activity
20232026
most citedContinual Learning of Large Language Models: A Comprehensive Survey

12 citations · 21 across the 13 of their papers we have counts for

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5 papers · 1 filter

cs.AI2026

When Debate Helps: Proposal Supply and Verification-Aware Readout in Multi-Agent Reasoning

Zihao Zhao, Tunyu Zhang, Haizhou Shi +3

Multi-agent debate can improve reasoning, yet often fails to beat simple majority voting. We argue that successful debate requires two distinct mechanisms: proposal supply must sur…

cs.AI2026

Reward Modeling for Multi-Agent Orchestration

King Yeung Tsang, Zihao Zhao, Vishal Venkataramani +5

Multi-Agent Systems (MAS) built on Large Language Models (LLMs) require effective orchestration to coordinate specialized agents, yet training such orchestrators is hindered by lim…

cs.LG2026

iLoRA: Bayesian Low-Rank Adaptation with Latent Interaction Graphs for Microbiome Diagnosis

Yang Song, Yixuan Zhang, Lingfa Meng +5

Parameter-efficient adaptation has made LLMs practical for domain prediction, but standard LoRA still relies on a static low-rank update and does not expose the latent interactions…

cs.CL2026

Few-Step Diffusion Language Models via Trajectory Self-Distillation

Tunyu Zhang, Xinxi Zhang, Ligong Han +9

Diffusion large language models (DLLMs) have emerged as powerful generative models with the promise of fast text generation through parallel decoding. However, realizing this poten…

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…