arXiv subjects

Ankur Sharma

Publications and source records attributed to Ankur Sharma.

At least 19 recordsLinked to original sources

arXiv2026

The Agent Operating System (AOS): A Reference Operating Architecture for Distributed Agentic Systems

Large language models have transformed artificial intelligence from isolated prediction services into components of long-running, distributed systems that reason, invoke tools, retrieve external state, delegate tasks, and act on behalf of users and organizations. The surrounding ecosystem has responded with agent frameworks, workflow engines, model-serving platforms, memory systems, communication protocols, and observability tools. These technologies improve execution, but they do not provide a stable, implementation-independent operating architecture for governing intent, selecting capabilities, preserving authority across delegation, controlling uncertainty, coordinating runtime behavior, and reconstructing why consequential actions occurred. This paper proposes the Agent Operating System (AOS), a vendor-neutral reference operating architecture for distributed agentic systems. AOS contains two internal planes: a Control & Governance Plane responsible for intent, policy, trust, authority, confidence, auditability, observability, and human oversight; and a Runtime & Coordination Plane responsible for agent lifecycle, workflow coordination, model and tool routing, context and memory coordination, scheduling, traffic management, and runtime assurance. Platform services, Linux or Windows, container runtimes, and physical infrastructure remain outside the AOS boundary and are integrated through explicit interfaces. The paper specifies AOS concepts, invariants, interface objects, optimization objectives, deployment profiles, and reliability responsibilities. It also identifies tradeoffs and unresolved research questions. AOS is not presented as a replacement for existing frameworks or infrastructure; it is proposed as the operating architecture through which heterogeneous components can be composed into governable, reliable, observable, and interoperable agentic systems.

cs.AI↗

arXiv2026

Agent Operating Systems (AOS): Integrating Agentic Control Planes into, and Beyond, Traditional Operating Systems

Traditional operating systems were designed around deterministic programs, explicit control flow, and human initiated workflows. Their core abstractions processes, threads, system calls, files, and permissions assume bounded behavior and predictable interaction patterns. Agentic AI systems introduce a different execution model: long-lived, goal-directed entities that reason probabilistically, invoke tools dynamically, and adapt behavior based on feedback. While agents can be implemented as user-space applications today, their execution characteristics stress OS boundaries in scheduling, memory and state management, security, observability, and governance. This paper introduces the concept of an Agent Operating System (AOS), a systems architecture that integrates an agentic control plane into existing operating systems or, in some models, subsumes selected OS responsibilities over time. We provide a precise definition of an AOS, explicit assumptions and non-goals, and a structured decomposition of AOS responsibilities into schedulers, context and memory management, tool and capability registries, policy and trust enforcement, and observability and audit. We analyze limitations of classical OS abstractions for agent workloads, propose integration models from user-space runtimes to distributed control planes, and map AOS concepts onto Linux and Windows primitives. We present security and safety implications, including agent specific threat models, and define evaluation criteria that emphasize deterministic enforcement, auditability, and operator comprehensibility. The objective is not to replace operating systems wholesale, but to establish a rigorous systems foundation for agentic computation that remains controllable, accountable, and secure at scale.

cs.CR↗

arXiv2026

Time, Causality, and Observability Failures in Distributed AI Inference Systems

Distributed AI inference pipelines rely heavily on timestamp-based observability to understand system behavior. This work demonstrates that even small clock skew between nodes can cause observability to become causally incorrect while the system itself remains functionally correct and performant. We present controlled experiments on a multi-node AI inference pipeline, where clock skew is introduced at a single stage. Results show that no violations are observed under synchronized conditions and up to 3 ms skew, while clear causality violations emerge by 5 ms. Despite this, system throughput and output correctness remain largely unaffected. We further observe that violation behavior is not strictly static. In longer runs, negative span rates may stabilize or decrease over time, indicating that effective skew evolves due to relative clock drift between nodes. Experiments were conducted using Kafka and ZeroMQ transports, with consistent results across both. Aeron is under active exploration but is not yet included in the completed validation set. These findings suggest that observability correctness depends not only on system functionality but also on precise time alignment, and that timing must be treated as a first-class concern in distributed AI systems.

