arXiv:2605.20132v1 Announce Type: new
Abstract: Distributed acoustic sensing (DAS) systems generate continuous, ultra-high-channel-count data streams at rates that exceed the capabilities of conventional batch-oriented analysis frameworks. As a result, essential tasks such as interactive exploration of long-duration recordings, scalable event annotation, and real-time algorithm-in-the-loop monitoring remain inadequately supported by workflows built around manually selected data segments and offline processing. This paper presents FiLark (Fiber Lark), a Python framework that applies a \emph{streaming-first} principle uniformly across data access, signal processing, visualization and monitoring for DAS. Instead of operating on manually selected data segments, FiLark presents any DAS sources-including continuous multi-file recordings-as a unified stream and builds all system components around that abstraction. An OpenGL-based ring-buffer renderer enables interactive browsing and visualization of arbitrarily long recordings with constant memory usage. An integrated annotation interface supports event labeling directly within continuous data streams, facilitating the creation of reproducible machine-learning-ready labeled datasets without offline preprocessing. The signal processing library includes temporal, spatial, spectral, and decomposition-based operators, with both CPU implementations and GPU-accelerated variants via PyTorch, alongside stateful chunked execution that preserves processing continuity and application semantics across segment boundaries. A standardized monitor interface further integrates streaming detectors and learning-based models into the visualization workflow. By sharing a common streaming abstraction across all layers, FiLark allows processing configurations and workflows developed interactively to transfer directly to scalable production pipelines without modification.
Science Journals
arXiv:2605.20169v1 Announce Type: new
Abstract: This paper presents an innovative solution designed to facilitate safe and flexible operation of nuclear power plants. The purpose of this new device, named OAPS system, is to provide optimal strategies (e.g., axial offset control, xenon oscillations mitigation, effluent minimization) and real-time recommendations (e.g., dilution and boration flowrates, turbine power setpoints and variation rates) to help NPP operators perform power variations confidently and efficiently. In fact, just as a GPS navigator optimizes and modifies its planned route according to the current position of the user, the OAPS system regularly updates its recommendations based on the latest plant measurements. To achieve this, the OAPS system relies on a well-established -yet cutting-edge in the nuclear industry -advanced control technique known as model predictive control. The conventional axial offset control strategy of the OAPS system was previously validated on both Framatome's full-scope PWR simulator and EDF's full-scope N4 simulator. In this paper, three new advanced strategies are showcased on an intermediate-complexity PWR simulator developed by Framatome: 1) determination of the fastest feasible power variation rates, 2) accelerated cancellation of axial power oscillations and 3) minimization of water and boron effluents.
arXiv:2605.20172v1 Announce Type: new
Abstract: The Power grid is a critical infrastructure underpinning all aspects of modern society and its services. Maintaining its effectiveness requires continuous adaptations. In particular, addressing sustainability targets, demand patterns, and urbanisation trends requires implementing changes to the network. Actual developments can potentially span over a decade, with supply continuity and service quality that must be preserved throughout by ensuring conformance to several topological and combinatorial invariants. Long-term power grid planning deals with the above process, and although planning languages could be a natural choice, the kind of properties and invariants needed are cumbersome to express in such languages; on the contrary, they can be elegantly and succinctly encoded in Answer Set Programming (ASP). In this paper, we propose the first approach to automate and optimise the long-term power grid planning process using ASP. Experimental evaluations conducted on synthetic and real-world grid data confirm the expressive power of the proposed ASP-based approach and demonstrate its effectiveness.
arXiv:2605.19770v1 Announce Type: cross
Abstract: A fully quantum multiphonon up-pumping model is proposed to characterize coherent energy transfer in energetic materials (EMs) subjected to external shock. After eliminating the degrees of freedom of the phonon bath within a mean-field approximation, we derive a quantum master equation governing the energy transfer among vibrational modes. Our analysis reveals that doorway modes of different frequencies undergo distinct levels of effective coherent driving and dissipation, induced by the shocked phonon environment. This not only clarifies the microscopic origin of coherent phonon generation, but also reveals the possibility of modulating such coherent driving and dissipation. Based on numerical simulations of a simplified model using the master equation, we demonstrate how doorway modes extract energy from the phonon environment and subsequently excite higher-frequency molecular vibrational modes. This work offers a renewed perspective for understanding the mechanisms of energy transfer in energetic materials.
