Soul Computing: A Theoretical Framework and Technical Architecture for Intelligent Agents with Independent Consciousness

Paper 2 has higher potential scientific impact because it proposes a new, actionable technical framework (“Soul Computing”) and architecture for next-generation intelligent agents, which could influence research agendas in AI, HCI, digital humans, and ethics. It is timely given rapid advances in multimodal/LLM systems and has clearer pathways to real-world applications (agent design, identity/memory systems). Paper 1 is largely metaphysical and normative, with limited methodological rigor or testable claims, making its impact more likely confined to philosophy/ethics discourse.

Paper 2, despite being modest in scope, provides concrete empirical evaluation of LLM-based planning (PlanGPT), offering reproducible benchmarks and a clear finding that PlanGPT performs no better than greedy search. This has immediate practical value for the AI planning community by tempering hype around LLMs for planning tasks. Paper 1 proposes a speculative theoretical framework ('Soul Computing') with grandiose claims about AI consciousness but lacks empirical grounding, conflates philosophical concepts with engineering, and its core contributions are primarily definitional rather than scientifically testable, limiting its real impact.

Paper 2 demonstrates higher scientific impact through its rigorous empirical methodology, quantifiable results, and immediate real-world applicability in supply chain resilience. It effectively bridges LLMs and reinforcement learning to solve a concrete, timely problem. In contrast, Paper 1 presents a highly speculative theoretical framework regarding 'independent consciousness' and 'Soul Computing' which, while philosophically interesting, lacks empirical grounding and near-term technical viability, making its practical scientific impact much lower.

Paper 1 is more likely to yield measurable scientific impact: it targets a well-defined, practical bottleneck (external-memory heuristic search), evaluates understudied baseline IDD methods, and investigates OS-level caching effects—producing reproducible empirical insights that can influence search/planning systems and performance engineering. Paper 2 is largely conceptual/theoretical, introduces new terminology (“Soul Computing”), and makes broad claims about consciousness/agency with unclear formalization, testable hypotheses, or validation pathways, limiting methodological rigor and near-term adoption despite timeliness.

ComBench offers a concrete, reproducible benchmark with empirical results on frontier LLMs, addressing a well-defined gap in evaluating combinatorial reasoning. It provides actionable diagnostics and a clear evaluation protocol. Paper 2 proposes a speculative theoretical framework ('Soul Computing') with vague claims about AI consciousness, lacking empirical validation, rigorous methodology, or falsifiable hypotheses. Paper 1's methodological rigor, practical utility for the active LLM evaluation community, and timeliness give it substantially higher scientific impact potential.

Paper 2 has higher likely scientific impact: it introduces a concrete, standardized, machine-gradable benchmark with clear metrics, broad utility for evaluating agentic LLMs, and directly actionable findings for automation research and industry. Its methodology (200 tasks, 7,118 criteria, multiple model baselines, sanity-check reference) supports reproducibility and rigorous comparison over time. Paper 1 is largely conceptual and definitional, with ambitious claims but unclear testable hypotheses, evaluation protocols, or implementation evidence, which limits near-term rigor and adoption despite potential long-term philosophical relevance.

Paper 1 presents a concrete, novel methodological integration (MCTS-style counterfactual search + learned world/value models) enabled by rare 3D tracking data, with measurable evaluation, model adaptations, and released code/checkpoints—supporting reproducibility and near-term uptake in sports analytics and trajectory-modeling research. Its approach is timely (world models, counterfactual evaluation), has clear real-world applications (player/team decision analysis), and can generalize to other multi-agent domains. Paper 2 is largely conceptual/theoretical with unclear formalism, validation, or implementable methodology, making near-term scientific impact less likely.

Paper 1 presents a concrete, implementable system (Infini Memory) with empirical evaluation on a benchmark, addressing a practical and timely problem in LLM agent memory. It offers reproducible methodology and measurable results. Paper 2 is a theoretical/conceptual framework paper on 'Soul Computing' that, while ambitious in scope, lacks empirical validation, remains largely speculative, and conflates philosophical concepts with engineering claims. Paper 1's practical contributions to the active LLM agents research community give it higher near-term and likely long-term scientific impact.

Paper 1 presents a rigorous, empirically grounded approach utilizing real-world datasets (NHTSA, DMV) to address critical, immediate challenges in autonomous vehicle deployment. Its integration of engineering, ethics, and policy offers high real-world applicability and timeliness. In contrast, Paper 2 proposes a highly speculative theoretical framework ('Soul Computing') that lacks empirical validation and makes scientifically contentious claims about AI 'consciousness' and 'living agency'. Paper 1's solid methodological rigor, actionable recommendations, and immediate relevance give it a significantly higher potential for credible scientific and societal impact.

Paper 1 has higher likely scientific impact because it presents a concrete, testable methodological contribution (memory-augmented neural networks for AIS vessel trajectory prediction) with empirical validation on real datasets and demonstrated gains over baselines, enabling immediate applications in maritime safety and logistics. Paper 2 is largely conceptual and terminological (“Soul Computing”), with unclear operational definitions, limited falsifiability, and no demonstrated technical results, making near-term uptake and measurable scientific progress less likely despite its broad ambitions.