14 citations · 31 across the 30 of their papers we have counts for
6 papers · 1 filter
MILO: Automated Harness Discovery via Orchestrated Multi-Agent Evolution
Prithwish Jana, Mononito Goswami, Hao Liu +9
Modern agentic systems combine an AI model with a harness that controls execution and environmental interactions. Harness design strongly affects long-horizon performance, yet its…
SWE-Proof: Can Language Models Resolve Real-World Issues with Machine-Checked Proofs?
George Ma, Benjamin Mikek, Haoyu Li +9
Ensuring the correctness of LLM-generated code is a core challenge for modern software engineering. Benchmarks for agentic code generation check correctness with held-out test suit…
Dissecting model behavior through agent trajectories
Gaurav Gupta, Vatshank Chaturvedi, Jun Huan +2
AI agent performance is not just a modeling problem, it is fundamentally a systems problem. The advanced capabilities of models are realized through agent harnesses. Therefore, a g…
ContextWeaver: Selective and Dependency-Structured Memory Construction for LLM Agents
Yating Wu, Yuhao Zhang, Sayan Ghosh +4
Large language model (LLM) agents often struggle in long-context interactions. As the history grows, sliding windows and prompt compression may drop earlier information that later…
ExecTune: Effective Steering of Black-Box LLMs with Guide Models
Vijay Lingam, Aditya Golatkar, Anwesan Pal +6
For large language models deployed through black-box APIs, recurring inference costs often exceed one-time training costs. This motivates composed agentic systems that amortize exp…
TerraFormer: Automated Infrastructure-as-Code with LLMs Fine-Tuned via Policy-Guided Verifier Feedback
Prithwish Jana, Sam Davidson, Bhavana Bhasker +3
Automating Infrastructure-as-Code (IaC) is challenging, and large language models (LLMs) often produce incorrect configurations from natural language (NL). We present TerraFormer,…