cs.AI↗

arXiv2025

Streaming Democratized: Ease Across the Latency Spectrum with Delayed View Semantics and Snowflake Dynamic Tables

Streaming data pipelines remain challenging and expensive to build and maintain, despite significant advancements in stronger consistency, event time semantics, and SQL support over the last decade. Persistent obstacles continue to hinder usability, such as the need for manual incrementalization, semantic discrepancies across SQL implementations, and the lack of enterprise-grade operational features. While the rise of incremental view maintenance (IVM) as a way to integrate streaming with databases has been a huge step forward, transaction isolation in the presence of IVM remains underspecified, leaving the maintenance of application-level invariants as a painful exercise for the user. Meanwhile, most streaming systems optimize for latencies of 100 ms to 3 sec, whereas many practical use cases are well-served by latencies ranging from seconds to tens of minutes. We present delayed view semantics (DVS), a conceptual foundation that bridges the semantic gap between streaming and databases, and introduce Dynamic Tables, Snowflake's declarative streaming transformation primitive designed to democratize analytical stream processing. DVS formalizes the intuition that stream processing is primarily a technique to eagerly compute derived results asynchronously, while also addressing the need to reason about the resulting system end to end. Dynamic Tables then offer two key advantages: ease of use through DVS, enterprise-grade features, and simplicity; as well as scalable cost efficiency via IVM with an architecture designed for diverse latency requirements. We first develop extensions to transaction isolation that permit the preservation of invariants in streaming applications. We then detail the implementation challenges of Dynamic Tables and our experience operating it at scale. Finally, we share insights into user adoption and discuss our vision for the future of stream processing.

cs.DB↗

arXiv2024

Multi-messenger connection in high-energy neutrino astronomy

Low fluxes of astrophysical neutrinos at TeV energies and the overwhelming background of atmospheric neutrinos below that, render the current paradigm of neutrino astronomy as a severely statistics limited one. While many hints have emerged, all the evidence gathered by IceCube and ANTARES, over the course of almost a decade and a half of operation, have fallen short of providing any conclusive answer to the puzzle of the origin of high-energy cosmic rays and neutrinos. The advancement of the field is thus closely associated with not only the neutrino observatories coming online in the next few years, but also on the coordinated efforts of the EM, GW and cosmic ray communities to develop dedicated channels and infrastructure that allows for swift and comprehensive multi-messenger follow-up of relevant events detected in any of the sectors. This paper highlights the strides that have been already taken in that direction and the fruits that they have born, as well as the challenges that lie ahead.

astro-ph.HE↗

arXiv2022

Database of Indian Social Media Influencers on Twitter

Databases of highly networked individuals have been indispensable in studying narratives and influence on social media. To support studies on Twitter in India, we present a systematically categorised database of accounts of influence on Twitter in India, identified and annotated through an iterative process of friends, networks, and self-described profile information, verified manually. We built an initial set of accounts based on the friend network of a seed set of accounts based on real-world renown in various fields, and then snowballed "friends of friends" multiple times, and rank ordered individuals based on the number of in-group connections, and overall followers. We then manually classified identified accounts under the categories of entertainment, sports, business, government, institutions, journalism, civil society accounts that have independent standing outside of social media, as well as a category of "digital first" referring to accounts that derive their primary influence from online activity. Overall, we annotated 11580 unique accounts across all categories. The database is useful studying various questions related to the role of influencers in polarisation, misinformation, extreme speech, political discourse etc.

cs.SI↗

arXiv2021

Rallying Adversarial Techniques against Deep Learning for Network Security

Recent advances in artificial intelligence and the increasing need for powerful defensive measures in the domain of network security, have led to the adoption of deep learning approaches for use in network intrusion detection systems. These methods have achieved superior performance against conventional network attacks, which enable the deployment of practical security systems to unique and dynamic sectors. Adversarial machine learning, unfortunately, has recently shown that deep learning models are inherently vulnerable to adversarial modifications on their input data. Because of this susceptibility, the deep learning models deployed to power a network defense could in fact be the weakest entry point for compromising a network system. In this paper, we show that by modifying on average as little as 1.38 of the input features, an adversary can generate malicious inputs which effectively fool a deep learning based NIDS. Therefore, when designing such systems, it is crucial to consider the performance from not only the conventional network security perspective but also the adversarial machine learning domain.

