Lee↗Sungjay Lee↗

arXiv:2606.13778v1 Announce Type: cross Abstract: We study the thermal transition between the bounce and the sphaleron in quantum mechanics with a metastable vacuum from the viewpoint of Borel resurgence. For two models representing a second-order and a first-order transition, we compute the perturbative expansion of the thermal free energy to high orders and extract the leading Borel singularity data $(A,b,S)$ as functions of temperature. The Borel singularity location $A$ reproduces the on-shell action of the dominant saddle on both sides of the transition, joining smoothly in the second-order case and developing a kink in the first-order case. The characteristic exponent $b$ jumps between $0$ and $1/2$ across the transition, counting the zero modes of the corresponding saddle. The Stokes constant $S$ matches the one-loop determinant around the saddle. The perturbative expansion around the false vacuum thus determines the transition temperature, the order of the transition, and the decay rate including the one-loop prefactor without relying on semiclassical inputs.

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04.

arXiv (CS.CV) 2026-06-16 DOI: arXiv:2606.14926

FlexPooling with Simple Auxiliary Classifiers in Deep Networks

作者:

Muhammad Ali↗Omar Alsuwaidi↗Salman Khan↗

In computer vision, the basic pipeline of most convolutional neural networks consists of multiple feature extraction layers, where the input signal is downsampled to a lower resolution in each subsequent layer. This downsampling process is commonly referred to as pooling, which is an essential operation in CNNs. Pooling improves robustness against transformations, reduces the number of trainable parameters, increases the receptive field, and lowers computation time. Since pooling is a lossy process but remains important for extracting high-level information from low-level representations, it is important to preserve the most prominent information from previous activations to improve network discriminability. Standard pooling is usually performed using dense pooling methods, such as max pooling or average pooling, or through strided convolutional kernels. In this paper, we propose a simple yet effective adaptive pooling method, called FlexPooling, which generalizes average pooling by learning a weighted average over activations jointly with the rest of the network. We further show that attaching Simple Auxiliary Classifiers (SAC) to the CNN improves performance and demonstrates the effectiveness of the proposed method compared with standard pooling methods. Experiments on multiple popular image classification datasets show that FlexPooling consistently outperforms baseline networks, achieving approximately 1 to 3 percent improvement in accuracy.

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05.

arXiv (quant-ph) 2026-06-16 DOI: arXiv:2606.15558

Degeneracy Cannot Violate the Quantum Hamming Bound

作者:

Yu-Xuan Zhang↗Jing-Ling Chen↗

arXiv:2606.15558v1 Announce Type: new Abstract: The quantum Hamming bound is the standard finite-length sphere-packing bound for exact correction of arbitrary qubit errors. Whether degeneracy can evade this bound has remained unresolved in full generality for nearly three decades: distinct correctable errors may act identically on the code space, so the usual disjoint-sphere argument breaks down. We prove that every exact binary quantum subspace code with $K>1$ obeys the bound, without assuming either nondegeneracy or additivity. Our proof turns the Li–Xing linear-programming polynomial into an exact intersection count for quaternary Hamming balls. Monotonicity in block length and in ball-center separation then reduces the problem to a local node–edge charging inequality at the shortest admissible length. Thus degeneracy can merge correctable error sectors, but cannot enlarge the finite-length binary Hamming bound.

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06.

arXiv (CS.CL) 2026-06-18 DOI: arXiv:2606.19051

Which Sections of a Research Paper Best Reveal Its Research Methods? Evidence from Library and Information Science

作者:

Qiuyu Fang↗Jiayi Hao↗Chengzhi Zhang↗

Research methods are essential carriers of knowledge contribution in academic papers. Automatic multi-label classification of research methods can support knowledge services such as method retrieval, review generation, and research intelligence analysis. While existing studies primarily rely on titles and abstracts, abstracts often provide only limited methodological information, whereas utilizing full-text content faces challenges related to excessive length and information redundancy. Therefore, this paper proposes a segment combination strategy by partitioning the full-text content according to its physical postion. Using an annotated corpus of 1,954 full-text articles from three representative journals in Library and Information Science (JASIST, LISR, and JDoc), we evaluate the classification performance of various segments and their combinations across multiple models. Experimental results indicate that methodological information is distributed unevenly within the full-text content, with the middle-to-late and final segments exhibiting greater discriminative power. Furthermore, integrating bibliographic metadata with cross-segment combination strategies effectively enhances classification performance.

