arXiv subjects

Rohit Yadav

Publications and source records attributed to Rohit Yadav.

11 recordsLinked to original sources

arXiv2026

Revisiting the Weight Spectrum of the Affine Grassmann Code $C^{\mathbb{A}}(2,m)$

Affine Grassmann codes, introduced by Beelen, Ghorpade and H{\o}holdt, are linear codes over $\mathbb{F}_q$ obtained by evaluating linear combinations of minors of a generic matrix. The weight spectrum of the affine Grassmann code $C^{\mathbb{A}}(2,m)$ was determined by Pi\~nero and Singh via a case analysis on the rank of an associated alternating matrix. We give an independent and more streamlined derivation, valid for all $m\ge4$ and every prime power $q$, in which each codeword is written in a compact matrix form and its Hamming weight is expressed through a single closed formula involving two explicit affine subspaces and their intersection.

cs.IT↗

arXiv2026

The Weight Spectrum of the Affine Grassmann Code $C^{\mathbb A}(3,6)$

In this article, we consider the affine Grassmann code $C^{\mathbb A}(3,6)$, obtained from the affine open cell ${\mathbb A}^9$ of the Grassmannian $G_{3,6}$. We exploit the representation of codewords as linear combinations of minors of all sizes of a generic $3\times3$ matrix and classify them according to the largest size of a minor occurring with a nonzero coefficient. Using this classification, we determine all possible Hamming weights of codewords of $C^{\mathbb A}(3,6)$ and, for each weight, compute the number of codewords attaining that weight. Consequently, we obtain the complete weight spectrum of the affine Grassmann code $C^{\mathbb A}(3,6)$.

cs.IT↗

arXiv2026

Self-selective growth of GaAs1-xBix on GaAs zinc blende/wurtzite nanowire heterostructures

Site-selective nanostructure growth and material incorporation at the atomic scale offer a promising pathway for engineering quantum materials and nanodevices. Here, GaAs nanowires (NWs) with an axial heterostructure of alternating zinc blende (Zb) and wurtzite (Wz) crystal phases are employed as templates for site-selective Ga and Bi overgrowth. Using X-ray photoemission electron microscopy (XPEEM) with nanoscale spatial resolution, we map elemental distribution and local chemical bonding to reveal the incorporation behavior of Bi atoms in {110} Zb and {11-20} Wz facets. Bi incorporation proceeds through an anion-exchange process, where Bi atoms replace As, forming local Ga-Bi bonds and producing a thin GaAs1-xBix shell. We observe crystal-phase-dependent Bi incorporation, with higher Bi concentration in the Zb segments than in the neighboring Wz segments within the same NW. Furthermore, the Zb segment with higher Bi content exhibits reduced susceptibility to oxidation compared with the Wz segment, resulting in increased Ga-oxide in the Wz surfaces. This study highlights GaAs NW Zb/Wz heterostructures as a template for controlled growth of GaBi and GaAs1-xBix nanostructures with tailored functionalities for quantum applications

cond-mat.mtrl-sci↗

arXiv2026

AutoResearch-RL: Perpetual Self-Evaluating Reinforcement Learning Agents for Autonomous Neural Architecture Discovery

We present AutoResearch-RL, a framework in which a reinforcement learning agent conducts open-ended neural architecture and hyperparameter research without human supervision, running perpetually until a termination oracle signals convergence or resource exhaustion. At each step the agent proposes a code modification to a target training script, executes it under a fixed wall clock time budget, observes a scalar reward derived from validation bits-per-byte (val-bpb), and updates its policy via Proximal Policy Optimisation (PPO). The key design insight is the separation of three concerns: (i) a frozen environment (data pipeline, evaluation protocol, and constants) that guarantees fair cross-experiment comparison; (ii) a mutable target file (train.py) that represents the agent's editable state; and (iii) a meta-learner (the RL agent itself) that accumulates a growing trajectory of experiment outcomes and uses them to inform subsequent proposals. We formalise this as a Markov Decision Process, derive convergence guarantees under mild assumptions, and demonstrate empirically on a single GPU nanochat pretraining benchmark that AutoResearch-RL discovers configurations that match or exceed hand-tuned baselines after approximately 300 overnight iterations, with no human in the loop.

cs.LG↗

arXiv2026

Bibby AI -- AI Latex Editor writing assistant for researchers vs Overleaf Alternative vs OpenAI Prism. (Bibby AI Latex Editor)

