7 papers
Router Sensitivity Under Lightweight Fine-Tuning Identifies Prunable Experts in Mixture-of-Experts Models
Ali Janati, Kaoutar El Maghraoui, Xinyi Luo +3
Mixture-of-Experts (MoE) models decouple total parameters from per-token compute, but deployment still requires storing every expert. Recent theory shows that pruning experts with…
Synthetic Scenario Generation for Evaluation of Industry 4.0 Agents
Sagar Chethan Kumar, Rohith Kanathur, Dhaval Patel +1
Industrial agent benchmarks require realistic evaluation scenarios that integrate telemetry, failure modes, maintenance records, and domain standards. However, existing benchmarks…
Beyond Static Leaderboards: Predictive Validity for the Evaluation of LLM Agents
Dhaval C. Patel, Kaoutar El Maghraoui, Shuxin Lin +58
Agent benchmarks are growing fast, but no single benchmark touches more than four or five of the dimensions that deployment exposes. This paper aggregates the largest coordinated d…
Internalizing Tool Knowledge in Small Language Models via QLoRA Fine-Tuning
Yuval Shemla, Ayal Yakobe, Tanmay Agarwal +2
Large language models are increasingly used as planning components in agentic systems, but current tool-use pipelines often require full tool schemas to be included in every prompt…
Evaluating Temporal Semantic Caching and Workflow Optimization in Agentic Plan-Execute Pipelines
Alimurtaza Mustafa Merchant, Krish Veera, Sajal Kumar Goyla +3
Industrial asset operations workflows are latency-sensitive because a single user query may require coordination over sensor data, work orders, failure modes, forecasting tools, an…
PHMForge: Evaluating LLM Agents on Industrial Prognostics through MCP-Native, Algorithm-Grounded Tools
Yusheng Li, Tianjun Feng, Yunfeng Chen +6
LLM agents are beginning to invoke industrial asset-management tools through the Model Context Protocol (MCP), yet whether they can act reliably on this substrate for safety-critic…