Paper List
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A Theoretical Framework for the Formation of Large Animal Groups: Topological Coordination, Subgroup Merging, and Velocity Inheritance
This paper addresses the core problem of how large, coordinated animal groups form in nature, challenging the classical view of gradual aggregation by...
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CONFIDE: Hallucination Assessment for Reliable Biomolecular Structure Prediction and Design
This paper addresses the critical limitation of current protein structure prediction models (like AlphaFold3) where high-confidence scores (pLDDT) can...
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Generative design and validation of therapeutic peptides for glioblastoma based on a potential target ATP5A
This paper addresses the critical bottleneck in therapeutic peptide design: how to efficiently optimize lead peptides with geometric constraints while...
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Pharmacophore-based design by learning on voxel grids
This paper addresses the computational bottleneck and limited novelty in conventional pharmacophore-based virtual screening by introducing a voxel cap...
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Human-Centred Evaluation of Text-to-Image Generation Models for Self-expression of Mental Distress: A Dataset Based on GPT-4o
This paper addresses the critical gap in evaluating how AI-generated images can effectively support cross-cultural mental distress communication, part...
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ANNE Apnea Paper
This paper addresses the core challenge of achieving accurate, event-level sleep apnea detection and characterization using a non-intrusive, multimoda...
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DeeDeeExperiment: Building an infrastructure for integrating and managing omics data analysis results in R/Bioconductor
This paper addresses the critical bottleneck of managing and organizing the growing volume of differential expression and functional enrichment analys...
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Cross-Species Antimicrobial Resistance Prediction from Genomic Foundation Models
This paper addresses the core challenge of predicting antimicrobial resistance across phylogenetically distinct bacterial species, where traditional m...
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.