The scaling of LLMs toward long-context inference has shifted the primary serving system bottleneck from computation to memory capacity. Traditional solutions for dense attention models rely on RDMA-based disaggregated memory pools, which perform coarse-grained fetching of the entire prefix KV cache from remote storage to local memory before decoding. However, this approach is fundamentally inefficient for emerging sparse attention models. While only a small fraction of KV entries are active during decoding, these systems still fetch the full KV cache locally, leading to severe transmission bottlenecks and local memory wastage. To address this, we propose SAC, the first efficient disaggregated KV cache system optimized for sparse attention models. By leveraging the low-latency, cache-line granularity load/store semantics of Compute Express Link (CXL), SAC fetches only the required top-k KV entries on demand during inference. Evaluations on DeepSeek-V3.2 using SGLang show that SAC achieves 2.1x higher throughput, 9.7x lower TTFT, and 1.8x lower TBT compared to RDMA-based baselines, establishing CXL-based disaggregation as the superior infrastructure for emerging sparse attention models.

Islamic learning often depends on reading the Qur'an, Hadith, and Seerah together, yet digital tools typically separate these sources across apps, screens, and search pathways. We examine this as a human-computer interaction problem through five semi-structured interviews with Muslim women recruited from an online Islamic learning community. Participants described a recurring tension: they wanted Qur'an-Hadith-Seerah context at the point of reading, but only when contextual expansion remained trustworthy, optional, and did not interrupt reading. Interpreting the interviews through gendered digital religion, epistemic trust, and seamless learning, we identify five themes concerning contextual understanding, authenticity, interface clutter, study modes, and guidance features. We introduce layered contextuality as an HCI account of this domain: contextual expansion must be balanced with interpretive accountability, devotional flow, and continuity across devices and study intensities.

Data visualization is essential for data analysis and communication, yet creating expressive visualizations remains labor-intensive. Recent AI-driven ``vibe coding'' tools enable users to generate visualizations through natural language interaction, lowering the barrier to entry. However, visualization implementation requires precise alignment between user intent and visual representation, which may differ from general software development practices. We present an empirical study with 16 participants of varying expertise to examine how users employ vibe coding tools for visualization implementation. Participants completed two visualization tasks and a semi-structured interview. Our findings characterize the diverse practices users adopt across prompting, evaluation, and iteration, and surface the challenges they encounter throughout the process.

Vector hubness, where a few points become nearest neighbors of many queries, creates a poisoning risk in retrieval-augmented generation (RAG): one injected document can influence unrelated requests. Existing defenses use periodic reverse-kNN scans, leaving an exposure window and repeated corpus-wide work. We study admission-time control, scoring each candidate against sentinel queries and quarantining hub-like documents before insertion. Across two 100,000-document corpora, five encoders, and disjoint attacker and defender query sets, a global gate achieves recall 1.0 at the decisive embedding-space point (>=0.92 across the effective range) and 0.91 +/- 0.07 on HotFlip attacks, with 1% false positives on general documents. A per-topic gate provides no reliable benefit, consistent with anisotropy coupling local and global visibility. Thresholds are maintained incrementally, with corpus-size-independent insertion cost and amortized deletion cost. On HNSW, admission adds about 3.1% to ingestion latency, scoring remains flat to 10^6 vectors, and 1.2% of decisions flip under approximate indexing, none involving attacks. Provenance complements the gate for natural or tight-domain hubs.

Traditional network representations, such as node-link views and adjacency matrices, can show dramatically different visual patterns, depending on the underlying layout or seriation algorithm. In contrast, invariant plots consistently surface the same visual pattern for the same input topology; yet researchers have underexplored them and have not integrated them into visualization systems. We present Syndesmoscope, an interactive system for network exploration that juxtaposes multiple views of the same network. Panes show a familiar a force-directed view alongside three panes with interpretable geometric layouts based on graph-theoretic properties: dense-sparse gradient, geodesic eccentricity, and spectral bisection. As a secondary contribution, we introduce kSnakes, a new invariant plot based on density decomposition. Syndesmoscope supports two key interactions: leapfrogging, or linked highlighting between different and interpretable visual patterns; and hopscotching, or hop-based traversal that extends data selections through the underlying topology. Through usage scenarios across a corpus of 72 diverse networks, we demonstrate how these interactions reveal network patterns inaccessible through any single view alone. Live demo available at https://syndesmoscope.vercel.app/.

