Montanaro's polynomial representation expresses amplitudes of quantum circuits over the gates $H$, $Z$, $CZ$, and $CCZ$ as normalized gaps of degree-three polynomials over $\mathbb{F}_2$. The normalization is governed by the circuit width $w(f)$, the minimum number of qubits in any circuit realizing a polynomial $f$. Thus, efficient width minimization would give an approximate-counting route toward a combinatorial characterization of $BQP$. We study the computational complexity of this parameter. For degree-three polynomials with no constant term, deciding whether $w(f)\le k$ is $NP$-complete, resolving Montanaro's open question. We also prove $NP$-hardness of approximation within any factor $49/48-ε$, and show via a twin-copy construction that the exact and approximation hardness results also hold for degree-two polynomials. Under the Exponential Time Hypothesis, the exact problem admits no $2^{o(n)}$-time algorithm when $k=Θ(n)$. Complementing these hardness results, we give a nondeterministic polynomial-time search algorithm using $2\log_2\binom{n}{k}=O(k\log(en/k))$ witness bits, and a constructive fixed-parameter algorithm parameterized by $k$ with running time $k^{6k+o(k)}n+O(m)$.

Group commit amortizes the fixed cost of a durable log flush across many committing transactions; the release rule - a timer, a batch size, or an adaptive policy - is a classic tuning knob. The textbook theory is open-loop: for Poisson arrivals the optimal timer is the EOQ square-root rule, and the wait-or-flush decision is ski-rental 2-competitive. We ask when that tuning is worth its machinery, and show that in closed-loop OLTP it usually is not. Real commit arrivals are closed-loop: a client issues its next transaction only after its last commits, so the arrival rate is induced by the policy's own latency. Modeling this as a closed queueing network, the parameter-free greedy-pipelined policy (flush the instant the device is free) self-clocks to a computable fixed point and is within about 0.1% of the best oracle-tuned timer at every load. The square-root rule prescribes waiting $T^\star=\sqrt{2F_0/λ}$, but $T^\star<F_0$ exactly when $λ>λ^\star=2/F_0$; above this device-set load threshold the timer collapses onto greedy and tuning is vacuous. The clean theory only bites below $λ^\star$ and in the open-loop world, where a parameter-free ski policy still beats a fixed tuned timer under rate shifts. We instantiate $λ^\star$ with measured fsync distributions on two AWS storage classes (EBS gp3 versus instance NVMe, a $25\times$ range), and confirm on PostgreSQL that commit_delay=0 is competitive with any tuned value. The contribution is a characterization that explains deployed practice; we add no new logger.

MRC is an open, production-grade transport designed for large-scale AI/ML training over best-effort Ethernet. It extends RoCEv2 with explicit, composable primitives for per-packet multipath and sender-based congestion control, decouples packet delivery from semantic processing, adds multiple new capabilities for accelerated packet-loss recovery and adds resilience against port and path failures. This paper presents MRC and details its core capabilities and mechanisms.

The Connectivity Augmentation Problem (CAP) is a fundamental problem in fault-tolerant network design and has been extensively studied in the context of approximation algorithms. In this work, we consider CAP in the online setting: given a $k$-edge-connected graph $G$ and a set $L$ of additional edges over the vertices of $G$, called links, online requests arrive one by one, each specifying two vertices that need to be $(k+1)$-edge-connected. We start with the graph $G$ and progressively add links to serve these requests. More specifically, upon the arrival of a request $\{u,v\}$, we must immediately and irrevocably add zero or more links from $L$ to the graph so that $u$ and $v$ are $(k+1)$-edge-connected in the resulting augmented graph. The goal is to minimize the total number of links added, and we evaluate an algorithm's performance by its competitive ratio relative to an optimal offline solution. In this work, improving upon previous bounds, we obtain a tight competitive ratio for online CAP, along with other related results.

