arXiv subjects

Rohan Kulkarni

Publications and source records attributed to Rohan Kulkarni.

8 recordsLinked to original sources

arXiv2026

Adaptive Fault Injection Planning for Multi-Layer Self-Healing AI Infrastructure

Modern GPU-accelerator platforms rely on multi-layer self-healing pipelines that span hardware, firmware, management software, and orchestration. When faults propagate across layer boundaries, they can bypass detection, corrupt diagnosis, or trigger conflicting remediations--yet conventional fault-injection campaigns test each layer in isolation. We present ADA-ST, an adaptive fault-injection methodology that uses a weighted fault-propagation graph to guide cross-layer scenario selection. We construct four-layer graphs for three successive platforms at a hyperscale operator: Alpha, Beta, and Gamma. Platform Alpha, a production system that accumulated 72,550 repair tickets over four years, provides the empirical foundation; 49% of those tickets involve cross-layer fault propagation. We show that existing static test campaigns cover only 20-25% of the modeled fault-propagation edges, leaving approximately three-quarters of the cross-layer attack surface unexercised. ADA-ST closes this gap through iterative, activity-guided scenario selection that maximizes marginal coverage gain per iteration, reaching full edge coverage within 10 iterations on Alpha, 12 on Beta, and 9 on Gamma. The Fault-Layer Abstraction Mapping (FLAM) transfers propagation knowledge across hardware generations with 100% fidelity from Alpha to Beta and 96% from Beta to Gamma. Physical spot-validation on the newest platform confirms all four tested propagation edges, revealing cross-layer vulnerabilities spanning telemetry blind spots, absence-based detection gaps, multi-signal correlation failures, and trust-without-verification propagation at the L2-to-L3 boundary.

cs.ET↗

arXiv2026

Automated Hardware Validation Test Plan Generation for Large Scale AI Datacenter Platforms Using a Generative AI Multi-Agents Architecture

Large-scale AI datacenter platforms comprise thousands of heterogeneous hardware components whose validation requires comprehensive fault injection test plans. Today these plans are authored manually: engineers review hardware self-healing validation documents and bills of materials, enumerate failure modes per field-replaceable unit, and produce flat lists of single-layer test cases. This process is labor-intensive, error-prone, and dependent on institutional knowledge; coverage gaps surface late, traceability to source specifications is implicit, and the effort is largely repeated per platform. This paper presents a generative AI multi-agent architecture that automates the generation of structured hardware validation test plans from two canonical inputs: self-healing validation documents, which enumerate known failure modes and their detection and remediation behaviors per field-replaceable unit, and component Bills of Material. An ingestion agent normalizes heterogeneous inputs into a canonical representation; a classification agent maps components to functional domains via contextual reasoning over part descriptions and sub-category hierarchies; and a generation agent synthesizes test cases by combining normalized failure modes with domain-classified data, filling gaps and producing edge cases. The output conforms to a standardized schema for direct import into internal validation software. Evaluated on two production platforms against manual baselines, the framework achieves coverage expansions of 74.2% and 51.4%, cutting authoring from days to hours. It yields fully traceable mappings from each test case to its source specification, and its multi-agent decomposition is portable across platform generations. Automated and expert evaluations confirm 100% extraction fidelity and high acceptance of new scenarios, validating the framework as a robust human-in-the-loop force multiplier.

cs.MA↗

arXiv2026

A Systematic Approach for Large Language Models Debugging

Large language models (LLMs) have become central to modern AI workflows, powering applications from open-ended text generation to complex agent-based reasoning. However, debugging these models remains a persistent challenge due to their opaque and probabilistic nature and the difficulty of diagnosing errors across diverse tasks and settings. This paper introduces a systematic approach for LLM debugging that treats models as observable systems, providing structured, model-agnostic methods from issue detection to model refinement. By unifying evaluation, interpretability, and error-analysis practices, our approach enables practitioners to iteratively diagnose model weaknesses, refine prompts and model parameters, and adapt data for fine-tuning or assessment, while remaining effective in contexts where standardized benchmarks and evaluation criteria are lacking. We argue that such a structured methodology not only accelerates troubleshooting but also fosters reproducibility, transparency, and scalability in the deployment of LLM-based systems.

