1 citations · 1 across the 4 of their papers we have counts for
28 papers
Harmonia: End-to-End RAG Serving Optimization
Saurabh Agarwal, Bodun Hu, Luis Pabon +3
Retrieval-Augmented Generation (RAG) improves the reliability of large language models by integrating external knowledge, but serving RAG pipelines efficiently is challenging becau…
AgentDS Technical Report: Benchmarking the Future of Human-AI Collaboration in Domain-Specific Data Science
An Luo, Jin Du, Xun Xian +12
Data science plays a critical role in transforming complex data into actionable insights across numerous domains. Recent developments in large language models (LLMs) and artificial…
LLMs Lean on Priors, Not Programming Language Semantics
Aditya Thimmaiah, Jiyang Zhang, Jayanth Srinivasa +2
Recent work asks whether large language models (LLMs) condition their reasoning on explicit rules rather than statistical regularities from pretraining. Program execution provides…
Survive or Collapse: The Asymmetric Roles of Data Gating and Reward Grounding in Self-Play RL
Sophia Xiao Pu, Zhaotian Weng, Chengzhi Liu +4
Self-play reinforcement learning trains language models on their own generated tasks, co-evolving a proposer and solver without human labels. Recent systems report strong reasoning…
TIER: Trajectory-Invariant Execution Rewards for Multi-Step Tool Composition
Anay Kulkarni, ChiaEn Lu, Dheeraj Mekala +3
Tool use enables large language models to solve complex tasks through sequences of API calls, yet existing reinforcement learning approaches fail to scale to multi-step composition…
EnactToM: An Evolving Benchmark for Functional Theory of Mind in Embodied Agents
Gurusha Juneja, Dylan Lu, Saaket Agashe +7
Theory of Mind (ToM), the ability to track others epistemic state, makes humans efficient collaborators. AI agents need the same capacity in multi agent settings, yet existing benc…