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1. Conformal Temporal Logic Planning using Large Language Models

   https://doi.org/10.1145/3769111  

   Wang, Jun; Tong, Jiaming; Tan, Kaiyuan; Vorobeychik, Yevgeniy; Kantaros, Yiannis (September 2025, ACM Transactions on Cyber-Physical Systems)

   This paper addresses temporal logic task planning problems for mobile robots. We consider missions that require accomplishing multiple high-level sub-tasks, expressed in natural language (NL), in a temporal and logical order. To formally define the mission, we treat these sub-tasks as atomic predicates in a Linear Temporal Logic (LTL) formula. We refer to this task specification framework as LTL-NL. Our goal is to design plans, defined as sequences of robot actions, accomplishing LTL-NL tasks. This action planning problem cannot be solved directly by existing LTL planners due to the NL nature of atomic predicates. Therefore, we propose HERACLEs, a hierarchical neuro-symbolic planner that relies on a novel integration of (i) existing symbolic planners generating high-level task plans determining the order at which the NL sub-tasks should be accomplished; (ii) pre-trained Large Language Models (LLMs) to design sequences of robot actions for each sub-task in these task plans; and (iii) conformal prediction acting as a formal interface between (i) and (ii) and managing uncertainties due to LLM imperfections. We show, both theoretically and empirically, that HERACLEs can achieve user-defined mission success rates. We demonstrate the efficiency of HERACLEs through comparative numerical experiments against recent LLM-based planners as well as hardware experiments on mobile manipulation tasks. Finally, we present examples demonstrating that our approach enhances user-friendliness compared to conventional symbolic approaches. 

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2. Race, Planning, and Emergency Management: Combating Inequities in Community Planning Policies

   https://doi.org/10.1177/08854122231188145  

   McKnight, Steve (May 2024, Journal of Planning Literature)

   Disaster preparedness and relief is a key service provided by the American system of government, but emergency management practices have produced varying outcomes. Based on the existing literature, emergency management, community planning, and a history of racial inequities are inextricably interconnected. Based on the concepts presented by social equity, community planning, emergency management, and environmental policy literature, an interdisciplinary approach was used to create an original checklist of considerations for emergency managers, community planners, and policymakers to use in a collaborative manner to proactively address potential threats to their constituents and communities. 

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3. SafeNet: A Neural-Symbolic Network for Safe Planning in Robotic Systems using Formal Method-Guided LLM Fine-Tuning

   Wang, Zifan; Fan, Jialiang; Zuo, Rui; Qiu, Qinru; Kong, Fanxin (June 2026, IEEE)

   Robotic systems present unique safety challenges due to their complex integration of computational and physical processes and direct interaction with humans and environments. Traditional approaches to robot safety planning either rely on conventional methods, which struggle with the complexity of modern robotic systems, or on pure machine learning techniques, which lack formal safety guarantees. While recent advances in Large Language Models (LLMs) offer promising capabilities, pre-trained LLMs alone lack the specific domain expertise required for effective robotic safety planning. This paper introduces SafeNet, a novel neural-symbolic network architecture that enhances LLMs' safety planning capabilities through formal method-guided fine-tuning for robotic applications. Our approach integrates formal logical knowledge and reward machines into pre-trained LLMs by carefully designed fine-tuning, creating a neural-symbolic approach that combines the flexibility of neural networks with the precision of formal methods for robot trajectory generation and task planning. Experimental results demonstrate significant improvements in safe trajectory generation for robotic systems, with planning success rates increasing from 1.17% to 91.60% for the block manipulation task and from 7.23% to 90.63% for the robotic path planning task. 

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4. Grounding Vision and Language to 3D Masks for Long-Horizon Box Rearrangement

   https://doi.org/10.1609/icaps.v36i1.42893  

   Malik, Ashish; Lowe, Caleb; Shrestha, Aayam; Lee, Stefan; Li, Fuxin; Fern, Alan (June 2026, Proceedings of the International Conference on Automated Planning and Scheduling)

   We study long-horizon planning in 3D environments from under-specified natural-language goals using only visual observations, focusing on multi-step 3D box rearrangement tasks. Existing approaches typically rely on symbolic planners with brittle relational grounding of states and goals, or on direct action-sequence generation from 2D vision-language models (VLMs). Both approaches struggle with reasoning over many objects, rich 3D geometry, and implicit semantic constraints. Recent advances in 3D VLMs demonstrate strong grounding of natural-language referents to 3D segmentation masks, suggesting the potential for more general planning capabilities. We extend existing 3D grounding models and propose Reactive Action Mask Planner (RAMP-3D), which formulates long-horizon planning as sequential reactive prediction of paired 3D masks: a which-object mask indicating what to pick and a which-target-region mask specifying where to place it. The resulting system processes RGB-D observations and natural-language task specifications to reactively generate multi-step pick-and-place actions for 3D box rearrangement. We conduct experiments across 11 task variants in warehouse-style environments with 1-30 boxes and diverse natural-language constraints. RAMP-3D achieves 79.5% success rate on long-horizon rearrangement tasks and significantly outperforms 2D VLM-based baselines, establishing mask-based reactive policies as a promising alternative to symbolic pipelines for long-horizon planning. 

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5. Verbal interference paradigms: A systematic review investigating the role of language in cognition

   https://doi.org/doi.org/10.3758/s13423-022-02144-7  

   Nedergaard, J.S.K.; Wallentin, M.; Lupyan, G. (January 2022, Psychonomic bulletin review)

   This paper presents a systematic review of the empirical literature that uses dual-task interference methods for investigating the on-line involvement of language in various cognitive tasks. In these studies, participants perform some primary task X putatively recruiting linguistic resources while also engaging in a secondary, concurrent task. If performance on the primary task decreases under interference, there is evidence for language involvement in the primary task. We assessed studies (N = 101) reporting at least one experiment with verbal interference and at least one control task (either primary or secondary). We excluded papers with an explicitly clinical, neurological, or developmental focus. The primary tasks identified include categorization, memory, mental arithmetic, motor control, reasoning (verbal and visuospatial), task switching, theory of mind, visual change, and visuospatial integration and wayfinding. Overall, the present review found that internal language is likely to play a facilitative role in memory and categorization when items to be remembered or categorized have readily available labels, when inner speech can act as a form of behavioral self-cuing (inhibitory control, task set reminders, verbal strategy), and when inner speech is plausibly useful as “workspace,” for example, for mental arithmetic. There is less evidence for the role of internal language in cross-modal integration, reasoning relying on a high degree of visual detail or items low on nameability, and theory of mind. We discuss potential pitfalls and suggestions for streamlining and improving the methodology. 

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