arXiv AI
Jun 16
LLM4RTL: Tool-Assisted LLM for RTL Generation
arXiv:2606. 15500v1 Announce Type: cross Abstract: Large language models (LLMs) have facilitated impressive progress in software engineering, code generation, tooling, and systems.
PrefixAgent: An LLM-Powered Design Framework for Efficient Prefix Adder Optimization
arXiv:2507. 06127v2 Announce Type: replace-cross Abstract: Prefix adders are fundamental arithmetic circuits, but their design space grows exponentially with bit-width, posing significant optimization challenges.
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arXiv Machine Learning
Jul 31
LLM Self-Correction with DeCRIM: Decompose, Critique, and Refine for Enhanced Following of Instructions with Multiple Constraints
arXiv:2410. 06458v2 Announce Type: replace-cross Abstract: Instruction following is a key capability for LLMs.
arXiv AI
Aug 3
Wrong Code, Right Structure: Learning Netlist Representations from Imperfect LLM-Generated RTL
arXiv:2603. 09161v2 Announce Type: replace-cross Abstract: Learning effective netlist representations is fundamentally constrained by the scarcity of labeled datasets, as real designs are protected by Intellectual Property (IP) and costly to annotate.
arXiv AI
Aug 6
XGrammar-2: Dynamic and Efficient Structured Generation Engine for Agentic LLMs
arXiv:2601. 04426v4 Announce Type: replace Abstract: Modern LLM agents increasingly rely on dynamic structured generation, such as tool calling and response protocols.
arXiv Machine Learning
Jul 22
Vectorizing the Trie: Efficient Constrained Decoding for LLM-based Generative Retrieval on Accelerators
arXiv:2602. 22647v2 Announce Type: replace-cross Abstract: Generative retrieval has emerged as a powerful paradigm for LLM-based recommendation.
arXiv AI
Oct 19
Operon: Incremental Construction of Ragged Data via Named Dimensions
arXiv:2511.16080v2 Announce Type: replace-cross Abstract: Modern data processing workflows frequently encounter ragged data: collections with variable-length elements that arise naturally in domains...
arXiv Machine Learning
Jul 1
GraphMend: Code Transformations for Fixing Graph Breaks in PyTorch 2
arXiv:2509. 16248v4 Announce Type: replace-cross Abstract: This paper presents GraphMend, a compiler technique that automatically fixes FX graph breaks in PyTorch 2 programs.
arXiv AI
Oct 12
Faithful and Complete LLM-Assisted Automated Construction of Hierarchical Hardware Knowledge Graphs
The paper introduces an automated pipeline that uses large language models to convert hardware datasheets into hierarchical knowledge graphs. By chunking documents, routing extracted properties, and iteratively refining the graph, the method preserves source provenance and reduces context size per query. Evaluation on 20 datasheets shows a 98.8% property faithfulness and 83.6% coverage of baseline properties, outperforming a one‑shot baseline.
arXiv AI
Jul 28
DataOrchestra: Learning to Orchestrate Per-Example Curation of Pretraining Data
arXiv:2607. 24717v1 Announce Type: cross Abstract: Pretraining data processing is critical to the downstream performance of Large Language Models (LLMs).
arXiv AI
Jul 10
UltraX: Refining Pre-Training Data at Scale with Adaptive Programmatic Editing
arXiv:2607. 08646v1 Announce Type: cross Abstract: As available training data approaches its physical limit, gains from Scaling Laws have begun to diminish.
arXiv AI
Jun 16
Imperfect Visual Verification for Code Edition : A Case Study on TikZ
arXiv:2606. 15693v1 Announce Type: cross Abstract: LLMs have significantly advanced code generation, enabling the synthesis of functional programs.
arXiv AI
Sep 1
StructSynth: Dependency Graphs as Generation Plans for Low-Data Tabular Synthesis with Language Models
arXiv:2508.02601v2 Announce Type: replace-cross Abstract: Tabular data derives its value from inter-feature dependencies, yet preserving them during synthesis is fragile when samples are scarce. Exis...