mlsanityData to 14 Oct 2026

LATCH: LLM-Assisted Timing Closure Harness for Post-Route FPGA Optimisation

arXiv 2610.16625 ↗First posted 12 Oct 2026cs.AIcited by 0

Not on the map. Why?

Abstract (arXiv)

Post-route FPGA timing closure remains expert-dependent and computationally expensive. We present LATCH (LLM-Assisted Timing Closure Harness), a two-layer framework that separates large language model (LLM) orchestration from deterministic FPGA implementation and evaluation. Across sixteen designs and seven LLMs, a single-iteration study compares three agent control granularities - expert-written recipes, direct EDA operations and LLM-constructed procedures - to ask how much control an agent needs per attempt. Functional-pass rates are 98.2%, 71.4% and 17.9%, respectively; yet direct operations give slightly larger 3.4% median Fmax gains when passing, versus 3.2% for recipes. This exposes a trade-off between execution reliability and timing improvement. Informed by this trade-off, a multi-iteration study fixes the action tier at expert recipes to ask how repeated attempts are best organised, comparing a fitted deterministic heuristic with sequential LLM reasoning, branching exploration and knowledge base-guided exploration within 12 checkpoints per run. Branching raises mean maximum-frequency improvement from 10.5% (sequential) to 16.0%, and the knowledge base to 20.3%, improving 15 of 16 designs. Characterisation across 2,421 recipe attempts profiled their gain and validation rates, showing hard-block re-placement gives the largest cumulative mean gain at 41.4MHz.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 20 of its 29 references that are in the map. First posted on arXiv 12 Oct 2026.

Builds on

20 of 29 references.

References · 20

  1. 2023ChatEDA: A Large Language Model Powered Autonomous Agent for EDAcited by 122
  2. 2021Machine Learning for Electronic Design Automation: A Surveycited by 72
  3. 2025A Survey of Research in Large Language Models for Electronic Design Automationcited by 41
  4. 2025ORFS-agent: Tool-Using Agents for Chip Design Optimizationcited by 14
  5. 2024EDA Corpus: A Large Language Model Dataset for Enhanced Interaction with OpenROADcited by 13
  6. 2025EEsizer: LLM-Based AI Agent for Sizing of Analog and Mixed Signal Circuitcited by 13
  7. 2022FlowTune: End-to-end Automatic Logic Optimization Exploration via Domain-specific Multi-armed Banditcited by 11
  8. 2026Dr. RTL: Autonomous Agentic RTL Optimization through Tool-Grounded Self-Improvementcited by 6
  9. 2025Nexus: A Lightweight and Scalable Multi-Agent Framework for Complex Tasks Automationcited by 5
  10. 2026PDAGENT-BENCH: Characterizing, Grounding, and Architecting LLM/VLM Agents for VLSI Physical Designcited by 4
  11. 2025Timing Analysis Agent: Autonomous Multi-Corner Multi-Mode (MCMM) Timing Debugging with Timing Debug Relation Graphcited by 3
  12. 2026Retrieve, Schedule, Reflect: LLM Agents for Chip QoR Optimizationcited by 3
8 more
  1. 2026Design Conductor 2.0: An agent builds a TurboQuant inference accelerator in 80 hourscited by 2
  2. 2026Bridging the Last Mile of Circuit Design: PostEDA-Bench, a Hierarchical Benchmark for PPA Convergence and DRC Fixingcited by 2
  3. 2023Imbalanced Large Graph Learning Framework for FPGA Logic Elements Packing Predictioncited by 1
  4. 2023LEAPS: Topological-Layout-Adaptable Multi-Die FPGA Placement for Super Long Line Minimizationcited by 1
  5. 2025Critical Path Aware Timing-Driven Global Placement for Large-Scale Heterogeneous FPGAscited by 1
  6. 2026FluxEDA: A Unified Execution Infrastructure for Stateful Agentic EDAcited by 1
  7. 2026PACT: Post-route Agentic Checkpoint Tuning for FPGA Timing Closurecited by 1
  8. 2026LLMs in Digital EDA: A perspective on shifting roles from Generation to Orchestrationcited by 1

Built on by

Not cited yet.

Similar, not cited

Close in wording, no citation either way

  1. 2026TicTacBench: Benchmarking Timing Closure Capabilities of Coding Agents
  2. 2026LLM-based Hardware Development with Hierarchical IRs and End-to-End Multi-Agent Workflow
  3. 2025TimelyHLS: LLM-Based Timing-Aware and Architecture-Specific FPGA HLS Optimization
  4. 2024New Solutions on LLM Acceleration, Optimization, and Application
  5. 2026Concurrency without Model Changes: Future-based Asynchronous Function Calling for LLMs
  6. 2025Rethinking LLM-Based RTL Code Optimization Via Timing Logic Metamorphosis
  7. 2026How LLMs Fail and Generalize in RTL Coding for Hardware Design?
  8. 2026LLM-FSM: Scaling Large Language Models for Finite-State Reasoning in RTL Code Generation