The prize-collecting stroll is the path version of the prize-collecting TSP. Given a complete metric graph, two prescribed terminal vertices $s,t$, and nonnegative penalties on vertices, the prize-collecting stroll asks for an $s$-$t$ tour minimizing the length of the tour plus the total penalty of vertices that are not visited by the $s,t$ tour. We study a common generalization of the prize-collecting stroll and several related prize-collecting routing problems, which we call the prize-collecting-$Φ$-TSP. In this model, $Φ$ specifies a set of prescribed vertices alongside their parity and connectivity requirements. We show that, if a $ρ$-approximation algorithm for the prize-collecting TSP is available, then, for any $\varepsilon>0$, there is a polynomial time $(ρ+\varepsilon)$-approximation algorithm for the prize-collecting-$Φ$-TSP when the number of prescribed vertices is bounded by a fixed constant. This yields a better-than-$1.6$-approximation algorithm for the prize-collecting stroll, improving the previous best-known approximation guarantee of $1.6662$.

Python dominates AI development, yet the numerical work behind frameworks like PyTorch and NumPy is executed in C, C++, or Rust. When a developer must implement an algorithm without such libraries -- because none exists, the target is resource-constrained, or a new system is being built -- which language should they choose? This paper answers that question empirically. Five algorithms covering data mining (k-means), machine learning (k-NN), neural networks (MLP with backpropagation), computational intelligence (genetic algorithm), and fuzzy systems (Mamdani inference) are implemented from scratch in Python, C, C++, Rust, Go, and Julia. All implementations share a common pseudo-random generator, consume identical inputs, and produce bit-identical outputs, so every measured difference reflects the language rather than the computation. Three performance tiers emerge: C and C++ are effectively tied; Rust trails them by 9% (geometric mean); Julia runs 3.3x slower than C and Go 5.0x; Python sits at 315x. Memory tells a different story -- Julia's JIT runtime carries a fixed ~224 MiB footprint regardless of workload, while C, C++, and Rust stay below 6 MiB. Crucially, rankings are not stable: Go's slowdown swings from 2.6x on k-NN to 8.0x on k-means, showing that workload characteristics can shift a language's position by a full tier. The results provide concrete, per-workload guidance for choosing an implementation language in AI systems.

Log-structured merge (LSM) trees attach an approximate-membership filter to every run and must split a fixed memory budget across them. The static optimum is known (Monkey); a large systems literature then makes the allocation adaptive, tracking shifting hotness online. We ask a prior question: when is that adaptivity worth its machinery? We give three analytical answers and validate them on synthetic sweeps, real Twitter production cache traces, and a real RocksDB engine. First, a log-law: optimal bits-per-key is affine in the logarithm of access frequency, at a fixed slope. Second, a robustness law: because the workload enters only logarithmically, the excess read cost from a hotness misestimate is half the size-weighted variance of the log error, and a common-factor misestimate is absorbed by the budget multiplier, so coarse estimates lose little. Third, an adaptivity-value frontier: since compaction rebuilds filters for free on its own clock, the value of continuous tracking over an allocation recomputed only at compaction grows quadratically in the within-epoch drift, with a closed-form scale. This yields a three-regime policy (coarse-at-compaction suffices, then track, then at extreme drift fall back to uniform) and predicts that more skew makes fine tracking matter less. On a real cluster, reallocating only at compaction captures 96-99% of tracking's benefit; on RocksDB the false-positive primitive holds within four percent to eight bits per key. The contribution is a characterization of when adaptive tuning pays; we add no new filter and no engine fork. Code and pre-registration are public.

