Designing high-performance system heuristics is a creative, iterative process requiring experts to form hypotheses and execute multi-step conceptual shifts. While Large Language Models (LLMs) show promise in automating this loop, they struggle with complex system problems due to two critical failure modes: evolutionary neighborhood bias and the coherence ceiling. Evolutionary methods often remain trapped in local optima by relying on scalar benchmark scores, failing when coordinated multi-step changes are required. Conversely, existing agentic frameworks suffer from context degradation over long horizons or fail to accumulate knowledge across independent runs. We present Engram, an agentic researcher architecture that addresses these limitations by decoupling long-horizon exploration from the constraints of a single context window. Engram organizes exploration into a sequence of agents that iteratively design, test, and analyze mechanisms. At the conclusion of each run, an agent stores code snapshots, logs, and results in a persistent Archive and distills high-level modeling insights into a compact, persistent Research Digest. Subsequent agents then begin with a fresh context window, reading the Research Digest to build on prior discoveries. We find that Engram exhibits superior performance across diverse domains including multi-cloud multicast, LLM inference request routing, and optimizing KV cache reuse in databases with natural language queries.

In this paper, we present a new city-scale decentralized mesh network system suited for disaster recovery and emergencies. When wide-area connectivity is unavailable or significantly degraded, our system, MapMesh, enables static access points and mobile devices equipped with Wi-Fi in a city to route packets via each other for intra-city connectivity and to/from any nodes that might have Internet access, e.g., via satellite. The chief contribution of our work is a new routing protocol that scales to millions of nodes, a significant improvement over prior work on wireless mesh and mobile ad hoc networks. Our approach uses detailed information about buildings from widely available maps–data that was unavailable at scale over a decade ago, but is widely available now–to compute paths in a scalable way.

Wi-Fi rate adaptation remains a persistent challenge in wireless networking. Deployed algorithms like Minstrel-HT have remained largely stagnant for over a decade, relying on hand-tuned heuristics that fail to generalize to the complexity of modern wireless environments. We present , an autonomous research system that closes the loop on rate control development. IteRate uses a multi-agent AI architecture to conduct the full scientific cycle: formulating hypotheses, writing eBPF programs that run inside the Linux kernel, deploying them over-the-air to Wi-Fi devices, collecting fine-grained telemetry for analysis, and iterating based on experimental evidence, all without human intervention. IteRate makes three contributions. (1) a novel kernel module that exposes per-frame hardware telemetry including modulation and coding schemes (MCS) and retry counts to eBPF programs, (2) a structured agentic AI architecture employing specialized agents for algorithm design, experiment execution, and data analysis, coordinated via a hypothesis-driven research protocol with persistent knowledge, and (3) a closed-loop pipeline that automates the cross-compilation, deployment, and evaluation of in-kernel logic onto embedded Wi-Fi targets. On a 58-node testbed running five workloads. relative to the well-known Minstrel algorithm, IteRate achieves 21

Assessing and enhancing human learning through question-answering is vital, yet automating this process remains challenging. While large language models (LLMs) excel at summarization and query responses, their ability to generate meaningful questions for learners is underexplored. We propose Savaal, a scalable question-generation system with three objectives: (i) scalability, enabling question generation from hundreds of pages of text (ii) depth of understanding, producing questions beyond factual recall to test conceptual reasoning, and (iii) domain-independence, automatically generating questions across diverse knowledge areas. Instead of providing an LLM with large documents as context, Savaal improves results with a three-stage processing pipeline. Our evaluation with 76 human experts on 71 papers and PhD dissertations shows that Savaal generates questions that better test depth of understanding by 6.5X for dissertations and 1.5X for papers compared to a direct-prompting LLM baseline. Notably, as document length increases, Savaal's advantages in higher question quality and lower cost become more pronounced.

Can an AI autonomously design mechanisms for computer systems on par with the creativity and reasoning of human experts? We present Glia, an AI architecture for networked systems design that uses large language models (LLMs) in a human-inspired, multi-agent workflow. Each agent specializes in reasoning, experimentation, and analysis, collaborating through an evaluation framework that grounds abstract reasoning in empirical feedback. Unlike prior ML-for-systems methods that optimize black-box policies, Glia generates interpretable designs and exposes its reasoning process. When applied to a distributed GPU cluster for LLM inference, it produces new algorithms for request routing, scheduling, and auto-scaling that perform at human-expert levels in significantly less time, while yielding novel insights into workload behavior. Our results suggest that by combining reasoning LLMs with structured experimentation, an AI can produce creative and understandable designs for complex systems problems.

