arXiv subjects

Adriana Szekeres

Publications and source records attributed to Adriana Szekeres.

8 recordsLinked to original sources

arXiv2026

ActionEngine: From Reactive to Programmatic Web Agents via State Machine Memory

Many web agents operate through a reactive execution loop: they observe the current interface, reason about the next action, execute it, and repeat. This design incurs latency and cost that grow with the number of actions, while requiring agents to repeatedly rediscover how the same web application works. We present ActionEngine, a novel architecture that replaces step-by-step reasoning with programmatic execution using reusable knowledge of the application. A Crawling Agent explores the application offline and constructs an updatable state-machine memory that represents its GUI states, the operations available in each state, and the transitions between states. Unlike trajectory memory, this representation stores how the application works rather than solutions to individual tasks. At runtime, an Execution Agent uses this memory to synthesize a complete executable program in a single planning step, which is then executed deterministically without further planning calls. When the interface changes or the memory is incomplete, a reactive fallback repairs the failed action and updates the memory for future tasks. On 655 tasks across four WebArena domains, ActionEngine achieves a 91.2% success rate, outperforming the strongest reactive baseline, Claude Code, by 8.5 percentage points while reducing average task latency by 3.2x and cost by 8x.

cs.AI↗

arXiv2026

AutoTailor: Automatic, User-Aligned Capability Selection and Adaptation for Web Agents

Web agents can utilize reusable tools to reduce the cost and latency of low-level browser interaction, but automatically discovered tool collections can be large, redundant, and poorly aligned with user demand. We present AutoTailor, a meta-agentic framework for constructing and maintaining a compact set of trajectory-derived Model Context Protocol (MCP) APIs. Offline, AutoTailor converts web trajectories into parameterized browser-automation programs, applies a Quality Filter to remove APIs with unsuitable granularity and redundant functionality, and applies a Usage Likelihood Filter to prioritize broadly useful capabilities while preserving semantic coverage. Online, Dynamic Reselection monitors task outcomes and API usage, identifies recurring coverage gaps, adds relevant candidates, and prunes persistently unused capabilities. We evaluate AutoTailor on 106 WebArena Postmill tasks. Offline filtering reduces the initial 1,283 unrefined APIs to 87, and Dynamic Reselection produces a 33-API set. With reasoning and acting (ReAct) fallback, this set achieves 90.6% correctness, compared with 87.5% for ReAct alone, while reducing average total request-token cost by 57.8% and latency by 29.4%. Without ReAct, it achieves 60.1% correctness, marginally matching the performance of unrefined set, while reducing request-token usage by 94.9%. Together, these results show that static filtering produces a compact inventory of APIs expected to support core, high-likelihood tasks, while dynamic reselection further tailors that inventory to observed user needs. This combination improves accuracy and latency while sharply reducing token usage and end-to-end cost, demonstrating the value of user-aligned capability management for efficient web agents.

cs.AI↗

arXiv2026

HONEYBEE: Efficient Role-based Access Control for Vector Databases via Dynamic Partitioning[Technical Report]

Enterprise deployments of vector databases require access control policies to protect sensitive data. These systems often implement access control through hybrid vector queries that combine nearest-neighbor search with relational predicates based on user permissions. However, existing approaches face a fundamental trade-off: dedicated per-user indexes minimize query latency but incur high memory redundancy, while shared indexes with post-search filtering reduce memory overhead at the cost of increased latency. This paper introduces HONEYBEE, a dynamic partitioning framework that leverages the structure of Role-Based Access Control (RBAC) policies to create a smooth trade-off between these extremes. RBAC policies organize users into roles and assign permissions at the role level, creating a natural ``thin waist`` in the permission structure that is ideal for partitioning decisions. Specifically, HONEYBEE produces overlapping partitions where vectors can be strategically replicated across different partitions to reduce query latency while controlling memory overhead. To guide these decisions, HONEYBEE develops analytical models of vector search performance and recall, and formulates partitioning as a constrained optimization problem that balances memory usage, query efficiency, and recall. Evaluations on RBAC workloads demonstrate that HONEYBEE achieves up to 13.5X lower query latency than row-level security with only a 1.24X increase in memory usage, while achieving comparable query performance to dedicated, per-role indexes with 90.4% reduction in additional memory consumption, offering a practical middle ground for secure and efficient vector search.

cs.DB↗

arXiv2023

The FIDS Theorems: Tensions between Multinode and Multicore Performance in Transactional Systems