Hypergraph partitioning is a critical step in the design of complex embedded systems, essential for optimizing task mapping on heterogeneous MPSoCs and enabling multi-FPGA prototyping. Many existing methods rely on community detection to identify modules with dense internal and sparse external connections, typically utilizing them to constrain the coarsening phase--a widely adopted paradigm. In this work, we propose ComPart, a generalized framework that integrates diverse community detection methods to uncover high-quality clusterings throughout the post-coarsening stages (i.e., initial partitioning and uncoarsening). These discovered clusterings serve as distinct structural guides, enabling the refinement process to identify superior partitioning solutions. Our framework offers two key advantages: (1) it establishes a new paradigm that leverages community structures detected during uncoarsening to escape local optima and explore globally meaningful solution subspaces, transcending the limitations of standard local refinements; and (2) it flexibly accommodates both existing and future community detection methods. Furthermore, we theoretically generalize locally-dense decomposition--originally from graphs--to the hypergraph domain. We provide the formal extension and necessary proofs to apply this technique to hypergraphs, marking its first application in hypergraph partitioning. Specifically, we utilize this rigorously derived decomposition to guide the initial partitioning phase toward superior starting points. Experimental results on standard benchmarks demonstrate that our method consistently outperforms state-of-the-art methods in solution quality.

This paper investigates decentralized decision-making for finite-state transition systems, i.e., discrete-event systems, under finite communication alphabet constraints. We consider a general decentralized observation framework in which a plant is observed by multiple local agents that transmit symbolic messages over a finite alphabet to a memoryless fusion center. The fusion center then produces a binary decision according to a prescribed fusion rule. We study the fundamental question of whether there exist local decision maps that enable exact reconstruction of a given regular specification language from decentralized observations. Contrary to classical results that rely on specific monotone fusion rules such as conjunction and disjunction, we show that the problem becomes undecidable even under a severely restricted information architecture: binary local decision alphabets and a fixed exclusive-or (XOR) fusion rule. The proof is based on a reduction from the Thue word problem, a classical undecidable problem in rewriting systems. We further show that decentralized supervisory control, decentralized fault diagnosis, and decentralized fault prognosis are also undecidable under finite communication alphabets. Our results reveal that existing decidability results fundamentally rely on structural properties of fusion rules, in particular their monotone order-preserving nature. In contrast, non-monotone fusion rules such as XOR break this structure, leading to undecidability even in highly restricted settings.

The problem of k-way hypergraph partitioning is fundamental with significant applications in various fields, including VLSI design and scientific computing. State-of-the-art hypergraph partitioners commonly employ a multi-level framework encompassing coarsening, initial partitioning, uncoarsening, and refinement phases. However, many existing methods do not scale well to problems requiring a large number of partitions (i.e., large k). In pursuit of exceptionally high solution quality, existing memetic approaches often execute their two key operations, recombination and mutation, by invoking separate, standalone multi-level partitioners. This design choice, however, renders them significantly more time-consuming than standard multi-level partitioners. To make such memetic approaches more practical, we propose an advanced memetic framework, IMPart, which introduces novel recombination and mutation operators and integrates them directly into the uncoarsening phase of a single multi-level framework. This transforms the local searches of different granularities in the traditional multi-level framework into a sophisticated, collaborative search. Experimental results on multiple standard benchmarks demonstrate our framework more effectively escapes local optima and explores the global solution space for higher-quality solutions, substantially outperforming all existing hypergraph partitioners for large-$k$-way hypergraph partitioning. Our framework highlights a new paradigm for the development of advanced hypergraph partitioners.

Encrypted control can preserve the privacy of data and parameters while the necessary computations can be outsourced to a cloud server. To ensure the integrity of the received values from the cloud, i.e., that they have not been changed, however, strong assumptions or verification algorithms are needed. Previous methods require computationally expensive cryptographic protocols or are only applicable to static computations. In this paper, we present a novel type of verification algorithm for linear dynamic encrypted control. We utilize system-theoretic input-output properties of the controller for artificial challenge signals, which are processed in the cloud in parallel with the requested control input, to check the correctness of the results at the plant. This results in almost no additional computational load, wrong computations are revealed with high probability, and no replay attacks are possible.