Many forms of static reasoning about program behaviours are known in the literature, yet formal relationships are studied surprisingly infrequently. While most type systems are well-known to be captured by abstract interpretations, the situation for type-and-effect systems is, in the general case, unsettled despite strong hypotheses and occasional framing of effect systems as abstract interpretations. We develop a formal relationship between abstract interpretations and a general class of effect systems. First, we describe an embedding of effect quantales into abstract domains. Second, we recover the general form of an effect quantale as an abstract interpretation -- not on states or values, but on event occurrences.

Longitudinal ophthalmic imaging analysis is an essential step for prognosis prediction in ophthalmic diseases. However, AI-assisted prognosis models are challenged by follow-up sequences, which tend to be sparse, irregularly sampled, and incomplete. Although advanced prognosis modeling methods, especially for the methods based on neural controlled differential equations (NCDEs), provide a principled continuous-time framework for sparse and irregular longitudinal data. Unfortunately, two major concerns remain unsolved in clinical follow-up modeling. First, the smooth latent dynamics of standard NCDEs is poorly matched to abrupt pathological changes induced by therapeutic intervention, lesion recurrence, or long follow-up gaps. Second, numerical integration over long horizons can accumulate errors, which will produce unstable latent trajectories and weakened class discrimination. To address these challenges, we propose ImProNCDE, an impulse-corrected NCDE framework with prototype learning for longitudinal ophthalmic prognosis prediction. To capture abrupt pathological changes beyond smooth latent dynamics, ImProNCDE introduces Residual Impulse Calibration (RIC), which injects residual-based impulse corrections at visit times and then recalibrates the latent state when observations deviate from continuous predictions. To further mitigate error accumulation over long horizons, we introduce a Prototype-guided Trajectory Stabilizer (PTS), which aims to attract latent trajectories toward learnable prognosis prototypes to reduce class overlap and which ultimately improves long-horizon stability. Experiments on multiple private and public longitudinal ophthalmic datasets (totalling over 1206 samples) show that ImProNCDE outperforms existing SOTA methods focusing on sequence modeling.

ReDoS attacks constitute a critical class of resource-exhaustion vulnerabilities. In such attacks, adversaries exploit the pathological worst-case execution behavior of regular expression (regex) engines to induce highly asymmetric computational workloads, ultimately exhausting system resources and degrading service availability. To protect systems against ReDoS attacks, numerous detection techniques have been proposed that simulate the attack process by generating attack strings to proactively exploit ReDoS vulnerabilities at the early development stage and facilitate remediation. Existing techniques broadly fall into two classes: static analyses that search for pathological regex structures, and dynamic exploration methods that synthesize candidate attack strings. However, the generated attack strings are often impractical for real-world exploitation because they usually assume unrealistic input-length budgets and do not validate the effectiveness and efficiency of the attack at the program level. Therefore, many generated strings fail to trigger vulnerable regexes when applied to real-world programs, further limiting the practical utility. To address these shortcomings, we introduce an effective and efficient attack string generator, PUFFERDOS, designed to synthesize attack inputs that are both feasible within realistic length budgets and validated at the program level, enabling effective exploitation of ReDoS vulnerabilities in real-world programs. Specifically, we first define three vulnerable patterns based on our observation and formal verification. According to the patterns, PUFFERDOS conducts a synthesis technique to generate attack strings, and then refines and validates the strings with ReDoS-specific compositional concolic execution to guarantee real-world exploitability.

