AI agents

Tool use, function calling, orchestration and the protocols that let models act rather than only answer.

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arXiv AI
Sep 1

LiteSearch-VL: Small Multimodal Search Agents via Trajectory Distillation and Synthetic Step-DPO

LiteSearch‑VL demonstrates that distilling released agent trajectories into small vision‑language backbones can transfer the agent’s behavioral contract, enabling a 2B model to produce usable answers in 28.4% of cases on multimodal benchmarks. The approach uses parameter‑efficient LoRA adapters and synthetic step‑level preferences derived from GPT‑5 hard negatives to refine tool use and query quality. While synthetic preference learning and tool distillation provide incremental improvements, the main bottleneck identified is answer verification rather than search depth.

By Saeed Khaki, Nima Safaei, Kamal Ginotra
arXiv AI
Sep 1

CrossAudit: A Git-Native, Cross-Vendor Audit Loop for Agentic Science

CrossAudit proposes a Git‑native, cross‑vendor audit protocol for autonomous research pipelines, ensuring each work increment is reviewed by an agent from a different vendor against a human‑written rulebook. Audit outcomes, disputes, and rulings are stored as git commits, providing a replayable, versioned supervision history. The authors implemented the protocol with GitHub Actions and Python, deployed it in a computational‑chemistry pipeline, and conducted a seeded‑defect trial that revealed differing interpretations of the same rulebook by two vendors.

By Zhaohe Dong, Yuhao Chen
arXiv Computer Vision
Sep 1

CL4D: Contrastive Language-4D Pretraining for Vision-Language Reasoning in Dynamic Scenes

arXiv:2608.18734v2 Announce Type: replace Abstract: 4D understanding and reasoning is a fundamental capability for embodied AI agents operating in dynamic physical environments. However, existing vis...

By Kumal Hewagamage, Isuranga Senavirathne, Sasika Amarasinghe, Hasitha Gallella, Dulanga Weerakoon, Vigneshwaran Subbaraju, Ranga Rodrigo
arXiv AI
Sep 1

Science sandboxes measure the scientific capability of AI agents

arXiv:2608.30165v1 Announce Type: cross Abstract: Scientific progress depends not only on finding solutions, but on learning the rules that explain why they work and using that understanding to desig...

By Arya S. Rao, Rodrigo I. Castro, Sager J. Gosai, Kenneth B. Hsu, Yasha Ektefaie, Shantanu Singh, Sangeeta N. Bhatia, Steven K. Reilly, Ryan Tewhey, Eric S. Lander, Pardis C. Sabeti