arXiv AI
Coco: An Agentic Copilot for the Hardware--Software Co-Design Lifecycle
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arXiv Machine Learning
Jun 30
SWE-INTERACT: Reimagining SWE Benchmarks as User-Driven Long-Horizon Coding Sessions
arXiv:2606. 30573v1 Announce Type: new Abstract: We introduce SWE-Interact, a new testbed for evaluating coding agents on multi-turn, interactive, user-driven software engineering tasks.
arXiv AI
Sep 3
Harness Engineering in LLM Tool Use via Agent-Native Reusable Tool Primitives
The paper introduces Tool Primitives, a design that replaces rigid API schemas with natural language interfaces for tool calling, enabling seamless inter-tool communication. It builds ToolFace, a repository of over 25,000 functions that LLMs can dynamically retrieve, and HEART, a harness engineering framework that orchestrates tool use with planning, routing, and verification. Experiments show HEART outperforms fine‑tuned models and leading commercial LLMs while cutting API costs by up to 85%.
arXiv Machine Learning
Aug 12
FlowScout: From Execution Feedback to Reliable Tool-Using Agent Workflows
arXiv:2608. 10039v1 Announce Type: new Abstract: Agentic workflows have become an important abstraction for building reliable LLM-based automation systems by organizing large language models (LLMs), tools, and control logic into explicit execution structures.
arXiv AI
Aug 12
DSAgentBench: Can Agents Automate End-to-End Data-Science Workflows in Real Computer Environments?
arXiv:2608. 10366v1 Announce Type: new Abstract: Real-world data science involves long-horizon workflows that span data wrangling, exploration, modeling, visualization, and validation, and require coordinated use of tools such as notebooks, IDEs, terminals, browsers, and databases within real operating environments.
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 AI
Sep 4
Terminal-Universe: Turning Agent Trajectories into Scalable Terminal Environments
Terminal-Universe is a framework that transforms large collections of terminal‑based agent trajectories into reusable, executable environments. By replaying recorded file operations and completing missing files, it reconstructs the original workspace and task, then synthesizes new tasks and multi‑round interactions. The resulting 37.3k task‑sufficient environments enable significant performance gains when fine‑tuning language models on terminal‑based benchmarks.