7 papers
Forgetting Without Restarting: Execution-State Unlearning for Stateful LLM Agents
Chao Yao, Yangbo Wei, Zhen Huang +5
Long-running LLM agents are stateful: beyond the transcript they accrete compressed summaries, plaintext memory, pending tool plans, and, under every serving API, a KV cache. Yet t…
Assessing Triple Modular Redundancy for Wide-Link, Low-Latency NoC Routers: Reliability and Physical Design Challenges
Chen Wu, Michael Rogenmoser, Luca Benini +1
Protecting the Network-on-Chip (NoC) of physical-AI tile-based accelerators deployed in harsh environments against single-event effects (SEEs) is paramount for preventing NoC failu…
Competence, Not Accuracy: A Diagnostic for Reference-Free Judge Gates in Skill Optimization
Chenle Chen, Yangbo Wei, Chao Yao +4
Text-space skill optimization adapts a frozen agent by evolving a natural-language skill document, accepting each candidate through a validation gate. Existing gates rely on verifi…
SkillSmith: Co-Evolving Skills and Tools for Self-Improving Agent Systems
Yangbo Wei, Zhen Huang, Shaoqiang Lu +4
LLM agents increasingly store capabilities in external skill and tool libraries. Libraries can grow, but execution must fit a finite context window and improvement consumes a finit…
Who Checks the Checker? Enhancing Component-level Architectural SEU Fault Tolerance for End-to-End SoC Protection
Michael Rogenmoser, Philippe Sauter, Chen Wu +2
Single-event upset (SEU) fault tolerance for systems-on-chip (SoCs) in radiation-heavy environments is often addressed by architectural fault-tolerance approaches protecting indivi…
A Lightweight High-Throughput Collective-Capable NoC for Large-Scale ML Accelerators
Luca Colagrande, Lorenzo Leone, Chen Wu +3
The exponential increase in Machine Learning (ML) model size and complexity has driven unprecedented demand for high-performance acceleration systems. As technology scaling enables…
AMSNet: Netlist Dataset for AMS Circuits
Zhuofu Tao, Yichen Shi, Yiru Huo +8
Today's analog/mixed-signal (AMS) integrated circuit (IC) designs demand substantial manual intervention. The advent of multimodal large language models (MLLMs) has unveiled signif…