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Pratik Prabhanjan Brahma

ACL 2026CVPR 2026IROS 2026多模态效率与系统推理智能体与工具评测与基准
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0 一作 · 3 篇 · ACL 2026 / CVPR 2026 / IROS 2026

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2026 顶会论文

3
作者位次 #2/4ACL 2026Findings· Findings of ACL

A dapt E volve: Improving Efficiency of Evolutionary AI Agents through Adaptive Model Selection

Pretam Ray · Pratik Prabhanjan Brahma · Zicheng Liu · Emad Barsoum

摘要

Evolutionary agentic systems intensify the trade-off between computational efficiency and reasoning capability by repeatedly invoking large language models (LLMs) during inference. This setting raises a central question: how can an agent dynamically select an LLM that is sufficiently capable for...

效率与系统推理智能体与工具OpenReview 项目页
作者位次 #3/5IROS 2026Poster· Building Better Benchmarks for Robot Intelligence

Pause and Think: A Dataset and Benchmark for Video-Grounded Assistive Action Suggestion

Shivam Singh · Saptarshi Majumder · Pratik Prabhanjan Brahma · Zicheng Liu · Emad Barsoum

多模态评测与基准数据与预训练Data Sets for Robotic VisionHuman-Robot CollaborationDeep Learning MethodsOpenReview

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