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Erik Cambria

8 papers hereh-index 342 citations13 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • middle author4
  • last author4

Across the 8 of 8 papers where every author was matched, so the position is known.

fields
  • cs.AI4
  • cs.CL2
  • cs.CV2
same name
  • Erik Cambria — 22 papers, h 20
  • Erik Cambria — 13 papers, h 6
  • Erik Cambria — 6 papers, h 15
  • Erik Cambria — 5 papers, h 6
  • Erik Cambria — 5 papers, h 5
  • Erik Cambria — 3 papers, h 3

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

collaborators
Showing cs.AIShow all

4 papers · 1 filter

cs.AI2026

From Static Risk to Dynamic Trajectories: Toward World-Model-Inspired Clinical Prediction

Pujun Feng, Xiaoyu Guo, Seyed Ehsan Saffari +10

Clinical decision-making is a feedback system where risk estimates influence treatment, which in turn changes disease trajectories, and both shape clinicians' measurement practices…

cs.AI2026

FlowSteer: Towards Agents Designing Agentic Workflows via Reinforced Progressive Canvas Editing

Mingda Zhang, Wenjin Liu, Tiesunlong Shen +5

In recent years, agentic workflows have been widely applied to solve complex human tasks. However, existing workflow construction still faces key challenges, including human-depend…

cs.AI2026

SkillFlow: Flow-Driven Recursive Skill Evolution for Agentic Orchestration

Mingda Zhang, Tiesunlong Shen, Haoran Luo +4

In recent years, a variety of powerful LLM-based agentic systems have been applied to automate complex tasks through task orchestration. However, existing orchestration methods sti…

cs.AI2026

LLMdoctor: Token-Level Flow-Guided Preference Optimization for Efficient Test-Time Alignment of Large Language Models

Tiesunlong Shen, Rui Mao, Jin Wang +4

Aligning Large Language Models (LLMs) with human preferences is critical, yet traditional fine-tuning methods are computationally expensive and inflexible. While test-time alignmen…

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