Motivational factors such as self-efficacy and how favorably students feel toward practice play a crucial role in shaping learning, particularly in technology-supported environments. Yet, educational interventions often overlook how these factors interact with practice format. This paper examines the influence of self-efficacy and favorability on learning outcomes across two common practice formats: paper-based and system-based tutoring practice. Using a counterbalanced within-subject design with matched problem sets, we isolate the effect of practice format while modeling motivational differences. Results indicate that students with lower baseline self-efficacy achieved greater learning gains regardless of practice format. Among students with lower baseline self-efficacy, greater favorability toward the tutor was associated with greater learning gains during tutor practice, whereas the pattern differed in paper-based practice. Intelligent Tutoring System (ITS)-based practice did not significantly improve post-training self-efficacy relative to paper-based methods. These findings underscore the potential value of tailoring practice format to students' motivational profiles, as the benefits of tutor- and paper-based practice varied with baseline self-efficacy and favorability. They lay the groundwork for future research on how instructional formats can be aligned more effectively with learners' motivational needs.

As enterprises deploy RAG-based systems to provide grounded responses to user queries, reranking has become a critical component for the final filtering step that separates relevant from distracting or irrelevant documents. Existing rerankers often rely on heuristic thresholds to achieve optimal filtering. Moreover, for relevance scoring, state-of-the-art methods use a language model's logit signals, which are designed for next-token prediction, not for assessing relevance. To address these limitations, we identify a principled signal for relevance: the representational shift (RS) induced in a query's internal state when conditioned on a document. We observe that the alignment between (a) RS induced by a candidate document and (b) RS induced by an oracle document-set provides a robust indicator of relevance. Building on this insight, we introduce a lightweight training framework that learns projections mapping RS to calibrated relevance scores. Our training objectives naturally filter irrelevant content at a zero threshold, reducing dependence on heuristic tuning. Across diverse retrieval datasets, our method delivers gains over SOTA rerankers.

With the rapid advancement of Deep Research Agents in knowledge-intensive domains such as finance, establishing reliable and domain-aligned evaluation standards remains a critical challenge. Existing benchmarks focus on either closed-ended question answering or open-ended report evaluation, failing to jointly capture retrieval-reasoning accuracy and end-to-end research quality required in real-world workflows. We introduce ICBCBench, a consortium-driven benchmark for financial deep research, developed in collaboration with domain experts from a broad range of financial institutions and academia, involving over 50 experts across more than 40 organizations. It adopts a dual-track paradigm integrating objective tasks with verifiable answers and subjective long-form report evaluation, enabling complementary assessment of retrieval-reasoning accuracy and end-to-end report quality in terms of expert alignment, citation consistency, and source quality. Experiments on state-of-the-art DRAs and large language models reveal substantial gaps in complex reasoning, factual grounding, and report quality, highlighting the challenges of achieving industry-level performance. Our dataset and evaluation framework are available at https://github.com/DeepFin-Intelligence/ICBCBench.

Cloud-hosted transformer and large language model (LLM) inference creates a direct confidentiality problem: user prompts may contain sensitive code, business data, personal information, or regulated documents, yet remote serving exposes intermediate state to the cloud software stack and accelerator runtime. Fully homomorphic encryption (FHE) keeps accelerator-side execution ciphertext-only, but end-to-end LLM inference remains expensive because linear layers are interleaved with non-linear, cache-state, and refresh-sensitive operators. CPU trusted execution environments (TEEs) can execute those operators natively, but a CPU TEE alone does not define how an untrusted accelerator should participate. We present Bifrost, a hybrid TEE-FHE serving architecture in which secrets are provisioned only to an attested CPU TEE, while the accelerator, device memory, driver/runtime stack, and host software remain outside the trusted computing base. Bifrost uses FHE as a secure delegation mechanism for projection and feed-forward linear layers on accelerator-backed CKKS, while non-linear operators, attention-side control logic, KV-state transitions, and decrypt-then-encrypt refresh execute inside the CPU TEE. Bifrost+ further applies a prefill/decode split: prompt-side KV state is built inside the CPU TEE, and only decode-side state enters the hybrid ciphertext path. In an estimator-style comparison matching Euston's methodology, Bifrost reduces projected latency by 9.25x on GPT-2 (1.5B) and 9.91x on LLaMA 3 (8B). In direct CKKS/FHE deployments, Bifrost+ reduces TTFT by 14.6-45.8x on GPT-2 (124M) and 15.3-53.4x on Qwen3 (0.6B). The systems lesson is selective encrypted execution: use FHE only where ciphertext-only accelerator delegation is required, and keep non-linear, refresh, and prompt-side work inside the CPU TEE.

