6 papers · 1 filter
RSIFlow: Adaptive Self-Improvement Workflows with an Evolving Meta-Agent
Huan Zhang, Mingju Chen, Dongxu Zhou +4
Large language model (LLM) agents have demonstrated strong capabilities in complex tasks, including code generation, tool use, and multi-hop question answering. Existing self-impro…
On the Chain-of-Thought Monitorability of Looped Language Models
Han Wang, Ishwar B Balappanawar, Huan Zhang
Chain-of-thought (CoT) monitoring provides a promising approach for detecting undesirable model behavior. Looped language models (LoopLMs) repeatedly apply shared transformer layer…
When LLMs Over-Answer: Measuring and Mitigating Quality Issues in LLM-Based Hardware Description Language Question Answering
Ziteng Hu, Jiachi Chen, Wenhao Lv +2
The rapid advancement of large language models (LLMs) has led practitioners to increasingly rely on them for answering questions about hardware description languages (HDLs). Becaus…
FORTIS: Benchmarking Over-Privilege in Agent Skills
Shawn Li, Chenxiao Yu, Han Wang +8
Large language model agents increasingly operate through an intermediate skill layer that mediates between user intent and concrete task execution. This layer is widely treated as…
Improving Code Translation with Syntax-Guided and Semantic-aware Preference Optimization
Yuhan Wu, Huan Zhang, Wei Cheng +3
LLMs have shown immense potential for code translation, yet they often struggle to ensure both syntactic correctness and semantic consistency. While preference-based learning offer…
MonitorBench: A Comprehensive Benchmark for Chain-of-Thought Monitorability in Large Language Models
Han Wang, Yifan Sun, Brian Ko +8
Large language models (LLMs) can generate chains of thought (CoTs) that are not always causally responsible for their final outputs. When such a mismatch occurs, the CoT no longer…