Superficial Beliefs in LLM Decision-Making

Paper 1 addresses a fundamental scientific question about LLM cognition—whether models have genuine internal decision structures versus superficial verbal rationalizations. The concept of 'superficial belief' is novel and contributes to the growing field of mechanistic interpretability and AI alignment. It uses rigorous behavioral methodology and has broad implications for trust in AI reasoning. Paper 2, while practically useful, is primarily an engineering reference architecture for enterprise governance—important for industry but narrower in scientific contribution, offering architectural patterns rather than new scientific insights.

Paper 1 addresses a fundamental question about LLM reasoning and self-knowledge—whether models truly understand the drivers of their own decisions. The concept of 'superficial belief' provides a novel theoretical framework with broad implications for AI alignment, interpretability, and trust in LLM outputs. This finding is relevant across virtually all LLM applications. Paper 2 presents a useful engineering framework for scientific discovery agents, but is more incremental and narrowly scoped. Paper 1's insights about the gap between LLM behavior and self-report have deeper, more cross-cutting implications for the field.

Paper 1 has higher estimated scientific impact due to a substantial enabling contribution: a million-scale, multi-source tactile reasoning dataset plus a new benchmark, alongside an action-aware representation tailored to tactile redundancy. These resources and methods can unlock broader progress in embodied AI, robotics manipulation, and multimodal foundation models, with clear real-world applications. Paper 2 offers a timely, rigorous behavioral analysis of LLM rationalization (“superficial belief”) with solid methodological controls, but its contribution is primarily conceptual/diagnostic without comparable new infrastructure, and likely narrower in downstream capability gains.

Paper 2 addresses a fundamental question about LLM reasoning and self-knowledge that has broad implications across AI safety, interpretability, and cognitive science. The concept of 'superficial belief' — where LLMs exhibit structured decision behavior but lack accurate self-report access — is novel and highly relevant given the widespread deployment of LLMs. This finding impacts trust, alignment, and evaluation methodologies across many fields. Paper 1, while technically solid, addresses a narrower domain-specific problem (BIM compliance checking) with more limited cross-disciplinary reach.

Paper 2 introduces a radical, paradigm-shifting theoretical framework for AI alignment, addressing core issues like deceptive alignment and instrumental convergence at their root. While Paper 1 offers valuable empirical insights into LLM interpretability, Paper 2's novel concept of 'Existential Indifference' and its bold methodological approach could fundamentally alter the trajectory of AGI safety research, offering a much higher ceiling for breadth of impact and real-world application in developing aligned superintelligence.

Paper 1 offers a clearer conceptual contribution: an empirically supported characterization (“superficial belief”) of the relationship between LLM choices and stated rationales, with controlled synthetic setups, behavioral model fitting, robustness checks, and interpretable analyses. This targets a timely, broadly relevant problem (faithfulness, interpretability, evaluation of reasoning) with impact across alignment, HCI, and cognitive modeling. Paper 2 is application/engineering-heavy (multi-agent + distillation + TTA/LoRA + formatting) and likely yields benchmark gains, but appears more incremental via composition of known techniques and its impact may be narrower to social-intelligence benchmarks.

Paper 1 addresses a fundamental question about LLM reasoning and self-knowledge—whether models truly understand their own decision processes—which has broad implications for AI interpretability, alignment, and trust. The concept of 'superficial belief' is novel and contributes to the growing body of work on LLM introspection. Paper 2, while methodologically solid and practically useful, is a benchmark contribution for a narrower subfield (tabular representation learning). Benchmarks have impact but are more incremental; Paper 1's findings about the gap between LLM behavior and self-reports have wider cross-disciplinary relevance and timeliness given current AI safety concerns.

Paper 2 likely has higher impact: it introduces a concrete, scalable systems-method (latent-space external memory with one-token evidence) that directly addresses pressing deployment constraints (token/storage costs) and demonstrates strong empirical results across many text and multimodal QA benchmarks with large efficiency gains. The approach is timely for RAG/VLM applications and broadly applicable to resource-constrained settings. Paper 1 is conceptually novel for interpretability of LLM decision structure, but its impact may be narrower and more diagnostic than enabling, with fewer immediate real-world deployments.

Paper 1 investigates a fundamental question regarding LLM interpretability and alignment, revealing a disconnect between stated rationales and actual decision drivers. This insight has profound implications for AI safety, cognitive science of LLMs, and trust in AI systems. While Paper 2 offers a valuable methodological improvement for long-horizon agents, Paper 1 addresses deeper theoretical issues with broader interdisciplinary impact.

Paper 1 addresses a fundamental question regarding the interpretability and reasoning mechanisms of LLMs, distinguishing between actual decision structures and superficial rationale generation. This foundational insight into how LLMs operate has broad implications across AI safety, alignment, and cognitive science. While Paper 2 offers a valuable, practical architectural improvement for GUI agents, Paper 1's focus on the core nature of LLM behavior provides deeper theoretical contributions with wider long-term scientific impact.