From the 1 of 9 linked papers with an AI index.
9 papers
PyroDash: Cost-Efficient Token-Level Small-Large Language Model Collaborative Inference
Niqi Lyu, Pengtao Shi, Wei Qiu +4
Large language models (LLMs) provide strong reasoning capabilities but are expensive to serve at scale, whereas small language models (SLMs) are cheaper but less reliable on diffic…
Who Grades the Grader? Co-Evolving Evaluation Metrics and Skills for Self-Improving LLM Agents
Xing Zhang, Guanghui Wang, Yanwei Cui +4
The paper introduces a framework that co‑evolves evaluation metrics and the skills of LLM agents using an evolutionary loop guided by anchored reference sets, enabling transparent…
The Blind Curator: How a Biased Judge Silently Disables Skill Retirement in Self-Evolving Agents
Xing Zhang, Yanwei Cui, Guanghui Wang +4
A self-evolving agent retires its bad skills by watching them fail, so what happens when the judge cannot see the failures? Skill retirement is the structural constraint that keeps…
Experience Compression Spectrum: Unifying Memory, Skills, and Rules in LLM Agents
Xing Zhang, Guanghui Wang, Yanwei Cui +4
As LLM agents scale to long-horizon, multi-session deployments, efficiently managing accumulated experience becomes a critical bottleneck. Agent memory systems and agent skill disc…
The Alignment Floor: How Persona Customization Breaks Safety in Weakly-Aligned LLMs
Xing Zhang, Guanghui Wang, Yanwei Cui +4
Telling an LLM to "be enthusiastic" raises its sycophancy rate from 30\% to 50\% on a lightly-aligned model, but has zero effect on a strongly-aligned one. We define this gap as th…
Guardrails Beat Guidance: A Large-Scale Study of Rules, Skills, and Persistent Configuration for Coding Agents
Xing Zhang, Guanghui Wang, Yanwei Cui +4
Random rules improve a coding agent's task performance as much as expert-curated ones (both pp on a discriminative subset of SWE-bench Verified), and in our data every indiv…