Electronic Design Automation (EDA) includes a set of tools for circuit design in different development stages especiallylogic synthesisstage andplacementstage (Fig.1).

Theorem 4.2.Thefollowingholdsfor Algorithm 2: withprobabilityatleast1 , forallt T hP Pt,Ci 32 log ( 3e / ) ( C)2 t+ 1 1 , where = (C) Theempirical implementation condition allowsusCt, with specified components, 7 1: Experimentsonsyntheticdata.

Through theoretical analyses andconstruction ofexamples intoyMDPs,wedemonstrate thatshared pessimistic targets can paradoxically lead to value estimates that are effectively optimistic.

We also propose an evaluation metric based on the intersection-over-union score of MSG edges.

Motivated by the success of ensembles for uncertainty estimation in supervised learning, we take a renewed look at how ensembles of $Q$-functions can be leveraged as the primary source of pessimism for offline reinforcement learning (RL). We begin by identifying a critical flaw in a popular algorithmic choice used by many ensemble-based RL algorithms, namely the use of shared pessimistic target values when computing each ensemble member's Bellman error. Through theoretical analyses and construction of examples in toy MDPs, we demonstrate that shared pessimistic targets can paradoxically lead to value estimates that are effectively optimistic. Given this result, we propose MSG, a practical offline RL algorithm that trains an ensemble of $Q$-functions with independently computed targets based on completely separate networks, and optimizes a policy with respect to the lower confidence bound of predicted action values. Our experiments on the popular D4RL and RL Unplugged offline RL benchmarks demonstrate that on challenging domains such as antmazes, MSG with deep ensembles surpasses highly well-tuned state-of-the-art methods by a wide margin. Additionally, through ablations on benchmarks domains, we verify the critical significance of using independently trained $Q$-functions, and study the role of ensemble size. Finally, as using separate networks per ensemble member can become computationally costly with larger neural network architectures, we investigate whether efficient ensemble approximations developed for supervised learning can be similarly effective, and demonstrate that they do not match the performance and robustness of MSG with separate networks, highlighting the need for new efforts into efficient uncertainty estimation directed at RL.

Grassroots platforms are distributed applications run by\linebreak cryptographically-identified people on their networked personal devices, where multiple disjoint platform instances emerge independently and coalesce when they interoperate. Their foundation is the grassroots social graph, upon which grassroots social networks, grassroots cryptocurrencies, and grassroots democratic federations can be built. Grassroots platforms have yet to be implemented, the key challenge being faulty and malicious participants: without secure programming support, correct participants cannot reliably identify each other, establish secure communication, or verify each other's code integrity. We present Grassroots Logic Programs (GLP), a secure, multiagent, concurrent, logic programming language for implementing grassroots platforms. GLP extends logic programs with paired single-reader/single-writer (SRSW) logic variables, providing secure communication channels among cryptographically-identified people through encrypted, signed and attested messages, which enable identity and code integrity verification. We present GLP progressively: logic programs, concurrent GLP, multiagent GLP, augmenting it with cryptographic security, and providing smartphone implementation-ready specifications. We prove safety properties including that GLP computations are deductions, SRSW preservation, acyclicity, and monotonicity. We prove multiagent GLP is grassroots and that GLP streams achieve blockchain security properties. We present a grassroots social graph protocol establishing authenticated peer-to-peer connections and demonstrate secure grassroots social networking applications.

Graphs are the ubiquitous, non-Euclidean structures that describe the relationship among objects.

We also propose an evaluation metric based on the intersection-over-union score of MSG edges.

Generative robot policies such as Flow Matching offer flexible, multi-modal policy learning but are sample-inefficient. Although object-centric policies improve sample efficiency, it does not resolve this limitation. In this work, we propose Multi-Stream Generative Policy (MSG), an inference-time composition framework that trains multiple object-centric policies and combines them at inference to improve generalization and sample efficiency. MSG is model-agnostic and inference-only, hence widely applicable to various generative policies and training paradigms. We perform extensive experiments both in simulation and on a real robot, demonstrating that our approach learns high-quality generative policies from as few as five demonstrations, resulting in a 95% reduction in demonstrations, and improves policy performance by 89 percent compared to single-stream approaches. Furthermore, we present comprehensive ablation studies on various composition strategies and provide practical recommendations for deployment. Finally, MSG enables zero-shot object instance transfer. We make our code publicly available at https://msg.cs.uni-freiburg.de.