arXiv:2605.19457v1 Announce Type: new
Abstract: Automated bidding is central to modern digital advertising. Early rule-based methods lacked adaptability, while subsequent Reinforcement Learning approaches modeled bidding as a Markov Decision Process but struggled with long-term dependencies. Recent generative models show promise, yet they lack explicit mechanisms to balance exploration and safety, relying solely on action perturbations or trajectory guidance without a safety fallback. This results in inefficient exploration and elevated financial risk for advertising platforms.
To address this gap, we propose GUIDE (Generative Auto-Bidding with Unified Modeling and Exploration), a framework that synergistically integrates directed exploration with a safe fallback mechanism. GUIDE employs a Decision Transformer (DT) to jointly model historical bidding actions and environmental state transitions. A Q-value module guides the DT's exploration via regularization constraints, while an Inverse Dynamics Module (IDM) leverages DT-predicted future states to infer robust, behaviorally consistent actions as a safe policy fallback. The Q-value module then adaptively selects the final action between these two options, balancing exploration and safety. Together, these components form an integrated "explore-safeguard-select" pipeline that unifies efficiency and safety.
We conduct extensive experiments on public datasets, in simulated auction environments, and through large-scale online deployment on Taobao, a leading Chinese advertising platform. Results show GUIDE consistently outperforms state-of-the-art baselines across all scenarios. In real-world deployment, GUIDE achieves notable gains: +4.10% ad GMV, +1.40% ad clicks, +1.66% ad cost, and +3.52% ad ROI, demonstrating its effectiveness and strong industrial applicability.
arXiv:2504.07756v2 Announce Type: replace
Abstract: Understanding human behaviour, neuroscience and psychology using concepts from the domain of AI is increasing in popularity. Given the massive integration of AI technologies into our daily lives, AI-related concepts are being used to compare AI systems with human behaviour, brain functions, and cognitive abilities like language acquisition. But scientists and philosophers are also increasingly tempted to take the AI-framing of the human conceptual domain as a literal one. This paper investigates the epistemic and practical success of these 'AI-framings': What does it mean to apply the conceptual constellation of AI to the human conceptual domain? We consider and compare two possible answers: either these examples are conceptual metaphors, or they are attempts at conceptual engineering. Firstly, we argue that when viewed as conceptual metaphors, the AI-framed descriptions risk committing the ''map-territory fallacy''. Secondly, we argue the comparisons also contain a misleading 'double metaphor' because of the metaphorical connection between human psychology and computation at the conceptual foundation of computation. But we also argue that there is a possible semantic catch to the AI-framing, which is captured by the conceptual engineering view. This is that the AI-framings point towards avenues for forms of conceptual engineering. If the challenges of conceptual ethics and reductionism are overcome, some AI-framings might enrich our epistemic and practical lives. So, at its worst - as implicit conceptual metaphor - the AI-framing leads us completely astray; at its best, it prompts us to reflect anew on how the boundaries of our current concepts serve us and how they could be improved.
arXiv:2508.15269v2 Announce Type: replace
Abstract: The behavior of capacitive coupled plasma (CCP) discharges is investigated in a mildly magnetized regime, defined by the condition 1 $\leq$ $f_{ce}/f_{rf}$ $\lt$ 2, where $f_{ce}$ and $f_{rf}$ are the cyclotron and radio-frequencies (RF), respectively. This regime exhibits complex and distinctive plasma dynamics due to the interplay between RF fields and the externally applied magnetic field. Two prominent phenomena are observed in this regime. First, the plasma density profile becomes asymmetric across the discharge, deviating from the typical symmetric distribution seen in unmagnetized CCPs. Second, electron Bernstein waves (EBWs), high-frequency electrostatic waves, are excited and propagate within the bulk plasma, particularly along steep electron density gradients. As the strength of the magnetic field increases within this regime, the CCP discharge undergoes a transition from a symmetric configuration to an asymmetric one, and then returns to a symmetric profile at higher field strengths. Notably, the excitation and propagation of EBWs are strongly correlated with the presence of discharge asymmetry and localized density gradients. These waves play a significant role in energy transport and electron heating under mildly magnetized conditions. To gain deeper insight into the underlying physics, detailed numerical simulations are carried out using the particle-in-cell Monte Carlo collision (PIC-MCC) technique. These simulations capture the kinetic behavior of electrons and ions, including the collisionless effects and sheath dynamics essential to understanding the excitation of EBWs and the evolution of discharge symmetry. The study thus sheds light on the role of weak magnetic fields in shaping plasma behavior and highlights the importance of wave-particle interactions in magnetized CCPs.
