IRIS-14B is the first LLM trained explicitly for GIMPLE-to-LLVM IR translation and outperforms much larger models by up to 44 percentage points on real-world C code.
arXiv preprint arXiv:2309.07062 , year=
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JETO-Mine and JETO-Bench provide a configurable, Docker-based pipeline and dataset of 91 verified Java execution-time improvement patches, on which OpenHands fixes 13 of 91 issues (14.3%).
TensorBench is a new benchmark of 199 tasks on a tensor framework used to evaluate seven coding agents, yielding pass rates from 22.1% to 64.8% with low inter-agent agreement.
Step-TP is a dataset providing grounded, atomic step-level IR transitions and CoT supervision to enable reliable multi-step LLM-guided tensor program optimization instead of end-to-end imitation.
A hybrid LLM-guided CUDA compiler for transformer decode reports 3.2-6.6x over PyTorch eager and 1.02-1.12x over vLLM, but only for single-request, fixed-shape generation.
InCoder-32B-Thinking uses error-feedback synthesized thinking traces and a code world model to reach top open-source scores on general and industrial code benchmarks including 81.3% on LiveCodeBench and 84.0% on CAD-Coder.
Fine-tuning LLMs on structured tasks inspired by maladaptive behaviors produces stable, context-general shifts in next-token distributions and response tendencies consistent with altered behavioral priors.
citing papers explorer
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LLM Translation of Compiler Intermediate Representation
IRIS-14B is the first LLM trained explicitly for GIMPLE-to-LLVM IR translation and outperforms much larger models by up to 44 percentage points on real-world C code.
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JETO-Bench: A Reproducible Benchmark for Execution Time Improvement Patches in Java
JETO-Mine and JETO-Bench provide a configurable, Docker-based pipeline and dataset of 91 verified Java execution-time improvement patches, on which OpenHands fixes 13 of 91 issues (14.3%).
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TensorBench: Benchmarking Coding Agents on a Compiler-Based Tensor Framework
TensorBench is a new benchmark of 199 tasks on a tensor framework used to evaluate seven coding agents, yielding pass rates from 22.1% to 64.8% with low inter-agent agreement.
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Step-TP: A Grounded, Step-Level Dataset with Chain-of-Thought Reasoning for LLM-Guided Tensor Program Optimization
Step-TP is a dataset providing grounded, atomic step-level IR transitions and CoT supervision to enable reliable multi-step LLM-guided tensor program optimization instead of end-to-end imitation.
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AgentCompile: An LLM-Guided Compiler for Direct CUDA Inference
A hybrid LLM-guided CUDA compiler for transformer decode reports 3.2-6.6x over PyTorch eager and 1.02-1.12x over vLLM, but only for single-request, fixed-shape generation.
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InCoder-32B-Thinking: Industrial Code World Model for Thinking
InCoder-32B-Thinking uses error-feedback synthesized thinking traces and a code world model to reach top open-source scores on general and industrial code benchmarks including 81.3% on LiveCodeBench and 84.0% on CAD-Coder.
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Modeling Pathology-Like Behavioral Patterns in Language Models Through Behavioral Fine-Tuning
Fine-tuning LLMs on structured tasks inspired by maladaptive behaviors produces stable, context-general shifts in next-token distributions and response tendencies consistent with altered behavioral priors.
- Source-Free Detection and Impact Analysis of Compiler Optimization Problems in Mobile Applications