Large language model (LLM)-powered tools such as ChatGPT are increasingly used in collaborative software engineering workflows, yet little is known about how prompt structure influences downstream pull request (PR) outcomes. Prior studies primarily examine conversational helpfulness, productivity, or coarse-grained adoption metrics, leaving the role of prompt structure in collaborative integration behavior insufficiently understood. We analyze 265 manually validated developer-ChatGPT interactions derived from self-admitted ChatGPT usage in open-source pull requests. Building on prior research on developer-facing artifacts and prompt engineering, we operationalize prompt structure using three dimensions: Context, Specificity, and Verification. We first evaluate whether LLM-assisted annotation can reliably reproduce human judgments of prompt structure, finding substantial variation across dimensions and workflow contexts. Specificity shows the most stable agreement with human judgments; Context is systematically under-scored by the LLM; and Verification remains difficult to assess consistently, motivating a hybrid human-LLM annotation strategy. Using this validated framework, we then examine how prompt structure influences actionable code generation, code adoption, and integration depth across AI-assisted PR workflows. Specificity and Context are most strongly associated with actionable code generation; Verification emerges as the primary predictor of code adoption; and integration depth is most strongly associated with Context. Overall, our findings show that prompt characteristics exert distinct, stage-dependent effects across AI-assisted software engineering workflows, influencing downstream adoption and integration through contextual grounding, task specificity, and evaluability cues.

We present G-Lox (group-adaptive Lox), a bridge-distribution system that preserves Lox-style distributor blindness while enabling hidden, stateful group-level adaptation. G-Lox places adaptive assignment logic behind a two-server privacy wall, so no single server learns group identifiers or group-to-bridge assignments. Private state access and state-dependent updates use two-server DPF/FSS protocols and secure two-party computation, supporting blockage reporting, transport-aware reassignment, and privacy-preserving group splitting. We evaluate G-Lox through system measurements and policy simulation. In our C++/EMP implementation over real TCP sockets, private state access has low client-visible overhead: across state sizes up to 2^16, communication remains in the low-KiB range per iteration. At M=1024, the client sends 1,968 bytes, receives 1,280 bytes, and completes an iteration in about 0.25 s. Simulations with group-specific blocking and Sybil enumeration show that G-Lox improves robustness over Lox- and rBridge-like baselines among systems that maintain broad issuance.

The Determinant Mutual Information (DMI) mechanism of Kong (2020, 2024) is dominantly truthful within the class of *consistent* reporting strategies, those that apply the same single-task strategy to every task. In settings where agents see multiple tasks before reporting, such as peer grading or peer review, it is natural to consider *joint-task* strategies that may condition reports on the full signal vector. Perhaps surprisingly, we show that the DMI mechanism preserves truthful reporting as a best response among all joint-task strategies when other agents play consistent strategies, so that truthfulness remains a Bayes--Nash equilibrium in the joint-task class. Without the restriction of peers to consistent strategies, however, both dominant truthfulness and informed truthfulness fail against joint-task peer strategies.

In a circular economy for construction, reclaimed materials carry prior lives of use and go on to have post-lives in future buildings. Yet working with such materials introduces unpredictability that requires on-site improvisation, making their reuse challenging to document and scale across building lifetimes. Without documentation, the on-site adaptations that make construction with reclaimed materials possible leave collaborators, evaluators, and inheritors without the information they need to continue, assess, and reuse materials. We call the collective deviation of the physical state from the digital model through these adaptations "building drift." Through a case study, ReShelter, a reclaimed timber pavilion constructed in the forest, we develop a taxonomy for building drift that characterizes the collective deviation across building lifetimes: Tending the Site, Foraging for Fit, Interpreting the Material, Marking Measurements, and Coordinating Across Communities. To put our taxonomy for building drift into practice, we present Pentimento, a documentation tool that leverages video documentation and 3D Gaussian Splatting to spatially, temporally, and semantically represent on-site adaptations in relation to the designed model. Pentimento enables each stakeholder to navigate material histories in ways that reduce barriers to material reuse. Together, these contributions open pathways towards computational tools that support the on-site improvisation essential to construction with reclaimed materials, enabling more sustainable cycles of recovery, repair, and reuse.

