arXiv:2607.09138v1 Announce Type: new
Abstract: Autonomous driving operates in partially observable environments where actors may become fully occluded by other vehicles or infrastructure. Most end-to-end driving systems implicitly couple actor existence to instantaneous observations, causing actor hypotheses to degrade or disappear during prolonged occlusion and removing potentially critical agents from downstream prediction and planning. We introduce BeyondSight, a permanence-aware end-to-end driving framework that decouples actor existence from observability by maintaining persistent actor hypotheses over time. BeyondSight propagates actor queries temporally and updates them with observation-conditioned evidence, enabling joint perception, prediction, and planning to reason about actors even when they are temporarily unobservable. To enable principled training and evaluation of persistence-aware models, we further introduce nuScenes-Permanence, an extension of nuScenes that provides supervision and observability-conditioned evaluation for unobservable actors. Experiments show that BeyondSight substantially improves reasoning under occlusion, increasing detection performance for unobservable actors from 0 to 0.249 mAP while reducing planning error from 0.61 to 0.54 L2avg. These results highlight object permanence as an important modeling principle for robust end-to-end autonomous driving.
Science Journals
arXiv:2607.09446v1 Announce Type: new
Abstract: This paper presents a robust, fully Lagrangian framework based on the Particle Finite Element Method (PFEM) capable of simulating multiphase flows with an arbitrary number of immiscible phases. Interface-tracking methods can sometimes suffer from numerical diffusion or allow the underlying mesh resolution to prematurely dictate topological changes. To address these limitations, we introduce a dynamic mesh adaptation strategy that naturally preserves sharp geometric interfaces without relying on classical constrained triangulation. A node-empty disk is assigned to each segment of the discretized interface, ensuring that the edge is part of the Delaunay triangulation. Our approach decouples the interface physics from the grid size, allowing the integration of sub-grid physical models to properly govern topological changes independently of the user-defined mesh size. The capabilities and accuracy of the framework are validated against standard multiphase benchmarks, closely matching references while maintaining a remarkably low overall node count. We demonstrate the scalability and geometric versatility of the method, in particular with a challenging 16-phase Rayleigh-Taylor simulation.
arXiv:2607.09032v1 Announce Type: cross
Abstract: Quantum logic is usually presented as a non-classical departure from ordinary reasoning forced on us by quantum mechanics, with classical logic kept as the secure starting point. We argue for the opposite order of explanation in a finite and fully computable setting. The free orthomodular lattice on two generators has ninety-six elements, the direct product of a six-element non-distributive factor and a sixteen-element Boolean factor. Reading the first factor as a register of contexts and the second as Boolean content, we obtain a calculus whose elements are context--bit-vector pairs and whose operations act component by component. With this calculus we establish three results. First, we classify the six layers by commutativity, identifying the central kernel of context-neutral propositions together with a dual central layer in which all complementary contexts are present. Second, we show that orthocomplementation rearranges the layers exactly as the complementation of the small factor rearranges its elements, which makes the duality among the layers rigid rather than accidental. Third, we prove that the operation forgetting the context is a surjective homomorphism of orthocomplemented lattices whose quotient is the classical Boolean algebra, so that classical logic is a six-to-one, information-losing image of the contextual calculus.
arXiv:2607.09525v1 Announce Type: cross
Abstract: Frequency combs, evenly spaced spectral lines locked to one fundamental frequency, are well known in optics and have also been found in phononic, magnonic, ferroelectric, and cosmological systems, but have not yet been studied in oscillating chemical reactions. In this work, we show that reaction-diffusion oscillators can also produce frequency combs. We use the Brusselator model and derive its Hopf bifurcation condition directly from the rate equations. We find that the trimolecular autocatalytic term is the only source of nonlinear harmonic content. Above the Hopf threshold, our simulations of target-wave patterns show a clear fundamental frequency followed by a long, evenly spaced ladder of harmonics, with each harmonic weaker than the one before it. We then vary the reactant concentrations and kinetic parameters one at a time and find that this comb structure holds across a wide range of values. We also test this idea experimentally using the Belousov-Zhabotinsky reaction. Intensity signals recorded at different points in a target pattern show a shared fundamental frequency with several weakening harmonics, matching the simulated pattern closely. Together, these results show that reaction-diffusion chemistry is a new platform for generating frequency combs.
