3 citations · 5 across the 13 of their papers we have counts for
13 papers
When Is Availability-Aware Training Worth It? A Benchmark and Empirical Study of Interruption-Resilient Optimization Under Predictable Compute Schedules
Subhadip Mitra
Training under non-stationary but predictable compute availability (satellites under eclipse, duty-cycled edge devices, power-capped datacenters) is often framed as needing special…
Extracting Work from Discrete Quantum Polytropic Processes
Vishal Anand, Swarup Kumar Giri, Avijit Misra +2
We establish an upper bound on extractable work for time-dependent, non-Markovian quantum heat engines operating with finite baths. This bound analytically isolates the distinct th…
Closing the Activation-Cone Blind Spot: Response-Time Probing and Unified Defense
Subhadip Mitra
Inference-time safety methods for large language models have proliferated, yet no systematic comparison exists. We evaluate five defense paradigms (no defense, static steering, CAS…
Cross-Generational Transfer of Adversarial Attacks Reveals Non-Monotonic Safety Alignment in LLMs
Subhadip Mitra
Safety alignment in LLMs does not improve monotonically across model generations. Studying four generations of Google's Gemma family (7B-31B) with quality-diversity evolution (MAP-…
Quality-Diversity Evolution for Discovering Diverse Vulnerabilities in LLM Safety
Subhadip Mitra
Current approaches to LLM adversarial testing suffer from coverage gaps: manual red-teaming does not scale, LLM-as-attacker methods exhibit mode collapse, and gradient-based approa…
Constraint-Aware Execution Planning for Hybrid Space-Ground Compute Workloads
Subhadip Mitra
Low Earth orbit (LEO) satellites increasingly carry compute hardware capable of on-board processing, yet each satellite generates roughly two orders of magnitude more data than it…
Cross-Platform Fused MoE Dispatch in Triton: Portable Expert Routing Without CUDA
Subhadip Mitra
Mixture-of-Experts (MoE) architectures power the majority of frontier large language models, but their inference is bottlenecked by irregular memory access patterns and expert rout…
Spark-LLM-Eval: A Distributed Framework for Statistically Rigorous Large Language Model Evaluation
Subhadip Mitra
Evaluating large language models at scale remains a practical bottleneck for many organizations. While existing evaluation frameworks work well for thousands of examples, they stru…
Kinematic budget of quantum correlations
Maaz Khan, Subhadip Mitra
The diversity of quantum correlations -- discord, entanglement, steering, and Bell nonlocality -- disappears at the kinematic level of observable second moments. By treating state…
Field-Theoretic Memory for AI Agents: Continuous Dynamics for Context Preservation
Subhadip Mitra
We present a memory system for AI agents that treats stored information as continuous fields governed by partial differential equations rather than discrete entries in a database.…
Unravelling the non-Markovian spin-boson model and quantum quasi-Otto cycle
Shreyas Harshal Pradhan, Hadi Mohammed Soufy, Vishal Anand +3
We use the spin-boson model to describe the dynamics of a two-level atom interacting with Fabry-Pérot cavity modes. We solve the Schrödinger equation for the system-bath model with…
Simulating quantum collision models with Hamiltonian simulations using early fault-tolerant quantum computers
Kushagra Garg, Zeeshan Ahmed, Subhadip Mitra +1
We develop randomized quantum algorithms to simulate quantum collision models, also known as repeated interaction schemes, which provide a rich framework to model various open-syst…
Teleportation fidelity of quantum repeater networks
Ganesh Mylavarapu, Subrata Ghosh, Chittaranjan Hens +2
We show that the average of the maximum teleportation fidelities between all pairs of nodes in a large quantum repeater network is a measure of the resourcefulness of the network a…