We study the robustness of the Strong Stackelberg Equilibrium (SSE) in finite leader--follower games when the follower's best-response correspondence is set-valued. While optimistic tie-breaking (in the leader's favor) is commonly adopted, it can hinge on knife-edge indifferences. We formalize a stability notion: an SSE is unstable if, at the leader's committed strategy, the follower has an alternative best response that strictly reduces the leader's payoff. Our main results establish a sharp generic dichotomy. Fixing the follower's utility and sampling the leader's utility from any continuous distribution, with probability one the optimal Stackelberg commitment is unique and is either (i) pure, or (ii) mixed and unstable. When both players' utilities are sampled generically, this strengthens to: with probability one, the unique optimal commitment is either pure and stable or mixed and unstable. These theorems complement the classic generic-value result of von Stengel and Zamir by showing that even when optimistic and pessimistic leader values coincide generically, the strategy-level SSE prediction is generically fragile whenever optimality requires genuine randomization. We further apply this perspective to Stackelberg satisfaction games, disproving a conjecture from prior work via counterexamples and identifying conditions under which it nonetheless holds.

Directed community detection is challenging because edge directions encode asymmetric source-target relations. Most directed modularity and random-walk methods assign one label to each vertex, whereas recent bimodularity-based methods cluster directed edges more freely. We propose TT-SR, a Two-Tier Sender-Receiver framework that lies between these two viewpoints. Each vertex is assigned a sender role and a receiver role, and each directed edge receives the type induced by the sender role of its source and the receiver role of its target. Thus, TT-SR is more expressive than one-label vertex clustering while remaining more interpretable than unrestricted edge clustering. The method generates candidate sender-receiver assignments from count-residual, stationary-flow, degree-corrected, and order-score views. The candidates are refined by local role updates and selected by a two-tier rule: a degree-corrected profile score provides the primary structural criterion, while Bernoulli density and order-flow scores are used only as secondary ranking signals. We justify the main spectral views through sender-receiver modularity relaxations and interpret the degree-corrected score as a likelihood-based residual comparison. Experiments on pathway-type, co-block, and ordered-flow synthetic benchmarks show that TT-SR achieves the strongest or essentially tied strongest edge-community recovery across three scale settings. The gains are most pronounced on degree-corrected co-block and ordered-flow graphs. Real-network diagnostics further indicate that TT-SR aligns well with Email-Eu-core metadata and extracts strong sender-receiverbicommunity summaries on unlabeled directed networks.

We present a fully differentiable, GPU-accelerated finite element framework for forward simulation and inverse characterization of finite-strain anisotropic elastoplastic materials. Built on JAX, the framework exploits modern accelerator architectures by parallelizing the three major computational bottlenecks in nonlinear FEM: elemental weak-form and tangent-stiffness evaluation, global sparse matrix assembly, and sparse linear solution. For a large-scale forward problem with 3 million degrees of freedom, JAX-FEM on a single NVIDIA H100 GPU achieves up to 9.4$\times$ speed-up over a 24-core CPU Abaqus baseline. Automatic differentiation is applied through the constitutive update and solver workflow, providing consistent Jacobians for complex constitutive models without manual derivation and accurate gradients for PDE-constrained inverse analysis. Compared with finite differences, the JAX-AD gradients avoid step-size sensitivity and provide the required sensitivities at substantially lower computational cost. For inverse characterization, we combine information-rich, topology-optimized heterogeneous specimen geometries with full-field displacement data to identify complex constitutive models with many parameters that would otherwise require many conventional experiments to characterize. We demonstrate accurate recovery of anisotropic yield and hardening parameters in progressively challenging settings, including uniform and spatially varying material properties. The resulting AD-based formulation enables efficient optimization in high-dimensional parameter spaces where finite-difference approaches are computationally infeasible. These results establish differentiable, GPU-accelerated FEM as a practical high-throughput engine for simulation, characterization, and optimization workflows in advanced manufacturing.