arXiv:2607.09639v1 Announce Type: cross
Abstract: Partial differential equations are a promising application area for fault-tolerant quantum algorithms, but the subject lies between two communities with different languages: numerical analysis and quantum computation. These lecture notes provide a numerically grounded introduction for readers entering from either field. Block encoding is the organizing principle: once a discretized differential operator is embedded in a unitary, primitives such as quantum singular value transformation, Hamiltonian simulation, linear combinations of unitaries, amplitude amplification, and measurement can be assembled into algorithms for elliptic, hyperbolic, and parabolic PDEs. Each chapter begins with a standard finite difference or finite element discretization and follows the full pipeline from the continuous PDE to quantum encoding, transformation, and extraction of a quantity of interest. Particular attention is paid to the factors governing end-to-end performance, including discretization error, state preparation, normalization, postselection, and measurement cost. A final chapter introduces nonlinear problems through Carleman and Koopman-von Neumann linearizations. The aim is not a comprehensive survey or a claim of universal quantum advantage, but a mathematically transparent entry point and a shared vocabulary for researchers in both communities.
arXiv:2211.00111v3 Announce Type: replace
Abstract: Rust, as an emerging system programming language, introduces $\texttt{unsafe}$ to allow developers to bypass safety checks during compilation. As a result, memory safety bugs are typically confined to the $\texttt{unsafe}$ regions, which have been the primary focus of Rust bug-finding tools. However, such tools rely on the presence of the $\texttt{unsafe}$ keyword in Rust source code; there are no tools available that can examine Rust binaries to pinpoint $\texttt{unsafe}$ areas. Therefore, we propose $\texttt{Ruby}$, the first tool that unmasks $\texttt{unsafe}$ regions in Rust binaries using machine learning. By capturing the subtle differences in the binary instructions, $\texttt{Ruby}$ can identify 91.75% of the total $\texttt{unsafe}$ regions with a false positive rate of 6.16%, beating SOTA LLM models including GPT-5.2, Claude-4.5 and Gemini-3. We further applied $\texttt{Ruby}$ to guide symbolic execution and fuzzing, showing a speed-up of 57.95% and 21.26%, with five bugs confirmed and patched by Google in Android library fuzzing.
arXiv:2311.00300v2 Announce Type: replace
Abstract: The entity alignment of science and technology patents aims to link the equivalent entities in the knowledge graph of different science and technology patent data sources. Most entity alignment methods only use graph neural network to obtain the embedding of graph structure or use attribute text description to obtain semantic representation, ignoring the process of multi-information fusion in science and technology patents. In order to make use of the graphic structure and auxiliary information such as the name, description and attribute of the patent entity, this paper proposes an entity alignment method based on the graph convolution network for science and technology patent information fusion. Through the graph convolution network and BERT model, the structure information and entity attribute information of the science and technology patent knowledge graph are embedded and represented to achieve multi-information fusion, thus improving the performance of entity alignment. Experiments on three benchmark data sets show that the proposed method has better Hits@$K$ evaluation indicators than existing methods.
arXiv:2607.09290v1 Announce Type: new
Abstract: In the digital era, Portable Document Format (PDF) is one of the most widely used file formats for storing and exchanging digital documents due to its platform independence and rich functionality. However, these same capabilities have also made PDF files an attractive attack vector for cyberattackers, who embed malicious code within seemingly legitimate documents to compromise target systems. This paper presents a novel interpretable Tsetlin Machine (TM)-based framework for PDF malware detection. The proposed framework extracts salient features from PDF documents through static analysis without executing the files and employs rule-based learning to accurately classify benign and malicious PDF documents. Numerical evaluation on the RIT-PDFMal-2026 dataset demonstrates that the proposed framework achieves competitive performance, attaining an accuracy of 98.02% compared with several ML classifiers and existing methods. Moreover, the proposed framework provides intrinsic interpretability by transparently explaining its classification decisions. The combination of competitive detection performance, computational efficiency, and intrinsic interpretability makes the proposed framework a promising solution for practical PDF malware detection.