Traditionally, distributed and parallel transactional systems have been studied in isolation, as they targeted different applications and experienced different bottlenecks. However, modern high-bandwidth networks have made the study of systems that are both distributed (i.e., employ multiple nodes) and parallel (i.e., employ multiple cores per node) necessary to truly make use of the available hardware. In this paper, we study the performance of these combined systems and show that there are inherent tradeoffs between a system's ability to have fast and robust distributed communication and its ability to scale to multiple cores. More precisely, we formalize the notions of a \emph{fast deciding} path of communication to commit transactions quickly in good executions, and \emph{seamless fault tolerance} that allows systems to remain robust to server failures. We then show that there is an inherent tension between these two natural distributed properties and well-known multicore scalability properties in transactional systems. Finally, we show positive results; it is possible to construct a parallel distributed transactional system if any one of the properties we study is removed.

cs.DC↗

arXiv2021

Scaling Replicated State Machines with Compartmentalization [Technical Report]

State machine replication protocols, like MultiPaxos and Raft, are a critical component of many distributed systems and databases. However, these protocols offer relatively low throughput due to several bottlenecked components. Numerous existing protocols fix different bottlenecks in isolation but fall short of a complete solution. When you fix one bottleneck, another arises. In this paper, we introduce compartmentalization, the first comprehensive technique to eliminate state machine replication bottlenecks. Compartmentalization involves decoupling individual bottlenecks into distinct components and scaling these components independently. Compartmentalization has two key strengths. First, compartmentalization leads to strong performance. In this paper, we demonstrate how to compartmentalize MultiPaxos to increase its throughput by 6x on a write-only workload and 16x on a mixed read-write workload. Unlike other approaches, we achieve this performance without the need for specialized hardware. Second, compartmentalization is a technique, not a protocol. Industry practitioners can apply compartmentalization to their protocols incrementally without having to adopt a completely new protocol.

cs.DC↗

arXiv2020

Making Distributed Mobile Applications SAFE: Enforcing User Privacy Policies on Untrusted Applications with Secure Application Flow Enforcement

Today's mobile devices sense, collect, and store huge amounts of personal information, which users share with family and friends through a wide range of applications. Once users give applications access to their data, they must implicitly trust that the apps correctly maintain data privacy. As we know from both experience and all-too-frequent press articles, that trust is often misplaced. While users do not trust applications, they do trust their mobile devices and operating systems. Unfortunately, sharing applications are not limited to mobile clients but must also run on cloud services to share data between users. In this paper, we leverage the trust that users have in their mobile OSes to vet cloud services. To do so, we define a new Secure Application Flow Enforcement (SAFE) framework, which requires cloud services to attest to a system stack that will enforce policies provided by the mobile OS for user data. We implement a mobile OS that enforces SAFE policies on unmodified mobile apps and two systems for enforcing policies on untrusted cloud services. Using these prototypes, we demonstrate that it is possible to enforce existing user privacy policies on unmodified applications.

cs.CR↗

arXiv2020

Matchmaker Paxos: A Reconfigurable Consensus Protocol [Technical Report]

State machine replication protocols, like MultiPaxos and Raft, are at the heart of nearly every strongly consistent distributed database. To tolerate machine failures, these protocols must replace failed machines with live machines, a process known as reconfiguration. Reconfiguration has become increasingly important over time as the need for frequent reconfiguration has grown. Despite this, reconfiguration has largely been neglected in the literature. In this paper, we present Matchmaker Paxos and Matchmaker MultiPaxos, a reconfigurable consensus and state machine replication protocol respectively. Our protocols can perform a reconfiguration with little to no impact on the latency or throughput of command processing; they can perform a reconfiguration in one round trip (theoretically) and a few milliseconds (empirically); they provide a number of theoretical insights; and they present a framework that can be generalized to other replication protocols in a way that previous reconfiguration techniques can not. We provide proofs of correctness for the protocols and optimizations, and present empirical results from an open source implementation.

cs.DC↗

arXiv2020

Bipartisan Paxos: A Modular State Machine Replication Protocol

There is no shortage of state machine replication protocols. From Generalized Paxos to EPaxos, a huge number of replication protocols have been proposed that achieve high throughput and low latency. However, these protocols all have two problems. First, they do not scale. Many protocols actually slow down when you scale them, instead of speeding up. For example, increasing the number of MultiPaxos acceptors increases quorum sizes and slows down the protocol. Second, they are too complicated. This is not a secret; state machine replication is notoriously difficult to understand. In this paper, we tackle both problems with a single solution: modularity. We present Bipartisan Paxos (BPaxos), a modular state machine replication protocol. Modularity yields high throughput via scaling. We note that while many replication protocol components do not scale, some do. By modularizing BPaxos, we are able to disentangle the two and scale the bottleneck components to increase the protocol's throughput. Modularity also yields simplicity. BPaxos is divided into a number of independent modules that can be understood and proven correct in isolation.

cs.DC↗