In recent decades, privacy-enhancing technologies (PETs) have been recognized as a means of meeting regulatory and user privacy requirements in software systems that process personal data. Despite substantial research efforts, support from regulators, contributions by large technology companies such as Google and Microsoft, and growing interest among software practitioners, the practical adoption of PETs remains limited. Existing research consistently identifies recurring challenges to PETs adoption in SE, such as technical complexity and insufficient training. Despite ongoing research efforts, these challenges largely remain unresolved in practice. In this industrial challenge paper, we apply a practical, requirements engineering (RE)-driven perspective to examine challenges to PET adoption across multiple stakeholder groups (PET developers, integrators, and adopters) as well as across different disciplinary perspectives (engineering, law, and business). We argue that RE can facilitate the adoption of PETs by systematically addressing each of the complementary engineering, business, and legal viewpoints on privacy. Neglecting challenges in any of these viewpoints (e.g., the impact of PETs on software architecture, their business implications, and their contribution to regulatory compliance) can increase the impediments or even lead to implementation failure. In practice, explicit specification of these viewpoints within RE can enable meaningful coordination among stakeholders to more effectively realize the benefits of PETs in software engineering.

Text-to-image diffusion models are increasingly deployed at the network edge to serve heterogeneous workloads with diverse quality and latency requirements. However, existing deployment strategies choose either large edge-side models with high fidelity but high latency or lightweight device-side models that offer speed at the cost of semantic coherence. Moreover, these approaches rarely split the denoising workload between models of different sizes across edge servers and user devices. To bridge this gap, we propose RISE, a method for edge-device diffusion model services that combines relay inference with online scheduling. Driven by the finding that the latent intensity exhibits minimal deviation after a model handoff, RISE uses a training-free relay mechanism that exploits the shared latent space within a model family: the large model on the edge handles the early denoising steps that shape semantic structure, then passes the intermediate latent to a small device-side model for detail refinement. To deploy this mechanism as a practical service, a contextual bandit scheduler selects the best relay configuration based on prompt complexity, user preferences, network quality and real-time node loads. Experiments on two benchmarks show that RISE's relay mechanism achieves up to 2.1$\times$ speedup while preserving full-model quality, and its context-aware scheduler effectively balances quality and latency under mixed workloads.

Rust's type system prevents many classes of memory errors, yet its standard library relies heavily on unsafe code whose correctness is validated through testing, including dynamic checks under Miri, but lacks static verification. We present what is, to the best of our knowledge, the largest verification campaign reported for a software library: an open, crowdsourced effort that integrates complementary verification tools into the continuous integration of a verification repository forked from the Rust standard library. We analyze the campaign's effectiveness, discuss the practical value of machine-checked proofs for a subset of undefined behaviors (e.g., out-of-bounds access, null and dangling pointer dereferences, and use of uninitialized memory), and frame the remaining obstacles as open challenges for the formal-methods community.

AI systems are increasingly deployed in real-world settings where their behavior is shaped by dynamic environments, evolving data distributions, and complex interactions with users and infrastructure. Traditional machine learning evaluation focuses on benchmarks and operates within sandboxed environments, providing only a limited view of the true system behavior in the wild. We argue for the development of principled auditing frameworks that monitor deployed AI systems throughout their lifecycle. We further propose framing auditing as a statistical problem of monitoring constraint violations under uncertainty, where desired properties (e.g., fairness and safety) are treated as risk-controlled constraints that must be continuously evaluated as systems evolve through iterative feedback. This perspective highlights the need for uncertainty-aware monitoring methods, socio-technical specifications of audit criteria, and auditing infrastructures that enable ongoing oversight of AI systems in the wild.

Computing's dominant concepts - innovation, efficiency, resilience, professionalism - often migrate from reflective ideals to instruments that limit behaviour, redirect responsibility, and foreclose critique. We call this drift weaponisation: the discursive repurposing of professional concepts so that they stabilise business-as-usual while making structural alternatives appear unreasonable, illegible, or out of scope. Using collective autoethnography across education, justice, public administration, research management, and computing, we identify recurring mechanisms (simplification, individualisation, binary framing, metric substitution, hero/resilience scripts, organised ignorance). From this synthesis we propose Reframing - peaceful, practice-ready shifts (e.g., From Simplified Slogans to Structural Literacy; From Performative Compliance to Meaningful Outcomes; From Coping to Justice) - each paired with a 'do-now' prompt. The levers restore complexity, surface assumptions, and redirect attention to structural conditions without requiring formal authority. We contribute: (1) a cross-field account of weaponisation as a patterned phenomenon; (2) a portable reframing toolkit grounded in narrative and systems thinking; and (3) implications for computing within limits, including day-to-day practice shifts toward care, sufficiency, and justice.

