Interpretability by Design for Efficient Multi-Objective Reinforcement Learningcs.AI updates on arXiv.org arXiv:2506.04022v2 Announce Type: replace
Abstract: Multi-objective reinforcement learning (MORL) aims at optimising several, often conflicting goals to improve the flexibility and reliability of RL in practical tasks. This is typically achieved by finding a set of diverse, non-dominated policies that form a Pareto front in the performance space. We introduce LLE-MORL, an approach that achieves interpretability by design by utilising a training scheme based on the local relationship between the parameter space and the performance space. By exploiting a locally linear map between these spaces, our method provides an interpretation of policy parameters in terms of the objectives, and this structured representation enables an efficient search within contiguous solution domains, allowing for the rapid generation of high-quality solutions without extensive retraining. Experiments across diverse continuous control domains demonstrate that LLE-MORL consistently achieves higher Pareto front quality and efficiency than state-of-the-art approaches.
arXiv:2506.04022v2 Announce Type: replace
Abstract: Multi-objective reinforcement learning (MORL) aims at optimising several, often conflicting goals to improve the flexibility and reliability of RL in practical tasks. This is typically achieved by finding a set of diverse, non-dominated policies that form a Pareto front in the performance space. We introduce LLE-MORL, an approach that achieves interpretability by design by utilising a training scheme based on the local relationship between the parameter space and the performance space. By exploiting a locally linear map between these spaces, our method provides an interpretation of policy parameters in terms of the objectives, and this structured representation enables an efficient search within contiguous solution domains, allowing for the rapid generation of high-quality solutions without extensive retraining. Experiments across diverse continuous control domains demonstrate that LLE-MORL consistently achieves higher Pareto front quality and efficiency than state-of-the-art approaches. Read More
Learning to Share: Selective Memory for Efficient Parallel Agentic Systemscs.AI updates on arXiv.org arXiv:2602.05965v1 Announce Type: cross
Abstract: Agentic systems solve complex tasks by coordinating multiple agents that iteratively reason, invoke tools, and exchange intermediate results. To improve robustness and solution quality, recent approaches deploy multiple agent teams running in parallel to explore diverse reasoning trajectories. However, parallel execution comes at a significant computational cost: when different teams independently reason about similar sub-problems or execute analogous steps, they repeatedly perform substantial overlapping computation. To address these limitations, in this paper, we propose Learning to Share (LTS), a learned shared-memory mechanism for parallel agentic frameworks that enables selective cross-team information reuse while controlling context growth. LTS introduces a global memory bank accessible to all teams and a lightweight controller that decides whether intermediate agent steps should be added to memory or not. The controller is trained using stepwise reinforcement learning with usage-aware credit assignment, allowing it to identify information that is globally useful across parallel executions. Experiments on the AssistantBench and GAIA benchmarks show that LTS significantly reduces overall runtime while matching or improving task performance compared to memory-free parallel baselines, demonstrating that learned memory admission is an effective strategy for improving the efficiency of parallel agentic systems. Project page: https://joefioresi718.github.io/LTS_webpage/
arXiv:2602.05965v1 Announce Type: cross
Abstract: Agentic systems solve complex tasks by coordinating multiple agents that iteratively reason, invoke tools, and exchange intermediate results. To improve robustness and solution quality, recent approaches deploy multiple agent teams running in parallel to explore diverse reasoning trajectories. However, parallel execution comes at a significant computational cost: when different teams independently reason about similar sub-problems or execute analogous steps, they repeatedly perform substantial overlapping computation. To address these limitations, in this paper, we propose Learning to Share (LTS), a learned shared-memory mechanism for parallel agentic frameworks that enables selective cross-team information reuse while controlling context growth. LTS introduces a global memory bank accessible to all teams and a lightweight controller that decides whether intermediate agent steps should be added to memory or not. The controller is trained using stepwise reinforcement learning with usage-aware credit assignment, allowing it to identify information that is globally useful across parallel executions. Experiments on the AssistantBench and GAIA benchmarks show that LTS significantly reduces overall runtime while matching or improving task performance compared to memory-free parallel baselines, demonstrating that learned memory admission is an effective strategy for improving the efficiency of parallel agentic systems. Project page: https://joefioresi718.github.io/LTS_webpage/ Read More
Vibe AIGC: A New Paradigm for Content Generation via Agentic Orchestrationcs.AI updates on arXiv.org arXiv:2602.04575v2 Announce Type: replace
Abstract: For the past decade, the trajectory of generative artificial intelligence (AI) has been dominated by a model-centric paradigm driven by scaling laws. Despite significant leaps in visual fidelity, this approach has encountered a “usability ceiling” manifested as the Intent-Execution Gap (i.e., the fundamental disparity between a creator’s high-level intent and the stochastic, black-box nature of current single-shot models). In this paper, inspired by the Vibe Coding, we introduce the textbf{Vibe AIGC}, a new paradigm for content generation via agentic orchestration, which represents the autonomous synthesis of hierarchical multi-agent workflows.