This paper presents DriveTrack, a new benchmark and data generation framework for long-range keypoint tracking in real-world videos. DriveTrack is motivated by the observation that the accuracy of state-of-the-art trackers depends strongly on visual attributes around the selected keypoints, such as texture and lighting. The problem is that these artifacts are especially pronounced in real-world videos, but these trackers are unable to train on such scenes due to a dearth of annotations. DriveTrack bridges this gap by building a framework to automatically annotate point tracks on autonomous driving datasets. We release a dataset consisting of 1 billion point tracks across 24 hours of video, which is seven orders of magnitude greater than prior real-world benchmarks and on par with the scale of synthetic benchmarks. DriveTrack unlocks new use cases for point tracking in real-world videos. First, we show that fine-tuning keypoint trackers on DriveTrack improves accuracy on real-world scenes by up to 7%. Second, we analyze the sensitivity of trackers to visual artifacts in real scenes and motivate the idea of running assistive keypoint selectors alongside trackers.

This paper argues that network and application delivery infrastructures have become highly centralized and are more vulnerable to attacks and disasters than is desirable. It proposes a research agenda for decentralized fallback networks and focuses on a key component-a city-scale decentralized network using existing Wi-Fi access points, which are deployed across almost all buildings in cities. It proposes a routing system that uses information about buildings from geospatial maps instead of traditional routing mechanisms to scale well to millions of Wi-Fi nodes.

Given the technical flaws with---and the increasing non-observance of---the TCP-friendliness paradigm, we must rethink how the Internet should manage bandwidth allocation. We explore this question from first principles, but remain within the constraints of the Internet's current architecture and commercial arrangements. We propose a new framework, Recursive Congestion Shares (RCS), that provides bandwidth allocations independent of which congestion control algorithms flows use but consistent with the Internet's economics. We show that RCS achieves this goal using game-theoretic calculations and simulations as well as network emulation.

Reconfiguring the network stack allows applications to specialize the implementations of communication libraries depending on where they run, the requests they serve, and the performance they need to provide. Specializing applications in this way is challenging because developers need to choose the libraries they use when writing a program and cannot easily change them at runtime. This paper introduces Bertha, which allows these choices to be changed at runtime without limiting developer flexibility in the choice of network and communication functions. Bertha allows applications to safely use optimized communication primitives (including ones with deployment limitations) without limiting deployability. Our evaluation shows cases where this results in 16x higher throughput and 63% lower latency than current portable approaches while imposing minimal overheads when compared to a hand-optimized versions that use deployment-specific communication primitives.

This paper introduces a new metric, "elasticity," which characterizes the nature of cross-traffic competing with a flow. Elasticity captures whether the cross traffic reacts to changes in available bandwidth. We show that it is possible to robustly detect the elasticity of cross traffic at a sender without router support, and that elasticity detection can reduce delays in the Internet by enabling delay-controlling congestion control protocols to be deployed without hurting flow throughput. Our results show that the proposed method achieves more than 85% accuracy under a variety of network conditions, and that congestion control using elasticity detection achieves throughput comparable to Cubic but with delays that are 50-70 ms lower when cross traffic is inelastic.

To overcome weaknesses in traditional loss-based congestion control algorithms (CCAs), researchers have developed and deployed several delay-bounding CCAs that achieve high utilization without bloating delays (e.g., Vegas, FAST, BBR, PCC, Copa, etc.). When run on a path with a fixed bottleneck rate, these CCAs converge to a small delay range in equilibrium. This paper proves a surprising result: although designed to achieve reasonable inter-flow fairness, current methods to develop delay-bounding CCAs cannot always avoid starvation, an extreme form of unfairness. Starvation may occur when such a CCA runs on paths where non-congestive network delay variations due to real-world factors such as ACK aggregation and end-host scheduling exceed double the delay range that the CCA converges to in equilibrium. We provide experimental evidence for this result for BBR, PCC Vivace, and Copa with a link emulator. We discuss the implications of this result and posit that to guarantee no starvation an efficient delay-bounding CCA should design for a certain amount of non-congestive jitter and ensure that its equilibrium delay oscillations are at least one-half of this jitter.

This paper introduces a new metric, "elasticity," which characterizes the nature of cross-traffic competing with a flow. Elasticity captures whether the cross traffic reacts to changes in available bandwidth. We show that it is possible to robustly detect the elasticity of cross traffic at a sender without router support, and that elasticity detection can reduce delays in the Internet by enabling delay-controlling congestion control protocols to be deployed without hurting flow throughput. Our results show that the proposed method achieves more than 85% accuracy under a variety of network conditions, and that congestion control using elasticity detection achieves throughput comparable to Cubic but with delays that are 50--70 ms lower when cross traffic is inelastic.

Digital maps with lane-level details are the foundation of many applications. However, creating and maintaining digital maps especially maps with lane-level details, are labor-intensive and expensive. In this work, we propose a mapping pipeline to extract lane-level street maps from aerial imagery automatically. Our mapping pipeline first extracts lanes at non-intersection areas, then it enumerates all the possible turning lanes at intersections, validates the connectivity of them, and extracts the valid turning lanes to complete the map. We evaluate the accuracy of our mapping pipeline on a dataset consisting of four U.S. cities, demonstrating the effectiveness of our proposed mapping pipeline and the potential of scalable mapping solutions based on aerial imagery.

