6 papers
FrontierScience: Evaluating AI's Ability to Perform Expert-Level Scientific Tasks
Miles Wang, Robi Lin, Kat Hu +4
We introduce FrontierScience, a benchmark evaluating expert-level scientific reasoning in frontier language models. Recent model progress has nearly saturated existing science benc…
The Unified Cognitive Consciousness Theory for Language Models: Anchoring Semantics, Thresholds of Activation, and Emergent Reasoning
Edward Y. Chang, Zeyneb N. Kaya, Ethan Chang
We propose semantic anchoring, a unified account of how large language models turn pretrained capacity into goal-directed behavior: external structure (in-context examples, retriev…
VMDT: Decoding the Trustworthiness of Video Foundation Models
Yujin Potter, Zhun Wang, Nicholas Crispino +11
As foundation models become more sophisticated, ensuring their trustworthiness becomes increasingly critical; yet, unlike text and image, the video modality still lacks comprehensi…
Inverse-Free Wilson Loops for Transformers: A Practical Diagnostic for Invariance and Order Sensitivity
Edward Y. Chang, Ethan Y. Chang
Large language models can change answers under harmless edits that matter in practice: RAG outputs flip when passages are reordered, fine-tuning erodes invariances learned at pretr…
Multi-Agent Collaborative Intelligence: Dual-Dial Control for Reliable LLM Reasoning
Edward Y. Chang, Ethan Y. Chang
Multi-agent debate often wastes compute by using a fixed adversarial stance, aggregating without deliberation, or stopping on heuristics. We introduce MACI, an active controller wi…
Knowledge Tagging with Large Language Model based Multi-Agent System
Hang Li, Tianlong Xu, Ethan Chang +1
Knowledge tagging for questions is vital in modern intelligent educational applications, including learning progress diagnosis, practice question recommendations, and course conten…