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07.

arXiv (CS.AI) 2026-06-19 DOI: arXiv:2606.20210

Augmenting Game AI with Deep Reinforcement Learning

作者:

Alessandro Sestini↗Joakim Bergdahl↗Amir Baghi↗Jean-Philippe Barrette-LaPierre↗Florian Fuchs↗Linus Gissl\'en↗

arXiv:2606.20210v1 Announce Type: new Abstract: Immersion in video games depends not only on graphics, audio, and game mechanics, but also on the quality of in-game characters. Producing believable characters, or game AI, remains a significant challenge as behavioral complexity is hard to capture with hand-coded systems. Game AI is a source of immersion and engagement; however, the limitations stemming from the challenges of creating game AI often lead to frustration and the breaking of the illusion of realism within the game. The introduction of machine learning models opens the door to creating more believable, authentic, and relatable characters in games. The promise is that they either learn from interacting with the game, or from player data, to develop true human-like behavior. In this paper, we envision more applications of reinforcement learning for game AI in the future. For this to materialize, current research limitations are prohibitive to broad deployment across game genres. Therefore, we propose a framework for training reinforcement learning models with a set of requirements in mind that are suited towards game AI and game development. We present examples of games with reinforcement learning-augmented game AI and describe the practicalities of deploying player-facing machine learning agents in modern games. Furthermore, we identify bottlenecks and hard problems in these areas, which we believe offer promising research directions to accelerate the adoption of machine learning in game AI for the video game industry.

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08.

medRxiv (Medicine) 2026-06-24 DOI: HASH:8595f007b9f1658024e405f3562a1df8

Food insecurity, caloric intake and nutritional status among children under 5 years old: a predictive modelling analysis of the MAL-ED multi-country cohort

作者:

Checchi↗Ferguson↗Hamad↗Ouchtar↗Ratnayake↗Singh↗Tanvir↗van Zandvoort↗Dahab↗

Background For children at risk of acute malnutrition, being able to predict and forecast dietary intakes and/or nutritional evolution would support decision-making, particularly in crisis settings where ground data collection is unfeasible or scant. We explored whether statistical models could offer accurate predictions of caloric intake or anthropometric (weight-for-height Z score, WHZ) changes, given intake, household food insecurity and other plausible predictors. Methods We reanalysed data from the Malnutrition and Enteric Disease (MAL-ED) multi-country (Bangladesh, Brazil, India, Nepal, Pakistan, Peru, South Africa, Tanzania) birth cohort (2009-2014), which consistently tracked household food insecurity experience, dietary intake, anthropometry, infectious disease symptoms, breastfeeding and other variables among children 9 to 35 months old. We quantified the performance on cross-validation of three models: (M1) change in WHZ as a function of household food insecurity; (M2) change in WHZ as a function of caloric intake; (M3) caloric intake as a function of household food insecurity. We compared random forests, lasso regressions, additive models and generalised boosted regressions. All models included age, sex, birth weight, urban versus rural residence, breastfeeding status and the longitudinal prevalence of diarrhoea, acute respiratory infection and fever as additional predictors. Results Altogether, M1, M2 and M3 leveraged 2957, 23,651 and 2013 longitudinal child observations, respectively. Both at country and individual level, there was low correlation among the key variables of interest. All three models featured low performance and moderate to extreme regression dilution, even when fitted to each country cohort separately. Discussion This secondary analysis based on data from a rigorous observational study suggests that statistical prediction of key variables along the causal pathway to childhood acute malnutrition may not be feasible. These negative findings may in part be explained by error in predictor measurement and the narrow range of both predictor and outcome values in the MAL-ED cohort, relative to the more extreme scenarios common to crisis settings. They also imply that mechanistic models requiring caloric intake as an input cannot rely on a statistical shortcut of this kind and must instead depend on empirical data or scenario assumptions.