cs.AI↗

arXiv2025

Generating Verifiable Chain of Thoughts from Exection-Traces

Getting language models to reason correctly about code requires training on data where each reasoning step can be checked. Current synthetic Chain-of-Thought (CoT) training data often consists of plausible-sounding explanations generated by teacher models, and not verifiable accounts of actual program behavior. Models trained on such data learn logically flawed reasoning patterns despite syntactic correctness. To address this, we build a pipeline that generates execution-trace-verified CoT rationales by instrumenting code to capture traces, narrating them into natural language, and cross-checking each narration against the original trace. We systematically create 54,000 verified, bi-directional rationales that teach models to reason both forward (input$\rightarrow$output) and backward (output$\rightarrow$input). Models fine-tuned on our verified data achieve substantial improvements, with a peak gain of +26.6 on LiveCodeBench-Exec, +22.2 on CruxEval, and +19.5 on HumanEval across our fine-tuned models, demonstrating that verification quality directly determines both reasoning and code generation capabilities. Complete synthesis pipeline is avilable as open-source: https://github.com/IBM/verified-code-cot/

cs.SE↗

arXiv2025

Secluded Dark Composites and Remnant Binding Fields

Dark matter may freeze-out and undergo composite assembly while decoupled from the Standard Model. In this secluded composite scenario, while individual dark matter particles may be too weakly-coupled to detect, the assembled composite can potentially be detected since its effective coupling scales with number of constituents. We examine models and observables for secluded composites, and in particular we investigate the cosmological abundance of the composite binding field, which is generated during freeze-out annihilation and secluded composite assembly. This binding field could be discovered as a new relativistic species in the early universe or through later interactions as a subdominant dark component.

hep-ph↗

arXiv2021

First-Generation Inference Accelerator Deployment at Facebook

In this paper, we provide a deep dive into the deployment of inference accelerators at Facebook. Many of our ML workloads have unique characteristics, such as sparse memory accesses, large model sizes, as well as high compute, memory and network bandwidth requirements. We co-designed a high-performance, energy-efficient inference accelerator platform based on these requirements. We describe the inference accelerator platform ecosystem we developed and deployed at Facebook: both hardware, through Open Compute Platform (OCP), and software framework and tooling, through Pytorch/Caffe2/Glow. A characteristic of this ecosystem from the start is its openness to enable a variety of AI accelerators from different vendors. This platform, with six low-power accelerator cards alongside a single-socket host CPU, allows us to serve models of high complexity that cannot be easily or efficiently run on CPUs. We describe various performance optimizations, at both platform and accelerator level, which enables this platform to serve production traffic at Facebook. We also share deployment challenges, lessons learned during performance optimization, as well as provide guidance for future inference hardware co-design.

cs.AR↗

arXiv2020

QnAMaker: Data to Bot in 2 Minutes

Having a bot for seamless conversations is a much-desired feature that products and services today seek for their websites and mobile apps. These bots help reduce traffic received by human support significantly by handling frequent and directly answerable known questions. Many such services have huge reference documents such as FAQ pages, which makes it hard for users to browse through this data. A conversation layer over such raw data can lower traffic to human support by a great margin. We demonstrate QnAMaker, a service that creates a conversational layer over semi-structured data such as FAQ pages, product manuals, and support documents. QnAMaker is the popular choice for Extraction and Question-Answering as a service and is used by over 15,000 bots in production. It is also used by search interfaces and not just bots.

cs.IR↗

arXiv2019

Black holes, Singularity theorems and The Global structure of Spacetime

This work is an extensive literature review focusing on a few of the important topics in the large-scale structure of spacetime. The work is a Bachelor's thesis submitted at the Institute of Theoretical Physics, University of Leipzig. The author starts the thesis by deriving the Schwarzchild metric, Reissner Nordstorm metric and discusses their properties with appropriate Penrose diagrams. The Kerr black hole is metric is assumed, and its features are examined. The causal structure of spacetime is discussed with a focus on different stages of causality on a spacetime, Cauchy surfaces, and what does it mean for spacetime to be Globally hyperbolic. Then, the author moves to Singularity theorems, where one can find an algorithm to deal with non-coordinate singularities in spacetime. A few singularity theorems are discussed in detail. Then, the asymptotic structure of spacetime is discussed, which leads to a precise definition of a Black hole and its event horizon. The Black hole area theorem by Prof. Hawking is dissected pointwise with exclusive author comments as an attempt to simplify it for a less advanced audience. After that, a few exotic propositions in General relativity like closed timelike curves and warp drives are discussed.

gr-qc↗