activity
20202026
most citedA Survey of Frontiers in LLM Reasoning: Inference Scaling, Learning to Reason, and Agentic Systems

3 citations · 11 across the 24 of their papers we have counts for

collaborators
Showing 2026Show all

5 papers · 1 filter

cs.LG2026

The Hidden Signal of Verifier Strictness: Controlling and Improving Step-Wise Verification via Selective Latent Steering

Yefan Zhou, Yilun Zhou, Austin Xu +3

Generative verifiers have emerged as a promising paradigm for step-wise verification, but their verification behavior is often poorly calibrated: they may be under-critical and mis…

cs.SE2026

VIBEPASS: Can Vibe Coders Really Pass the Vibe Check?

Srijan Bansal, Jiao Fangkai, Yilun Zhou +3

As Large Language Models shift the programming toward human-guided ''vibe coding'', agentic coding tools increasingly rely on models to self-diagnose and repair their own subtle fa…

cs.AI2026

MAS-ProVe: Understanding the Process Verification of Multi-Agent Systems

Vishal Venkataramani, Haizhou Shi, Zixuan Ke +6

Multi-Agent Systems (MAS) built on Large Language Models (LLMs) often exhibit high variance in their reasoning trajectories. Process verification, which evaluates intermediate step…

cs.LG2026

Least-Loaded Expert Parallelism: Load Balancing An Imbalanced Mixture-of-Experts

Xuan-Phi Nguyen, Shrey Pandit, Austin Xu +2

Mixture-of-Experts (MoE) models are typically pre-trained with explicit load-balancing constraints to ensure statistically balanced expert routing. Despite this, we observe that ev…

cs.AI2026

MAS-Orchestra: Understanding and Improving Multi-Agent Reasoning Through Holistic Orchestration and Controlled Benchmarks

Zixuan Ke, Yifei Ming, Austin Xu +7

While multi-agent systems (MAS) promise elevated intelligence through coordination of agents, current approaches to automatic MAS design under-deliver. Such shortcomings stem from…