arXiv:2605.09252v3 Announce Type: replace
Abstract: Tool-augmented LLM agents tend to call tools indiscriminately, even when the model can answer directly. Each unnecessary call wastes API fees and l...
By Chung-En Sun, Linbo Liu, Ge Yan, Zimo Wang, Tsui-Wei Weng
The paper presents a closed‑form model that compares the coordination tax of multi‑agent LLM pipelines—comprising decomposition overhead and per‑handoff protocol cost—to the heterogeneity dividend gained by routing work to cheaper models. Using the Open Floor Protocol (OFP) on 135 HumanEval and ClassEval tasks, the authors find that the protocol adds only 7.2 ms per handoff, yet a planner‑coder‑reviewer pipeline is 3.22× more expensive and slower than a single strong model call, and does not solve more tasks. The study concludes that the benefit lies in selecting the appropriate model per task rather than in pipeline decomposition, as an oracle choice between two single calls solves 94.3 % of runs at half the cost of the strong call.
By Andreas Zettl, Diego Gosmar
arXiv:2610.15993v1 Announce Type: new
Abstract: We study how initial model selection, later handoff, and training targets affect success and cost in LLM tasks that span multiple turns. We compare sev...
By Daniel Matos, Lu Liu, Yanfeng Zhang
AgentBaton explores how capable and efficient language models can collaborate on long‑horizon agentic tasks by handoffs, where one model takes over from another. The study finds that handoffs from a capable to an efficient model at appropriate stages preserve or improve accuracy while significantly cutting cost, whereas the reverse handoff is less effective. AgentBaton, a lightweight detector, learns the optimal handoff point and achieves an average API cost reduction of 65.9% across 11 model pairs while largely maintaining task accuracy, even gaining up to 8.8 percentage points over capable‑only execution.
By Junlong Li, Jindong Jiang, Yonggan Fu, Jiarui Yao, Hanrong Ye, Wonmin Byeon, Peter Belcak, Hao Zhang, Pavlo Molchanov
Mid‑Harness proposes a test‑time compute strategy that samples and verifies candidate actions before execution, keeping the underlying generator and harness unchanged. Experiments show that with a strong verifier, sampling more actions significantly boosts success rates—e.g., a GPT‑5.6 verifier raises Pass@1 from 50.00 % to 68.03 % on TerminalBench‑Lite using eight samples. The approach also improves performance across various models, benchmarks, and harnesses, demonstrating that action scaling is a promising target for enhancing terminal agent reliability.
By Minki Kang, Ryo Hachiuma, Shaokun Zhang, Subhashree Radhakrishnan, Yonggan Fu, Jindong Jiang, Mingjie Liu, Ehsan Hosseini-Asl, Yi Dong, Yu-Chiang Frank Wang, Byung-Kwan Lee
arXiv:2608. 04804v1 Announce Type: cross Abstract: Frontier language models can resolve repository-level software issues, but each attempt is expensive, and existing routers select a model from the issue text alone.
By Ishaan Bhola, Adithyan Krishnan, Mukunda NS