arXiv:2401.14341v4 Announce Type: replace
Abstract: An orientable sequence of order $n$ is a cyclic binary sequence such that each length-$n$ substring appears at most once \emph{in either direction}. Maximal length orientable sequences are known only for $n\leq 7$, and a trivial upper bound on their length is $2^{n-1} - 2^{\lfloor(n-1)/2\rfloor}$. This paper presents the first efficient algorithm to construct orientable sequences that reach this upper bound, asymptotically; more specifically, our algorithm constructs orientable sequences via cycle-joining and a successor-rule approach requiring $O(n)$ time per bit and $O(n)$ space. This answers a longstanding open question from Dai, Martin, Robshaw, Wild [Cryptography and Coding III (1993)]. Applying a recent concatenation-tree framework, the same sequences can be generated in $O(1)$-amortized time per bit using $O(n^2)$ space. Our sequences are applied to find new longest-known (aperiodic) orientable sequences for $n\leq 20$.
arXiv:2405.16361v4 Announce Type: replace
Abstract: To protect privacy in regulated domains such as healthcare and finance, model owners may allow only remote API access while keeping both the training data and model parameters private. However, model users performing inference on such remotely hosted models may be required to transmit potentially sensitive inputs, raising privacy concerns. In this work, we present LDPKiT, a framework for non-adversarial, privacy-preserving model distillation that leverages a user's private in-distribution data while bounding privacy leakage. LDPKiT introduces a novel superimposition technique that generates approximately in-distribution samples, enabling effective knowledge transfer under local differential privacy (LDP). Experiments on Fashion-MNIST, SVHN, and PathMNIST demonstrate that LDPKiT consistently improves utility while maintaining privacy, with benefits that become more pronounced at stronger noise levels. For example, on SVHN, LDPKiT achieves nearly the same inference accuracy at $\epsilon=1.25$ as at $\epsilon=2.0$, yielding stronger privacy guarantees with less than a 2\% accuracy reduction. We further conduct sensitivity analyses to examine the effect of dataset size on performance and provide a systematic analysis of latent space representations, offering intuitive and empirical insights into the accuracy gains of LDPKiT.
arXiv:2411.02650v4 Announce Type: replace
Abstract: Game-integrated learning systems (GILS) are a growing form of multimedia education. Brain-based evidence can help researchers and designers understand how GILS design choices shape how learners think and process information. This scoping review follows PRISMA-ScR and synthesizes 20 empirical studies (2014-2025) in which functional near-infrared spectroscopy (fNIRS) measured brain activity during GILS use. This corpus shows that fNIRS can capture brain responses across GILS platforms and game elements, and points to how neurophysiological evidence can inform multimedia design decisions, such as that different platforms activate different brain regions, that adaptive difficulty reduces cognitive load and improves performance simultaneously, and that collaborative gameplay predicts knowledge retention. The 20 studies in this corpus reflect a field with substantial room to grow. Causal links between brain activation and learning outcomes would give designers more reliable evidence for platform decisions. As fNIRS and multimedia devices improve, standardized methods, classroom settings, and real-time neural adaptation represent directions where future work can translate these findings into practical multimedia learning systems.
arXiv:2502.02843v2 Announce Type: replace
Abstract: In this paper, we take a step towards developing efficient hard thresholding methods for low-rank tensor recovery from memory-efficient linear measurements with tensorial structure. Theoretical guarantees for many standard iterative low-rank recovery methods, such as iterative hard thresholding (IHT), are based on model assumptions on the measurement operator, like the restricted isometry property (RIP). However, tensor-structured random linear maps -- while memory-efficient and convenient to apply -- lack good restricted isometry properties; that is, they do not preserve the norms of low-rank tensors sufficiently well.