As LLM-driven multi-agent systems (MAS) transition from lab to production, system behavior exhibits nonlinear degradation. We introduce the Intelligence Entropy Principle: probability-driven systems spontaneously drift toward disorder, formalized as S(t) = S0 * exp(alpha*t/Cm), where Cm is a model capability coefficient we propose. Lyapunov analysis yields the stabilization condition lambda > alpha/Cm. We construct the ADE (Agent Delivery Engineering) four-layer framework (L1 Physical Laws through L4 User Adaptation) with 23 core components. Validation spans 100K-scale experiments and 33.6 days of production monitoring. We propose a Five-Layer Disorder Taxonomy unifying failures under structural collapse, and present Elastic Organization as an original MAS morphology. Results: channel fracture reduced from 69-98% to near 0%; system death probability below 0.02%.

Move is a smart-contract language used to execute transactions on the Aptos blockchain. Move programs execute in a sandboxed VM as typed bytecode. The VM statically verifies foundational safety properties like type safety and reference safety at code loading time. In principle, this design gives strong guarantees for Move. However, the static verification logic is complex and continually evolving with the language; like any software, it is not immune to bugs. In a live blockchain setting, a missed rule violation can translate directly into loss of assets, forged authority, or unrecoverable corruption of on-chain state. For this reason, Aptos relies on defense-in-depth runtime safety checks that independently verify the critical invariants during execution, providing protection against latent verifier bugs and malicious bytecode. This paper motivates and describes the runtime safety checks for Move on Aptos.

Generative AI models lower the bar for content creation, making it easy for any user to create professional-looking images, text and music with minimal effort. This has enabled a new cottage industry around creation of "AI slop" mass quantities of mediocre content produced to generate revenue, often through misrepresentation as human-authored content, or scams involving automated scripts and fake consumption. While there are obvious parallels between the AI-slop industry and "traditional" email spam networks, it might be too early to determine if AI slop generation can grow into a similar self-sustaining industry. In this paper, we look specifically at the music industry, and explore the question: Can we prevent AI music slop from growing into a self-sustaining shadow industry? To answer this question, we characterize the current state of AI slop in music, and its pipeline from generation, distribution, and consumption by users on streaming platforms. By examining growth and engagement on Spotify, we confirm that AI music exhibits AI slop characteristics: the overwhelming majority (93%) of AI music receive few, if any listener plays, and are rarely recommended. AI musicians "spray and pray," releasing large volumes of music across multiple genres in hopes of generating a hit. We also explore the AI slop pipeline by generating and publishing our own AI tracks onto streaming through 11 indie music distributors. We find distributors have inconsistent and largely unenforced policies on AI music, making it surprisingly easy to publish mass produced AI songs. Finally, we consider AI music detection, and find that current methods lack accuracy or robustness. As generation costs decrease, we believe slop generation in music will become self-sustainable, unless concrete steps are taken by the music industry. We consider and discuss potential mitigation methods based on our findings.

This paper presents a low-cost home automation system implemented in a municipal swimming pool to address various challenges, including security concerns, air quality control, gas leakage detection, energy consumption reduction, and temperature and humidity control on the pool deck. The system utilises Arduino microcontrollers with sensors and actuators, enabling real-time data collection and analysis. The project is divided into two phases: hardware assembly and data analysis. In the hardware assembly phase, the Arduino sends data to a web Application Programming Interface (API) and stores it in a time-series database, with results presented in an Android application. The data analysis phase involves statistical exploration using libraries such as Pandas, NumPy, and Matplotlib. The proposed system aims to enhance decision-making based on collected and analysed data.

Social media posts often include misinformative or misleading content, diminishing the expected credibility of content feeds. We present an optimization-based method to improve the credibility of news content on social media feeds by refining existing content rankings. This method is based on a dual-objective optimization approach that minimizes the Spearman's footrule distance to the original ranking to maintain the original content order while incorporating an additional linear cost objective to elevate the expected credibility of the content feed. Additionally, we propose a robust semi-automated pipeline for assigning credibility scores to content based on a mixture of retrieval-augmented score assignments and human-generated fact-checks. This semi-automated pipeline helps ground the credibility assignment using human-generated labels while ensuring the algorithm extends to posts with few or no human-generated labels. We showcase our approach through an experimental setup using real-world data collected over X (Twitter), where we assign the credibility scores based on a mixture of user-generated community notes and retrieval augmented generation. The method we present leads to at most 7% deviation in both optimization objectives from the Pareto optimal front with known initial ranking values. Additionally, the algorithm allows for incorporating different measures for source credibility, making it applicable across various social media platforms.

