encode control signals

This study addresses the challenges of control design in complex industrial processes characterized by multivariable coupled dynamics by proposing an automated control strategy generation framework that integrates large language models (LLMs) with Bayesian optimization. The approach decomposes control design into structured code generation steps, ensuring physical consistency through execution-based validation and feedback-driven repair. It pioneers the automatic synthesis of decentralized PI controller architectures and their tuning environments directly from dynamic process models. Evaluated on a nonlinear gas preheater benchmark, the generated control schemes—subsequently refined via Bayesian optimization—achieve a 26.5% improvement in closed-loop performance and significantly enhance the transient response of pressure loops, thereby demonstrating the method’s effectiveness and novelty.

This work clarifies common misconceptions that dynamic parameterization inherently enables dynamic inference or computational savings, explicitly distinguishing among coefficient variation, dependence of frozen models on coefficient assignment, and conditional execution. To this end, the authors propose Frozen Controller Audit (FCA), a method that caches coefficient tensors and replays frozen models under various strategies—including cross-input reallocation, token shuffling, and static configurations—to quantify a model’s reliance on content-conditional coefficient assignment. Experiments on FeatureGate Transformers and MUDDPythia reveal that static configurations retain over 98.7% of original performance, while layer identity accounts for 87%–96% of coefficient variance. Notably, dynamic parameterization fails to accelerate inference—actually slowing it by 30.8%—and reallocation substantially increases negative log-likelihood, demonstrating strong dependence on content-conditional assignment without tangible efficiency gains.

Digital twin modeling for industrial processes faces challenges under data scarcity, hindering proactive control. Method: This paper proposes a lightweight, actionable world model that employs disentangled latent representations, integrating joint embedding prediction with contrastive learning to ensure bidirectional predictability among process inputs, latent space, and outputs—thereby preserving causal interpretability of control actions. Crucially, the method reduces reliance on large-scale labeled datasets. Contribution/Results: Evaluated on highly dynamic, strongly nonlinear injection molding, the model generates concrete, executable control policies. Experiments demonstrate significant improvements in process stability and operational boundary preservation, enabling a paradigm shift in industrial process monitoring—from passive observation to active intervention.

This paper addresses discrete-time interconnected systems whose subsystem dynamics and interconnection topology are partially unknown. Method: We propose a data-driven, compositional approach to construct finite-state abstractions for formal verification and distributed controller synthesis. Subsystems are modeled individually from input-output data, and—novelly—the unknown static interconnection mapping is treated as a learnable object, enabling its symbolic abstraction. Compositionality and rigorous error propagation analysis ensure that the resulting abstraction strictly satisfies an approximate simulation relation. Contribution/Results: We theoretically establish scalability and verifiability of the abstraction. Experiments demonstrate substantial mitigation of the curse of dimensionality, enabling high-precision, low-complexity controller synthesis while preserving formal guarantees.

Existing runtime harnesses for programming agents suffer from either oversimplification or excessive complexity, lacking a clear and concise architectural paradigm. This work proposes a harness design centered on the request lifecycle, explicitly delineating three core boundaries: model, execution, and state. By orchestrating a structured sequence—comprising context construction, model decision-making, environmental action, observation feedback, and state continuation—the design enables cross-request state persistence and continual self-improvement through bootstrapping. We implement this paradigm in Coderlet, an open-source prototype system, demonstrating its efficacy in coordinating code generation, environment interaction, and state management. The resulting framework provides a scalable foundation for building high-performance programming agents.

This study investigates whether large language models genuinely leverage their explicitly generated intermediate states for subsequent reasoning, thereby validating the efficacy of process supervision. To this end, the authors design a controlled state-tracking task that requires models to produce intermediate states before final answers and employ causal interventions by editing internal representations to test whether subsequent predictions adhere to known state-transition rules. The work provides the first empirical evidence of the causal role of intermediate states and introduces the concept of a “causal register,” emphasizing that such states must substantively participate in computation. Experiments show that Qwen2.5-Coder-7B achieves 80%–91% prediction accuracy under state edits—significantly outperforming models that output only final answers or rely on pretrained baselines—with consistent results replicated across multiple model families.

Existing controllable generation methods often rely on fine-tuning, auxiliary networks, or test-time search, lacking a flexible, training-free control mechanism. This work proposes a “follow-the-mean” principle within the flow matching framework: by adjusting the conditional terminal mean and leveraging a reference set, it guides a pre-trained generative model to achieve desired attribute control. The approach employs deterministic interpolation-based flow matching, closed-form terminal mean correction, and semi-parametric guidance combining a frozen FLUX.2-klein model with a learnable residual refiner, enabling reference set swapping at inference time. Under fixed prompts, seeds, and weights, it effectively controls color, identity, style, and structure. Notably, this semi-parametric method attains unconditional DiT-B/4-level generation quality on AFHQv2.