We study ramping procurement co-optimized with economic dispatch under net-demand uncertainty. We examine two flexible ramp product designs implemented by grid operators: single-interval and multi-interval co-optimization. Both rely on rolling-window stochastic optimization with binding and advisory interval decisions. We develop analytical frameworks to evaluate generator profits, consumer payments, bid cost recovery (BCR), and operational efficiency. In particular, net-demand uncertainty may lead to generator under-compensation, requiring discriminatory BCR. While operational efficiency is invariant to energy and ramp prices, producer profits and consumer payments depend critically on pricing. We examine locational marginal pricing (LMP) and two uniform pricing: maximum dispatch cost pricing (MDCP) and maximum temporal locational marginal pricing (MTLMP). With out-of-market BCR, LMP yields discriminatory energy prices, whereas MDCP eliminates BCR and MTLMP does so in most cases. This property enables us to establish truthful bidding incentives for price-taking generators under MDCP. Our analysis highlights trade-offs between single- and multi-interval co-optimization and pricing designs: single-interval energy-ramp co-optimization is advantageous under high forecast uncertainty and moderate ramping requirements, whereas multi-interval co-optimization is superior when net-demand forecasts are relatively accurate and ramp needs are challenging. Empirical results on CAISO and ERCOT data show that MDCP and MTLMP increase producer profits with negligible BCR, albeit at the expense of higher consumer payments relative to LMP.

Remote and disaggregated memory tiers expand the effective memory capacity of analytical database engines, but they also reshape the cost structure of out-of-memory query processing. When an operator spills beyond local DRAM, moving pages to remote memory incurs both data-transfer time and a fixed round-trip latency per transfer. Classical operator analyses and buffer-allocation heuristics primarily target disk spilling by minimizing total I/O volume. Under remote memory, these strategies can be suboptimal because they may trigger excessive transfer rounds. We present REMOP, a remote-memory-aware operator optimization framework that uses transfer-round-aware intra-operator memory policies to improve out-of-memory execution under tight memory budgets. REMOP introduces the number of transfer rounds into the latency cost model and derives operator-specific buffer-partitioning strategies, instantiating the approach for blocked nested-loop join, external merge sort, and external hash join in DuckDB. Our evaluation on a two-node compute-memory testbed shows that REMOP reduces transfer rounds by up to 97% and operator runtime by up to 48% on spill-heavy microbenchmarks, and lowers the average runtime of spilling TPC-H and TPC-DS queries by 22.7% and 26.4% end-to-end.

Program visualizations are widely used to support novice programmers, yet students often ignore or resist well-designed visual scaffolds. Research on multiple external representations (MERs) offers cognitive design principles for coordinating views, but less is known about what shapes learners' engagement with available representations. We conducted a within-subjects study with 19 undergraduates who had completed CS1 and CS2. Students completed think-aloud tasks, reflective interviews, and webcam-based gaze tracking while using a multi-representational probe with synchronized code, memory, and metaphor views, and Python Tutor, across scope, while loops, and linked lists. Gaze analysis showed that students spent nearly half their time focused on code despite available visual scaffolds. Students without prior experience anchored even more heavily in code and engaged minimally with metaphor views. Interviews identified three factors shaping selective engagement: agency, as students sought control over cognitive effort rather than simply having it reduced; representational fit, as identical designs differed in whether they felt helpful or overwhelming; and legitimacy, as some students avoided metaphorical scaffolds they perceived as childish or insufficiently rigorous for university-level work. These findings suggest that multi-representational tools in computing education require attention to affective and social factors alongside cognitive design. Practical considerations include positioning visualizations as verification instruments, offering toggleable abstraction levels, and framing tools to signal disciplinary legitimacy. More broadly, the themes help explain why cognitively sound visualization tools may fail to engage the students they are designed to help.

