GAE: Graph-Augmented Evolution for Scientific Discovery via Reinforcement Optimization

Evolutionary program search guided by Large Language Models (LLMs) has emerged as a powerful paradigm for automated scientific discovery. However, current approaches are fundamentally constrained by three bottlenecks: structurally blind parent selection, sparse whole-program evaluation rewards, and static mutation operators that fail to adapt during search. We present GAE (Graph-Augmented Evolution), a framework that resolves these limitations through a tightly coupled, three-pillar architecture. First, a relational graph neural network (GNN) parses programs into typed computation graphs, producing structure-aware embeddings. Second, an RL-optimized meta-controller leverages these embeddings to replace blind evolutionary sampling with a directed policy, dynamically selecting optimal parents and mutation directions based on reward history. Third, an online GRPO fine-tuning loop continuously updates the LLM mutation operator at test-time using group-normalized evaluation rewards, directly aligning the model's generation distribution with high-fitness structural edits. We evaluate GAE on a challenging scientific discovery task: symbolic regression for complex nonlinear oscillator systems. By transforming stochastic search into a directed, self-improving trajectory, GAE efficiently discovers closed-form physical equations, consistently matching or outperforming static LLM-driven baselines and achieving state-of-the-art out-of-distribution performance.

Agentic AI for Self-Driving Laboratories in Soft Matter: Taxonomy, Benchmarks,and Open Challenges

Self-driving laboratories (SDLs) close the loop between experiment design, automated execution, and data-driven decision making, and they provide a demanding testbed for agentic AI under expensive actions, noisy and delayed feedback, strict feasibility and safety constraints, and non-stationarity. This survey uses soft matter as a representative setting but focuses on the AI questions that arise in real laboratories. We frame SDL autonomy as an agent environment interaction problem with explicit observations, actions, costs, and constraints, and we use this formulation to connect common SDL pipelines to established AI principles. We review the main method families that enable closed loop experimentation, including Bayesian optimization and active learning for sample efficient experiment selection, planning and reinforcement learning for long horizon protocol optimization, and tool using agents that orchestrate heterogeneous instruments and software. We emphasize verifiable and provenance aware policies that support debugging, reproducibility, and safe operation. We then propose a capability driven taxonomy that organizes systems by decision horizon, uncertainty modeling, action parameterization, constraint handling, failure recovery, and human involvement. To enable meaningful comparison, we synthesize benchmark task templates and evaluation metrics that prioritize cost aware performance, robustness to drift, constraint violation behavior, and reproducibility. Finally, we distill lessons from deployed SDLs and outline open challenges in multi-modal representation, calibrated uncertainty, safe exploration, and shared benchmark infrastructure.

Understanding Transformer Encoder-Decoder Representations through Bernoulli Dropout

We study Transformer overparameterization through the lens of angular similarity in high-dimensional encoder-decoder embeddings. We apply Bernoulli dropout between the encoder and the decoder, varying the keep probability $p$ to identify a sparsity-dependent threshold above which the Top-1 prediction is preserved. Theoretically, we prove that, if the effective sparsity embeddings is sufficiently large, and thus decoder performance, remain stable under moderate coordinate dropout. Empirically, we implement the Bernoulli dropout by constructing a new Transformer model augmented with Binary Erasure Channel (BEC) and test its performance on an English-French translation task. Experimental results visualize the trends for validation accuracies and BLEU scores, both decline sharply at some threshold.

Plan, Verify and Fill: A Structured Parallel Decoding Approach for Diffusion Language Models

Diffusion Language Models (DLMs) present a promising non-sequential paradigm for text generation, distinct from standard autoregressive (AR) approaches. However, current decoding strategies often adopt a reactive stance, underutilizing the global bidirectional context to dictate global trajectories. To address this, we propose Plan-Verify-Fill (PVF), a training-free paradigm that grounds planning via quantitative validation. PVF actively constructs a hierarchical skeleton by prioritizing high-leverage semantic anchors and employs a verification protocol to operationalize pragmatic structural stopping where further deliberation yields diminishing returns. Extensive evaluations on LLaDA-8B-Instruct and Dream-7B-Instruct demonstrate that PVF reduces the Number of Function Evaluations (NFE) by up to 65% compared to confidence-based parallel decoding across benchmark datasets, unlocking superior efficiency without compromising accuracy.