Skip to content
Papers.

Procedural Graphs: Self-Evolving Execution Structures for LLM Agents research paper by Google, 2026

Google · Sep 8, 2026 · Foundation models · 0 citations · 41 upvotes · unverified

Read on arXiv

What it shows

A procedural graph framework organizes agent actions into structured relational triplets, providing situational guidance and self-evolving topology to improve long-horizon tool use.

UnverifiedHugging Face's summary; not yet checked by hand.

More from Google

All 33
PaperCitations
RRSI: Regularized Recursive Self-Improvement of Agent HarnessesRRSI keeps self-improving agent harnesses from memorising their training tasks, so gains carry over to new benchmarks.Agents and evaluation · Sep 20260
Verifiable Social Reasoning for LLM AssistantsLLM assistants are widely used for daily social advice, yet evaluating their social reasoning in such consultation settings remains challenging since (i) it requires setups where the assistant learns about social...Reasoning · Sep 2026 · Unverified0
Dream-RSI: Recursive Self-Improvement through Evolving WorldsDream-RSI enables scalable recursive self-improvement by using historical discovery replay to evaluate exploration policies offline, reducing costly online evaluations.Retrieval and data · Sep 2026 · Unverified0
WikiSkill: Compiling Agent Experience into Persistent Knowledge for Skill EvolutionWikiSkill co-evolves reusable agent skills with a persistent knowledge base to systematically accumulate experience and improve performance across models.Agents and evaluation · Aug 2026 · Unverified0
EnvHarness: Awakening Static Worlds for Agent LearningEnvHarness and EnvRigger dynamically reshape static environments via programmable plugins to target agent weaknesses and improve reinforcement learning co-evolution.Agents and evaluation · Aug 2026 · Unverified2
Concurrent Image Understanding and Generation: Self-Correcting Coupled Markov Jump ProcessesA coupled Markov jump process with cross-modal attention and remasking enables a training-free single-pass sampler for joint multimodal generation that improves with more denoising steps.Multimodal and robotics · Jul 2026 · Unverified1
Reinforcement Learning with Metacognitive Feedback Elicits Faithful Uncertainty Expression in LLMsReinforcement learning with metacognitive feedback and metacognitive data selection improve large language model calibration by enabling accurate self-assessment of performance and uncertainty.Foundation models · Jun 2026 · Unverified1
FLAT: Feedforward Latent Triangle Splatting for Geometrically Accurate Scene GenerationVideo diffusion models are adapted to decode explicit surface primitives directly from latent space, enabling high-quality 3D scene generation with improved geometric accuracy and real-time rendering capabilities.Multimodal and robotics · Jun 2026 · Unverified1
Topic
About this paper
Authors
Yuxing Lu, Yicheng Chen, Shanchan Wu and 1 more
arXiv
2609.09153 · PDF
Citations
0, 0 influential · Semantic Scholar
Upvotes
41 · Hugging Face
Lab
Google · on Companies · on Acquisitions · on Paydays · on TechConf · on Releases

Changes

What changed
Influential citationsfirst count: 0Sep 25, 2026
Citationsfirst count: 0Sep 25, 2026
New paperFound by the weekly scan, unverifiedSep 25, 2026

Sources: each lab's own papers and arXiv, with citation and upvote counts from Semantic Scholar and Hugging Face. One-line summaries are for orientation, not a substitute for the paper. Logos via logo.dev; trademarks belong to their owners.

New papers by email

Monday afternoons, only in weeks with new papers from the labs.

Double opt-in. Unsubscribe any time.