The Value Iteration Network (VIN) is an end-to-end differentiable neural network architecture for planning. It exhibits strong generalization to unseen domains by incorporating a differentiable planning module that operates on a latent Markov Decision Process (MDP). However, VINs struggle to scale to long-term and large-scale planning tasks, such as navigating a $100\times 100$ maze---a task that typically requires thousands of planning steps to solve. We observe that this deficiency is due to two issues: the representation capacity of the latent MDP and the planning module's depth. We address these by augmenting the latent MDP with a dynamic transition kernel, dramatically improving its representational capacity, and, to mitigate the vanishing gradient problem, introduce an "adaptive highway loss" that constructs skip connections to improve gradient flow. We evaluate our method on 2D/3D maze navigation environments, continuous control, and the real-world Lunar rover navigation task. We find that our new method, named Dynamic Transition VIN (DT-VIN), scales to 5000 layers and solves challenging versions of the above tasks. Altogether, we believe that DT-VIN represents a concrete step forward in performing long-term large-scale planning in complex environments.

Contemporary evaluation techniques are inadequate for agentic systems. These approaches either focus exclusively on final outcomes—ignoring the step-by-step nature of the thinking done by agentic systems—or require excessive manual labour. To address this, we introduce the Agent-as-a-Judge framework, wherein agentic systems are used to evaluate agentic systems. This is a natural extension of the LLM-as-a-Judge framework, incorporating agentic features that enable intermediate feedback for the entire task-solving processes for more precise evaluations. We apply the Agent-as-a-Judge framework to the task of code generation. To overcome issues with existing benchmarks and provide a proof-of-concept testbed for Agent-as-a-Judge, we present DevAI, a new benchmark of 55 realistic AI code generation tasks. DevAI includes rich manual annotations, like a total of 365 hierarchical solution requirements, which make it particularly suitable for an agentic evaluator. We benchmark three of the top code-generating agentic systems using Agent-as-a-Judge and find that our framework dramatically outperforms LLM-as-a-Judge and is as reliable as our human evaluation baseline. Altogether, we believe that this work represents a concrete step towards enabling vastly more sophisticated agentic systems. To help that, our dataset and the full implementation of Agent-as-a-Judge will be publically available at https://github.com/metauto-ai/agent-as-a-judge

Advanced machine learning algorithms require platforms that are extremely robust and equipped with rich sensory feedback to handle extensive trial-and-error learning without relying on overwhelming inductive biases. Traditional robotic designs, while well-suited for their specific use cases, are often fragile when used with these algorithms as they fail to address the intermediate sub-optimal posterior-based behavior these algorithms exhibit. To address this gap—and inspired by the vision of enabling curiosity-driven baby robots—we present a novel robotic limb designed from scratch. Our design features a semi-soft structure, a high degree of redundancy achieved through rich non-contact sensors (exclusively cameras), and strategically designed, easily replaceable failure points. Proof-of-concept experiments using two contemporary reinforcement learning algorithms on a physical prototype demonstrate that our design is able to succeed in a target-finding task even under simulated sensor failures, all with minimal human oversight during extended learning periods. Additional experiments on the robustness of the design show that it is able to withstand relatively large amounts of mechanical stress. We believe this design represents a concrete step toward more tailored robotic designs capable of supporting general-purpose, generally intelligent robots.

Inspired by Minsky's Society of Mind, Schmidhuber's Learning to Think, and other more recent works, this paper proposes and advocates for the concept of natural language-based societies of mind (NLSOMs). We imagine these societies as consisting of a collection of multimodal neural networks, including large language models, which engage in a “mindstorm” to solve problems using a shared natural language interface. Here, we work to identify and discuss key questions about the social structure, governance, and economic principles for NLSOMs, emphasizing their impact on the future of AI. Our demonstrations with NLSOMs-which feature up to 129 agents-show their effectiveness in various tasks, including visual question answering, image captioning, and prompt generation for text-to-image synthesis.

The act of telling stories is a fundamental part of what it means to be human. This work introduces the concept of narrative information, which we define as the overlap in information space between a story and the items that compose the story. Using contrastive learning methods, we show how modern artificial neural networks can be leveraged to distill stories and extract a representation of the narrative information. We then demonstrate how evolutionary algorithms can leverage this to extract a set of narrative template curves and how these-in tandem with a novel curve-fitting algorithm we introduce-can reorder music albums to automatically induce stories in them. In doing so, we give statistically significant evidence that (1) these narrative information template curves are present in existing albums and that (2) people prefer an album ordered through one of these learned template curves over a random one. The premises of our work extend to any form of (largely) independent media, and as evidence, we also show that our method works with image data.

Reward-Weighted Regression (RWR) belongs to a family of widely known iterative Reinforcement Learning algorithms based on the Expectation-Maximization framework. In this family, learning at each iteration consists of sampling a batch of trajectories using the current policy and fitting a new policy to maximize a return-weighted log-likelihood of actions. Although RWR is known to yield monotonic improvement of the policy under certain circumstances, whether and under which conditions RWR converges to the optimal policy have remained open questions. In this paper, we provide for the first time a proof that RWR converges to a global optimum when no function approximation is used, in a general compact setting. Furthermore, for the simpler case with finite state and action spaces we prove R-linear convergence of the state-value function to the optimum.

Procedural content generation (PCG) is an active area of research with the potential to significantly reduce game development costs as well as create game experiences meaningfully personalized to each player. Evolutionary methods are a promising method of generating content procedurally. In particular asynchronous evolution of AI agents in an artificial life (A-life) setting is notably similar to the online evolution of non-playable characters in a video game. In this paper, we are concerned with improving the efficiency of evolution via more effective mate selection. In the spirit of PCG, we genetically encode each agent's preference for mating partners and thereby allowing the mate-selection process to evolve. We evaluate this approach in a simple predator-prey A-life environment and demonstrate that the ability to evolve a per-agent mate-selection preference function indeed significantly increases the extinction time of the population. Additionally, an inspection of the evolved preference function parameters shows that agents evolve to favor mates who have survival traits.

A proper evaluation of techniques that require multiple training and evaluation executions of a benchmark, such as Feedback-Directed Optimization (FDO), requires multiple workloads that can be used to characterize variations on the behaviour of a program based on the workload. This paper aims to improve the performance evaluation of computer systems - including compilers, computer architecture simulation, and operating-system prototypes - that rely on the industrystandard SPEC CPU benchmark suite. A main concern with the use of this suite in research is that it is distributed with a very small number of workloads. This paper describes the process to create additional workloads for this suite and offers useful insights in many of its benchmarks. The set of additional workloads created, named the Alberta Workloads for the SPEC CPU 2017 Benchmark Suite1 is made freely available with the goal of providing additional data points for the exploration of learning in computing systems. These workloads should also contribute to ameliorate the hidden learning problem where a researcher sets parameters to a system during development based on a set of benchmarks and then evaluates the system using the very same set of benchmarks with the very same workloads.