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."
A recurring design choice in building AI systems is whether, when a task is too hard for a model, to expand the model (make it larger, better-prompted, fine-tuned), or to decompose the task (extract t...
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...
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...
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.
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.
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...
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...
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...
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...
arXiv:2610.17757v1 Announce Type: new Abstract: Large language model agents can follow complex human instructions to solve real-world tasks. In practice, however, initial instructions are often incom...
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.
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%.