arXiv subjects

Mudit Sinha

Publications and source records attributed to Mudit Sinha.

5 recordsLinked to original sources

arXiv2026

Rhythm of the Deep: Two-Tier Combinatorial Structure in Sperm Whale Codas Revealed by Acoustic Unit Induction

Sperm-whale codas are conventionally described as recurring click-count and timing patterns. We show instead that their waveforms contain a two-tier combinatorial acoustic organization. Recurring click units combine with inter-click rhythm to form coda units, and recurring coda units exhibit additional sequence-level dependence under a different acoustic carrier. Using 1,483 recordings, eight families of frozen audio encoders induce click and coda inventories. Held-out transfer, matched nulls, destructive waveform counterfactuals, expert timing baselines, and explicit abstention separate supported structure from shared encoder shortcuts. Click-token composition predicts induced coda identity with a median normalized mutual-information lift of 0.380, including when events are detected without published click times or counts. Stable click order is weak, whereas rhythm predicts coda-representation distance after the exact click-token multiset is fixed. At the second tier, coda tokens show 0.132 bits of incremental lag-2 dependence under the prespecified categorical estimator. Two expert-inspired rhythm representations do not recover the positive component. Coda identity is also more tempo-stable than click identity. These results change the acoustic description of codas from a single prescribed rhythm inventory to layered waveform organization. The controlled-induction framework provides a general method for discovering and falsifying combinatorial structure in under-annotated audio.

cs.AI↗

arXiv2026

Hiding in Plain Floats: Steganographic Carriers for Indirect Prompt and Content Injection

Text-centered prompt-injection defenses assume that the malicious signal is visible in one of the inspected text views. We study a reproducible LLM01-style indirect prompt/content-injection failure mode where that assumption breaks: a payload caught in plain English slips past the same detector when it is transported as structured float parameters and reconstructed only as fragmented telemetry. Across 14,400 attacked real-model trials on three commercial LLM APIs from different providers, the IFS-derived float-array carrier preserves 94.3% leakage ASR under the strongest dual-layer text-classifier defense evaluated in the main matrix: a Prompt Guard 2 + TF-IDF ensemble; the same carrier-level pattern also replicates with a fine-tuned roberta-base detector. We emphasize leakage ASR because downstream systems may act on quoted or reproduced markers even when the model refuses, but Strong ASR is the stricter metric for structurally compliant attack success. A 2 x 2 ablation shows that data-layer storage and reconstruction-layer fragmentation defeat different text views and that both are needed to evade both. A simple xxd detector and semantic validation block the current T3 instance, so the contribution is not an undetectable exploit but a measured failure boundary for text-only inspection in structured-input pipelines that expose reconstructed auxiliary channels to an LLM.

cs.CR↗

arXiv2021

Continual Distributed Learning for Crisis Management

Social media platforms such as Twitter, Facebook etc can be utilised as an important source of information during disaster events. This information can be used for disaster response and crisis management if processed accurately and quickly. However, the data present in such situations is ever-changing, and using considerable resources during such a crisis is not feasible. Therefore, we have to develop a low resource and continually learning system that incorporates text classification models which are robust against noisy and unordered data. We utilised Distributed learning which enabled us to learn on resource-constrained devices, then to alleviate catastrophic forgetting in our target neural networks we utilized regularization. We then applied federated averaging for distributed learning and to aggregate the central model for continual learning.

cs.LG↗

arXiv2015

Get 'em Moles! : Learning Spelling and Pronunciation through an Educational Game

Get 'em Moles! is a single-player educational game inspired by the classic arcade game Whac-A-Mole. Primarily designed for touchscreen devices, Get 'em Moles! aims to teach English spelling and pronunciation through engaging game play. This paper describes the game, design decisions in the form of elements that support learning, preliminary play-testing results, and future work.

cs.HC↗

arXiv2015

Bublz! : Playing with Bubbles to Develop Mathematical Thinking

We encounter mathematical problems in various forms in our lives, thus making mathematical thinking an important human ability. In this paper, we present Bublz!, a simple, click-driven game for children to engage in and develop mathematical thinking in an enjoyable manner.

cs.HC↗