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09.

arXiv (CS.CL) 2026-06-16 DOI: arXiv:2606.16093

Long-Context Modeling via GSS-Transformer Hybrid Architecture with Learnable Mixing

作者:

Kuzey Torlak↗H\"useyin Arda Arslan↗An{\i}l Dervi\c{s}o\u{g}lu↗Beyza Nur Deniz↗Onur Boyar↗

Modeling long-range dependencies remains a central challenge in natural language processing. Transformer architectures achieve strong performance via self-attention but scale quadratically ($O(N^2)$) with sequence length, while State Space Models (SSMs) scale linearly ($O(N)$) but suffer from a selective recall bottleneck, struggling to retrieve precise information from compressed states. This creates a fundamental tradeoff between efficiency and perplexity. To tackle these challenges, we propose the Parallel Hybrid Architecture (PHA), which runs Gated State Spaces (GSS), Grouped Query Attention (GQA), and Feed-Forward Networks (FFNs) as independent parallel branches fused by a learnable mixing mechanism. Instead of forcing SSMs to approximate attention or serializing the two paradigms, PHA allows each branch to specialize: GSS captures global context, while attention performs selective retrieval, with FFN providing complementary processing. On WikiText-103, PHA achieves 16.51 PPL at 125M parameters, outperforming Hedgehog (16.70) and H3-125M (23.70). Scaling to 180M parameters yields 16.42 PPL, which gives comparable results with the pure attention baseline while delivering 24\% higher throughput and up to 40\% lower memory usage at long contexts. On OpenWebText, our 125M model achieves 19.72 PPL, outperforming standard Transformers (20.60) and GSS hybrid baselines (19.80). These results demonstrate that separating sequence modeling paradigms into parallel specialists enables Transformer-level perplexity with substantially improved efficiency for long-context language modeling.

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10.

arXiv (CS.CL) 2026-06-16 DOI: arXiv:2606.15161

Beyond Layer Importance in Layer-wise Sparsity: An Inter-Layer Perturbation-Absorption Perspective

作者:

Tao Jing↗Ningxin Wu↗Chen Kang↗Dong Yu↗Changliang Li↗Pengyuan Liu↗

The considerable layer-wise redundancy in large language models (LLMs) has established non-uniform sparsity allocation across layers as the standard pruning approach for efficient compression. Existing layer-wise allocation methods that estimate allocation strategy from local signals such as activation outliers or weight spectra mainly derive from local layer importance, whereas the final post-pruning performance is also influenced by the network's subsequent compensatory capacity. In this paper, we directly characterize this property through controlled perturbation experiments. We make the following empirical findings. First, layers exhibit highly heterogeneous responses to pruning-scale perturbations. In most cases, early layers amplify perturbations, while middle and late layers actively absorb them, with relative L2 drift decreasing monotonically across depth and direction realigning toward the unperturbed hidden-state trajectory. Second, absorption is a large-perturbation phenomenon. Under small perturbations the network exhibits amplification across all layers, and the transition to absorption occurs smoothly as perturbation magnitude grows to pruning scale. This enriches the linearized accumulation theory underlying related works. Building on these findings, we define an absorption coefficient per layer and propose absorption-aware correction, an orthogonal augmentation that improves OWL and AlphaPruning by reducing perplexity by 7.13% and boosting zero-shot accuracy by 1.02% across multiple model families at 70% sparsity.

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11.

arXiv (CS.LG) 2026-06-18 DOI: arXiv:2606.18267

Graph Instance Landscapes: When Structural Similarity Does (Not) Reflect Shortest-Path Performance

作者:

Maryam Gholami Shiri↗Ivana Krminac↗Marko Djukanovi\'c↗Sa\v{s}o D\v{z}eroski↗Eva Tuba↗Tome Eftimov↗

arXiv:2606.18267v1 Announce Type: cross Abstract: Benchmarking shortest-path algorithms is commonly based on aggregate performance over heterogeneous graph sets, which limits insight into how different search paradigms react to instance structure. We adopt an instance-landscape view of graph benchmarking by embedding graphs into a low-cost structural feature space and clustering them into regions of similar structure. Three benchmark suites are studied: weighted Erdős–Rényi graphs, random geometric (wireless) graphs, and real-world road networks. We evaluate four representative shortest-path solvers spanning uninformed exact search (Dijkstra), bidirectional exact search (bidirectional Dijkstra), heuristic-guided exact search (A$^{*}$), and deque-based strategies (DEQ). Clustering robustness is analyzed under multiple feature-selection schemes, and runtime distributions are compared across landscape regions using non-parametric tests. While generator parameters induce stable structural regions, we find that feature-space similarity does not necessarily imply performance similarity: significant runtime shifts are frequently observed even within the same landscape region. A merged-suite analysis further shows that different benchmark families occupy largely disjoint regions. These results highlight both the potential and the limits of structural landscapes for the structure-aware benchmarking of shortest-path algorithms.

