8 citations · 19 across the 14 of their papers we have counts for
17 papers
Bias Control and Injection for Multi-Agent Aggregation
Sungjae Lee, Soheil Abbasloo, Jabin Koo +1
Multi-agent reasoning with large language models (LLMs) raises a central question for collective decision-making: when agents disagree, whose decision should be trusted? Resolving…
Dynamic Expert Pruning for Multi-Agent Systems
Jabin Koo, Soheil Abbasloo, Sungjae Lee +1
Mixture-of-Experts (MoE) architectures scale language models efficiently by activating only a few experts per token, but the saving is confined to computation: every expert must st…
Do AI Agents Understand Computer Architecture?
Ambika Sharan, Grigory Chirkov, Soheil Abbasloo
Agents are increasingly asked to design hardware, and increasingly reported to succeed. Such reports establish that a design improved; they cannot establish why. An agent that impr…
CornerCase: Automated Extremal Testing of Protocol Implementations using LLMs
Rathin Singha, Kuan Qian, Srinath Saikrishnan +6
Many software bugs in network protocol implementations arise near specification boundaries, such as inputs just within or outside allowed ranges, or messages that are valid in isol…
Beyond Correctness: Learning Robust Reasoning via Transfer
Hyunseok Lee, Soheil Abbasloo, Jihoon Tack +1
Reinforcement Learning with Verifiable Rewards (RLVR) has recently strengthened LLM reasoning, but its focus on final answer correctness leaves a critical gap: it does not ensure t…
Evaluating LLM Reasoning Beyond Correctness and CoT
Soheil Abbasloo
What does it truly mean for a language model to "reason"? Current evaluations reward models' correct standalone answers-but correctness alone reveals little about the process that…