most citedIntegrative Decoding: Improve Factuality via Implicit Self-consistency

1 citations · 1 across the 6 of their papers we have counts for

collaborators

7 papers

cs.AI2026

SOMA: Continuous Monitoring and Intervention for System Two Reasoning with System One Models

Ruizhi Qian, Lei Shi, Tianyu Hu +3

Reasoning models often run into problems without noticing them, even when symptoms are visible in their own text. In humans, metacognition separates monitoring from control: fast,…

cs.CL2026

NS-Copilot: An LLM-Driven Agent System for Autonomous Neuroscience Analysis

Wuche Liu, Yiran Qiao, Linlin Hou +4

AI is rapidly advancing neuroscience, yet many laboratories fail to fully unleash its potential due to significant interdisciplinary barriers. While pre-trained neural models for p…

cs.AI2026

The Menu Is an Execution Prior: State-Path Tool Menus for Online Agents

Bo Yan, Weikai Lin, Song Wang

Language models act through tools, yet practical agents face libraries containing thousands of interfaces. We introduce the tool menu as the short, ordered subset of available tool…

cs.CL2026

MemRouter: Memory-as-Embedding Routing for Long-Term Conversational Agents

Tianyu Hu, Weikai Lin, Weizhi Zhang +2

Long-term conversational agents must decide which turns to store in external memory, yet recent systems rely on autoregressive LLM generation at every turn to make that decision. W…

cs.CR2026

SafeDream: Safety World Model for Proactive Early Jailbreak Detection

Bo Yan, Weikai Lin, Yada Zhu +1

Multi-turn jailbreak attacks progressively erode LLM safety alignment across seemingly innocuous conversation turns, achieving success rates exceeding 90% against state-of-the-art…

cs.CL2024★ 1 cited

Integrative Decoding: Improve Factuality via Implicit Self-consistency

Yi Cheng, Xiao Liang, Yeyun Gong +11

Self-consistency-based approaches, which involve repeatedly sampling multiple outputs and selecting the most consistent one as the final response, prove to be remarkably effective…

cs.HC2024

Can we only use guideline instead of shot in prompt?

Jiaxiang Chen, Song Wang, Zhucong Li +4

Currently, prompting techniques can be mainly divided into two categories:1)shot method implicitly inspires the model to answer the question by mimicing the steps in the given exam…