Predictive simulation of melt pool dynamics in laser-based processing of metals, e.g., laser beam welding or laser powder bed fusion additive manufacturing, requires accurate resolution of thermo-hydrodynamic interactions at the melt-gas interface. Here, evaporation-induced recoil pressure and temperature-dependent surface tension govern the flow. Because these mechanisms depend sensitively, often exponentially, on the interface temperature, reliable predictions demand highly accurate heat transfer models. Popular diffuse-interface formulations smear the extreme thermal gradients as typical for laser-metal interactions, leading to interface temperature errors that critically degrade the accuracy of interface force predictions and melt pool dynamics. We present a hybrid sharp-diffuse interface approach for high-fidelity modelling of melt pool thermo-hydrodynamics with rapid evaporation. The heat transfer problem is represented using a sharp-interface unfitted finite element (CutFEM) formulation, enabling accurate prediction of the temperature field. The multi-phase flow problem, characterized by large density ratios and complex interface dynamics, is accurately captured using a robust level-set-based one-fluid diffuse-interface finite element formulation. Consistent coupling is achieved by extending the sharp-interface temperature into a narrow interface region to evaluate temperature-dependent interface forces within the diffuse-interface flow framework. In practically relevant benchmarks, the sharp-interface thermal model exhibits second-order spatial convergence, enabling finite element sizes two orders of magnitude larger than the diffuse-interface approach for 1 accuracy. In a novel coupled thermo-hydrodynamic benchmark representative of laser-metal interactions, the hybrid approach is one order of magnitude more accurate than a purely diffuse-interface model on the same mesh. Robu

We present a formal model of mesh inference: how a population of independent agents, each holding private state and exchanging only admitted, typed observations, derives a conclusion none of them holds alone, with no central coordinator and no agent exposed. No agent shares weights, gradients, or hidden state, and the agents may span different teams, networks, and organizations. Motivated by the observation that asking a model is energy-minimizing inference, we model the mesh as a coupled free energy that each agent relaxes locally. We show that a single admission/emission policy governs three properties. First, mesh inference converges to a unique answer for any admission, symmetric or not, because the coupling is always an M-matrix. Second, it is identification-complete: it derives the centralized optimum exactly when the contributing views are carrier-connected. Third, it is observation-only: no node transmits its internals, and confidentiality is the dual of identification. Content-addressed lineage is the only global side-channel. In the linear-Gaussian regime every derived answer is determined, hence equal to the centralized optimum, at O(diam^2) latency, the measured price of removing the center. One such derivation is one turn of a center-free learning loop, which we formalize as architecture rather than prove. The open problem we state is when asking improves the collective rather than corrupting it: whether the non-linear closure derives an upgraded answer or a confident error. To our knowledge, this is the first formal model of mesh inference.

Neural network controllers for autonomous decision-making are well-established in cyber-physical systems, yet their deployment in safety-critical healthcare settings remains largely unverified. This paper presents a methodology and case study for the formal verification of a neural network controller for antibiotic dosing, motivated by the challenge of systems that must be simultaneously adaptive and provably safe across unbounded time horizons. We construct a simplified yet clinically-interpretable model that tracks drug concentration, body temperature, and white blood cell count. Vancomycin is selected as a representative antibiotic, widely prescribed for severe infections yet carrying a narrow therapeutic window, where supratherapeutic concentrations risk nephrotoxicity and subtherapeutic dosing risks treatment failure. A supervised neural network controller is trained on synthetic clinician-style dosing data. We establish formal verification of input-output safety properties, specifically verifying a property of a neural network that implies an infinite-horizon proof that automated dosing never exceeds the supratherapeutic boundary. This system property is proven in Rocq using the Vehicle interactive theorem prover back-end to integrate the different proof systems. The end result is a verification pipeline that allows for a wide variety of treatment approaches whilst maintaining safety for each specific patient.

We argue here that traditional network models, which are overwhelmingly based on the mathematical construct of a simple graph, are fundamentally insufficient for capturing the complexity of modern distributed systems. Such systems are characterized by heterogeneous agents with diverse capabilities, high-dimensional and multi-modal data streams, and intricate, context-dependent relationships that cannot be adequately described by a simple connection or a scalar weight. The limitations of these classical models necessitate a new mathematical language, one with far greater expressive power. We have found that sheaf theory provides us with such a language. Moreover, we show that the sheaf Laplacian is a suitable mechanism for data fusion and establishing consensus within distributed sensing networks.

