AI Agent Papers2026
Chengyi Yang, Renxu Bao +11Affiliation: Xiamen University, Affiliation: Washington University in St. Louis +2
Converts real repositories into verifiable, sandboxed training environments with black-box tests to train coding agents on long-horizon program reproduction. Provides Program-Env for automated environment construction and Program-Agent for a role-isolated development workflow, yielding 1,648 C/C++/Go/Rust tasks and improving benchmark performance.
AI Agent Papers2026
Proposes TerminalHorizon, an automated engine that synthesizes long-horizon task environments and agentic trajectories that preserve exploration, evidence accumulation, and iterative improvement; uses ~1.5K environments and ~2.8K trajectories to fine-tune Qwen3.5-27B and improves performance on multiple benchmarks.
AI Model2026
A System 1 multimodal decision model that picks among up to 256 options and returns calibrated probabilities for choice, yes/no and 0–5 score decisions from text and images. Fine-tuned on Qwen3.5-35B-A3B, available in bf16 and NVFP4 builds (NVFP4 fits ~23.3 GiB GPU), and served via vLLM's POST /v1/decide API.