cs.CR↗

arXiv2021

KM3NeT/ARCA expectations in view of a novel multimessenger study of starburst galaxies

Starburst galaxies (SBGs) and more in general starforming galaxies represent a class of galaxies with a high star formation rate (up to 100 solar masses/year). Despite their low luminosity, they can 19 be considered as guaranteed "factories" of high energy neutrinos, being "reservoirs" of accelerated 20 cosmic rays and hosting a high density target gas in the central region. In this contribution 21 we present a novel multimessenger study of these sources and the possibility of observing their 22 neutrino signals with the KM3NeT/ARCA telescope. The differential sensitivity for different SBG 23 scenarios is reported considering track-like neutrino events in the 100 GeV-100 PeV energy range.

astro-ph.HE↗

arXiv2021

Studies of systematic uncertainty effects on IceCube's real-time angular uncertainty

Sources of astrophysical neutrinos can potentially be discovered through the detection of neutrinos in coincidence with electromagnetic or gravitational waves. Real-time alerts generated by IceCube play an important role in this search, acting as triggers for follow-up observations with instruments sensitive to other wavelengths. Once a high-energy event is detected by the IceCube real-time program, a complex and time consuming direction reconstruction method is run in order to calculate an accurate localisation. To investigate the effect of systematic uncertainties on the uncertainty estimate of the location, we simulate a set of high-energy events with a wide range of directions for different ice model realisations, the dominant systematic error in our localization uncertainty. This makes use of a novel simulation tool, which allows the treatment of systematic uncertainties with multiple continuously varied nuisance parameters. These events will be reconstructed using various reconstruction methods. This study will enable us to include systematic uncertainties in a robust manner in the real-time direction and error estimates.

astro-ph.HE↗

arXiv2021

A model-independent analysis of neutrino flares detected in IceCube from X-ray selected blazars

Blazars are among the most powerful steady sources in the Universe. Multi-messenger searches for blazars have traditionally focused on their gamma-ray emission, which can be produced simultaneously with neutrinos in photohadronic interactions. However, X-ray data can be equally vital to constrain the SED of these sources, since the hadronically co-produced gamma-rays could get absorbed by the ambient photon fields and cascade down to X-ray energies before escaping. In this work, we present the outline for an untriggered, time-dependent analysis of neutrino flares from the direction of X-ray selected blazars using 10 years of IceCube data. A binomial test will be performed on the population to reveal if a subcategory of sources has statistically significant emission. The sources are selected from RomaBZCat, and the p-values and best-fit flare parameters are obtained for each source using the method of unbinned likelihood maximisation.

astro-ph.HE↗

arXiv2021

Analyzing the gamma-ray activity of neutrino emitter candidates: comparing TXS 0506+056 with other blazars

On 22nd of September 2017 the IceCube collaboration sent an alert for an EHE (Extreme High Energy) event, corresponding to the reconstruction of a muonic neutrino (IC-170922A) with energy $\sim 290$ TeV. A multi-wavelength follow-up campaign associated this neutrino event with a gamma-ray flaring state of the BL Lac TXS 0506+056 located at z=0.3365. From the same position of the sky a muonic neutrino excess is observed in a time window of 110 days around 13th of December 2014. These observations together suggest TXS 0506+056 as a possible neutrino emitter. We report here a long term gamma-ray monitoring of this source and we compare it with other blazars spatially correlated with astrophysical muonic neutrino events observed by IceCube. We characterise the most significant gamma-ray flares of the blazars in the sample and introduce the blazar duty cycle as an important parameter to be considered when assessing a possible neutrino counterpart. For the selected blazars we show the expected neutrino flux variability with different time binning assuming the gamma-rays observed by Fermi-LAT as a product of a leptohadronic emission scenario. The neutrino expectations from the blazar sample are then compared with the IceCube discovery flux searching for the optimal time bin in a multi-messenger context. This analysis indicates that the detection of a single flare represents a challenge for a single cubic kilometer detector, underlining the importance of increasing the size of current neutrino telescopes and obtaining a good monitoring of the entire sky through a global neutrino network.