Deploying deep neural networks at the edge demands efficient inference under strict cost and power constraints. Quantized neural networks address these demands by replacing floating-point parameters with low-precision integers, yet their weights remain continuously exposed to radiation-induced bit-flips during inference. Fault Injection can be used to simulate those environments, but existing studies fail to characterize how accumulated upsets translate into mispredictions under realistic memory layouts. This paper presents a GDB-driven profiling framework that injects cumulative weight bit-flips directly onto the target binary of edge CPUs, generating per-layer fault profiles without requiring model retraining or code modification. Evaluated across multiple topologies, quantization efforts, and memory layouts, the results indicate how selective hardening strategies should be applied to effectively protect neural networks.

Electric-vehicle grid-integration studies need large, behaviorally realistic populations of individual charging profiles, but real charging telemetry is scarce and privacy-restricted, and the existing open generators are calibrated to non-U.S. mobility surveys or flatten the regional, seasonal, and equipment heterogeneity that drives aggregate demand. We present \texttt{ev-flow} (import name \texttt{pev\_synth}), an open-source, MIT-licensed Python package that generates synthetic plug-in electric vehicle charging behavior for eight U.S. regions, grounded in 2017 National Household Travel Survey (NHTS) microdata and regional sales-mix models. A deterministic nine-stage pipeline (M1--M9) carries each vehicle from survey records to a time-stamped charging profile: it stitches survey person-days into donor-matched 365-day travel calendars with a temperature-dependent winter energy uplift, samples behavioral plug-in start times from the published SPEECh K=16 Gaussian-mixture parameterization, evaluates a three-layer Bernoulli plug-in model, propagates a continuous-time state-of-charge ledger with an explicit PHEV gasoline range-extension term, and rasterizes plug status to 15-minute and hourly grids. The package generates residential and workplace profile types with descriptive EVSE brand and connector enrichment; every output is UTC-stored, timezone-aware, and bit-reproducible from a single master seed. A validation runner compares the generated distributions against published bounds and classifies every divergence with literature provenance: the reference \texttt{bay\_area} residential profile rolls up to 11 PASS, 0 unexplained FAIL, 6 explained failures, and 4 explained skips across 21 applicable checks. \texttt{ev-flow} fills a U.S.-focused, NHTS-grounded niche complementary to European generators such as emobpy and VencoPy and to charging simulators such as datafev and ACN-Sim.

The predominant formal models for blockchain systems, particularly smart contracts, have largely been drawn from the classical theory of computation, with the finite state machine (FSM) or labeled transition system serving as the primary conceptual tool. However, the FSM relegates the most difficult and novel aspect of a blockchain -- the achievement of consensus in a decentralized environment -- to a complex, often messy, implementation detail that lies outside the formal model itself. But the process of consensus is not an ancillary feature; it is the very essence of the computational phenomenon. To model it faithfully, a new mathematical language is required. The central thesis of this work is that topos theory, the theory of categories of sheaves, provides the native mathematical language for systems defined by local consistency and the construction of global truth.

Human-AI teaming (HAT) increasingly involves AI systems that provide real-time, context-aware guidance in complex tasks. While such systems can improve performance, their effectiveness depends on how they shape human cognition and behavior. In particular, AI assistance can introduce cognitive demands and influence attention, planning, and interaction with the task environment, with effects that can vary across levels of expertise. This work investigates these mechanisms in a simulated search and rescue (SAR) environment. We compare human performance under two LLM (Large Language Model)-guided conditions and a no-LLM baseline, and analyze interaction at multiple levels, including task performance, eye-tracking measures, and planning behavior. Eye tracking provides fine-grained insight into attention allocation and interaction with AI guidance, while behavioral measures capture how users structure and adapt their decisions over time. Results indicate that LLM guidance enhanced task efficiency (higher rewards and victims-per-step) but did not increase total victims saved. Eye-tracking data revealed an attention-guidance trade-off, with visual resources shifting to the chat interface alongside increased pupil size variability. Expertise moderated this effect: novices exhibited passive AI reliance, whereas experts maintained a "verification loop" through persistent environmental scanning. These findings suggest that LLM-mediated teaming efficacy depends on the operator's ability to cross-reference AI guidance with ground truth to maintain situational awareness.