arXiv:2605.19926v1 Announce Type: new
Abstract: The progression of reinforcement learning algorithms have been driven by challenging benchmarks. The rate in which a researcher can iterate on a problem setting directly impacts the speed of algorithm development. Modern machine learning has produced tools that allow for fast and scalable algorithm development like the JAX library. With the availability of these tools, a serious bottleneck in algorithm development is the availability of large and complex domains for experimentation. Most notably, the JAX reinforcement learning ecosystem does not have any benchmarks that test visual first-person tasks; these domains are crucial for testing both exploration and an agent's ability to overcome partial observability. We introduce JAXenstein: an open-source JAX-based benchmark that implements the Wolfenstein 3D rendering engine for fast and scalable experimentation in visual first-person tasks. JAXenstein is several times faster than comparable vision-based benchmarks, and is easily extensible to more complex first-person domains.
arXiv:2605.18808v1 Announce Type: new
Abstract: We characterize a compositional architecture of literary primitives in two instruction-tuned large language models (Llama 3.1 8B-Instruct and Gemma 2 9B-IT) via sparse autoencoders on mid-depth residual streams. Four feature classes emerge: naming-gates that promote lexical tokens of a target affect, an eleven-self cluster of first-person register features, stylistic register modulators (show-don't-tell and defamiliarization), and compositional emotions that arise only from multi-feature steering. Under a forced-choice 5-LLM judge panel applied to a 27-category emotion taxonomy (Cowen-Keltner), Llama reaches full 27/27 coverage by combining naming-gates, multi-feature recipes, and single self-feature steering; Gemma reaches 23/27 with adoration as the single residual strict-fail. Under random judging, the per-cell pass probability is on the order of $10^{-3}$ and the expected number of two-seed false-positive cells across the catalog is negligible, so the observed coverage is not consistent with chance. A cross-architectural asymmetry sits in the strict-versus-soft judge contrast: on the same generations, judges agree more often on Llama outputs than on Gemma outputs because Llama outputs name the target affect more directly while Gemma outputs evoke it through scene and imagery. Both architectures contain self-features that serve simultaneously as register markers and as emotion emitters, including a single most-RLHF-loaded self-feature per architecture that intensifies the institutional Helper-AI persona at one operating regime and produces affect-categorizable output at the same calibrated coefficient. Methodologically, the paper presents a three-stage validation pipeline (logit-lens, LLM-rate, 5-LLM judge) with documented anti-patterns; the total compute is single-GPU and about 15 minutes per emotion-feature discovery cycle.
arXiv:2605.19010v1 Announce Type: new
Abstract: Natural language to SQL (NL2SQL) conversion is an important problem for researchers and enterprises due to the ubiquitous importance of relational databases in broad-ranging practical problems. Despite the rapid advancements in the capabilities of LLMs, NL2SQL has not reached parity in accuracy with human expert SQL writers, hence needing additional improvements in NL2SQL algorithms. This study presents a new multi-agent method for NL2SQL that achieves 78.1% semantic accuracy on the BIg Bench for LaRge-scale Database (BIRD) benchmark. Our method leverages a semantically enriched representation of user-provided schema, adds user-provided business rules, and produces accurate SQL queries. The main contributions of this study are (a) We designed an optimized new orchestrator in a multi-agent solution that uses LLMs to plan, orchestrate, reflect, and self-correct to generate accurate SQL queries, (b) We developed an advanced schema enrichment method that creates context-aware metadata to improve accuracy, and (c) We demonstrated the accuracy and generalizability of the method across different domains and datasets by evaluating it on the BIRD-SQL benchmark.