Under this paradigm, the user’s role transcends traditional prompt engineering, evolving into a Commander who provides a Vibe, a high-level representation encompassing aesthetic preferences, functional logic, and etc. A centralized Meta-Planner then functions as a system architect, deconstructing this “Vibe” into executable, verifiable, and adaptive agentic pipelines. By transitioning from stochastic inference to logical orchestration, Vibe AIGC bridges the gap between human imagination and machine execution. We contend that this shift will redefine the human-AI collaborative economy, transforming AI from a fragile inference engine into a robust system-level engineering partner that democratizes the creation of complex, long-horizon digital assets.
arXiv:2602.04575v2 Announce Type: replace
Abstract: For the past decade, the trajectory of generative artificial intelligence (AI) has been dominated by a model-centric paradigm driven by scaling laws. Despite significant leaps in visual fidelity, this approach has encountered a “usability ceiling” manifested as the Intent-Execution Gap (i.e., the fundamental disparity between a creator’s high-level intent and the stochastic, black-box nature of current single-shot models). In this paper, inspired by the Vibe Coding, we introduce the textbf{Vibe AIGC}, a new paradigm for content generation via agentic orchestration, which represents the autonomous synthesis of hierarchical multi-agent workflows.
Under this paradigm, the user’s role transcends traditional prompt engineering, evolving into a Commander who provides a Vibe, a high-level representation encompassing aesthetic preferences, functional logic, and etc. A centralized Meta-Planner then functions as a system architect, deconstructing this “Vibe” into executable, verifiable, and adaptive agentic pipelines. By transitioning from stochastic inference to logical orchestration, Vibe AIGC bridges the gap between human imagination and machine execution. We contend that this shift will redefine the human-AI collaborative economy, transforming AI from a fragile inference engine into a robust system-level engineering partner that democratizes the creation of complex, long-horizon digital assets. Read More
How to Build a Production-Grade Agentic AI System with Hybrid Retrieval, Provenance-First Citations, Repair Loops, and Episodic MemoryMarkTechPost In this tutorial, we build an ultra-advanced agentic AI workflow that behaves like a production-grade research and reasoning system rather than a single prompt call. We ingest real web sources asynchronously, split them into provenance-tracked chunks, and run hybrid retrieval using both TF-IDF (sparse) and OpenAI embeddings (dense), then fuse results for higher recall and
The post How to Build a Production-Grade Agentic AI System with Hybrid Retrieval, Provenance-First Citations, Repair Loops, and Episodic Memory appeared first on MarkTechPost.
