Paper List
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MCP-AI: Protocol-Driven Intelligence Framework for Autonomous Reasoning in Healthcare
This paper addresses the critical gap in healthcare AI systems that lack contextual reasoning, long-term state management, and verifiable workflows by...
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Model Gateway: Model Management Platform for Model-Driven Drug Discovery
This paper addresses the critical bottleneck of fragmented, ad-hoc model management in pharmaceutical research by providing a centralized, scalable ML...
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Tree Thinking in the Genomic Era: Unifying Models Across Cells, Populations, and Species
This paper addresses the fragmentation of tree-based inference methods across biological scales by identifying shared algorithmic principles and stati...
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SSDLabeler: Realistic semi-synthetic data generation for multi-label artifact classification in EEG
This paper addresses the core challenge of training robust multi-label EEG artifact classifiers by overcoming the scarcity and limited diversity of ma...
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Decoding Selective Auditory Attention to Musical Elements in Ecologically Valid Music Listening
This paper addresses the core challenge of objectively quantifying listeners' selective attention to specific musical components (e.g., vocals, drums,...
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Physics-Guided Surrogate Modeling for Machine Learning–Driven DLD Design Optimization
This paper addresses the core bottleneck of translating microfluidic DLD devices from research prototypes to clinical applications by replacing weeks-...
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Mechanistic Interpretability of Antibody Language Models Using SAEs
This work addresses the core challenge of achieving both interpretability and controllable generation in domain-specific protein language models, spec...
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Fluctuating Environments Favor Extreme Dormancy Strategies and Penalize Intermediate Ones
This paper addresses the core challenge of determining how organisms should tune dormancy duration to match the temporal autocorrelation of their envi...
Dual-Laws Model for a theory of artificial consciousness
Department of mechano-informatics, The University of Tokyo, Japan
30秒速读
IN SHORT: This paper addresses the core challenge of developing a comprehensive, testable theory of consciousness that bridges biological and artificial systems, moving beyond narrow generative mechanisms to encompass functional aspects and causal efficacy.
核心创新
- Methodology Proposes seven fundamental questions (phenomena, self, causation, state, function, contents, universality) as a minimum necessary framework for evaluating consciousness theories, shifting focus from purely generative mechanisms to functional aspects.
- Theory Introduces the Dual-Laws Model (DLM) that formalizes consciousness through supervenience relationships with independent dynamics at two levels, enabling inter-level causation without relying on neural-specific implementations.
- Methodology Unifies the DLM with dual-process theories by mapping Type 1 processes to continuous feedback control at the base level and Type 2 processes to discrete algorithmic control at the supervenience level.
主要结论
- The DLM provides a formal framework where supervenient functions (X_i = b_i(x_i)) enable independent dynamics at two levels, allowing inter-level causation through negative feedback control mechanisms.
- Conscious systems require two unique capabilities: autonomy in goal construction and cognitive decoupling from external stimuli, distinguishing them from instruction-following machines.
- The theory rejects panpsychism and single-layer dynamical systems, proposing that consciousness emerges from dual-level feedback control where the supervenience level (corresponding to 'I') modifies index sequences that determine error functions.
摘要: Objectively verifying the generative mechanism of consciousness is extremely difficult because of its subjective nature. As long as theories of consciousness focus solely on its generative mechanism, developing a theory remains challenging. We believe that broadening the theoretical scope and enhancing theoretical unification are necessary to establish a theory of consciousness. This study proposes seven questions that theories of consciousness should address: phenomena, self, causation, state, function, contents, and universality. The questions were designed to examine the functional aspects of consciousness and its applicability to system design. Next, we will examine how our proposed Dual-Laws Model (DLM) can address these questions. Based on our theory, we anticipate two unique features of a conscious system: autonomy in constructing its own goals and cognitive decoupling from external stimuli. We contend that systems with these capabilities differ fundamentally from machines that merely follow human instructions. This makes a design theory that enables high moral behavior indispensable.