astro-ph.HE↗

arXiv2020

Anatomy of a Rumour: Social media and the suicide of Sushant Singh Rajput

The suicide of Indian actor Sushant Singh Rajput in the midst of the COVID-19 lockdown triggered a media frenzy of prime time coverage that lasted several months and became a political hot button issue. Using data from Twitter, YouTube, and an archive of debunked misinformation stories, we found two important patterns. First, that retweet rates on Twitter clearly suggest that commentators benefited from talking about the case, which got higher engagement than other topics. Second, that politicians, in particular, were instrumental in changing the course of the discourse by referring to the case as 'murder', rather than 'suicide'. In conclusion, we consider the effects of Rajput's outsider status as a small-town implant in the film industry within the broader narrative of systemic injustice, as well as the gendered aspects of mob justice that have taken aim at his former partner in the months since.

cs.SI↗

arXiv2020

Direct Measurement of Folding Angle and Strain Vector in Atomically thin WS$_2$ using Second Harmonic Generation

Structural engineering techniques such as local strain engineering and folding provide functional control over critical optoelectronic properties of 2D materials. Accurate monitoring of local strain vector (both strain amplitude and direction) and folding angle in 2D materials is important to optimize the device performance. Conventionally, the accurate measurement of both strain amplitude and direction requires the combined usage of multiple tools, such as atomic force microscopy (AFM), electron microscopy, Raman spectroscopy, etc. Here, we demonstrated the usage of a single tool, polarization-dependent second harmonic generation (SHG) imaging, to determine the folding angle and strain vector accurately in atomically thin tungsten disulfide (WS2). We find that trilayer WS2 folds with folding angle of 600 show 9 times SHG enhancement due to vector superposition of SH wave vectors coming from the individual folding layers. Strain dependent SHG quenching and enhancement is found parallel and perpendicular respectively to the direction of the compressive strain vector. However, despite a variation in strain angle, the total SHG remains constant which allows us to determine the local strain vector accurately using photoelastic approach. We also demonstrate that band-nesting induced transition (C peak) can highly enhance SHG, which can be significantly modulated by strain. Our results would pave the way to enable novel applications of the TMDs in nonlinear optical device.

physics.optics↗

arXiv2020

Super-transport of Excitons in Atomically Thin Organic Semiconductors at the 2D Quantum Limit

Long-range and fast transport of coherent excitons is important for development of high-speed excitonic circuits and quantum computing applications. However, most of these coherent excitons have only been observed in some low-dimensional semiconductors when coupled with cavities, as there are large inhomogeneous broadening and dephasing effects on the exciton transport in their native states of the materials. Here, by confining coherent excitons at the 2D quantum limit, we firstly observed molecular aggregation enabled super-transport of excitons in atomically thin two-dimensional (2D) organic semiconductors between coherent states, with a measured a high effective exciton diffusion coefficient of 346.9 cm2/sec at room temperature. This value is one to several orders of magnitude higher than the reported values from other organic molecular aggregates and low-dimensional inorganic materials. Without coupling to any optical cavities, the monolayer pentacene sample, a very clean 2D quantum system (1.2 nm thick) with high crystallinity (J type aggregation) and minimal interfacial states, showed superradiant emissions from the Frenkel excitons, which was experimentally confirmed by the temperature-dependent photoluminescence (PL) emission, highly enhanced radiative decay rate, significantly narrowed PL peak width and strongly directional in-plane emission. The coherence in monolayer pentacene samples was observed to be delocalized over 135 molecules, which is significantly larger than the values (a few molecules) observed from other organic thin films. In addition, the super-transport of excitons in monolayer pentacene samples showed highly anisotropic behaviour. Our results pave the way for the development of future high-speed excitonic circuits, fast OLEDs, and other opto-electronic devices.