Large language models are increasingly integrated into academic writing workflows; however, the most widely used \LaTeX\ editors remain AI-peripheral -- offering compilation and collaboration, but no native intelligence. This separation forces researchers to leave their editing environment for AI assistance, fragmenting document context and interrupting writing flow. We present Bibby AI (trybibby.com), a native, AI-first \LaTeX\ editor that unifies the complete research writing lifecycle within a single interface. Bibby embeds an AI writing assistant, smart citation search, AI table and equation generation, an AI paper reviewer, abstract generator, literature review drafting, a deep research assistant, and real-time \LaTeX\ error detection and auto-fix -- all natively, without plugins or copy-paste workflows. We introduce LaTeXBench-500, a benchmark of 500 real-world compilation errors across six categories. Bibby achieves 91.4\% detection accuracy and 83.7\% one-click fix accuracy, outperforming Overleaf's native diagnostics (61.2\%) and OpenAI Prism (78.3 / 64.1\%) by large margins. Bibby demonstrates that a privacy-preserving, research-first AI editor can meaningfully accelerate every stage of academic manuscript preparation. We found that Bibby AI is a far superior alternative to overleaf latex and better than OpenAI Prism functionalities and AI.

cs.ET↗

arXiv2025

A Concise Review of Hallucinations in LLMs and their Mitigation

Traditional language models face a challenge from hallucinations. Their very presence casts a large, dangerous shadow over the promising realm of natural language processing. It becomes crucial to understand the various kinds of hallucinations that occur nowadays, their origins, and ways of reducing them. This document provides a concise and straightforward summary of that. It serves as a one-stop resource for a general understanding of hallucinations and how to mitigate them.

cs.CL↗

arXiv2025

Majority Logic Decoding of Affine Grassmann Codes Over Nonbinary Fields

In this article, we consider the decoding problem of affine Grassmann codes over nonbinary fields. We use matrices of different ranks to construct a large set consisting of parity checks of affine Grassmann codes, which are orthogonal with respect to a fixed coordinate. By leveraging the automorphism groups of these codes, we generate a set of orthogonal parity checks for each coordinate. Using these parity checks, we perform majority logic decoding to correct a large number of errors in affine Grassmann codes. The order of error correction capability and the complexity of this decoder for affine Grassmann codes are the same as those of the majority logic decoder for Grassmann codes proposed in [BS21].

cs.IT↗

arXiv2025

On a Recursive Integer Sequence Implying the Nonexistence of Odd Perfect Numbers

We define a sequence of positive integers recursively, where each term is determined as follows: starting with a given positive integer, if the term is odd, the next is the sum of its positive divisors; if the term is even, the subsequent term is half the term. In this paper, we conjecture that this sequence eventually reaches one for all initial values. Furthermore, we classify a family of integers for which this conjecture holds.

math.NT↗

arXiv2023

Population-wise Labeling of Sulcal Graphs using Multi-graph Matching

Population-wise matching of the cortical fold is necessary to identify biomarkers of neurological or psychiatric disorders. The difficulty comes from the massive interindividual variations in the morphology and spatial organization of the folds. This task is challenging at both methodological and conceptual levels. In the widely used registration-based techniques, these variations are considered as noise and the matching of folds is only implicit. Alternative approaches are based on the extraction and explicit identification of the cortical folds. In particular, representing cortical folding patterns as graphs of sulcal basins-termed sulcal graphs-enables to formalize the task as a graph-matching problem. In this paper, we propose to address the problem of sulcal graph matching directly at the population level using multi-graph matching techniques. First, we motivate the relevance of multi-graph matching framework in this context. We then introduce a procedure to generate populations of artificial sulcal graphs, which allows us benchmarking several state of the art multi-graph matching methods. Our results on both artificial and real data demonstrate the effectiveness of multi-graph matching techniques to obtain a population-wise consistent labeling of cortical folds at the sulcal basins level.

stat.ML↗

arXiv2023

A Family of surjective word map on SU(2)

Let $\mathbf{F}$ be the free group on two generators $a, b$ and let a family of words $w = [[a, b], [a^3, b^n]]$ in $\mathbf{F}$. In this paper we examine surjectivity of word map $w$ on special unitary group SU(2) over complex field $\mathbf{C}$.

math.GR↗

arXiv2022

Kernelized multi-graph matching

Multigraph matching is a recent variant of the graph matching problem. In this framework, the optimization procedure considers several graphs and enforces the consistency of the matches along the graphs. This constraint can be formalized as a cycle consistency across the pairwise permutation matrices, which implies the definition of a universe of vertex~\citep{pachauri2013solving}. The label of each vertex is encoded by a sparse vector and the dimension of this space corresponds to the rank of the bulk permutation matrix, the matrix built from the aggregation of all the pairwise permutation matrices. The matching problem can then be formulated as a non-convex quadratic optimization problem (QAP) under constraints imposed on the rank and the permutations. In this paper, we introduce a novel kernelized multigraph matching technique that handles vectors of attributes on both the vertices and edges of the graphs, while maintaining a low memory usage. We solve the QAP problem using a projected power optimization approach and propose several projectors leading to improved stability of the results. We provide several experiments showing that our method is competitive against other unsupervised methods.

cs.LG↗