Parent child tutoring is a collaborative learning setting with asymmetric roles, where parents guide children s problem solving while children engage in understanding and reasoning. However, most LLM based learning systems are designed for either single users or symmetric collaboration, leaving parent child tutoring with distinct instructional roles underexplored. Through a formative study, we find that effective parent child tutoring depends on preserving these distinct roles, with parents guiding the learning process and children remaining actively engaged in reasoning. We also identify recurring challenges when parents struggle to understand problem structure, lack sufficient knowledge to provide support, or encounter communication difficulties that disrupt shared understanding. To address these challenges, we present ParaTutor, a scaffolding system that provides different forms of support to parents and children. ParaTutor supports parents with guidance for tutoring and provides children with visual grounding for problem solving. We evaluate ParaTutor with 23 parent child dyads (children aged 10 to 12) under four tutoring conditions that vary how LLM assistance is delivered. Results show that generic LLM assistance tends to reduce the parent s role in tutoring, whereas ParaTutor better preserves parent led support and sustains children s participation in reasoning. These findings suggest that in multi users learning, the value of LLM support depends not only on model capability but also on how support is distributed across users with different roles. Our work contributes design implications for LLM systems that support family learning.

User mobility creates stochastic, time-varying backhaul demand that static capacity provisioning cannot match. We propose a closed-loop, load-aware hysteresis controller for hybrid fiber-IAB-FSO backhaul and show that energy drops faster than coverage: 8% to 44% energy savings cost only 0.9% to 6.7% coverage.

Metaheuristic optimization of hybrid fiber-IAB networks demonstrates that integrating user dynamics into topology design enables more adaptive and cost-efficient backhaul architectures, contributing to the development of scalable and flexible 6G network infrastructures.

Tabletop role-playing games provide a unique environment for interaction with artificial intelligence (AI) due to their complex and collaborative nature. We analyze Adventure AI, a podcast featuring human-AI interactions in Dungeons & Dragons play, to examine how AI is and can be used in tabletop role-playing gaming and how players perceive this use. We complete a qualitative analysis of three seasons of this podcast, from 2023 to 2025, reporting on the overarching themes of roles of AI, roles of humans, the evaluations and failures of AI, and its treatment as a person and character at the table. There are many aspects of the game where artificial intelligence succeeds, while there are others where it is less appropriate. This analysis gives a basis for future work on where artificial intelligence should and should not be used in gaming spaces.

In online vector balancing, vectors $t_1,\dots,t_n$ arrive one by one from a given set $T$ and the goal is to assign signs $s_1,\dots,s_n\in\{\pm1\}$ in an online manner so as to minimize the largest norm of any signed prefix sum $\sum_{i=1}^ks_i t_i$, $k \in [n]$. In this paper, we analyze the natural Euclidean greedy vector balancing algorithm for this problem: at each step $k$, the sign $s_k\in\{\pm1\}$ is chosen so that $s_k t_k$ has non-positive inner product with $\sum_{i=1}^{k-1} s_i\cdot t_i$. Our main result is the first finite bound, independent of the sequence length $n$, on the performance of greedy whenever $T$ is finite. When $T \subset \mathbb{R}^d$ consists of unit vectors, we prove that the signed sums produced by greedy have Euclidean norm at most $(2/δ_T)^{d-1}$, where $δ_T$ is the minimum non-zero distance between vectors in $T$ and subspaces spanned by vectors in $T$. The same upper bound holds when the sequences are composed of scaled down vectors in $T$. We also provide a simple set $T$ for which $Ω(\sqrt{d}/δ_T)$ is a lower bound. We analyze the greedy algorithm by proving the existence of a bounded convex $K_T$ that is $T$-absorbing: $\forall x\in K_T$ and $t \in\pm T$, $\langle x,t\rangle\leq0\Rightarrow x+t\in K_T$. We give an explicit construction of a set $K_T$ contained in a ball of radius $(2/δ_T)^{d-1}$, based on chains of subspaces spanned by vectors in $T$, which may be of independent interest. We generalize our greedy vector balancing bound to online vector partitioning, where the sequence $t_1,\dots,t_n$ must be partitioned in an online manner into $p$ subsequences. As an application, we prove a special case of a conjecture of Bosman et al. (arxiv:2402.19259), showing that a lexicographic version of total completion time scheduling under scenarios is polynomial time solvable when the number of scenarios is fixed.