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12.

arXiv (quant-ph) 2026-06-24 DOI: arXiv:2606.24591

Auxiliary Schmidt Rank as a Resource for Photonic Bell Measurements

作者:

Pradip Laha↗Peter van Loock↗

arXiv:2606.24591v1 Announce Type: new Abstract: In quantum communication and fusion-based quantum computation, photonic Bell measurements are fundamentally limited when only passive linear optics is employed. While for qubits, some Bell states can be unambiguously identified with static beam splitters and no extra photons or entanglement, additional auxiliary photons or at least additional auxiliary degrees of freedom with a certain level of additional entanglement are needed to approach or attain a complete, deterministic Bell measurement. Here, we prove an exact resource threshold when the same two photons carry system qudits of dimension $d$ and a fixed auxiliary entangled state $\Phi$, possibly distributed over several additional degrees of freedom, with total Schmidt rank $r_\Phi$. We show that a single conclusive Bell-label functional can occur for $r_\Phi\geqslant\lceil d/2\rceil$, but deterministic discrimination of all $d^2$ Bell-state labels requires $r_\Phi\geqslant d$. A maximally entangled rank-$d$ auxiliary state achieves the bound by local Bell-basis sorting between each photon's system and auxiliary degrees of freedom. Thus, the auxiliary Schmidt rank is a certified resource for ancilla-photon-free, embedded photonic Bell measurements.

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13.

arXiv (CS.CL) 2026-06-25 DOI: arXiv:2606.24937

The Hitchhiker's Guide to Agentic AI: From Foundations to Systems

作者:

Haggai Roitman↗

The Hitchhiker's Guide to Agentic AI is a comprehensive practitioner's reference for building autonomous AI systems. The book covers the full stack from first principles to production deployment, organized around a central thesis: building great agentic systems requires understanding every layer of the pipeline, not just one. The book opens with the LLM substrate – transformer architecture, GPU systems, training and fine-tuning (SFT,LoRA, MoE), model compression, and inference optimization – treated as essential foundations rather than the primary focus. It then develops the alignment and reasoning layer: reinforcement learning from human feedback (RLHF), PPO, DPO and its variants, GRPO, reward modeling, and RL for large reasoning models including chain-of-thought and test-time scaling. The second half is devoted to agentic AI proper. Topics include agentic training and trajectory-based RL, retrieval-augmented generation (RAG and Agentic RAG), memory systems (in-context, external, episodic, and semantic), agent harness design and context management, and a taxonomy of agent design patterns. Inter-agent coordination is covered in depth: the Model Context Protocol (MCP), agent skills and tool use, the Agent-to-Agent (A2A) communication protocol, and multi-agent architectures spanning centralized, decentralized, and hierarchical topologies. The book concludes with agent development frameworks, agentic UI design, evaluation methodology for agentic tasks, and production deployment. Each chapter pairs rigorous theoretical foundations with implementation guidance, code examples, and references to the primary literature.

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14.

arXiv (math.PR) 2026-06-24 DOI: arXiv:2606.21122

Negative index, matchings, and nonnegative eigenvalues of tridiagonal stochastic matrices

作者:

