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Ankit Shah

11 papers hereh-index 5200 citations13 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • middle author10
  • last author1

Across the 11 of 11 papers where every author was matched, so the position is known.

fields
  • cs.CL9
  • cs.AI2
same name
  • Ankit Shah — 27 papers, h 16
  • Ankit Shah — 8 papers, h 15
  • Ankit Shah — 5 papers, h 3
  • Ankit Shah — 4 papers, h 1
  • Ankit Shah — 2 papers, h 3
  • Ankit Shah — 2 papers, h 3

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20242026
most citedMCP-Bench: Benchmarking Tool-Using LLM Agents with Complex Real-World Tasks via MCP Servers

4 citations · 6 across the 10 of their papers we have counts for

collaborators
Showing 2026Show all

4 papers · 1 filter

cs.AI2026

HiComm: Hierarchical Communication for Multi-agent Reinforcement Learning

Runze Zhao, Dongruo Zhou, Sumit Kumar Jha +2

Cooperative multi-agent reinforcement learning (MARL) often relies on communication to mitigate partial observability, yet most existing protocols treat messages as flat dense vect…

cs.CL2026

PreUnlearn: Auditing Collateral Knowledge Damage Before Large Language Model Unlearning

Bo Su, Ankit Shah, Thai Le

Machine unlearning for large language models (LLMs) aims to remove specified knowledge while preserving the rest of the model's capabilities. However, the boundary between knowledg…

cs.CL2026

Inference Time Optimization with Confidence Dynamics

Yu Wang, Minghao Liu, Jiayun Wang +3

Inference time optimization techniques, such as repeated sampling, have significantly advanced the reasoning capabilities of Large Language Models (LLMs). However, the critical rol…

cs.CL2026

Training-Free Agentic AI: Probabilistic Control and Coordination in Multi-Agent LLM Systems

Mohammad Parsa Hosseini, Ankit Shah, Saiyra Qureshi +3

Multi-agent large language model (LLM) systems enable complex, long-horizon reasoning by composing specialized agents, but practical deployment remains hindered by inefficient rout…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.