The CPU-side large language model (LLM) tokenizer is a critical security gap in LLM serving through a confidential computing stack with CPU and GPU trusted execution environments (TEEs). Tokenizers converts the prompts through table-driven lookups, and the resulting memory access patterns are a powerful source of side-channel leakage. Recent work demonstrates end-to-end recovery of user prompts from tokenizer access pattern on production Intel TDX. However, a drop-in use of the popular tree-based Oblivious RAMs (e.g., PathORAM) to prevent access-pattern leakage introduces $\sim$13$\times$ tokenizer slowdown, resulting in 10-58% higher time-to-first-token (TTFT). In this paper, we present OTRO, an efficient, oblivious tokenization path tailored to latency-critical LLM serving. OTRO relies on square-root ORAM for fast single-access lookups, but avoids its prohibitive $O(N\log^2N$) rebuild cost every $\sqrt{N}$ accesses through three key innovations. First, OTRO provides a pool of replicated square-root ORAM instances that utilize the read-only nature of tokenizer table. Second, an epoch-based rotation policy decouples accesses from rebuilds and pads each epoch with dummy accesses to its boundaries, minimizing observable information. Lastly, chunked KV-cache-aware tokenization further overlaps rebuilds with GPU prefill and minimizes the instance count. Implemented as modules in HuggingFace Tokenizers and nano-vLLM, running within a TDX-enabled CVM with an NVIDIA H100 GPU, OTRO limits TTFT overhead to at most 4.5%, keeps tokenizer-induced latency under 10\% of total TTFT, and adds less than 0.5 GB of memory overhead while reducing the tokenizer's observable leakage across various model families and sizes.

This article proposes a novel methodology for the classification of transient and stationary regimes in dynamic systems. Several sensor-based solutions for regime classification in the literature require the setting of several parameters, or are not suitable for scenarios involving multivariate systems that may contain periodic signals. The proposed method introduces a spatial curve representation of the considered system based on its sample mathematical moments. Then, by connecting concepts of stability theory, geometrical properties of spatial curves and stationary stochastic processes, two regime classifiers are designed using the arc length and the curvatures of the proposed curve. Both classifiers are capable of describing and detecting transient regimes, considering behaviors such as: multivariate asymptotically, marginally stability, and cyclostationarity. Furthermore, a quantitative comparison in performance and computation resources of the proposed classifiers against existing classifiers in the literature illustrates that the proposed regime classifier based on the arc length outperforms other techniques in classifying transient regimes for simulated linear, non-linear, and discontinuous multivariate systems under the specified studied conditions.

What happens to permacomputing when electricity grids shift to decentralised green energy, and local communities and municipalities have increased governance over this vital public service? Electricity and computational networks are more than just separate systems that plug together. Shifts to renewable energy generation in the grid are impacting computational systems, and computational demands on electrical power are impacting the electricity grid; one infrastructure limits the other. Permacomputing research tends to focus on 'off-grid' or 'behind-the-meter' energy. This sacrifices some of the social justice benefits that the public electricity grid, managed and regulated for universal service, was designed to provide. Our paper uses empirically-grounded 'speculative fabulation' to identify research opportunities in permacomputing that open up when energy systems are federated across communities. The speculative fabulation takes the form of an interview between the energy manager of a future energy island in the North Sea and a permacomputing podcaster. This allows us to conceive of computing-grid integration in tractable social and ecological terms, and introduce a notion of 'fair trade energy'.