arXiv:2604.24427v2 Announce Type: replace
Abstract: An updated design for the LEP3 electron-positron collider is presented. The machine is designed to operate in the existing tunnel infrastructure currently hosting the Large Hadron Collider and aims to deliver high luminosity for precision studies of the Z, W, and Higgs boson.
arXiv:2509.22202v3 Announce Type: replace
Abstract: Large language models (LLMs) now play a central role in code generation, yet they continue to hallucinate, frequently inventing non-existent libraries. Such library hallucinations are not just benign errors: they can mislead developers, break builds, and expose systems to supply chain threats such as slopsquatting. Despite growing awareness of these risks, there is limited understanding of how library hallucinations manifest under realistic usage conditions. To fill this gap, we present the first systematic study of how user-level prompt variations influence library hallucinations in LLM-generated code. Across seven diverse LLMs, we analyse library name hallucinations (invalid imports) and library member hallucinations (invalid calls from valid libraries), examining the effects of realistic developer language and controlled user mistakes, including misspellings and fabricated libraries or members. Our findings expose systemic vulnerabilities: one-character misspellings trigger hallucinations in up to 26% of tasks; fabricated library names are accepted in up to 99%; and time-based prompts induce hallucinations in up to 85%. Grounded in the highest-risk prompts identified in our study, we introduce LibHalluBench, a benchmark that enables a systematic and reproducible evaluation of these library hallucinations. Our findings underscore the fragility of LLMs to natural prompt variation and highlight the urgent need for safeguards against library-related hallucinations and their downstream risks.
arXiv:2605.20134v1 Announce Type: new
Abstract: Learning generalizable trajectory representations from raw GPS traces remains difficult because the data is continuous, noisy, and irregularly sampled. Spatial tokenization is also challenging: fine grids yield sparse cells with weak embeddings, while coarse grids merge heterogeneous movement patterns into the same token. We present TrajTok, a trajectory encoder with a simple pretraining recipe for transferable trajectory embeddings. TrajTok first learns a multi-resolution hexagonal cell partition from the spatial distribution of GPS points, converting noisy GPS sequences into discrete cell tokens. To capture both geometry and kinematics, it uses a factorized transformer encoder with early per-modality self-attention blocks, cross-attention fusion layers, and spatiotemporal rotary position embeddings, ST-RoPE, to encode where and when each token occurs. TrajTok is pretrained with masked-token modeling that recovers both geometric structure and kinematic patterns from partial trajectory observations. On the Porto dataset, a frozen TrajTok encoder with lightweight task adapters achieves strong performance across trajectory similarity search, classification, estimated time of arrival, and full travel-time regression, outperforming multiple task-specific methods. The same frozen encoder supports both geometry-dominated and kinematics-dominated tasks, suggesting that TrajTok learns transferable trajectory structure rather than task-specific shortcuts. These results indicate that learned multi-resolution spatial tokenization combined with masked-token pretraining is a promising direction for general-purpose trajectory foundation models.
arXiv:2605.20139v1 Announce Type: new
Abstract: Online, comprehensive molecular profiling of exhaled breath provides a non-invasive window into human metabolism, yet current optical platforms are restricted by narrow instantaneous spectral coverage. Here, we present a novel ultra-broadband mid-infrared spectroscopic platform that enables simultaneous, high-sensitivity detection of a comprehensive profile of breath biomarkers. By integrating an intrapulse difference-frequency generation (IDFG) supercontinuum source spanning 2.9-11.5 $\mu$m (2580 cm$^{-1}$) with a custom-built Fourier transform spectrometer, we achieve a spectral resolution of 0.1 cm$^{-1}$ - surpassing current laser-based approaches. Combined with a standardized online sampling system, the platform achieves sensitivities in the tens of parts per billion over three minutes, resolving dynamic metabolic changes of ammonia, methane, isoprene, acetone, carbon monoxide, and nitrous oxide. We demonstrate the system's utility through proof-of-concept case studies tracking responses to fasting, protein intake, and smoking. This calibration-free platform establishes a powerful and versatile tool for online breath analysis, with broad potential in clinical diagnostics and exposure monitoring.