To address this, we propose local trimming techniques that provably restore point-wise geometry-preservation properties of tensor-structured maps, making them comparable to those of unstructured sub-Gaussian measurements. Then, we propose two novel versions of tensor IHT algorithms: an adaptive gradient trimming algorithm and a randomized Kaczmarz-based IHT algorithm, that efficiently recover low-rank tensors from linear measurements. We provide initial theoretical guarantees for the proposed methods and present numerical experiments on real and synthetic data, highlighting their efficiency over the original TensorIHT for low HOSVD and CP-rank tensors.
arXiv:2607.09488v1 Announce Type: new
Abstract: High-quality visual representation is a long-standing pursuit in computer vision. In the context of multimodal LLMs (MLLMs), feeding higher-resolution images can produce more fine-grained visual tokens. However, it introduces additional computational and design complexity, due to multiple forward passes and post-processing of increased tokens. Before simply adopting a higher resolution, have we truly unlocked the model's full perception capability at a standard resolution? Therefore, we study an interesting problem: how to achieve fine visual perception under lower cost without larger images. We present SigLIP-HD in this work. The core is a highly simple fine-to-coarse supervision design. We enforce the coarse feature of a mid-resolution image to mimic the fine-grained feature of its high-resolution version. We build this framework on the advanced SigLIP 2 model. Our final model produces better visual tokens at exactly the same inference budget. It is validated on extensive MLLM benchmarks and consistently delivers stronger results than our baseline model, especially on OCR-related tasks.
arXiv:2607.09329v1 Announce Type: new
Abstract: Decomposing outgoing surface radiance into material and illumination during inverse rendering is essential for applications such as relighting and augmented reality, yet it is severely ill-posed since multiple combinations can result in the same observed colour. Capturing an object under multiple lighting conditions usually helps resolve this ambiguity as it constrains the optimization towards correct solutions. In this work, we explore the potential of reconstructing rigidly moving objects -- which provides observations of diverse light-surface interactions -- to resolve the material-lighting ambiguity in inverse rendering. For this purpose, we introduce a relightable approach that marries object tracking and reconstruction with inverse rendering for general rigidly moving objects. Our experimental analysis on synthetic data demonstrates that motion can be an advantage for disentangling material and lighting: the reconstructed material is significantly more accurate when the object is observed under rigid motion than when it is static. Moreover, results on RGB videos of real hand-held objects show that our pipeline preserves this advantage even under noisy real-world conditions.
arXiv:2607.09489v1 Announce Type: new
Abstract: An AI system's output is not the fact or world state it appears to describe, but rather an engineered representation. We propose a semantic framework to describe AI systems, to be able to examine the correctness of such representations. To do so, we distinguish what is justified by accepted domain knowledge, what reference sources say, and what the system can currently use. This allows us to give precise definitions to common failures: extrapolation, refuted or unsupported assertion, sources versus knowledge mismatch, stale or refuted source, added hypotheses, unsupported use... We hope our framework gives a useful vocabulary for specifying and checking AI systems whose outputs, citations, tool calls, and world-changing actions must be justified by reliable claims and explicit authority rather than apparent fluency.
arXiv:2502.18497v2 Announce Type: replace
Abstract: Dynamic community detection must update high-quality modularity partitions after edge batches, yet full Leiden reruns make small changes scale with the whole snapshot. Existing dynamic methods reduce work but often alter Leiden refinement, keep limited hierarchy state, or restrict graph support. This paper presents LD-Leiden, a local dynamic Leiden method for weighted directed and undirected graphs that preserves the move-refine-aggregate pipeline and updates only repaired affected regions. Its novelty is the combination of an affected-frontier rule after statistic repair, exact subtract-add aggregate repair, and conflict-filtered parallel local moves; together these mechanisms bound update cost by the visited frontier rather than the full graph. On real streams and streamed static graphs with up to 214M vertices and 3.30B edges, LD-Leiden is 48.77x faster than warm-started Leidenalg in 100-batch runs while preserving a 0.996 final modularity ratio. On the common undirected benchmark set, it is 6.94x faster than DF-Leiden and 9.73x faster than NetworKit while obtaining higher final modularity; synthetic sequences support the predicted local edge-volume scaling.
arXiv:2502.20336v5 Announce Type: replace
Abstract: We propose rigorous and efficiently computable lower and upper a posteriori error bounds for given approximations to PDEs on a given domain, which might be geometrically complex. This is done by embedding or enveloping the original domain towards geometrically simpler domains, enabling the use of fast numerical solvers. To this end, we extend and restrict the residual and provide efficient methods to compute those Hahn-Banach extensions. Then, we efficiently compute their Riesz representations on the geometrically simpler domains and obtain the desired a posteriori bounds for which we prove that they are sharp.