Clustering in a big data setting is an intensively studied problem, with coresets emerging as one of the important paradigms in this line of work. Given a cost function $\text{cost}(P,S)$ mapping input points $P$ and a solution $S$ to an objective value, a coreset is a typically weighted sketch $Ω\subseteq P$ such that $\text{cost}(Ω,S)\approx \text{cost}(P,S)$. In practice, coreset sizes much smaller than those suggested by theoretical guarantees are often found to be sufficient. In this paper, we offer an explanation for this phenomenon. Smaller coreset sizes suffice if we only wish to preserve the costs of \emph{good} solutions, i.e., solutions with low cost. We define and devise \emph{approximation-preserving coresets}, which provide a weaker guarantee than strong coresets, which apply to all solutions, while providing stronger guarantees than weak coresets, which apply only to the optimum solution. We complement this result by showing that even a very small distortion in the approximation factor cannot admit coresets of this size.

Linear probing is one of the simplest and most space-efficient approaches to hash table design, and is widely used in sequential settings due to its compact memory layout. However, designing a concurrent linear-probing hash table with strong liveness guarantees has proved difficult, and only a handful of such algorithms have been proposed, all of which either restrict concurrency or rely on large per-entry metadata, thereby compromising space efficiency. We present a lock-free linear-probing hash table with wait-free lookups that retains the core advantages of sequential linear probing while handling contention gracefully. Our design uses only a small amount of metadata per table entry: a constant number of additional bits when using LL/SC, or a logarithmic number of bits when using CAS. The algorithm is linearizable and lock-free, supports insert, delete, and wait-free lookup operations, and is able to safely reclaim space used by deleted elements without rebuilding the table. We analyze the amortized step complexity of our hash table assuming no concurrent insertions of the same key, and show that each operation has expected amortized step complexity matching that of sequential linear probing, up to the point contention per key.

Transmission failures can lead to cascading failures and system blackout affecting millions of customers if not handled in time, and choosing the best locations to monitor the condition of the transmission system is crucial for power system reliability. In this paper, we propose a new sensitivity factor, the line outage impact factor (LOIF), which is especially useful for power system monitoring and can reveal the impacts of a transmission outage on the power flow of other lines more effectively than existing sensitivity factors, such as the line outage distribution factors (LODF). In this study, we apply the LOIF in transmission line outage detection in three test systems and compare it with LODF using a number of observed transmission line (OTL) selection methods based on these two sensitivity factors. Then we apply a machine learning algorithm to detect the outages of other lines by monitoring the selected OTLs, and the detection accuracy is evaluated using the F1-score. The results show that, in general, with the same number of OTLs, detection using the OTLs selected using LOIF achieved higher F1-scores. The pattern was especially consistent in large-scale systems, showing its potential in real-world applications.

Counts of $k$-cliques are commonly used metrics in subgraph mining. Since graphs often have sensitive data, there also has been a lot of work on $k$-clique counts with differential privacy. However, these metrics have very high global sensitivity, and so more sophisticated techniques have been developed for counting $k$-cliques with privacy. Smooth sensitivity and ladder functions were developed for reducing the noise magnitude for private estimates of these metrics. However, these are computationally very inefficient to estimate. No polynomial time algorithms are known for smooth sensitivity of $k$-cliques for $k>3$, while the time complexity of ladder functions is lower bounded by the time for exact counts, which does not scale very well. In this paper, we develop a new highly scalable algorithm for estimating $k$-clique counts with differential privacy. Our algorithm adapts the ladder function to serve as a smooth upper bound on its local sensitivity, and utilizes the approximation sensitivity framework to calibrate noise with magnitude proportional to an approximation of the bound. This gives us a significant improvement in the running time. Experiments show that our method is several orders of magnitude faster than the ladder function based estimates of $k$-clique counts, while the accuracy is similar. Our algorithm is the first to scale to graphs with millions of edges, and for larger $k$, for which the ladder function algorithm doesn't complete.

Safe set computation is a fundamental challenge in safety-critical control systems, especially in direct data-driven settings where safety analysis is performed directly from noise-affected measurements, without explicit modeling. A recently proposed method, Data-Driven Hamiltonian (DDH), enables reachability analysis directly from measurements, without relying on prior knowledge of the underlying system dynamics. This paper extends the DDH framework to a robust setting that accounts for measurement noise, exogenous disturbances, and sampling-induced state-velocity estimation error. A Robust Data-Driven Hamiltonian (R-DDH) is derived from noisy measurements and shown to yield a certified lower bound on the exact Hamiltonian. This results in a provable under-approximation of the value function and an inner approximation of the associated safe set. The gap between the data-driven and exact Hamiltonians is quantified, and it is shown to converge to zero with more data in a noise-free setting with additive disturbances. The effectiveness of the approach is shown through two case studies: a constrained double integrator and an aircraft taxiing system with a nonlinear closed-loop controller operating under perceptual uncertainty.

