In briefPlotkin-Type Bounds for Function-Correcting Codes on Monomial Maps
SummaryThis paper studies function-correcting codes (FCCs), which give the receiver stronger protection for a function of the message than for the message itself. For arbitrary functions over finite fields under the Hamming metric, the authors derive a generalised Plotkin-type bound on FCC redundancy that reduces to the classical Plotkin bound for ordinary error-correcting codes. Explicit bounds are also obtained for linear functions, Hamming-weight functions, and Hamming-weight threshold functions. The paper further derives an explicit redundancy bound for monomial mappings f(x)=x^n over the field F_{q^k}.
In briefResource pattern design for V2X-ISAC under communication quality constraints
SummaryThis paper proposes a method for selecting sensing resource patterns in vehicle-to-everything integrated sensing and communication (ISAC) systems, considering both communication reliability and sensing performance. Sensing is based on the Fisher information matrix for joint range and velocity estimation, with a minimum packet reception ratio imposed as a QoS constraint. A greedy selection algorithm with one-swap refinement is developed to avoid exhaustive search. Simulations show improved sensing over conventional patterns while meeting the PRR requirement, with performance close to the exhaustive-search optimum.
In briefTimely persuasion by two opposing senders: does competition help the receiver?
SummaryTwo senders with opposing preferences each send status updates about a binary continuous-time Markov chain under individual budgets. The receiver follows the reports only if doing so yields at least the long-run utility of a prior-based default estimate. The authors model the interaction as a Stackelberg game, characterize its pure-strategy equilibria in closed form, and compare receiver utility with a single sender and with a receiver-optimal reporting benchmark.
In briefRecovering a hidden linear system and its starting states from partial observations.
SummaryThis paper studies how to jointly recover an unknown system matrix A and a low-rank initial-state matrix X0 from observations of the state trajectories A^t X0 that cover only some coordinates. It gives necessary identifiability conditions, lower bounds on the number of observations, and a rank-based principle for generic local identifiability. When trajectories from a basis of the initial-state range are fully observed, it provides necessary and sufficient rank conditions for global recovery with explicit formulas. Numerical experiments on synthetic and real data assess performance under different sampling rates and horizons.
In briefComputing Equilibria in Integer Games with Shared Constraints
SummaryThe paper presents a cutting-plane algorithm for computing pure Nash equilibria in finite integer games where deviations depend on opponents' choices due to shared constraints. Conditional equilibrium inequalities are embedded as lazy constraints in branch-and-cut to optimize over exact or approximate equilibria. Evaluated on 3,300 instances, the method finds a socially optimal equilibrium in nearly all cases and certifies nonexistence in 16.
Bainian Hao, Hyunwoo Lee, Robert Hildebrand et al.
In briefSyclKittens: A Tile Programming Model for Intel GPU Programmers and Coding Agents
SummaryThe paper presents SyclKittens, a hardware-aware tile programming model for Intel GPUs that encodes known-good methods such as matrix-engine operand layouts, L1 prefetching, and fabric communication. Under the same agent and budget, an agent using SyclKittens reaches 82.1% of Intel's oneDNN library on a GEMM kernel, compared with 57.5% using raw SYCL. The resulting kernel suite reaches about 96% of oneDNN in geometric mean across GEMM shapes and runs Llama-3.1-8B inference 1.59x faster than torch.compile on one GPU.
WhyProposes a hardware-aware tile model encoding known methods, validated by controlled GEMM and Llama-3.1-8B inference comparisons against oneDNN and oneCCL.
In briefOACM: An Optimistic Asynchronous Communication Model for Large-Scale Spiking Neural Network Simulation
SummaryThis paper proposes OACM, a communication model for simulating large-scale spiking neural networks across distributed computers. It combines optimistic hybrid simulation with rollbacks, asynchronous one-sided communication via the UNR library, and adaptive message aggregation and routing on a 2D-HyperX topology. On a high-performance computing cluster simulating a multi-area marmoset brain model with up to 174 nodes, the authors report up to 1.4x speedup over the original CORTEX simulator and over 22.6x speedup compared to NEST.
In briefLearning in large populations with anticipatory payoffs, combining Bregman divergence with Willems and Lyapunov methods
SummaryThe paper studies evolutionary Nash equilibrium learning in large populations whose payoffs come from dynamic mechanisms. Using counterclockwise dissipativity (CCW), it shows convergence for a broad class of well-behaved learning rules that align with payoffs. It then builds rule-independent Lyapunov functions from CCW storage functions, filling the gap where earlier results gave convergence but no Lyapunov stability. For a nonlinear anticipatory mechanism, the storage function is a Bregman divergence, and the resulting Lyapunov function establishes global asymptotic stability. A congestion game with a nonlinear congestion-growth toll illustrates the results.
In briefBitIR: A Cross-Architecture Fault Injection Tool for Testing How Heterogeneous GPU Programs Handle Faults
SummaryThis paper presents BitIR, a framework that injects single-bit faults at the LLVM IR level so the same perturbation can be compared across NVIDIA, Intel, and AMD GPUs. The authors ran large injection campaigns on the Polaris, Aurora, and Frontier supercomputers using heterogeneous benchmarks. Outcomes were sorted into masked results, silent data corruptions, and failures. The results show that identical faults behave differently across vendors, so resilience depends on the backend.