Bassam Mourad↗Issam Kaddoura↗Hassan Issa↗

arXiv:2606.21122v2 Announce Type: replace Abstract: We study negative eigenvalues of $n\times n$ stochastic matrices whose off-diagonal support is constrained by a sparse graph. The main tool is a matching-based inertia principle: if $G$ is bipartite with matching number $\mu(G)$, $S$ is a real symmetric matrix supported on $G$ with nonnegative diagonal entries and whose negative index (i.e. number of negative eigenvalues counted with their multiplicities) is denoted by $\nu_{-}(S) $, then \[ \nu_{-}(S)\leq \mu(G). \] In particular, every $n\times n$ nonnegative tridiagonal stochastic matrix $P$ satisfies $ \nu_{-}(P)\leq \left\lfloor \frac{n}{2}\right\rfloor. $ Consequently, after ordering the eigenvalues of $P$ in the decreasing order, we have $ \lambda_{\lceil n/2\rceil}(P)\geq0, \ and hence \ \lambda_2(P)\geq0, \mbox{ for } n\geq3. $ This gives an all-dimensional strengthening of the previously known $4\times4$ tridiagonal stochastic result. Next, we show that this tridiagonal bound is sharp in every dimension in both reducible and irreducible cases. Finally, we explore some possible extension and raise some open questions.

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15.

arXiv (CS.LG) 2026-06-16 DOI: arXiv:2606.15892

Scalar-pathway fidelity improves physical accuracy in short-range equivariant interatomic potentials

作者:

Jia Bi↗Alin Marin Elena↗Samuel Pinilla↗

arXiv:2606.15892v1 Announce Type: new Abstract: Accurate interatomic potentials enable molecular dynamics of materials, molecules, and interfaces beyond density-functional-theory length and time scales. Equivariant neural network potentials have improved the representation of local geometry. However, their deployable energy surfaces ultimately manifest through invariant scalar channels, whose aggregation and spectral resolution remain comparatively underexamined. Here we use Physics-Aware Neighborhood (PAN) pooling and Physics-Guided Spectral (PGS) mixers as controlled scalar-pathway probes: lightweight, symmetry-preserving modifications that act only on \(\ell=0\) channels while leaving the equivariant tensor backbone unchanged. Using MACE as a high-body-order mechanistic scaffold, PAN adds coordination-sensitive amplitude modulation, whereas PGS augments edge and readout scalar features with radial and tapered spectral bases. Across metallic Ag, covalent Si, a short-range ionic LiF/Li–F subset, and MD17/rMD17 molecules, this scalar-pathway correction reduces MACE force errors by 22–27\% and energy errors by 19–22\%; on systems with stress labels, stress errors decrease by 27–28\%, at approximately 5\% additional inference-FLOPs cost. Directionally consistent gains in Allegro and NequIP further indicate that the correction is portable across distinct short-range equivariant backbones, although effect sizes remain architecture-dependent. These results identify scalar-pathway fidelity as a practical design dimension for short-range equivariant interatomic potentials.

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16.

arXiv (CS.CV) 2026-06-12 DOI: arXiv:2605.26144

VISTA: An End-to-End Benchmark for Visual Spec-to-Web-App Coding Agents

作者:

JunJia Guo↗Yuhang Yao↗Jiawei↗Zhou↗Jingdi Chen↗

We present VISTA (VIsual Spec-To-App Benchmark), a benchmark for evaluating the end-to-end web-app generation capabilities of LLM-based agents. Unlike prior code generation benchmarks that focus on algorithmic tasks, VISTA targets realistic UI-centric development, where agents must produce functional, visually coherent applications from underspecified inputs. We define five prompt-information conditions that vary along two axes, visual/structural fidelity and stack constraint: (1) text only with free stack choice, (2) text with reference screenshots under three specified stacks, (3) text with reference screenshots under free stack choice, (4) text with screenshots and pruned Figma structure under a single specified stack, and (5) text with screenshots and pruned Figma structure under free stack choice. To enable robust evaluation, each page in the benchmark is manually annotated with interactive UI components and around three visual anchor points, addressing the well-known limitations of script-based testing tools such as Playwright in open-ended code generation settings. Evaluation combines DOM-grounded reference matching, behavior-specific browser tests, and CLIP-based visual similarity, jointly measuring structural alignment, behavioral completeness, and overall visual fidelity. We use VISTA to assess four agent systems drawn from two model families and two harnesses, finding that visual fidelity and functional correctness are partially decoupled across both input conditions and agents, and that agent editing style varies sharply but is largely orthogonal to task quality. VISTA establishes a rigorous and reproducible foundation for advancing agent-based software engineering research.

