Papers for

network infrastructure teams

Papers whose findings have a practical use for this group, as judged from the abstract. Open a paper to read what it means in practice.

Strategyproof facility location on a circle with improved approximation ratio

A Randomized $\frac32$-Approximation for Strategic Facility Location on a Circle

Abstract: We study strategyproof mechanisms for locating a single facility on a circle so as to serve a set of strategic agents under the utilitarian social cost objective. We analyze a simple parity-dependent mechanism that randomizes between the Random Dictator (RD) mechanism and the Proportional Circle Distance (PCD) mechanism. For an odd number \(n\) of agents, this mechanism mixes RD and PCD with equal probability, as originally proposed by Rogowski and Dziubi{ń}ski (IJCAI 2025). For even \(n\), it mixes RD with a random-deletion extension of PCD, in which one agent is removed uniformly at random before PCD is applied to the remaining agents. Our main result shows that this mechanism is strategyproof and achieves an approximation ratio of \(\frac32\) for every \(n\ge 3\). This improves the \(\frac74\) upper bound of Rogowski and Dziubi{ń}ski, which applied only to odd \(n\), and extends the guarantee to even numbers of agents. Finally, we establish a lower bound of \(\frac{11}{10}\) on the approximation ratio of any randomized strategyproof mechanism, improving on the previous lower bound of \(1.0456\) due to Meir (SAGT 2019).

Fri 11 SeptComputer Science and Game Theory
The gist
This paper looks at how to fairly choose a spot on a circular path to build one facility so that a group of people, each with their own honest preferences, are served well. The authors study a method that mixes two known ways to pick this spot, ensuring that no one can benefit by lying about their location. They prove this mixed method works better than previous approaches, giving a closer-to-optimal solution for any number of people. They also show that no random method can do much better than their guaranteed performance.
Open 2609.12792v1

SemBridge creates communication plans linking distributed tensor systems

SemBridge: Compiling Consumer Observations into Cross-Stack Communication Plans

Abstract: Distributed-tensor systems specify where values reside, while collective systems optimize how requested operations execute. At a boundary between vendor runtimes that cannot share a native communicator, neither abstraction states what a remote consumer must observe. SemBridge fills this gap by compiling graph and runtime facts into a typed contract for the consumer-visible result and its delivery obligations. The contract captures provenance, substitutability, completion, authority, demand, and native-domain locality. A deterministic lowerer constructs backend-neutral communication plans, and a symbolic checker validates each plan before execution across CUDA/NCCL and CANN/HCCL. An independent layout-only planner handles all 72 structural transitions but establishes only 54 complete obligations; a byte-only minimizer proposes 40 semantically invalid candidates, all rejected by SemBridge. On nine real edges, SemBridge produces distinct observation-aware plans that reduce startups on all nine and payload bytes on the three result edges. A live CUDA/CANN run derives and executes full-logit reconstruction, source projection, and owner-token delivery from log-probability, token-only, and owner-scoped requests. On a measured two-host 1-GbE capacity-spillover deployment, source projection cuts result traffic by more than 99.97% and increases throughput by 8.92-80.20% across Dense, MoE, and MiniMax workloads. All 18 MiniMax restart pairs at concurrency 1, 8, and 16 favor source projection. A Qwen3-14B MLP slice additionally verifies bitwise activation-shard delivery and HCCL completion of row-parallel partials. These results establish consumer observation as a semantic layer between placement and collective execution.

Tue 8 SeptDistributed, Parallel, and Cluster Computing
The gist
When computers work together to process complex data, they need clear rules about who sees what and when. This paper presents SemBridge, a system that builds precise contracts explaining how data moves between parts of a distributed system. The authors show that these contracts help create efficient communication plans that work across different hardware and software setups. Their tests demonstrate big reductions in data traffic and faster processing in real-world workloads.
Open 2609.08231v1