The paper argues that many AI tasks can be split into a deterministic core and a fuzzy residue, and that decomposing tasks rather than expanding models yields a property called parity by construction—where model‑size no longer affects performance on the deterministic sub‑tasks. Across nine models ranging from 494 M to Opus‑class size, the authors show that once the deterministic context is resolved, operation selection and selector resolution scores become identical, while a non‑decomposable task (labelling) remains sensitive to model size. They present a general method for identifying decomposition points, constructing deterministic surfaces and grammars, and ensuring safe residues, and demonstrate the approach in physics, motion retargeting, object‑model editing, and a Web‑CLI tool.
whyItMatters":"The study demonstrates that task decomposition can eliminate the need for model scaling or fine‑tuning, offering a systematic design discipline applicable across diverse AI domains."
By Tejaswi Gowda
SCOPE compiles natural‑language policies into neurosymbolic predicate programs that combine small neural models, deterministic extractors, and logical composition. The system is jointly optimized with adversarially generated counterexamples, achieving higher accuracy than prompt‑optimization, task‑decomposition, and workflow‑generation baselines on real‑world policy benchmarks. Programs trained with the weakest executor transfer to other models, enabling deployment on inexpensive models with stable, interpretable predictions.
By Arnon Mazza, Elad Levi
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
arXiv:2610.02826v1 Announce Type: new
Abstract: Successful trajectories on difficult tasks provide valuable supervision for model improvement, but specialized harnesses introduce interventions that m...
By Zongxia Li, Yucheng Shi, Zhongzhi Li, Junyao Yang, Ruhan Wang, Chengsong Huang, Fuxiao Liu, Haitao Mi, Jordan Boyd-Graber, LeoweiLiang
arXiv:2608. 02680v1 Announce Type: cross Abstract: Tool-using language-model agents repeatedly rediscover procedures they have already executed, producing traces that mix reusable structure with retries, exploration, accidental ordering, and repeated lookups.
By Salma El Yadouni (EPFL), Guanyi Li (Binome Technologies)
arXiv:2609.06396v2 Announce Type: new
Abstract: Recursive self-improvement (RSI) lets a system improve the model-building machinery from its own failures, so every later model inherits the gain. Yet...
By Zihan Tan, Leixin Sun, Zitong Shi, Yitao Liu, Jiajun Wu, Nathaniel Brooks, Jiaru Qian, Xiaoran Shang, Suyuan Huang, Yi Ding, Yangxu Liao, Mukai Li, Qiushi Sun, Shudong Liu, Xuankun Rong, Xiaohang Yu, Zhuo Chen, Hejia Geng, Chenxin Li, Aozhou Wang, Zengji Tu, Robert Tang, Yuxin Zhan, Eric Jiang, Yuxin Wu, Jianqing Zhang, Xiao Liang, Fang Wu, Haochi Zhang, Alexander Marlow, Guancheng Wan
Interlock is a framework that transforms long‑horizon tasks into typed completion graphs, linking evidence, decisions, and required effects through grounding, coupling, and realization edges. It compiles these graphs into a resettable workspace, a public request, and an evaluation rubric, and uses source‑coverage, public‑input, and agent‑as‑judge reviews to ensure that all mandatory requirements are met. Fine‑tuning Qwen3.6 backbones on Interlock‑generated trajectories improves pass rates on AutomationBench, JobBench, and Harvey LAB, showing that the synthesized experience benefits external task environments.
By Linji Hao, Yanji He, Zihan Wang, Hansi Yang, Jiayuan Zhang, Hongyan Hao, Qi Gu, Yan Feng, Han Wu
arXiv:2610.40934v1 Announce Type: new
Abstract: In this work, we provide a formal framework and mathematical definition for the subset of foundation models focused on enabling the property of "any-ta...
By Matthew McDermott, Florent Pollet, Payal Chandak, Gregory Kondas, Liat Friedman Antwarg, Noa Dagan, Isaac Kohane
arXiv:2603. 22016v3 Announce Type: replace-cross Abstract: Large Reasoning Models (LRMs) often reach a correct solution before their long Chain-of-Thought trace ends, yet continue with redundant verification, repeated attempts, or unnecessary exploration that wastes computation and can even overturn the correct answer.
By Xinyan Wang, Xiaogeng Liu, Ming Pei, Chaowei Xiao
arXiv:2610.09624v1 Announce Type: cross
Abstract: Tool calling, invoking external tools on demand, is central to agentic LLMs, yet the mechanism that decides whether a model calls a tool or responds...
By Xijie Gong, Tingxu Han, Jiahao Zhang, Wei Song, Ziqi Ding, Hanqi Yan, Youcheng Sun, Lijie Hu
arXiv:2611.07461v1 Announce Type: cross
Abstract: How can an agent handle a real-world task end to end? Frontier models with their own harnesses still fall short on real-world benchmarks: on Terminal...
By Haibo Jin, Peng Kuang, Xiaopeng Yuan, Lucheng Fu, Jerry Wang, Xucheng Yu, Yaoning Yu, Dehao Wu, Runguo Li, Colin Su, Jingxuan Sun, Xuyang Liu, Haojing Luo, Haohan Wang
LeafSkill introduces an external reuse layer for recursive LLM agents that retrieves semantically related action grounding experiences, binds action templates to current objects, and uses a lightweight 1.7B selector to choose or defer actions. This approach preserves high coverage of candidate actions under paraphrases (96–97%) and achieves a 97.34% adoption rate of unseen skill types. In experiments on Robotouille tasks, LeafSkill outperforms ReCAP, completing more tasks and reducing token usage and calls by over 14%.
By Shen Liu, Ze Zhou, Zhenguo Xu, Shaopu Wang, Yike Gao, Chunlei Wang