This paper presents STORX: Shape and Topology Optimization for Research and Experimentation, an open-source MATLAB-based educational framework for learning and teaching computational design optimization. STORX provides a platform for parametric and level-set shape optimization, as well as topology optimization methods including density, level-set, and topological sensitivity approaches such as evolutionary and Pareto-tracing methods. All modules follow a consistent object-oriented structure and integrate visualization, sensitivity analysis, and finite element routines, enabling users to explore the continuum between shape and topology optimization in a transparent and reproducible manner. The code is designed to complement graduate-level coursework and independent research by emphasizing modularity and extensibility through a clear separation of intent. Core software interfaces are defined via abstract base classes, enabling new objective functionals and design/manufacturing constraints to be implemented by adding derived classes without modifying the core code. The paper also describes the software architecture and demonstrates how the framework maps mathematical formulations directly to executable code through a series of illustrative problems.

We start from a small open question, where Hetzl and Vierling asked whether two theories of induction, open induction and clause set cycles, are incomparable. They proved one direction and left the other open. Here we close it, and the proof is almost embarrassingly short, because the rules for addition can only fire when the first argument is $0$ or a successor, a Skolem constant is neither, so the terms $a{+}b$ and $b{+}a$ can never be touched, and a machine that can never touch them can never prove they are equal. The thing that separates the two theories is the order of two constants, and that order is a fact about numbers, not about symbols. We extract from this proof a small general principle, the Syntactic Invariance Principle, that names the shape of such arguments. We then close with a few speculative remarks on how this same shape appears, informally, in the known barriers to settling $\mathsf{P}$ versus $\mathsf{NP}$, where each barrier seems to point to a level of description that the techniques in the barrier cannot reach. We raise this as a suggestion rather than a theorem, since the analogy is real but we do not push it past the point where we can defend it. Along the way we raise an open question that the analogy suggests but does not settle, on whether a fast algorithm for $\SAT$, were it to exist, would always be exhibitable as a machine you can write down or whether it could be found, in some cases, only as a function on the numbers.

In the perpetual pursuit of performance, modern computing systems rely ever more on stateful mechanisms to accommodate the dynamics of workloads and physical environments, bolstering efficiency but confounding benchmarking and thereby the optimization of software. Indeed, by their nature, adaptive mechanisms introduce temporal dependencies between measurements and render naive estimators of individual program performance biased. Observing that rectifying such biases necessitates speculative assumptions about system dynamics, we call for prioritizing performance differentials over absolute measures and formalize software benchmarking as the decision problem of identifying the fastest program, for which relative knowledge suffices. To this end, we propose simple experiment designs admitting consistent estimators of contrasts, whereby program-specific biases cancel under tenable assumptions. These designs asymptotically yield the correct decision and afford a robust methodology for finite-budget benchmarking in stateful environments, bearing broad implications for the development of performance-sensitive software.

Large Language Models and vision-language models have shown remarkable success in the front-end design of Very Large-Scale Integrated Circuits, yet their capabilities for VLSI physical design remain significantly underexplored. The primary cause is the lack of standardized benchmarks for evaluating agentic physical design workflows that require high-dimensional, multi-stage optimization under strict design constraints, coordinated interaction with diverse Electronic Design Automation tools, and iterative refinement. This work introduces PDAGENT-BENCH, a comprehensive and multi-dimensional benchmark for evaluating LLM/VLM-based agents across the physical design stack. PDAGENT-BENCH integrates both task-level assessment and workflow-level execution. The benchmark suite contains 353 curated problems that combine conceptual questions with real-world industrial artifacts, with expert-validated references and executable solutions. These tasks cover five key capability dimensions: foundational knowledge, report comprehension, root-cause analysis, script generation, and full-flow implementation. In addition, the benchmark provides a unified, human-aligned agentic physical design workflow framework that enables closed-loop evaluation of holistic physical design in realistic EDA environments. Experiments on 11 state-of-the-art models reveal that while modern LLMs/VLMs perform competitively on conceptual tasks, they remain substantially limited in tool-centric execution (e.g., 42.2% on Innovus script generation) and long-horizon, multi-stage reasoning. Our studies further show that human-skill-enhanced agentic workflows significantly improve end-to-end physical design performance. PDAGENT-BENCH establishes a standardized, reproducible, and realistic evaluation framework for advancing LLM/VLM-driven holistic physical design automation. We will open source the benchmark and framework soon.