Modern distributed applications run across numerous microservices and components deployed in cloud datacenters, using shared cloud services for computing and storage, edge services such as content distribution networks, network functions such as rate limiters and firewalls, security infrastructures, network routers, and physical links. When a user-visible fault occurs, the first step toward diagnosis is localization to determine where the fault has occurred. However, because application delivery spans different layers and different organizations, no entity has complete visibility or access to the information required to localize faults quickly. This paper proposes a cross-layer, cross-domain, and cross-application fault localization primitive with a simple and standardized information interface for the Internet.

The rapid proliferation of commodity drones has expanded interest in building applications that acquire imagery, video, and sensor data at scale. In addition, recent work on drone programming frameworks have simplified the development of aerial computing apps that gather this data. These advancements have popularized the drones-as-a-service model, where large drone fleets serve multiple apps simultaneously. This paper proposes a marketplace for aerial computing, where apps can gather aerial data on demand and providers can offer up their drones for aerial computing. We introduce Aerialis, a drones-as-a-service platform that schedules tasks to drones by arbitrating bids submitted by apps. Aerialis allows apps with different semantics and spatiotemporal preferences to express how much they would like to pay for each aerial computing task. It then aggregates requests across apps, and schedules tasks on drones according to a marketplace policy (e.g., maximizing revenue or guaranteeing quality-of-service to apps). We build a prototype of Aerialis, and implement urban sensing apps to monitor air pollution, measure road traffic, and profile cellular throughput. We discuss operational challenges in deploying Aerialis, and show how the measurements collected from our real-world experiments offer valuable insights for engineers and city planners.

The diversity of paths on the Internet makes it difficult for designers and operators to confidently deploy new congestion control algorithms (CCAs) without extensive real-world experiments, but such capabilities are not available to most of the networking community. And even when they are available, understanding why a CCA underperforms by trawling through massive amounts of statistical data from network connections is challenging. The history of congestion control is replete with many examples of surprising and unanticipated behaviors unseen in simulation but observed on real-world paths. In this paper, we propose initial steps toward modeling and improving our confidence in a CCA's behavior. We have developed CCAC, a tool that uses formal verification to establish certain properties of CCAs. It is able to prove hypotheses about CCAs or generate counterexamples for invalid hypotheses. With CCAC, a designer can not only gain greater confidence prior to deployment to avoid unpleasant surprises, but can also use the counterexamples to iteratively improvetheir algorithm. We have modeled additive-increase/multiplicative-decrease (AIMD), Copa, and BBR with CCAC, and describe some surprising results from the exercise.

Traffic accidents cost about 3% of the world’s GDP and are the leading cause of death in children and young adults. Accident risk maps are useful tools to monitor and mitigate accident risk. We present a technique to generate high-resolution (5 meters) accident risk maps. At this high resolution, accidents are sparse and risk estimation is limited by bias-variance trade-off. Prior accident risk maps either estimate low-resolution maps that are of low utility (high bias), or they use frequency-based estimation techniques that inaccurately predict where accidents actually happen (high variance). To improve this trade-off, we use an end-to-end deep architecture that can input satellite imagery, GPS trajectories, road maps and the history of accidents. Our evaluation on four metropolitan areas in the US with a total area of 7,488 km2 shows that our technique outperform prior work in terms of resolution and accuracy.

Accurately maintaining digital street maps is labor-intensive. To address this challenge, much work has studied automatically processing geospatial data sources such as GPS trajectories and satellite images to reduce the cost of maintaining digital maps. An end-to-end map update system would first process geospatial data sources to extract insights, and second leverage those insights to update and improve the map. However, prior work largely focuses on the first step of this pipeline: these map extraction methods infer road networks from scratch given geospatial data sources (in effect creating entirely new maps), but do not address the second step of leveraging this extracted information to update the existing map data. In this paper, we first explain why current map extraction techniques yield low accuracy when extended to update existing maps. We then propose a novel method that leverages the progression of satellite imagery over time to substantially improve accuracy. Our approach first compares satellite images captured at different times to identify portions of the physical road network that have visibly changed, and then updates the existing map accordingly. We show that our change-based approach reduces error rates four-fold.

There is now a significant and growing functional gap between the public Internet, whose basic architecture has remained unchanged for several decades, and a new generation of more sophisticated private networks. To address this increasing divergence of functionality and overcome the Internet's architectural stagnation, we argue for the creation of an Extensible Internet (EI) that supports in-network services that go beyond best-effort packet delivery. To gain experience with this approach, we hope to soon deploy both an experimental version (for researchers) and a prototype version (for early adopters) of EI. In the longer term, making the Internet extensible will require a community to initiate and oversee the effort; this paper is the first step in creating such a community.