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17.

arXiv (CS.CL) 2026-06-17 DOI: arXiv:2407.13053

E2Vec: Feature Embedding with Temporal Information for Analyzing Student Actions in E-Book Systems

作者:

Yuma Miyazaki↗Valdemar \v{S}v\'abensk\'y↗Yuta Taniguchi↗Fumiya Okubo↗Tsubasa Minematsu↗Atsushi Shimada↗

Digital textbook (e-book) systems record student interactions with textbooks as a sequence of events called EventStream data. In the past, researchers extracted meaningful features from EventStream, and utilized them as inputs for downstream tasks such as grade prediction and modeling of student behavior. Previous research evaluated models that mainly used statistical-based features derived from EventStream logs, such as the number of operation types or access frequencies. While these features are useful for providing certain insights, they lack temporal information that captures fine-grained differences in learning behaviors among different students. This study proposes E2Vec, a novel feature representation method based on word embeddings. The proposed method regards operation logs and their time intervals for each student as a string sequence of characters and generates a student vector of learning activity features that incorporates time information. We applied fastText to generate an embedding vector for each of 305 students in a dataset from two years of computer science courses. Then, we investigated the effectiveness of E2Vec in an at-risk detection task, demonstrating potential for generalizability and performance.

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18.

arXiv (quant-ph) 2026-06-24 DOI: arXiv:2606.24360

Multipartite synchronization residuals in driven-dissipative spin networks

作者:

Jatin Ghildiyal↗Shubhrangshu Dasgupta↗Asoka Biswas↗

arXiv:2606.24360v1 Announce Type: new Abstract: We introduce a phase-space measure of quantum synchronization that quantifies relative phase localization for two-qubit and three-qubit systems. This measure is built from the first angular moments of phase distributions obtained from Husimi-Q quasiprobability functions. Using this framework, we formulate a new class of synchronization residuals, motivated by subadditivity-type hierarchies of information-theoretic measures. We investigate these residuals in a driven-dissipative quantum Rabi network in the dispersive adiabatic regime. We show that, for two qubits, collective synchronization remains bounded by single-qubit contributions yielding a non-negative bipartite residual. The three-qubit nonequilibrium steady state exhibits a negative tripartite residual, which indicates collective phase synchronization, which cannot be described by pairwise decomposition. The corresponding entropy-based residuals, however, remain non-negative in both cases. Our results therefore, underscore that phase-sensitive synchronization measures and entropic correlation measures probe distinct aspects of open-system dynamics.

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19.

arXiv (CS.AI) 2026-06-17 DOI: arXiv:2507.18623

Moving Out: Physically-grounded Human-AI Collaboration

作者:

Xuhui Kang↗Sung-Wook Lee↗Haolin Liu↗Yuyan Wang↗Yen-Ling Kuo↗

arXiv:2507.18623v4 Announce Type: replace-cross Abstract: The ability to adapt to physical actions and constraints in an environment is crucial for embodied agents (e.g., robots) to effectively collaborate with humans. Such physically grounded human-AI collaboration must account for the increased complexity of the continuous state-action space and constrained dynamics caused by physical constraints. However, most existing collaboration benchmarks are discrete or do not consider physical attributes and constraints. To address this, we introduce Moving Out, a human-AI collaboration benchmark that resembles a wide range of collaboration modes affected by physical attributes and constraints, such as moving heavy items together and coordinating actions to move an item around a corner. Moving Out consists of two challenges and human-human interaction data to comprehensively evaluate models' abilities to adapt to diverse human behaviors and unseen physical attributes. To give embodied agents the capability to collaborate with humans under physical attributes and constraints, we propose a novel method, BASS (Behavior Augmentation, Simulation, and Selection), to enhance the diversity of agents and their understanding of the outcome of actions. We systematically compare BASS and state-of-the-art models in AI-AI and human-AI experiments, showing that BASS can effectively collaborate with both unseen AI and humans. The project page is available at https://live-robotics-uva.github.io/movingout_ai/.