We present MonaVec, a deterministic, embedded vector-search kernel for edge and offline AI -- settings where server infrastructure, network connectivity, and training data are all unavailable. Existing vector-search systems assume a persistent server, gigabytes of RAM, or a training pass over the corpus; MonaVec instead targets the deployment profile of SQLite: one file, one function call, runs anywhere. Its quantization core is training-free by default and data-oblivious: a Randomized Hadamard Transform (RHDH) conditions any input distribution toward N(0,1), so precomputed Lloyd-Max tables quantize to 4 bits (8x smaller) with no learned codebook and no data pass. The index persists as a single .mvec file whose embedded ChaCha20 rotation seed makes results reproducible across architectures and byte-identical within a build -- a determinism guarantee that parallel-build graph libraries cannot offer. On semantic embeddings (AG News, 45K x 1024-dim BGE-M3, cosine), MonaVec 4-bit BruteForce reaches 0.960 Recall@10 in 27 MB -- leading float32 FAISS-IVF and 8-bit usearch on recall -- while trading peak throughput for byte-identical determinism. A single-pass global standardization (fit()) extends the same data-oblivious pipeline to magnitude-sensitive L2 data, and optional IvfFlat and HNSW backends carry it to million-vector corpora. MonaVec is implemented in pure Rust with Python bindings and runtime SIMD dispatch (AVX-512/AVX2/NEON/scalar). It targets on-device RAG, offline agents, and embedded retrieval -- the niche SQLite occupies for relational data: one file, one call, runs anywhere.

Modern agent systems often suffer from fragmented runtime state: transcripts, tool effects, memory events, workspace placement, branch provenance, and replay evidence are recorded separately and become difficult to inspect or reproduce. OpenRath addresses this issue with a PyTorch-like programming model for multi-agent, multi-session systems. The analogy concerns the role of a central first-class runtime abstraction, not tensor computation. Its core abstraction is Session, the runtime value passed between agents and workflows. A Session is branchable, inspectable, replayable, backend-aware, and composable. It records conversation chunks, sandbox placement, lineage metadata, token usage, pending work, and tool evidence, while defining where memory interactions enter the runtime record. Since this state is carried by the same value used in program execution, fork, merge, and replay become explicit runtime operations rather than states reconstructed from external traces. OpenRath further defines Sandbox, Tool, Agent, Memory, Workflow, and Selector, with Selector turning control flow into runtime-routed decisions. This report presents the programming model, architecture, audited milestones, and evidence protocol. Its claims are limited to controlled runtime properties, while broad quantitative comparisons, live-provider quality, optional-backend availability, and memory quality are left for follow-on evaluation. The central thesis is that Session provides agent systems with a first-class runtime value for auditable composition.

A formal explanation certifies a prediction with a subset-minimal sufficient reason. Under incremental disclosure, however, evidence arrives field by field, and a normally sufficient reason can be overturned by later information. We study the smallest reason core that remains sufficient under all admissible later disclosures; its size is the robustness radius. We compile a defeasible classifier into an explicit boundary atlas of entry anchors and exit defeaters, and chart the complexity of auditing it (all statements are in the atlas size). Prediction and standing anchors are read by polynomial-time scans of the atlas, without iterative fixpoint computation; a reason's defeater frontier is obtained by scanning and subset-minimizing the defeaters above it. But verifying that a reason core is robust is coNP-complete, and deciding whether a robust core of size at most theta exists is $Σ_2^p$-complete -- a four-cell P / coNP-complete / NP-complete / $Σ_2^p$-complete landscape, with the accepted (A(t)=1) case reaching the second level of the polynomial hierarchy. The decision version of minimal certified disclosure is NP-complete; its optimization version is fixed-parameter tractable in the number of excluded worlds without defeaters, with the general-defeater case open. On exact audits of depth-limited decision trees over standard tabular datasets under a deliberately small Boolean abstraction, the governing parameters fall in a small-parameter regime (robust cores in the low single digits), so exact robust auditing is tractable in these audited cubes; on adversarial instances built from our reductions the hardness bites, with robust cores of size Theta(n). To our knowledge this is the first $Σ_2^p$-complete audit query for disclosure-robust formal explanations.

