arXiv subjects

Chunbo Li

Publications and source records attributed to Chunbo Li.

5 recordsLinked to original sources

arXiv2024

Reflection-Bench: Evaluating Epistemic Agency in Large Language Models

With large language models (LLMs) increasingly deployed as cognitive engines for AI agents, the reliability and effectiveness critically hinge on their intrinsic epistemic agency, which remains understudied. Epistemic agency, the ability to flexibly construct, adapt, and monitor beliefs about dynamic environments, represents a base-model-level capacity independent of specific tools, modules, or applications. We characterize the holistic process underlying epistemic agency, which unfolds in seven interrelated dimensions: prediction, decision-making, perception, memory, counterfactual thinking, belief updating, and meta-reflection. Correspondingly, we propose Reflection-Bench, a cognitive-psychology-inspired benchmark consisting of seven tasks with long-term relevance and minimization of data leakage. Through a comprehensive evaluation of 16 models using three prompting strategies, we identify a clear three-tier performance hierarchy and significant limitations of current LLMs, particularly in meta-reflection capabilities. While state-of-the-art LLMs demonstrate rudimentary signs of epistemic agency, our findings suggest several promising research directions, including enhancing core cognitive functions, improving cross-functional coordination, and developing adaptive processing mechanisms. Our code and data are available at https://github.com/AI45Lab/ReflectionBench.

cs.AI↗

arXiv2024

D&A: Resource Optimisation in Personalised PageRank Computations Using Multi-Core Machines

Resource optimisation is commonly used in workload management, ensuring efficient and timely task completion utilising available resources. It serves to minimise costs, prompting the development of numerous algorithms tailored to this end. The majority of these techniques focus on scheduling and executing workloads effectively within the provided resource constraints. In this paper, we tackle this problem using another approach. We propose a novel framework D&A to determine the number of cores required in completing a workload under time constraint. We first preprocess a small portion of queries to derive the number of required slots, allowing for the allocation of the remaining workloads into each slot. We introduce a scaling factor in handling the time fluctuation issue caused by random functions. We further establish a lower bound of the number of cores required under this scenario, serving as a baseline for comparison purposes. We examine the framework by computing personalised PageRank values involving intensive computations. Our experimental results show that D&A surpasses the baseline, achieving reductions in the required number of cores ranging from 38.89% to 73.68% across benchmark datasets comprising millions of vertices and edges.

cs.DC↗

arXiv2024

Enabling self-identification in intelligent agent: insights from computational psychoanalysis

Building upon prior framework of computational Lacanian psychoanalysis with the theory of active inference, this paper aims to further explore the concept of self-identification and its potential applications. Beginning with two classic paradigms in psychology, mirror self-recognition and rubber hand illusion, we suggest that imaginary identification is characterized by an integrated body schema with minimal free energy. Next, we briefly survey three dimensions of symbolic identification (sociological, psychoanalytic, and linguistical) and corresponding active inference accounts. To provide intuition, we respectively employ a convolutional neural network (CNN) and a multi-layer perceptron (MLP) supervised by ChatGPT to showcase optimization of free energy during motor skill and language mastery underlying identification formation. We then introduce Lacan's Graph II of desire, unifying imaginary and symbolic identification, and propose an illustrative model called FreeAgent. In concluding remarks, we discuss some key issues in the potential of computational Lacanian psychoanalysis to advance mental health and artificial intelligence, including digital twin mind, large language models as avatars of the Lacanian Other, and the feasibility of human-level artificial general intelligence with self-awareness in the context of post-structuralism.

q-bio.NC↗

arXiv2023

Schizophrenia research under the framework of predictive coding: body, language, and others

Although there have been so many studies on schizophrenia under the framework of predictive coding, works focusing on treatment are very preliminary. A model-oriented, operationalist, and comprehensive understanding of schizophrenia would promote the therapy turn of further research. We summarize predictive coding models of embodiment, co-occurrence of over- and under-weighting priors, subjective time processing, language production or comprehension, self-or-other inference, and social interaction. Corresponding impairments and clinical manifestations of schizophrenia are reviewed under these models at the same time. Finally, we discuss why and how to inaugurate a therapy turn of further research under the framework of predictive coding.

q-bio.NC↗

arXiv2023

Return to Lacan: an approach to digital twin mind with free energy principle

Free energy principle (FEP) is a burgeoning theory in theoretical neuroscience that provides a universal law for modelling living systems of any scale. Expecting a digital twin mind from this first principle, we propose a macro-level interpretation that bridge neuroscience and psychoanalysis through the lens of computational Lacanian psychoanalysis. In this article, we claim three fundamental parallels between FEP and Lacanian psychoanalysis, and suggest a FEP approach to formalizing Lacan's theory. Sharing the non-linear temporal structure that combines prediction and retrospection (logical time), both of two theories focus on epistemological questions that how systems represented themselves and external world, and those elements failed to be represented (lacks and free energy) significantly influence the systems' subsequent states. Additionally, the fundamental hypothesis of FEP that the precise state of environment is always concealed, accounts for object petit a, the core concept in Lacan's theory. With neuropsychoanalytic mapping from three orders (the Real, the Symbolic, and the Imaginary, RSI) onto brain regions, we propose a brain-wide FEP model for a minimal definition of Lacanian mind - composite state of RSI that is perturbated by desire running over the logical time. The FEP-RSI model involves three FEP units connected by respective free energy with a natural compliance with logical time, mimicking core dynamics of Lacanian mind. The biological plausibility of current model is considered from perspectives of cognitive neuroscience. In conclusion, the FEP-RSI model encapsulates a unified framework for digital twin modeling at the macro level.

q-bio.NC↗