NFV/SDN orchestration lets operators instantiate and steer traffic through virtual firewalls, IDS/IPS replicas, WAF clusters, zero-trust gateways, backup inspection paths, and migration targets on demand. Operators often treat these options as monotone improvements: more inspection capacity, lower nominal latency, or broader placement flexibility should not degrade the service. That intuition can fail even when the new option is locally attractive. We study a security-induced Braess paradox in service function chain (SFC) orchestration, where adding a defensive option worsens the post-adaptation equilibrium by concentrating traffic and adversarial value on shared security resources. We define Braessian security-management actions, derive a sufficient condition for paradox emergence under affine load-dependent VNF delay, and give a pre-deployment orchestration screen that rejects, caps, or reserves harmful options. A multi-tenant SFC experiment suite applies the model to four topology-derived settings: a fat-tree datacenter, NSFNET-style WAN, GEANT-style WAN, and edge/fog topology. Under default parameters in the Braessian regime identified by the theory, naive defensive expansion raises equilibrium service cost by 27.2-30.8% and increases risk concentration by factors of 6.1-9.7. Paradox-aware constrained use keeps the residual penalty below 1.9%, reduces service cost by 20.0-22.1% relative to naive expansion, and lowers a concentration-sensitive attack-loss proxy by 93.5% on average.

Cyber deception and Moving Target Defense are promising strategies that aim to disrupt adversaries by increasing uncertainty. However, sustaining long-lived, credible interactive sessions with adversaries remains an open challenge. Large Language Models (LLMs) offer a promising path toward more dynamic deception systems, but suffer from key limitations that fundamentally limit their applicability, including: lack of persistent state, output inconsistencies, hallucinations, latency, and susceptibility to behavioral subversion that may reveal the deception. We propose ShellGames, an SSH shell simulator based on LLM designed to address these limitations. ShellGames combines five complementary techniques: (i) Automatic Chain-of-Thought and few-shot learning to improve correctness; (ii) memory management to maintain system state coherency; (iii) speculative command execution to reduce response latency; (iv) smart routing of complex interactive commands to a sandboxed environment; and (v) subversion detection leveraging the constrained input-output domain of shell environments. To enable systematic evaluation, we introduce a standardized benchmarking protocol and dataset spanning correctness, consistency, state tracking, and robustness tasks. ShellGames achieves $0.898$ command accuracy on correctness ($+5.3pp$ over baselines), $0.918$ sequence-level accuracy on consistency ($+36pp$), $0.98$ state tracking accuracy ($+18.3pp$), and $0.95$ accuracy on robustness ($+37pp$). A user study with $n=20$ participants confirms that ShellGames achieves realism comparable to a real shell under free exploration and outperforms traditional honeypots on perceived command coverage.