The resulting bounds control the error in the natural norm induced by a well-posed variational formulation, require only minimal regularity assumptions, and thus remain applicable on complex geometries. The framework is detailed for elliptic as well as parabolic problems. Numerical experiments demonstrate the good quantitative behavior of the derived upper and lower error bounds.
A central motivation for this paper comes from physics-informed and related neural-network approximations of PDEs, which are naturally mesh-free and can be evaluated pointwise on complex or parameter-dependent geometries. Nevertheless, the framework applies to any approximation for which the variational residual can be evaluated.
arXiv:2607.09161v1 Announce Type: new
Abstract: Keyword-based retrieval is limited for source-code repositories when queries are expressed in natural language or concern implementation intent and code quality rather than exact tokens. This study evaluates a prototype retrieval system that combines function-level fragmentation, text-and-code embeddings, ChromaDB vector storage, LLM-derived quality metadata, and four retrieval modes: semantic, quality-filtered, hybrid, and automatic routing. The concrete evaluation uses an educational C-code corpus. The full corpus contains 563 anonymized programmer identifiers and 8,951 C files; a reproducible 10% indexed sample contains 56 programmer identifiers, 847 files, and 3,839 fragments. Across 15 manually judged queries, semantic retrieval achieved nDCG@5 of 0.820, Success@5 of 0.800, and MRR of 0.644. The automatic router selected the expected mode for all 15 queries. In a small manual audit, LLM-derived quality scores were within one point of the manual assessment for 9 of 12 fragments. Within the reported query set, semantic retrieval was the strongest overall mode, while explicit quality metadata was most useful for explicitly quality-oriented queries.
arXiv:2504.07459v2 Announce Type: replace
Abstract: We propose a novel framework for generating causal graphs from narrative texts, bridging high-level causality and detailed event-specific relationships. Our method first extracts concise, agent-centered vertices using large language model (LLM)-based summarization. We introduce an "Expert Index," comprising seven linguistically informed features, integrated into a Situation-Task-Action-Consequence (STAC) classification model. This hybrid system, combining RoBERTa embeddings with the Expert Index, achieves superior precision in causal link identification compared to pure LLM-based approaches. Finally, a structured five-iteration prompting process refines and constructs connected causal graphs. Experiments on 100 narrative chapters and short stories demonstrate that our approach consistently outperforms GPT-4o and Claude 3.5 in causal graph quality, while maintaining readability. The open-source tool provides an interpretable, efficient solution for capturing nuanced causal chains in narratives.
arXiv:2411.02612v3 Announce Type: replace
Abstract: We discover a novel connection between two classical mathematical notions, Eulerian orientations and Hadamard codes by studying the counting problem of Eulerian orientations (\#EO) with local constraint functions imposed on vertices. We present two special classes of constraint functions and a chain reaction algorithm, and show that the \#EO problem defined by each class alone is polynomial-time solvable by the algorithm. These tractable classes of functions are defined inductively, and quite remarkably the base level of these classes is characterized perfectly by the well-known Hadamard code. Thus, we establish a novel connection between counting Eulerian orientations and coding theory. We also prove a \#P-hardness result for the \#EO problem when constraint functions from the two tractable classes appear together.
arXiv:2607.09452v1 Announce Type: new
Abstract: We present a practical pipeline for recovering source code from stripped binary functions by combining reverse engineering, anchor-based source code retrieval, and large language model reasoning. Our binary-to-source-code retrieval method attempts to identify the source function from a source code database, rather than generating approximate decompiled pseudocode. It extracts anchors such as strings, constants, external calls, and available function names using Ghidra, retrieves candidate files via an inverted-index search database, narrows candidates to likely function snippets, and re-ranks them with a large language model (LLM) based on disassembly, decompiled code, and source metadata. Confident matches can also serve as anchors in later passes. In an evaluation backed by our high-fidelity source code database on a stripped, optimized tcpdump binary, our proposed binary-to-source matching method achieves 95.2% assembly instruction coverage. Experiments on a GitHub-based retrieval database showed lower performance with 35.5% instruction coverage on average, mainly due to retrieval misses. These results show that source-level binary recovery excels with high-quality databases and remains a useful tool in noisy environments.
arXiv:2607.09457v1 Announce Type: new
Abstract: The 7th ALEGRO workshop, hosted by INFN Frascati National Laboratories from 3rd to 5th March 2026, brought together the international Advanced Novel Accelerators (ANA) community to discuss the role of advanced wakefield accelerators (AWA) in particle and high-energy physics. Organized under the ICFA-ANA panel, the workshop highlighted recent progress in plasma- and structure-based wakefield acceleration and strengthened international collaboration toward future energy-frontier colliders.