Internet blackouts, occurring due to technological mishaps or intentional governmental action, prevent citizens from accessing the internet. Citizens in regions where internet blackouts are common have utilized blackout-resistant technologies to maintain communication. Such technologies often rely on mobile mesh networks to provide limited messaging services. However, no technology currently exists which can provide continued access to knowledge sources on the web during a blackout. We present Cache to the Future (CttF): a system to cache and deliver static content hosted on the web during a blackout. CttF's distributed community ratings crowdsources caching at scale while cryptographic constructs (digital signatures, proofs-of-work) mitigate adversarial interference. Our realistic simulations demonstrate CttF delivering content at city-scale across a wide range of benign and adversarial scenarios.

This paper develops a compact collecting semantics for the residual behaviour left by nonterminating computation. For sequential time this is the tail-cluster set of the stream in the Stone-Cech compactification of the observation space. It gives a common semantics to ordinary recurrence, mixed recurrent behaviour, and escape through noncompact parts of the observation space. The basic theory establishes tail invariance, functoriality under continuous observations, and a temporal reading for clopen observations: containment in the corresponding clopen region of beta-X is eventual truth, while nonempty intersection is recurrence. Progress and fairness assumptions are represented by strengthening the time filter. Relational meanings are obtained by compactifying products, so correlations between observations made along the same asymptotic view of time are retained. The main application is to residual behaviour in CCS. Infinite executions are read as streams of residual processes modulo structural congruence. The resulting semantics distinguishes stable divergence, finite recurrent divergence, mixed recurrence with escape, and escape through unbounded residual growth. It validates residual-tail laws for prefixing, guarded unfolding, finite choice, and finite prefix-choice forms, while also identifying the boundary of those laws under parallel composition and synchronisation. Finite observational quotients provide the computational interface to the compact semantics: abstract meanings become recurrent states and strongly connected component calculations, and resource observations detect unbounded escape without requiring individual points of the Stone-Cech remainder to be inspected.

Neuro-symbolic AI (NeSy) pairs neural perception with symbolic reasoning, making it attractive for high-stakes domains where explainability and structured inference are required. However, this hybrid architecture introduces an enlarged attack surface spanning five layers: neural perception, symbolic knowledge bases, reasoning engines, agentic orchestration, and data stores -- each exploitable in ways absent from purely neural systems. This paper makes six contributions: (1) formal definitions of NeSy Attack Surface, Symbolic Integrity Violation (SIV), and Cross-Layer Amplification Ratio $\mathcal{X}$, decomposed into neural-caused and autonomous symbolic sensitivity components; (2) a unified threat model extending MITRE ATLAS with 11 NeSy-specific tactic extensions and a five-profile attacker taxonomy; (3) a symbolic-layer threat catalogue covering knowledge graph (KG) poisoning, ontology-merging, and inference-engine subversion; (4) analysis of cognitive risks -- automation bias, authority bias, and sycophantic reinforcement -- structurally amplified by NeSy's explicit logical explanations relative to black-box neural outputs; (5) interdisciplinary mitigations with measurable acceptance criteria aligned to NIST AI 600-1 and the EU AI Act; (6) three empirical benchmarks: (E1) targeted KG poisoning achieves break-even SIV at injection budget $B=5$ on a 205-entity medical KG, with a KG-specific stealth/SIV trade-off; (E2) PGD-10 at $\varepsilon=0.01$ yields $\mathcal{X}=5.884$ (95% CI $[4.64,\, 8.00]$, $p<0.0001$), confirmed adversarially specific by a matched-random baseline ($E^{R}_{\mathrm{rand}}=0$), on a DistilBERT+ProbLog pipeline; (E3) single-axiom OWL edits achieve 93.3% SIV success with 100% Pellet-consistency stealth, but held-out STIX detection fails at 50% (random-guessing level), an open problem.