cond-mat.mtrl-sci↗

arXiv2019

ChainifyDB: How to Blockchainify any Data Management System

Today's permissioned blockchain systems come in a stand-alone fashion and require the users to integrate yet another full-fledged transaction processing system into their already complex data management landscape. This seems odd as blockchains and traditional DBMSs share large parts of their processing stack. Thus, rather than replacing the established data systems altogether, we advocate to simply 'chainify' them with a blockchain layer on top. Unfortunately, this task is far more challenging than it sounds: As we want to build upon heterogeneous transaction processing systems, which potentially behave differently, we cannot rely on every organization to execute every transaction deterministically in the same way. Further, as these systems are already filled with data and being used by top-level applications, we also cannot rely on every organization being resilient against tampering with its local data. Therefore, in this work, we will drop these assumptions and introduce a powerful processing model that avoids them in the first place: The so-called Whatever-LedgerConsensus (WLC) model allows us to create a highly flexible permissioned blockchain layer coined ChainifyDB that (a) is centered around bullet-proof database technology, (b) makes even stronger guarantees than existing permissioned systems, (c) provides a sophisticated recovery mechanism, (d) has an up to 6x higher throughput than the permissioned blockchain system Fabric, and (e) can easily be integrated into an existing heterogeneous database landscape.

cs.DB↗

arXiv2019

An Adaptive Soft Plasmonic Nanosheet Resonator

Current micro nanomechanical system are usually based on rigid crystalline semiconductors that normally have high quality factors but lack adaptive responses to variable frequencies, a capability ubiquitous for communications in the biological world, such as bat and whale calls. Here, we demonstrate a soft mechanical resonator based on a freestanding organic-inorganic hybrid plasmonic superlattice nanosheet, which can respond adaptively to either incident light intensity or wavelength. This is achieved because of strong plasmonic coupling in closely-packed nanocrystals which can efficiently concentrate and convert photons into heat. The heat causes the polymer matrix to expand, leading to a change in the nanomechanical properties of the plasmonic nanosheet. Notably, the adaptive frequency responses are also reversible and the responsive ranges are fine-tunable by adjusting the constituent nanocrystal building blocks. We believe that our plasmonic nanosheets may open a new route to design next-generation intelligent bio-mimicking opto-mechanical resonance systems.

physics.app-ph↗

arXiv2018

Radiation tolerance of two-dimensional material-based devices for space applications

Characteristic for devices based on two-dimensional materials are their low size, weight and power requirements. This makes them advantageous for use in space instrumentation, including photovoltaics, batteries, electronics, sensors and light sources for long-distance quantum communication. Here, we present for the first time a comprehensive study on combined radiation effects in earth's atmosphere on various devices based on these nanomaterials. Using theoretical modeling packages, we estimate relevant radiation levels and then expose field-effect transistors, single-photon sources and monolayers as building blocks for future electronics to gamma-rays, protons and electrons. The devices show negligible change in performance after the irradiation, suggesting robust suitability for space use. Under excessive $γ$-radiation, however, monolayer WS$_2$ showed decreased defect densities, identified by an increase in photoluminescence, carrier lifetime and a change in doping ratio proportional to the photon flux. The underlying mechanism was traced back to radiation-induced defect healing, wherein dissociated oxygen passivates sulfur vacancies.

cond-mat.mtrl-sci↗

arXiv2018

How to Databasify a Blockchain: the Case of Hyperledger Fabric

Within the last few years, a countless number of blockchain systems have emerged on the market, each one claiming to revolutionize the way of distributed transaction processing in one way or the other. Many blockchain features, such as byzantine fault tolerance (BFT), are indeed valuable additions in modern environments. However, despite all the hype around the technology, many of the challenges that blockchain systems have to face are fundamental transaction management problems. These are largely shared with traditional database systems, which have been around for decades already. These similarities become especially visible for systems, that blur the lines between blockchain systems and classical database systems. A great example of this is Hyperledger Fabric, an open-source permissioned blockchain system under development by IBM. By having a relaxed view on BFT, the transaction pipeline of Fabric highly resembles the workflow of classical distributed databases systems. This raises two questions: (1) Which conceptual similarities and differences do actually exist between a system such as Fabric and a classical distributed database system? (2) Is it possible to improve on the performance of Fabric by transitioning technology from the database world to blockchains and thus blurring the lines between these two types of systems even further? To tackle these questions, we first explore Fabric from the perspective of database research, where we observe weaknesses in the transaction pipeline. We then solve these issues by transitioning well-understood database concepts to Fabric, namely transaction reordering as well as early transaction abort. Our experimental evaluation shows that our improved version Fabric++ significantly increases the throughput of successful transactions over the vanilla version by up to a factor of 3x.

cs.DC↗