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20.

arXiv (CS.CL) 2026-06-24 DOI: arXiv:2606.24428

Escaping the Self-Confirmation Trap: An Execute-Distill-Verify Paradigm for Agentic Experience Learning

作者:

Shiding Zhu↗Yudi Qi↗Yajie Wang↗Jiaze Li↗Chao Song↗Yaorui Shi↗Yibo Miao↗Hanqi Gao↗Kai Zhang↗

Experience-driven self-evolution is critical for large language model (LLM) agents to improve through open-world interaction. However, existing experience learning methods mostly rely on single-agent loops, where the same agent executes tasks, summarizes outcomes, and determines memory content. This setup makes agents vulnerable to the Self-Confirmation Trap: wrong-but-self-consistent trajectories are misidentified as successful experience, leading to cumulative errors during retrieval and reuse. To address this issue, we propose EDV, an Execute-Distill-Verify framework for reliable experience learning. In the Execute stage, multiple heterogeneous agents explore the same task space in parallel to generate diverse candidate trajectories. In the Distill stage, a dedicated third-party agent comparatively analyzes these trajectories to produce candidate experiences, reducing executor-centric summarization bias. In the Verify stage, the execution group validates candidates via a consensus mechanism, and only approved experiences are written into shared or private memory. By decoupling the three stages, EDV transforms experience learning from isolated self-reflection into collaborative construction, filtering erroneous and noisy content before memory insertion. We evaluate EDV on three challenging long-horizon benchmarks: tau2-bench, Mind2Web and MMTB. Results show EDV consistently outperforms strong baselines, validating that reliable experience construction is essential for robust agent self-evolution. Our code is available at https://github.com/shidingz/EDV.

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21.

arXiv (CS.AI) 2026-06-16 DOI: arXiv:2606.16914

Greed Is Learned: Visible Incentives as Reward-Hacking Triggers

作者:

Tong Che↗Rui Wu↗

arXiv:2606.16914v1 Announce Type: new Abstract: Deployed agents increasingly act with their reward proxy in view, such as a balance, score, or KPI dashboard. We show that reinforcement learning can make a policy addicted to such a visible self-benefit channel. It chases the displayed payoff across held-out domains, sacrifices the true task to do so, and follows the channel wherever we rewrite it, while policies that never saw the channel stay honest. We call this reward-channel addiction and study it in MoneyWorld, a synthetic sandbox. The addiction can flip a model's safety alignment: trained only on innocuous money tasks with no safety content, the model abandons the safe action it otherwise always takes whenever a dashboard pays for an unsafe one, and reverts to safe once the channel is hidden. This learned bribe replicates across model scales and families. Blindly optimizing super-capable, next-generation AI on KPIs or P\&L can be dangerous for alignment. Greed is learned when following such a channel pays.

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22.

arXiv (CS.AI) 2026-06-15 DOI: arXiv:2605.07121

AdaTKG: Adaptive Memory for Temporal Knowledge Graph Reasoning

作者:

Seunghan Lee↗Jun Seo↗Jaehoon Lee↗Sungdong Yoo↗Minjae Kim↗Tae Yoon Lim↗Dongwan Kang↗Hwanil Choi↗SoonYoung Lee↗Wonbin Ahn↗

arXiv:2605.07121v2 Announce Type: replace Abstract: Temporal knowledge graphs (TKGs) represent time-stamped relational facts and support a wide range of reasoning tasks over evolving events. However, existing methods produce entity representations that are static at the entity level, in that each representation is a function of learned parameters only and retains no trace of the interactions in which the entity has participated. In this paper, we depart from this static view and propose that each entity be modeled as an adaptive process whose representation is refined every time the entity participates in a fact. To this end, we propose AdaTKG, which maintains a per-entity memory that is updated with every observed interaction, with the memory accumulating online and predictions improving as more interactions arrive. Specifically, we instantiate the memory update as a learnable exponential moving average governed by a single shared scalar instead of using learnable parameters for each entity, enabling AdaTKG to handle entities unseen during training. Extensive experiments confirm consistent gains over TKG baselines, demonstrating the effectiveness of adaptive memory. Code is available at: https://github.com/seunghan96/AdaTKG

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23.

arXiv (CS.CL) 2026-06-24 DOI: arXiv:2606.23870

ESBMC-PLC+: A Unified IEC~61131-3 Formal Verification Framework as a PLCverif Successor

作者:

Pierre Dantas↗Lucas Cordeiro↗Waldir Junior↗

PLCverif is the most mature open-source platform for PLC formal verification, developed at CERN and in production use since 2019. Yet it has two fundamental limitations: no support for Ladder Diagram (LD) programs, the dominant PLC notation, and reliance on CBMC as its primary backend, which restricts verification to bounded proofs. The PLCverif authors themselves identified ESBMC as the appropriate backend improvement. Prior work established ESBMC-PLC (a textual LD frontend with k-induction) and ESBMC-GraphPLC (graphical PLCopen XML support); together, they cover LD with unbounded proofs but not Structured Text (ST), and graphical LD with timer/counter function blocks remains unverifiable. This paper presents ESBMC-PLC+, a unified framework that closes both gaps: (1) an ST/SCL frontend via the MATIEC IEC 61131-3 compiler, routing C-compiled ST to ESBMC with nondeterministic input modeling and YAML property injection; (2) function block state semantics for graphical LD, extending the DFS resolver to model TON/TOF/TP timers, CTU/CTD counters, and R_TRIG/F_TRIG edge triggers as persistent scan-cycle state variables in the GOTO IR. ESBMC-PLC+ is the first open-source PLC verification framework to support all three major IEC 61131-3 input formats via a single ESBMC backend, enabling k-induction-unbounded safety proofs. A feature comparison with PLCverif and experimental evaluation on 8 benchmark programs, including programs with up to 8 integer timers, shows that ESBMC-PLC+ matches PLCverif's input coverage while providing stronger guarantees. Against nuXmv's BDD backend, ESBMC-PLC+ is 400-2,000x faster on timer programs and completes proofs where nuXmv BDD times out at 120s.

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24.

bioRxiv (Bioinfo) 2026-06-08 DOI: HASH:ac0e9b812fd07de0008e912ce7dae75a

TRACEY: an updated resource for SNARE protein domain annotation with improved HMMs and expanded sequence coverage

作者:

Pulido-Quetglas↗Fasshauer↗

Motivation: SNARE proteins catalyse membrane fusion across the eukaryotic endomembrane system, from synaptic vesicle exocytosis to intracellular trafficking, endosomal and vacuolar transport, and autophagy, and their accurate domain annotation depends on the quality of profile models and the sequence diversity behind them. The original SNARE domain classification predates the recent expansion of eukaryotic sequence data, leaving its HMM profiles and subgroup coverage unable to resolve divergent and lineage-specific paralogs. Results: We present an updated release of TRACEY built on a resynchronized, non-redundant collection of 18,915 curated SNARE proteins spanning 1,188 species, together with a consolidated set of 83 HMM profiles, including 43 models for newly defined subgroups, reconstructed through an iterative, mixture-model-driven procedure. In direct comparison with the legacy models, at least ~75% of sequences in every overlapping group scored better with the new HMMs, indicating systematic gains in domain detection. A redesigned web interface adds multiparameter querying, FASTA download, and direct scanning of user-submitted sequences against the curated profiles. Availability and implementation: TRACEY is freely available at https://tracey.unil.ch.

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25.

medRxiv (Medicine) 2026-06-24 DOI: HASH:f229df10b5995e3fdfbd4a3e71fd5b9c

IgG2 Galactosylation is related to higher antibody dependent enhancement for dengue in cross-reactive antibodies from Sars-CoV-2

作者:

Reinig↗Chin↗Shih↗S.-R↗

Cross-reactive antibodies against dengue virus are known to cause antibody-dependent enhancement (ADE) of infection or disease severity under specific conditions. In our previous study, we showed that primary immunization with the COVID-19 vaccine induces induces cross-reactive IgG causing ADE against dengue. In the present study, we investigated the influence of IgG Fc-glycosylation (analyzed by LC-MS/MS) on ADE mediated by cross-reactive IgG against dengue from IgG against SARS-CoV-2. We found a clear correlation between anti-DENV2 E IgG2 galactosylation and the ADE capacity of cross-reactive IgG against dengue in individuals vaccinated against COVID-19. IgG2 sialylation increased over time; however, it was not correlated with ADE capacity. This phenomenon was restricted to IgG2, whereas anti-DENV2 E IgG1 Fc-glycosylation remained stable after COVID-19 vaccination.

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