This paper develops and analyses the Fair Play Automatic Market Maker (FP-AMM), a programmable electricity allocation mechanism in which scarcity allocation is treated as a controlled, stateful, and auditable cyber-physical process. Existing mechanisms such as locational marginal pricing are memoryless and cannot account for historical service outcomes, preventing guarantees of equitable treatment across market intervals. The FP-AMM employs a two-stage stochastic clearing rule comprising service-priority sampling and inverse-fairness weighting, coupled with a DC-OPF feasibility set and bounded shortage memory updated through a saturated integrator. Four main results are established. First, the shortage-memory state is invariant in $[0,1]^N$ and the update map is a contraction with rate $1-β$. Second, the intra-interval clearing operator converges linearly to a unique fixed point with contraction factor $q\in(0,1)$. Third, under the Fair Play priority rule, the per-node delivery ratio converges almost surely to the contracted target $F^\star$, with a finite-time $O(1/\sqrt{T})$ bound obtained via Lyapunov analysis of the deficit recursion. Fourth, event-triggered execution guarantees practical ultimate boundedness of the allocation tracking error and quantifies the computation-fidelity trade-off. The mechanism is validated on the IEEE 14-, 57-, and 118-bus systems over $T=5000$ market intervals. Fairness convergence to $F^\star$ is achieved on all benchmarks, peak weak-bus fairness error is reduced by 54% on the IEEE-57 network and by up to 55% relative to an equal-weight baseline during scarcity periods, and DC feasibility is maintained throughout.

Dynamic Gaussian compression is normally optimized for complete files or complete progressive prefixes, but interactive rendering encounters partial representations: some spatiotemporal regions are present, others missing, and late refinements cannot affect the displayed frame. We study dynamic Gaussian representations whose incomplete delivery states remain directly renderable and whose degradation is optimized in image space. Gaussian primitives are organized into independently addressable spatiotemporal clusters with a base level and three refinements; training samples partial dependency graphs, renders many counterfactual states in one GPU batch, and minimizes expected distortion, tail distortion, temporal inconsistency, rate, and prefix regressions. A counterfactual utility layer measures the marginal render contribution of each completion group across valid receiver contexts. The same graph admits a concrete delivery realization with MTU-bounded entropy-coded chunks, deadline-aware scheduling, and receiver-side dependency closure. On held-out views, the finest refinement has negative mean marginal utility in 3/32 D-NeRF bouncingballs, 49/64 HyperNeRF broom2, and 28/64 HyperNeRF chicken clusters; its lower-tail utility is negative in 21/32, 61/64, and 42/64 clusters, respectively. On broom2, render-utility ordering removes both PSNR regressions produced by nominal layer order at matched byte budgets; on chicken, utilities measured on disjoint training cameras improve held-out PSNR by 3.03 dB at the lowest matched budget. These scoped results show why nominal refinement order cannot substitute for render-conditioned utility: the formulation treats network delivery as a distribution over renderable scene states rather than as an external wrapper around a graphics codec.

High-Level Synthesis (HLS) enables rapid hardware development by translating C/C++ programs into hardware implementations. Functional consistency verification between golden C specifications and HLS-oriented C implementations is a critical yet labor-intensive task in HLS design flows. While Large Language Models (LLMs) have recently shown promise in automated testbench generation, their stochastic nature often leads to insufficient coverage, inconsistent verification environments, and unreliable equivalence checking results. To address these limitations, we propose a knowledge-augmented, agent-driven shift-left verification framework for automated functional consistency checking between golden C and HLS-C implementations before synthesis. The framework introduces a Dual-Tier Consistency Checking mechanism that jointly enforces static structural alignment and dynamic behavioral equivalence between paired testbenches, while integrating symbolic execution and coverage-driven refinement to improve verification completeness. Furthermore, we construct a heterogeneous HLS Verification Knowledge Graph to provide topology-aware reasoning priors for testbench generation, and design an autonomous verification agent to orchestrate iterative refinement and failure diagnosis across heterogeneous toolchains. Experimental results on 107 HLS benchmark pairs demonstrate that the proposed framework achieves 98.26\% average coverage and 95.33\% dynamic consistency, outperforming representative AST-based, retrieval-augmented, and iterative agent-based baselines. https://github.com/cz-5f/HLS-LeVeri.git