In briefDeterministic Stackelberg Strategies for Robotaxi Vehicle Assignment
SummaryThis paper studies competition between two robotaxi platforms, where an incumbent must dispatch its fleet to limit riders poached by a reactive entrant. The authors model this as a zero-sum assignment game and give an exact integer linear program, plus a polynomial-time approximation algorithm. They prove that computing or approximating the optimal payoff is hard in general, and provide an exact dynamic programming method for riders and vehicles on a line. Experiments on real ride-hailing data show the LP-based method performs well in practice, and simple heuristics are competitive.
Ioannis Caragiannis, Kostas Kollias, Mohammad Roghani et al.
In briefSIC-based multi-way communication using reconfigurable intelligent surfaces
SummaryThis paper studies multi-way communication systems aided by a reconfigurable intelligent surface (RIS) using QPSK, where multiple users exchange information by exploiting the RIS's full-duplex capability. Three successive interference cancellation (SIC) ordering methods are compared by bit error rate through Monte-Carlo simulations, and an extension to RIS elements with phase-dependent amplitudes is considered. Results show that with few RIS elements, SIC ordered by increasing mean square error performs best, while with more elements, the optimization-based ordering performs best.
In briefNon-isomorphic graphs have distinct vertex-Ramsey classes
SummaryThis paper studies the k-colour vertex Ramsey class of a graph H, the set of graphs G where any k-colouring of vertices yields a monochromatic induced copy of H. The authors prove that, for any k, non-isomorphic graphs have distinct Ramsey classes.
In briefSingle-sample prophet inequalities via reduction from combinatorial to single-item
SummaryThis paper studies single-sample prophet inequalities for online combinatorial allocation, where items arrive sequentially and must be allocated. The authors give a general reduction that converts combinatorial problems into single-item prophet inequalities, using a free-disposal value to separate buyer and item constraints. The framework yields competitive ratios for XOS valuations in the single-sample and k-sample settings, and extends to divisible resources with capped-XOS valuations.
In briefA tighter upper bound on the size of recovering pairs
SummaryThe paper proves that for any recovering pair of nonempty families of subsets of an n-element set, the product of the family sizes is at most (2.23949)^n. Previously, combining a result of Mond, Souza and Versteegen with the Fang-Huang bound gave (2.2499)^n. The proof combines two entropy inequalities through a convex combination.
WhyCombines two entropy inequalities via convex combination to improve the exponential bound for recovering pairs from 2.2499^n to 2.23949^n.
In briefOpenMP Meta-Lowering: A Declarative Way to Generate Parallel Code Portably
SummaryThis paper presents a modular approach to parallel code generation built on MLIR, in which a domain-specific language specifies how OpenMP constructs are lowered for each target. The authors call this OpenMP meta-lowering. On PolyBench/C-OMP, it matches state-of-the-art toolchains with under 0.7% code-size overhead. The OpenMP subset is expressed in about 32% fewer lines than Clang's lowering code and about 76% fewer than GCC's, and new runtimes need only a few specification lines.
In briefCode distances of matrix rank-metric codes under Add-and-Remove transformations: how subcode distances change when a code is modified by removing and adding subspaces.
SummaryThe paper studies how subcode distances of matrix rank-metric codes change under the Add-and-Remove construction, which keeps a subcode of codimension ℓs and adds a space of dimension ℓa. It gives inequalities between the subcode distances of the original and new codes for arbitrary matrix codes, and shows Delsarte duality interchanges ℓs and ℓa. For matrix Gabidulin codes it gives explicit behavior of subcode distances and expresses the new code's minimum distance via rank distance between the added space and the common subcode, with relevance to the MIRANDA signature scheme.
Gianira N. Alfarano, Rakhi Pratihar, Adrien Vin\c{c}otte
In briefTraining one LLM across three supercomputers in two continents
SummaryThis paper presents a system that pools compute allocations from three supercomputers (Snellius, LUMI, and Frontier), located up to 7,400 km apart, to pre-train a single language model. It combines elastic two-loop training, a heartbeat-backed data-leasing protocol that prevents samples from being duplicated or lost, and queue-aware job placement. Training Qwen3-0.6B on C4, the three-site run reached a held-out perplexity of 34.7, compared with 28.2 for a centralised baseline.
WhyCombines elastic cross-site training, data leasing, and queue-aware placement, validated on three supercomputers, though comparisons and scale are limited.
Zar\`e Palanciyan, Thomas van Osch, Douwe van der Wal et al.
In briefRailWave: adaptive scheduling that speeds up data transfers between expert GPUs in large AI models
SummaryThis paper addresses slow, uneven data exchange (All-to-All communication) in Mixture-of-Experts AI models. It presents RailWave, a communication layer built on DeepEP that redistributes traffic across network Rails and limits concurrent senders per receiver. On communication workloads from the 106B GLM-4.5-Air model, it reports speedups of up to 5.84x on H800 and 4.36x on H20 compared with Native.