Purpose: To investigate the distinct roles of DevOps specialists and general software developers, examining their varying use of tools, technologies, methodologies, and demographics in the current software development environment. In addition, to differentiate these two professional groups regarding their unique contributions and challenges in the field. Design/Methodology/Approach: The research uses a quantitative approach to analyze data from the Stack Overflow 2023 Developer Survey. It focuses on a comparative analysis of technological preferences, demographic information, and professional experiences between DevOps specialists and general developers, highlighting key trends and differences. The data analysis was conducted using Python's Pandas library for data analysis. Findings: The research indicates no significant difference in the tool and technology preferences between DevOps specialists and general software developers, highlighting their complementary roles. DevOps specialists and general software developers use tools like Docker and Kubernetes, emphasizing efficiency and automation. While general developers employ diverse tools for various role demands, demographic trends show younger general developers and mid-career DevOps professionals. This age range reflects growing experience in DevOps, and both groups are adapting to remote and hybrid work models in the evolving tech industry. Practical Implications: This research offers perspectives on the dynamic roles within software development, emphasizing the growing importance of DevOps. It is a valuable resource for academic and industry professionals to understand the evolving dynamics in software development roles. Originality/Value: This research fills a significant gap in the existing literature regarding the evolving dynamics of software development roles.

Hypergraphic automata are automata whose state sets and output symbol sets are hypergraphs invariant under the actions of the transition and output functions. Universally attracting objects in the category of such automata are called universal hypergraphic automata; their semigroups of input symbols are algebras of mappings whose properties are tightly linked to the algebraic structure of the automata themselves. This paper establishes a complete characterisation of epimorphisms of universal hypergraphic automata and of their semigroups of input symbols. A central contribution is the introduction of two distinct notions of epimorphism for hypergraphs including weak, strong and the proof that these notions diverge in general but necessarily coincide for the important subclass of $p^*$-hypergraphs, which includes automata whose state hypergraphs and output hypergraphs are projective or affine planes. The main results give necessary and sufficient conditions for a triple $(f, \mathbb{P}_s, g)$ to be an epimorphism of universal hypergraphic automata, expressed in terms of the component maps on the state and output hypergraphs.

Model Driven Engineering relies on model transformations to automate the derivation of target models or source code from source models. However, the design decisions that govern how source elements are mapped to target artifacts typically remain embedded in the transformation source code, limiting flexibility, reusability, and traceability. This paper proposes an approach to explicitly model and manage design decisions in rule-based model transformations. Design decisions are separated from transformation implementation through three mechanisms. First, a decision model captures design decisions and their options independently of any source modeling language for a given transformation domain. Second, a binding model connects these decisions to specific metamodel concepts in a given source modeling language, enabling reuse of the same design knowledge across transformations from similar languages. Third, a configuration model records the specific option chosen for each applicable source model element, with defaults pre-selected automatically. During execution, variability points in transformation rules are resolved dynamically according to configured choices. A trace model records which rules and options were applied to produce each target element. We establish a formal mathematical framework that defines the core concepts of variability-based transformations and proves key properties of the resulting transformations. The formal concepts are realized in a practical architecture of four interconnected artifact models. We demonstrate practical feasibility by extending an existing embedded domain-specific language for model-driven engineering with variability support and illustrate the approach with a complete ER-to-Relational transformation example.

Integrated Sensing and Communication (ISAC) refers to the capability for the network to provide communications services whilst also being able to sense the environment in a scalable manner. One of the key functions of ISAC is the accurate localization of passive and mobile sensing targets. This paper introduces a novel hybrid TRP-UE sensing mechanism that improves network-based sensing performance. Evaluation results are provided using 3GPP-compliant ISAC channel models. The results demonstrate the significant benefit in complimenting TRP-based sensing with UE-assisted sensing in challenging propagation environments such as indoor factory.