A major focus was the ongoing 10 TeV linear collider design study, launched in 2025 following the US P5 recommendations. A dedicated session covered accelerator concepts, enabling technologies, and the associated physics case, while recognizing that future priorities will depend on the outcome of the European Strategy for Particle Physics Update.
The workshop also reviewed nearer-term applications of advanced accelerators, including fixed-target experiments, injectors for future colliders and light sources, plasma-based Higgs factory concepts, and proton-driven plasma wakefield acceleration. Beyond high-energy physics, sessions covered free-electron lasers, synchrotron light sources, and strong-field QED. Recent demonstrations of FEL lasing with plasma-accelerated electron beams highlighted significant progress in beam quality and accelerator performance.
Operational challenges for reliable user facilities, including high-availability laser and electron-beam systems, were discussed alongside the growing role of artificial intelligence and machine learning for accelerator optimization and control.
This report summarizes the workshop discussions and conclusions from the chairs, together with short contributions from the presenters, providing an overview of the current status and future prospects of advanced wakefield accelerators.
arXiv:2504.15650v3 Announce Type: replace
Abstract: Building a generalized affordance grounding model to identify actionable regions on objects is vital for real-world applications. Existing methods to train the model can be divided into weakly and fully supervised ways. However, the former method requires a complex training framework design and can not infer new actions without an auxiliary prior. While the latter often struggle with limited annotated data and components trained from scratch despite being simpler. This study focuses on fully supervised affordance grounding and overcomes its limitations by proposing AffordanceSAM, which extends SAM's generalization capacity in segmentation to affordance grounding. Specifically, we design an affordance-adaption module and curate a coarse-to-fine annotated dataset called C2F-Aff to thoroughly transfer SAM's robust performance to affordance in a three-stage training manner. Experimental results confirm that AffordanceSAM achieves state-of-the-art (SOTA) performance on the AGD20K benchmark and exhibits strong generalized capacity.
arXiv:2506.01692v2 Announce Type: replace
Abstract: Human preferences in RLHF are typically modeled as a function of the human's reward function or corresponding optimal state-action values. In this work, we propose that human beliefs about the capabilities of the agent being trained also play a key role in preference generation. We examine two questions related to this hypothesis, one descriptive and one normative, respectively: Do human labelers' beliefs about agent capabilities affect the preferences that they provide? And what is the ideal set of beliefs about an agent -- and resulting preferences -- for humans to have? We propose a new preference model that incorporates human beliefs and provide a normative theory that bounds the error on the final learned policy based on the mismatch between the human's beliefs and an idealized set of beliefs. We then confirm via a human study that beliefs about agent capabilities do, in fact, significantly affect preferences and can be influenced through simple interventions. Additionally, we empirically show through synthetic experiments that it is often suboptimal for human preference labelers to assume agent optimality. Collectively, these results theoretically and empirically demonstrate how reducing the mismatch between human beliefs and agent capabilities can lead to more performant RLHF and point toward new best practices for RLHF practitioners.
arXiv:2506.03062v2 Announce Type: replace
Abstract: A/B tests in online experiments face statistical power challenges when testing multiple candidates simultaneously, while adaptive experimental designs (AED) alone fall short in inferring experiment statistics such as the average treatment effect, especially with many metrics (e.g., revenue, safety) and heterogeneous variances. This paper proposes a fixed-budget multi-metric AED framework with a two-phase structure: an adaptive exploration phase to identify the best treatment, and a validation phase with an A/B test to verify the treatment's quality and infer statistics. We propose SHRVar, which generalizes sequential halving (SH) with a novel relative-variance-based sampling and an elimination strategy built on reward z values. It achieves a provable error probability that decreases exponentially, where the exponent H3 generalizes the complexity measure for SH and SHVar with homogeneous and heterogeneous variances, respectively. Numerical experiments demonstrate its performance and robustness.