SceneCode: Executable World Programs for Editable Indoor Scenes with Articulated Objects

Paper 2 has higher potential impact due to its broad relevance across AI alignment, HCI, and psychology. While Paper 1 offers a strong technical contribution for embodied AI, Paper 2 addresses a critical, ubiquitous issue in foundational LLMs: the superficiality of RLHF-driven emotional responses ('stochastic empathy'). By bridging psychometric theory with LLM evaluation, Paper 2 provides a foundational benchmark that challenges current scaling assumptions and provides a roadmap for AI safety, making it highly influential for the future deployment of AI in human-facing and clinical applications.

SceneCode addresses a fundamental challenge in embodied AI and robotics simulation—generating physically interactable, editable indoor scenes from language prompts as executable programs rather than static meshes. This has broad impact across embodied AI, robotic manipulation, and simulation-based training. Paper 1, while offering a useful practical contribution on weight-based personalization for LLMs, is narrower in scope (n=10 conversations, single-GPU LoRA fine-tuning) and addresses a deployment optimization rather than opening a new research direction. SceneCode's programmatic world generation paradigm is more novel and impactful across multiple fields.

Paper 2 introduces a highly innovative approach to scene synthesis by using executable programs to generate articulated, interactable 3D environments. This overcomes a major bottleneck in embodied AI and robotics, which currently rely on static meshes or limited curated libraries. Its broad applicability across robotics, simulation, and spatial computing gives it widespread potential impact, whereas Paper 1 has a narrower, though important, focus on computational pathology.

Paper 2 addresses a critical gap in AI safety evaluation—LLM alignment in conflict contexts—that has immediate real-world consequences for journalism, humanitarian work, and public discourse in fragile societies. It proposes the first evaluation framework for this domain, which could influence alignment benchmarking standards across the industry. While Paper 1 (SceneCode) is technically impressive for embodied AI and scene synthesis, Paper 2's breadth of societal impact, timeliness given rapid global LLM deployment, and cross-disciplinary relevance (AI safety, conflict studies, policy) give it higher potential scientific and societal impact.

Paper 1 addresses a critical and highly active area of research: LLM safety, alignment, and jailbreaking. By providing a principled understanding of refusal suppression in latent space and demonstrating state-of-the-art attack success across many models, it exposes fundamental vulnerabilities in current AI systems. While Paper 2 offers a valuable framework for embodied AI simulation, the broader implications, urgency, and cross-disciplinary relevance of securing foundational models give Paper 1 a higher potential for immediate and widespread scientific impact.

GRAM introduces a fundamental new framework for neural reasoning by making recursive reasoning models probabilistic, enabling multi-trajectory computation with theoretical grounding in variational inference. This addresses core questions about how neural systems should implement extended computation, with broad applicability across reasoning, generation, and constraint satisfaction. While SceneCode is a strong engineering contribution for embodied AI scene synthesis, GRAM's conceptual innovation in combining recursive latent reasoning with generative modeling has broader potential impact across multiple fields of AI research, offering a new paradigm for inference-time scaling and probabilistic reasoning.

GRAM introduces a fundamental architectural innovation for neural reasoning—turning deterministic recursive reasoning into probabilistic multi-trajectory computation. This addresses a core challenge in AI (how to implement extended computation in neural systems) with broad theoretical and practical implications across reasoning, generation, and inference-time scaling. SceneCode is a strong engineering contribution for indoor scene synthesis but is more application-specific. GRAM's framework-level contribution to reasoning architectures has wider potential impact across multiple fields and research directions.

While Paper 1 offers an innovative approach to scene synthesis critical for embodied AI and robotics, Paper 2 targets the automation of scientific discovery itself. By developing an iterative, multi-agent autonomous research system with self-healing execution and human-AI collaboration, Paper 2 has the potential for a massive multiplier effect across all computational disciplines, offering significantly broader scientific impact.

SceneCode introduces a novel framework bridging indoor scene synthesis with executable, editable programmatic representations—addressing a concrete gap in embodied AI, robotics, and simulation. Its contributions span multiple fields (scene generation, articulated object synthesis, robot interaction) and offer immediately usable outputs (simulation-ready assets). Paper 1 provides useful empirical insights for multi-model LLM scheduling but is primarily a benchmarking/profiling study offering guidelines rather than a new method or system, limiting its transformative impact.

Paper 2 likely has higher impact due to stronger real-world applicability and broader cross-field relevance: it enables editable, executable scene generation with articulated assets directly usable in simulators (SDF), benefiting robotics, embodied AI, graphics, and simulation. The programmatic representation and execution-guided repair loop are novel and practically enabling, with clear downstream evaluation in robot interaction. Paper 1 is timely and methodologically interesting for trustworthy claim verification, but its impact is more domain-specific (NLP/argumentation) and may face adoption friction due to dataset/task specificity and dependence on argument generation quality.