arXiv Machine Learning
Jun 3
KForge: LLM-Driven Cross-Platform Kernel Generation for AI Accelerators
arXiv:2606. 02963v1 Announce Type: new Abstract: Production inference increasingly targets a heterogeneous mix of accelerators.
Towards Direct Latent-Space Synthesis for Parallel Branches in LLM-Agent Workflows
arXiv:2606. 14672v1 Announce Type: new Abstract: Large language models increasingly serve as execution engines for agentic systems, yet they still consume context through a sequential text interface.
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arXiv AI
Jul 28
CodeEvo: Interaction-Driven Synthesis of Code-centric Data through Hybrid and Iterative Feedback
arXiv:2507. 22080v2 Announce Type: replace-cross Abstract: Acquiring high-quality instruction-code pairs is essential for training Large Language Models for code generation.
arXiv AI
Jul 28
E-Bench: Benchmarking Multi-Step Tool-Use Agents in Real-World Product Scenarios
arXiv:2607. 23722v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly deployed as agents that interact with stateful environments over multiple steps: gathering hidden information, composing tool calls, and committing state changes.
arXiv AI
Jul 21
DataFlow-Harness: A Grounded Code-Agent Platform for Constructing Editable LLM Data Pipelines
arXiv:2607. 16617v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used to automate data-processing workflows, yet coding agents typically produce scripts that are not automatically materialized as persistent, editable platform artifacts.
arXiv Machine Learning
Jul 7
ParEVO: Synthesizing Code for Irregular Data: High-Performance Parallelism through Agentic Evolution
arXiv:2603. 02510v2 Announce Type: replace Abstract: The transition from sequential to parallel computing is essential for modern high-performance applications but is hindered by the steep learning curve of concurrent programming.
arXiv AI
Jun 12
Functional Cache Grafting: Robust and Rapid Code-Policy Synthesis for Embodied Agents
arXiv:2606. 13097v1 Announce Type: cross Abstract: Code-writing large language models (CodeLLMs) generate executable code policies for embodied agents by translating natural language goals and environmental constraints into structured control programs.
arXiv Machine Learning
Jun 2
LRAgent: Efficient KV Cache Sharing for Multi-LoRA LLM Agents
arXiv:2602. 01053v2 Announce Type: replace Abstract: Role specialization in multi-LLM agent systems is often realized via multi-LoRA, where agents share a pretrained backbone and differ only by lightweight adapters.
arXiv AI
Jul 29
Speculate While You Reason: Teaching Agents to Predict Their Next Tool Call via Joint Agent-Speculator RL
arXiv:2607. 25816v1 Announce Type: new Abstract: Large language model agents often spend substantial wall-clock time waiting for tool call results.
arXiv AI
Jul 24
MKEvolve: A Modular Multi-Agent Framework for Kernel Code Generation
arXiv:2607. 20501v1 Announce Type: new Abstract: Despite rapid progress in LLM-based code generation, writing correct and performant kernels for hardware accelerators remains a key bottleneck in scaling modern ML workloads.
arXiv Machine Learning
Jul 17
FlowBot: Inducing LLM Workflows with Bilevel Optimization and Textual Gradients
arXiv:2604. 26258v3 Announce Type: replace-cross Abstract: LLM workflows, which coordinate structured calls to individual LLMs/agents to achieve a particular goal, offer a promising path towards building powerful AI systems that can tackle diverse tasks.
arXiv AI
Jun 30
Scalable Synthesis of distributed LLM workloads through Symbolic Tensor Graphs
arXiv:2511. 10480v3 Announce Type: replace-cross Abstract: Optimizing the performance of large language models (LLMs) on large-scale AI training and inference systems requires a scalable and expressive mechanism to model distributed workload execution.
arXiv Machine Learning
Jun 10
CodeAlchemy: Synthetic Code Rewriting at Scale
arXiv:2606. 10087v1 Announce Type: cross Abstract: Pre-training on raw code teaches syntax but provides sparse signal for diverse real-world task formats.