In this tutorial, we build an ultra-advanced agentic AI workflow that behaves like a production-grade research and reasoning system rather than a single prompt call. We ingest real web sources asynchronously, split them into provenance-tracked chunks, and run hybrid retrieval using both TF-IDF (sparse) and OpenAI embeddings (dense), then fuse results for higher recall and
The post How to Build a Production-Grade Agentic AI System with Hybrid Retrieval, Provenance-First Citations, Repair Loops, and Episodic Memory appeared first on MarkTechPost. Read More
Is Your Machine Learning Pipeline as Efficient as it Could Be?KDnuggets Here are five critical pipeline areas to audit, with practical strategies to reclaim your team’s time.
Here are five critical pipeline areas to audit, with practical strategies to reclaim your team’s time. Read More
Making AI work for everyone, everywhere: our approach to localizationOpenAI News OpenAI shares its approach to AI localization, showing how globally shared frontier models can be adapted to local languages, laws, and cultures without compromising safety.
OpenAI shares its approach to AI localization, showing how globally shared frontier models can be adapted to local languages, laws, and cultures without compromising safety. Read More
Pydantic Performance: 4 Tips on How to Validate Large Amounts of Data EfficientlyTowards Data Science The real value lies in writing clearer code and using your tools right
The post Pydantic Performance: 4 Tips on How to Validate Large Amounts of Data Efficiently appeared first on Towards Data Science.
The real value lies in writing clearer code and using your tools right
The post Pydantic Performance: 4 Tips on How to Validate Large Amounts of Data Efficiently appeared first on Towards Data Science. Read More
Evaluate generative AI models with an Amazon Nova rubric-based LLM judge on Amazon SageMaker AI (Part 2)Artificial Intelligence In this post, we explore the Amazon Nova rubric-based judge feature: what a rubric-based judge is, how the judge is trained, what metrics to consider, and how to calibrate the judge. We chare notebook code of the Amazon Nova rubric-based LLM-as-a-judge methodology to evaluate and compare the outputs of two different LLMs using SageMaker training jobs.
In this post, we explore the Amazon Nova rubric-based judge feature: what a rubric-based judge is, how the judge is trained, what metrics to consider, and how to calibrate the judge. We chare notebook code of the Amazon Nova rubric-based LLM-as-a-judge methodology to evaluate and compare the outputs of two different LLMs using SageMaker training jobs. Read More
Scientists create smart synthetic skin that can hide images and change shapeArtificial Intelligence News — ScienceDaily Inspired by the shape-shifting skin of octopuses, Penn State researchers developed a smart hydrogel that can change appearance, texture, and shape on command. The material is programmed using a special printing technique that embeds digital instructions directly into the skin. Images and information can remain invisible until triggered by heat, liquids, or stretching.
Inspired by the shape-shifting skin of octopuses, Penn State researchers developed a smart hydrogel that can change appearance, texture, and shape on command. The material is programmed using a special printing technique that embeds digital instructions directly into the skin. Images and information can remain invisible until triggered by heat, liquids, or stretching. Read More
Waymo Introduces the Waymo World Model: A New Frontier Simulator Model for Autonomous Driving and Built on Top of Genie 3MarkTechPost Waymo is introducing the Waymo World Model, a frontier generative model that drives its next generation of autonomous driving simulation. The system is built on top of Genie 3, Google DeepMind’s general-purpose world model, and adapts it to produce photorealistic, controllable, multi-sensor driving scenes at scale. Waymo already reports nearly 200 million fully autonomous miles
The post Waymo Introduces the Waymo World Model: A New Frontier Simulator Model for Autonomous Driving and Built on Top of Genie 3 appeared first on MarkTechPost.
Waymo is introducing the Waymo World Model, a frontier generative model that drives its next generation of autonomous driving simulation. The system is built on top of Genie 3, Google DeepMind’s general-purpose world model, and adapts it to produce photorealistic, controllable, multi-sensor driving scenes at scale. Waymo already reports nearly 200 million fully autonomous miles
The post Waymo Introduces the Waymo World Model: A New Frontier Simulator Model for Autonomous Driving and Built on Top of Genie 3 appeared first on MarkTechPost. Read More