Vision-language models (VLMs) have demonstrated remarkable capabilities in robotic planning, particularly for long-horizon tasks that require a holistic understanding of the environment for task decomposition. Existing methods typically rely on prior environmental knowledge or carefully designed task-specific prompts, making them struggle with dynamic scene changes or unexpected task conditions. Such challenges underscore two critical issues: adaptability and efficiency. To address them, in this work, we propose an adaptive multi-agent planning framework, termed REMAC, that enables efficient, scene-agnostic multi-robot long-horizon task planning and execution through continuous reflection and self-evolvement. REMAC incorporates two key modules: a self-reflection module performing pre-condition and post-condition checks in the loop to evaluate progress and refine plans, and a self-evolvement module dynamically adapting plans based on scene-specific reasoning. It offers appealing benefits: (1) Robots can keep reflecting on potential planning errors and adapting the plan based on task-specific insights. (2) After iterations, a robot can call another one to coordinate tasks in parallel, maximizing the task execution efficiency. To validate REMAC’s effectiveness, we build a multi-agent environment for long-horizon robot manipulation and navigation based on RoboCasa, featuring 4 task categories with 27 task styles and 50+ different objects. Based on it, we further benchmark state-of-the-art reasoning models, including DeepSeek-R1, o3, QwQ, Qwen3, and Grok3. Extensive experiments demonstrate REMAC’s superiority by boosting average success rates by 40% and execution efficiency by 52.7% over the single robot baseline without any task-specific prompting or fine-tuning in initial planning. Code, demos, and appendix are available at the website https://reamac-repo.github.io/remac-project-page/.
Existing or planned power grids need to evaluate survivability under extreme events, like a number of peak load overloading conditions, which could possibly cause system collapses (i.e. blackouts). For realistic extreme events that are correlated or share similar patterns, it is reasonable to expect that the dominant vulnerability or failure sources behind them share the same locations but with different severity. Early warning diagnosis that proactively identifies the key vulnerabilities responsible for a number of system collapses of interest can significantly enhance resilience. This paper proposes a multi-period sparse optimization method, enabling the discovery of persistent failure sources across a sequence of collapsed systems with increasing system stress, such as rising demand or worsening contingencies. This work defines persistency and efficiently integrates persistency constraints to capture the “hidden” evolving vulnerabilities. Circuit-theory based power flow formulations and circuit-inspired optimization heuristics are used to facilitate the scalability of the method. Experiments on benchmark systems show that the method reliably tracks persistent vulnerability locations under increasing load stress, and solves with scalability to large systems (on average taking around 200 s per scenario on 2000+ bus systems).
In an earlier paper, we proved that Montgomery's Pair Correlation Conjecture (PCC) for zeros of the Riemann zeta-function can be used to prove without the assumption of the Riemann Hypothesis (RH) that asymptotically 100% of the zeros are both simple and on the critical line. This is based on a method of Gallagher and Mueller from 1978. We formulate an appropriate form of the Alternative Hypothesis (AH), which determines a different PCC, and, using the same method as above, prove that asymptotically, 100% of the zeros are both simple and on the critical line. As in our previous paper, we do not assume RH.
As financial incentives for EVs wane and price parity approaches, engaging mainstream adopters is critical to sustaining market growth. While early adopters are driven by environmentalism and proactive information-seeking, the broader market remains hesitant due to knowledge gaps regarding EV benefits. We evaluate how communication of state-specific emission estimates for five vehicle types (ICE, HEV, PHEV, BEV, and FCEV) influences vehicle perceptions, rankings, and fleet composition. Surveying 908 participants across four states (CA, FL, PA, TX), we find that, while emissions information shifts vehicle rankings, political identity and charging feasibility remain the primary determinants of top preferences. Furthermore, participants favor fleets with a diverse composition over a single technology. Our findings suggest that providing state-level emission estimates effectively reshapes consumer preferences and reduces the desired share of high-emission vehicles. This research provides a strategic framework for policymakers and manufacturers to sustain market momentum through transparency and targeted communication.
We give a geometric model for the non-τ-rigid modules over acyclic path algebras of type D˜n. Similar models have been provided for module categories over path algebras of types An,Dn, and A˜n as well as the τ-rigid modules of type D˜n. A major draw of these geometric models is the “intersection-dimension formulas” they often come with. These formulas give an equality between the intersection number of the curves representing the modules in the geometric model and the dimension of the extension spaces between the two modules. This formula allows us to calculate the homological data between two modules combinatorially. Since there are infinitely many distinct homogeneous stable tubes in the regular component of the Auslander-Reiten quiver of type D˜n, all of which are disjoint, our geometric data requires an extra decoration on the admissible edges in our geometric model to prevent intersections between curves corresponding to modules in distinct stable tubes of the Auslander-Reiten quiver.