arXiv:2601.05437v3 Announce Type: replace
Abstract: Large language models often produce human-like moral judgments, but it is unclear whether this reflects an internal conceptual structure or superficial ``moral mimicry.'' Using Moral Foundations Theory (MFT) as an analytic framework, we study how moral foundations are encoded, organized, and expressed across 14 base and instruction-tuned LLMs spanning four model families (Llama, Qwen2.5, Qwen3-MoE, Mistral) and scales from 7B to 70B. We employ a multi-level approach combining (i) layer-wise analysis of MFT concept representations and their alignment with human moral perceptions, (ii) pretrained sparse autoencoders (SAEs) over the residual stream to identify sparse features that support moral concepts, and (iii) causal steering interventions using dense MFT vectors and sparse SAE features. We find that models represent and distinguish moral foundations in a manner that aligns with human judgments, and that this moral geometry naturally emerges from pretraining and is selectively rewired by post-training. At a finer scale, SAE features show clear semantic links to specific foundations, suggesting partially disentangled mechanisms within shared representations. Finally, steering along either dense vectors or sparse features produces predictable shifts in foundation-relevant behavior, demonstrating a causal connection between internal representations and moral outputs. Together, our results provide mechanistic evidence that moral concepts in LLMs are distributed, layered, and partly disentangled, suggesting that pluralistic moral structure can emerge as a latent pattern from the statistical regularities of language alone.
arXiv:2605.19915v1 Announce Type: new
Abstract: Classical models of opinion dynamics assume human participants with bounded rationality and limited coordination. The rise of LLM-based agents introduces a qualitative shift: agents can now participate in online discussions at scale, maintain consistent persuasion strategies, and coordinate systematically. This paper argues that LLM agents make collective belief dynamics programmable, enabling deliberate steering of population-level beliefs. We term this emerging problem programmable collective belief control. Through controlled multi-agent simulations, we provide proof-of-concept evidence that coordinated AI agents can induce measurable belief shifts that stabilize within a few interaction rounds. We identify four structural properties (indistinguishability, persistence, contextuality, and configurability) that make detection and defense fundamentally difficult. Based on these findings, we outline a research agenda spanning theoretical foundations for adversarial belief dynamics, operational methods for system-level detection and intervention, and simulation infrastructure for scalable experimentation. Our goal is not to present a complete solution, but to articulate why this problem demands urgent attention and to provide a conceptual foundation for future work.
arXiv:2605.19462v1 Announce Type: new
Abstract: The success of self-supervised learning (SSL) in vision and NLP has motivated its rapid adoption for time series. However, research has focused primarily on Generative paradigms and forecasting tasks, leaving the broader utility of learned representations unquantified. We establish a controlled framework to evaluate the "pre-training dividend": the value added by SSL across diverse temporal tasks. We systematically compare Generative paradigms against Latent Alignment architectures, introducing adaptations of LeJEPA and DINO for time series. These adaptations utilize Discrete Wavelet Transform (DWT) augmentations to enforce invariance to local fluctuations. Our analysis reveals that the pre-training dividend is highly asymmetric: SSL yields gains of up to 375% for anomaly detection and classification, yet remains marginal for forecasting. We demonstrate that representational utility is non-universal, governed by a precision-invariance trade-off where the specific signal resolution required by the task must align with the objective. Finally, we show that representation quality is largely independent of data origin and saturates at moderate architectural depths, suggesting a path to scaling via massive synthetic generation. Our code is available at: https://github.com/noammajor/Models
arXiv:2602.07777v2 Announce Type: replace
Abstract: Indirect reciprocity, which means helping those who have helped others, is difficult to sustain among decentralized, self-interested LLM agents without reliable reputation systems. We address this challenge with the Agentic Linguistic Gossip Network (ALIGN), an automated framework that enables decentralized agents to form reputations, evaluate trustworthiness, and coordinate social norms by strategically sharing open-ended gossip with hierarchical tones. We demonstrate that ALIGN consistently improves indirect reciprocity and resists malicious entrants by identifying and ostracizing defectors. Notably, we find that stronger reasoning capabilities in LLMs lead to more incentive-aligned cooperation, whereas chat models often over-cooperate even when strategically suboptimal. These results suggest that leveraging LLM reasoning through decentralized gossip is a promising path for maintaining social welfare in agentic ecosystems. Our code is available at https://github.com/shuhui-zhu/ALIGN.
