2 citations · 4 across the 15 of their papers we have counts for
12 papers · 1 filter
SKILL.state: Scalable Long-Horizon Agent Skills
Sanket Badhe, Priyanka Tiwari, Jonghyun Chung
Large Language Models (LLMs) increasingly act as autonomous agents executing complex, long-running procedural skills. Existing agent runtimes maintain execution by continually appe…
A Systematic Survey of Agentic Skills: Architecture, Lifecycle, and Security
Sanket Badhe, Deep Shah, Priyanka Tiwari +1
Autonomous large language model (LLM) agents increasingly face reliability, context consumption, and execution stability bottlenecks when deployed on complex, long-horizon tasks. W…
Agent Skill Security: Threat Models, Attacks, Defenses, and Evaluation
Sanket Badhe, Priyanka Tiwari
Reusable skills are becoming a fundamental building block of Large Language Model (LLM) agents, enabling capabilities to be packaged, shared, and reused across diverse applications…
Pluralis v0.1: Towards a Multicultural, Multimodal, Multilingual Benchmark for AI Risk and Reliability
Alicia Parrish, Rajat Shinde, Sanket Badhe +57
Current AI safety evaluation and benchmarking frameworks predominantly rely on Western-centric culture-agnostic defaults that mask critical regional laws, socio-linguistic nuances,…
Beyond Locality: Feature-Boundary Auditing for OS-Integrated AI
Jonghyun Chung, Sanket Badhe
On-device inference reduces exposure to remote infrastructure, but it does not establish privacy for an operating-system assistant that aggregates cross-application data, retains d…
Generative AI and Digital Ecosystem Resilience: A Proactive Lifecycle-Based Survey
Jonghyun Chung, Rishabh Chaddha, Sanket Badhe +3
The proliferation of adversarial synthetic content, accelerated by Generative AI (GenAI) is rendering traditional reactive detection methods ineffective. This survey synthesizes em…
The Silent Vote: Improving Zero-Shot LLM Reliability by Aggregating Semantic Neighborhoods
Sanket Badhe, Priyanka Tiwari, Deep Shah
Large Language Models are increasingly used as zero-shot classifiers in complex reasoning tasks. However, standard constrained decoding suffers from a phenomenon we define as Renor…
CROP: Token-Efficient Reasoning in Large Language Models via Regularized Prompt Optimization
Deep Shah, Sanket Badhe, Nehal Kathrotia +1
Large Language Models utilizing reasoning techniques improve task performance but incur significant latency and token costs due to verbose generation. Existing automatic prompt opt…
Large Language Models in the Abuse Detection Pipeline
Suraj Kath, Sanket Badhe, Preet Shah +2
Online abuse has grown increasingly complex, spanning toxic language, harassment, manipulation, and fraudulent behavior. Traditional machine-learning approaches dependent on static…
Long-Tail Knowledge in Large Language Models: Taxonomy, Mechanisms, Interventions and Implications
Sanket Badhe, Deep Shah, Nehal Kathrotia
Large language models (LLMs) are trained on web-scale corpora that exhibit steep power-law distributions, in which the distribution of knowledge is highly long-tailed, with most ap…
Prompt-Level Distillation: A Non-Parametric Alternative to Model Fine-Tuning for Efficient Reasoning
Sanket Badhe, Deep Shah
Advanced reasoning typically requires Chain-of-Thought prompting, which is accurate but incurs prohibitive latency and substantial test-time inference costs. The standard alternati…
Taxonomy of the Retrieval System Framework: Pitfalls and Paradigms
Deep Shah, Sanket Badhe, Nehal Kathrotia
Designing an embedding retrieval system requires navigating a complex design space of conflicting trade-offs between efficiency and effectiveness. This work structures these decisi…