As AI-generated code proliferates, formal verification, particularly through interactive theorem provers such as Rocq and Isabelle, becomes increasingly important for ensuring software correctness. However, producing machine-checked proofs in such provers remains a bottleneck. Existing solutions bring complementary strengths to proof automation: large language models (LLMs) can propose high-level proof strategies but lack local rigor, while automated tactics such as CoqHammer can reliably discharge many local goals but lack long-range planning capabilities. To combine the best of both worlds, we present Quarry, a planning-based proof synthesis framework that separates proof planning from proof execution. Specifically, Quarry asks an LLM to actively propose multiple proof decompositions with arbitrary sublemmas, type-checks them in Rocq under temporarily admitted sublemmas, and ranks candidates using a proof-state-based difficulty model that estimates hammer solvability. It then recursively proves sublemmas within a bounded budget, effectively turning long proofs into sequences of hammer-solvable obligations. We implement Quarry on top of SerAPI and CoqHammer and evaluate it using multiple frontier LLMs across multiple benchmarks. The experimental results show that planning-based decomposition with solvability-aware ranking substantially improves automation while maintaining predictable cost. Under a uniform 10-minute wall-clock budget, Quarry improves over the strongest baseline by 7% to 13% in success rate across three Rocq benchmarks. These results demonstrate that reliable proof automation can be achieved by coordinating neural planning with symbolic execution rather than replacing either.

Digital technologies are now central to children's learning, play, communication, identity formation, and social participation. Yet dominant approaches to children's online safety often rely on containment mechanisms, including bans, age gates, parental controls, monitoring, and screen-time restrictions. These approaches can be useful in specific contexts, but they often frame child protection primarily as a problem of restricting access to systems designed for adults. In this paper, we argue that this framing is inadequate for children's digital lives and insufficient as a security paradigm. We propose Child-fit security, a design paradigm in which technologies likely to be used by children treat a child as legitimate users, not attackers to be excluded, vulnerabilities to be patched, or risks to be managed. In this paradigm, children's wellbeing, development, privacy, safety, agency, and rights become core security requirements. This shifts the focus of protection from apps, accounts, and data to the child-system relationship, which means protecting both the child and their participation. We conceptualise child-fit security, contrast it with containment-oriented approaches, define its core principles, and discuss its implications for security design. We conclude by presenting a research agenda for making child-fit security operational.

Heterogeneous LLM serving stacks split scheduling into two layers that optimize in isolation: model routers pick a model from quality and cost signals while ignoring instance load, and serving load balancers optimize queues while ignoring quality. We present RouteBalance, a serving-aware scheduling layer that fuses both into a single online assignment over concrete model instances, jointly trading off quality, latency, and cost. A batched in-process predictor stack and dead-reckoned instance state keep the joint decision cheap on the request hot path ($\approx$32 ms at 12 req/s). On a 13-instance, 28-GPU heterogeneous cluster serving four model sizes, a single deployed RouteBalance stack traces the upper region of the three-way quality-cost-throughput frontier. Sweeping one weight vector reaches both the highest routing-decision quality (DeepEval $0.419$, $+0.013$ over the strongest baseline, $95\%$ CI $[{+}0.005,{+}0.022]$; the ordering holds when a second judge re-scores the actually served text) and, at its cost-priority corner, per-request cost that ties the cheapest baseline. With router engineering equalized against concurrent-scoring baseline variants we build, its balanced preset serves at $2.8$ s and $30$ req/s, leading $2.6$ to $4.1\times$ ahead of enhanced BEST-Route at high load. (Deploying those routers as published, one serial scoring call per request, makes them collapse $23\times$ under load, a deployment-architecture effect we isolate separately, not the routing result.) A four-arm isolation shows the benefit follows from pricing latency at model-selection time; the learned predictors contribute calibration and SLO headroom rather than the headline frontier. Code: https://github.com/AKafakA/route-balance

This work extends recent developments in studying human--machine interaction by scaling from individual game-theoretic models to a societal-level model. We adopt a Graphon Mean-Field Game (GMFG) that models the interaction among four groups of internally-homogeneous but externally-heterogeneous agents in a shared environment. Our results show that parasitism can masquerade as productive learning, with knowledge distribution and actions appearing healthy while being driven by machine coupling rather than independent investigation. To detect this, we measure the direction of information flow and belief entropy of the environment, revealing that human to machine channel dominates across all scenarios, with the asymmetry intensifying under parasitism. We further demonstrate that the system exhibits coexisting mutualistic and parasitic equilibria, where environmental noise can induce a tipping point that shifts agents past the cognitive cost barrier. These emergent phenomena are not designed into any individual agent but arise from the collective interaction structure, underscoring the need to study the sociology of humans and machines holistically as a complex system.

