arXiv AI
Worst-Case Discovery and Runtime Protection for RL-Based Network Controllers
Read the original on arXiv AI →The Flow has not summarised this story yet — read it at arXiv AI.
Related stories
arXiv Machine Learning
Sep 3
Sim2Signal: Sim-to-Real Benchmarks for Traffic Signal Control
Sim2Signal is a benchmark designed to systematically measure the Sim-to-Real gap in traffic signal control by decomposing it into observation, action, transition, and reward gaps. The study evaluates 18 mitigation methods across 33 gap settings and 10 calibrated networks from five real-world locations, finding that direct transfer degrades performance but mitigation effectiveness varies by network and gap type. The most effective approaches tend to estimate the specific changes caused by each gap rather than relying on domain randomization or invariant representations.
arXiv Machine Learning
Aug 13
Grounding Large Language Models as Generalizable Policies in Network Control
arXiv:2512. 11839v2 Announce Type: replace Abstract: Designing generalizable control policies that operate reliably under changing conditions is essential for robust network services in modern digital infrastructure.
arXiv AI
Sep 16
Large Language Models in the Loop: A Stability- and Network-Aware Survey in Networked Control, Cyber-Physical, and Multi-Agent Systems
The article surveys how large language models (LLMs) can be incorporated into networked control systems, cyber‑physical systems, and multi‑agent networks without violating stability and safety guarantees. It proposes treating the LLM as a slow supervisor that sets high‑level goals, while a fast, certified inner loop preserves physical stability. The survey maps LLM characteristics—such as inference latency, API failures, tokenization, and hallucinations—to classical control challenges and highlights the growing gap between model capability and formal safety assurances, calling for future research on stability proofs.
arXiv AI
Sep 30
Neuro-Symbolic Computer Use: Learning Reusable Policies for Reliable and Efficient Execution
The paper introduces neuro‑symbolic computer use, a method that learns reusable policies to execute recurring computer workflows efficiently. Instead of re‑planning each run, the learned policy encodes stable decisions (ordering, variables, loops, branches) into executable code while delegating observation‑dependent decisions to neural models. Using neuro‑symbolic policy iteration, the approach iteratively refines the policy from a single agent trajectory, diagnoses failures, and revises the code with a coding model, achieving superior Pass^3 scores and significant reductions in per‑run cost and latency on OSWorld‑Verified and ScienceBoard benchmarks.
arXiv AI
Aug 5
TraceCompiler: Skill-Guided Mining and Compilation of LLM Agent Traces into Mostly Deterministic Workflows
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.
arXiv AI
Aug 11
ROM: Real-time Overthinking Mitigation via Streaming Detection and Intervention
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.