arXiv:2605.19522v1 Announce Type: new
Abstract: Pairwise image quality assessment (IQA) in professional photography requires a model not only to identify the preferred image between two candidates, but also to provide convincing and image-grounded reasoning. In the NTIRE 2026 RAIM challenge, this requirement is further emphasized by jointly evaluating preference prediction and rationale generation. To address this task, we propose iDiff, an Interpretable Difference-aware framework for pairwise image quality assessment. Our method adopts a dual-branch design consisting of an Answer Model and a Thinking Model. The Answer Model performs robust preference prediction by explicitly decomposing each sample into left/right global and local views, followed by content-aware specialization for person and scene images and ensemble-based aggregation across backbones. The Thinking Model focuses on rationale generation and is progressively enhanced with expert-style templates, multi-source quality features, and answer-aware supervision conditioned on the Answer Model prediction. In this way, iDiff jointly models discriminative decision making and structured explanation, improving both robustness and interpretability. Extensive experiments demonstrate the effectiveness of the proposed framework on both accuracy and reasoning-quality metrics. Our method achieved first place in the NTIRE 2026 RAIM challenge, showing the effectiveness of integrating explicit difference modeling with structured multimodal reasoning for pairwise IQA.
arXiv:2602.11910v2 Announce Type: replace
Abstract: Audio diffusion models can synthesize high-fidelity music from text, yet achieving fine-grained control over specific musical attributes remains challenging, as their internal mechanisms for representing high-level concepts are poorly understood. In this work, we use activation patching to demonstrate that recent audio diffusion architectures exhibit a semantic bottleneck, where a small, shared subset of consecutive attention layers controls distinct musical concepts, such as the presence of specific instruments, vocals, or genres. Building on this, we systematically evaluate a broad spectrum of steering paradigms, comparing activation steering against prompt-level, score-space, and weight-space interventions, analyzing the interaction between the steering mechanism and the intervention site. Our new benchmark, supported by an extensive user study, demonstrates that localized activation steering establishes a new state-of-the-art in audio concept modulation.
arXiv:2605.19631v1 Announce Type: new
Abstract: End-to-end autonomous driving has emerged as a compelling alternative to traditional modular pipelines by directly mapping raw sensor data to driving actions. While recent approaches achieve strong performance on single-domain datasets, their performance degrades significantly when trained jointly across multiple heterogeneous domains. In practice, however, autonomous systems must operate across diverse environments with heterogeneous distributions, including different cities, sensor configurations, and traffic patterns, without domain-specific retraining. This gap highlights a key challenge in multi-domain learning: domain-specific variations across heterogeneous domains introduce conflicting learning signals, driving models toward compromised solutions that are suboptimal across domains. To address this, we propose a trajectory-driven learning paradigm that organizes training around planning trajectories, enabling the model to capture domain-invariant representations of driving intent. Furthermore, we incorporate a world model that predicts future latent features conditioned on ego actions, improving feature consistency and mitigating domain-induced biases. We evaluate our approach on three benchmarks, nuScenes, NAVSIM, and the Waymo end-to-end dataset, and show substantial improvements over existing methods across all domains. Our results demonstrate that a single unified model can be trained on heterogeneous datasets while maintaining strong performance within each domain, highlighting a step toward scalable real-world deployment. We will make our code publicly available.