Automated video-odor matching predicts scents aligned with audiovisual content for scent-enhanced media. Existing methods usually treat odor labels as determined only by scene content, but odor judgment also depends on individual olfactory profiles, including scent sensitivity, tolerance to unpleasant odors, and affective preference. Ignoring this observer context limits current systems' ability to predict scents that match perceived experience. We present OlfactProfile, a framework for profile-conditioned odor prediction from audiovisual content. Our results show that olfactory profiles are not beneficial by default: with matched feature backbones, naive profile concatenation and uniform profile modulation can degrade performance, while structured field-wise profile conditioning consistently improves prediction. Thus, the key challenge is not merely whether observer context is available, but how it is integrated into multimodal reasoning. To study this setting, we construct an audiovisual benchmark pairing temporally aligned odor annotations with annotator olfactory preference profiles. It contains 1,350 video clips, a 99-class scent vocabulary, and three semantic odor tracks: Foreground Odor, Background Odor, and Emotion Odor. We also propose OAR (Olfactory-Aware Routing), a multimodal fusion module that performs track-aware audiovisual routing with field-wise profile modulation, allowing profile dimensions to influence odor reasoning according to perceptual role. Experiments show that OlfactProfile outperforms supervised baselines and general-purpose multimodal large models, is competitive with odor experts in a small human comparison, and improves perceived scent fit in scent-enhanced applications without task-specific fine-tuning. Per-track analysis shows that gains are strongest for Background Odor and Emotion Odor, where observer-dependent judgment is most important.

Classical DistFlow equations for steady-state distribution network analysis fail to capture the inherent imbalances of three-phase systems arising from asymmetrical lines, loads, and distributed energy resources (DERs). This paper extends the classical power flow (PF) equations into a rigorous, non-approximated three-phase formulation, termed Dist3Flow. The proposed branch flow model (BFM) utilizes the real and imaginary components of nodal voltages and the active and reactive power flows as state variables. Lines are modelled by nonlinear forward and backward equations, while loads and DERs are represented via ZIP models and P-Q control, respectively. By incorporating specific boundary conditions at the terminal nodes, the formulation generalizes PF analysis to both radial and closed-ring topologies. The solution is obtained by using a backward/borward sweep (BFS) algorithm. The approach is validated against OpenDSS across various configurations, considering open-ring and closed-ring topologies with and without DERs.

Space heating remains the dominant energy consumer in buildings. While structural retrofitting can substantially reduce demand, it is often costly and time-intensive. As an alternative, this study investigates the potential of intelligent heating control strategies to reduce heat consumption with lower investment and faster implementation. Previous studies have shown that replacing conventional heating-curve-based controllers with model predictive controllers (MPCs) can reduce heating energy demand. Whereas most studies compare MPC to conventional control, this work evaluates two MPC strategies with different control objectives and quantifies their impact on indoor temperature tracking and heating demand. A virtual residential building model was developed in Python based on ISO 52016-1 to generate synthetic measurement data. A simplified resistance-capacitance (RC) model was parametrised using this dataset and used as the internal model for two MPC strategies implemented in MATLAB. The strategies differ only in their optimisation objective: one minimises quadratic heating power, while the other prioritises indoor temperature tracking for thermal comfort. Simulations over six days show that both strategies satisfy comfort and system constraints, but differ in energy use and temperature variation. The comfort-oriented controller achieves lower total heat consumption than the controller minimising heating power, which is attributed to the penalisation of high heating rates in the quadratic objective function. The results demonstrate the importance of objective function formulation in MPC design and show that high comfort levels can be maintained while achieving lower heating demand without structural modifications to the building envelope.