25 citations · 31 across the 10 of their papers we have counts for
11 papers
FIDES: A Concordance Protocol for LLM-Generated Trading Strategies
Arther Tian, Alex Ding, Simon Wu +1
An LLM asked for a trading strategy returns three artifacts at once: a natural-language rationale, an executable implementation, and once run, a track record. Whether these are the…
SFGA: A Statistics-First Gating Architecture with Adjudicative Escalation for Trustworthy SFT Data Procurement
Arther Tian, Alex Ding, Simon Wu +1
Procuring supervised fine-tuning (SFT) data forces a buyer to decide, before any downstream training, whether a candidate corpus is worth acquiring. We present \sys{}, a statistics…
PoQ-Judge: A Multi-Architecture Evaluation Framework for Cost-Aware Proof-of-Quality in Decentralized LLM Inference
Arther Tian, Alex Ding, Frank Chen +2
Decentralized LLM inference networks need lightweight, reference-free quality evaluation for Proof of Quality (PoQ). We present PoQ-Judge, a framework that trains dedicated judge m…
A Multi-Dimensional Quality Scoring Framework for Decentralized LLM Inference with Proof of Quality
Arther Tian, Alex Ding, Frank Chen +2
Decentralized large language model (LLM) inference networks can pool heterogeneous compute to scale serving, but they require lightweight and incentive-compatible mechanisms to ass…
Adaptive and Robust Cost-Aware Proof of Quality for Decentralized LLM Inference Networks
Arther Tian, Alex Ding, Frank Chen +2
Decentralized large language model inference networks require lightweight mechanisms to reward high quality outputs under heterogeneous latency and cost. Proof of Quality provides…
Optimistic TEE-Rollups: A Hybrid Architecture for Scalable and Verifiable Generative AI Inference on Blockchain
Aaron Chan, Alex Ding, Frank Chen +3
The rapid integration of Large Language Models (LLMs) into decentralized physical infrastructure networks (DePIN) is currently bottlenecked by the Verifiability Trilemma, which pos…
Design and Evaluation of Cost-Aware PoQ for Decentralized LLM Inference
Arther Tian, Alex Ding, Frank Chen +3
Decentralized large language model (LLM) inference promises transparent and censorship resistant access to advanced AI, yet existing verification approaches struggle to scale to mo…
SOP-Bench: Complex Industrial SOPs for Evaluating LLM Agents
Subhrangshu Nandi, Arghya Datta, Rohith Nama +21
LLM-based agents struggle to execute complex, multi-step Standard Operating Procedures (SOPs) that are fundamental to industrial automation. Existing benchmarks fail to capture the…
ReviewFlow: Intelligent Scaffolding to Support Academic Peer Reviewing
Lu Sun, Aaron Chan, Yun Seo Chang +1
Peer review is a cornerstone of science. Research communities conduct peer reviews to assess contributions and to improve the overall quality of science work. Every year, new commu…
Copilot Evaluation Harness: Evaluating LLM-Guided Software Programming
Anisha Agarwal, Aaron Chan, Shubham Chandel +6
The integration of Large Language Models (LLMs) into Development Environments (IDEs) has become a focal point in modern software development. LLMs such as OpenAI GPT-3.5/4 and Code…
Resprompt: Residual Connection Prompting Advances Multi-Step Reasoning in Large Language Models
Song Jiang, Zahra Shakeri, Aaron Chan +8
Chain-of-thought (CoT) prompting, which offers step-by-step problem-solving rationales, has impressively unlocked the reasoning potential of large language models (LLMs). Yet, the…