arXiv:2605.19645v1 Announce Type: new
Abstract: Recent advancements in large language models (LLMs) have shown their remarkable capacities in many NLP tasks. However, their substantial size often presents challenges for deployment. This necessitates efficient techniques for model compression, with quantization emerging as a prominent solution. Despite its benefits, the exact impact of quantization (from 2- to 6-bit) on the performance and accuracy of LLMs remains an active area of research. This paper investigates the performance of eight LLMs at various quantization levels, focusing on tasks such as MMLU-Pro for knowledge processing and reasoning, CRUXEval for code comprehension, and MuSR for reading comprehension. Our results show a consistent trend where higher precision (e.g., 8-bit Q8\_0) yields improved performance, albeit with diminishing returns. Aggressive quantization (e.g., 2-bit Q2\_K) usually retains acceptable accuracy, though some models show a substantial loss in performance. Our findings indicate that while lower bit precision generally reduces performance, the impact varies across models and tasks. Larger models show greater resilience to aggressive quantization, but can still undergo significant drops at lower precision levels. Mid-sized models in the 7-9 billion parameter range strike an optimal balance between efficiency and resource usage. Such results provide insights into the trade-offs between model size, quantization, and performance.
arXiv:2605.19654v1 Announce Type: new
Abstract: The dichromatic number $\vec\chi(D)$ of a digraph is the minimum number $k$ such that $V(D)$ can be partitioned into $k$ subsets, each inducing an acyclic digraph. The acyclic number $\vec\alpha(D)$ is the cardinality of a largest induced acyclic subdigraph of $D$.
We study these problems from an approximation point of view. We begin with establishing that even when restricted to tournaments, approximating $\vec\chi$ and $\vec\alpha$ remain as challenging as their undirected counterparts on general graphs. Specifically, we establish that for every $\epsilon >0$, it is hard to approximate both $\vec\alpha$ and $\vec\chi$ up to a factor of $n^{1-\epsilon}$ even when restricted to tournaments.
We next consider approximate coloring of digraphs in special cases. We begin with establishing that we can color $\ell$-dicolorable digraphs using at most $\ell \cdot n^{1-\frac{1}{\ell}}$ colors in time $O(n^{2\ell})$; in particular, we can color $2$-dicolorable digraphs with $2\sqrt{n}$ colors in polynomial time. We then focus on bounding the dichromatic number of dense digraphs as a function of the independence number $\alpha$ of the underlying graph. We consider two special cases in this regard: digraphs with $\vec\chi(D)\leq 2$ and digraphs that do not contain any directed triangle. For these cases, we present algorithms which generalize and improve existing tools and results.
arXiv:2605.19655v1 Announce Type: new
Abstract: Automated driving systems require monitoring mechanisms to ensure operation as intended, especially when system elements degrade and/or fail. Hence, capability monitoring is crucial in order to evaluate the system's remaining performance and implement capability-based behavior. In this paper, we investigate the dynamics of a highly over-actuated automated vehicle under actuator degradations and failures, affecting the vehicle's motion control capabilities. We propose a lightweight prediction model based on conformalized quantile regression that predicts whether an automated vehicle can be controlled with sufficiently low lateral deviation from a planned trajectory under nominal, degraded, and failed actuator conditions. We recognize that statistical guarantees should hold not only across all data (marginal coverage) but also for different regimes within the data (conditional coverage). We therefore employ equalized coverage methods to address this challenge. During runtime behavior generation our predictor can provide a heuristic for determining the admissible action space. Its application and limitations are discussed in this paper.
arXiv:2605.19664v1 Announce Type: new
Abstract: Optical microcavities have emerged as a powerful platform for emulating topological phases challenging to realize in conventional materials, offering precise control over dispersion, light confinement, and interactions. Among them, liquid crystal microcavities (LCMCs) offer exceptional tunability at room temperature, enabling voltage-controlled polarisation splitting, photonic spin-orbit coupling, and photonic potentials generated by self-assembled textures, such as cholesteric torons and uniform lying helix (ULH). Here, we design a LCMC hosting a dimerized ULH texture and show that the corresponding photonic potential describes two coupled Su-Schrieffer-Heeger chains with orthogonal linear polarisations, acting as an effective pseudospin degree of freedom. The applied voltage tunes the interchain coupling, enabling polarisation-dependent interactions. These results establish LCMCs as a versatile platform for tunable synthetic topological Hamiltonians.