Erik Cambria

ScienceClaw: Benchmarking Continual Self-Evolution of AI-for-Science Agents Across the Natural and Social Sciences

Large language model agents are accelerating scientific automation, yet verified executions rarely become persistent program-level improvements, and existing evaluations do not examine this process across sequential tasks in both the natural and social sciences. We formalize ScienceClaw as fixed-parameter program self-...

Reinforcement Learning with Comparative Evidence for Social Intelligence

Developing socially intelligent AI remains heavily dependent on human-annotated data, limiting the scale and breadth of social understanding models can acquire. Methods that derive training signals from unlabeled data offer a path beyond this dependence, but social predictions lack the verification oracles available in...

Implicit Bias in Peer Review: Through the Lens of Language Abstraction

Affective Flow Language Model for Emotional Support Conversation

The Affective Flow Language Model is proposed, which models multi-turn emotional support as an affective utility flow evolving along dialogue trajectories, providing a process-based approach to dynamic affect modeling and continuous strategy optimization.

Reasoning through Evolution: Automatic Meta-path Discovery for LLM-based Fake News Detection

MAGER is a multi-agent genetic evolution framework that automatically discovers meta-paths optimized for LLM reasoning, enabling frozen LLMs to perform structure-aware veracity reasoning and introduces a graph in-context learning strategy that retrieves semantically and structurally similar demonstrations to strengthen...