arXiv subjects

Bin Ling

Publications and source records attributed to Bin Ling.

5 recordsLinked to original sources

arXiv2026

Modeling Decision-Making with Will for Cooperation in Social Dilemmas

Standard rational actor models often attribute cooperation failures in social dilemmas to insufficient incentives, overlooking the destabilizing effects of continuous utility maximization. To address this, we propose a framework of ``will" defined as a mechanism that persistently pursues goals while ignoring local cost-benefit fluctuations. We formalize the Willed Agents as potential minimizers, distinguishing them from cumulative utility maximization. Dynamical analysis of infinite population demonstrates that willed agents shrink the feasible state space, acting as boundary constraints that accelerate convergence in canonical social dilemmas. Through multi-agent simulations in a spatiotemporal Stag Hunt Game, we show that willed agents function as ``cooperation catalysts", enabling groups to surmount high-risk thresholds where purely utility maximization fails. We find that heterogeneous will strength promotes cooperation, and that agents who autonomously suspend rational re-evaluation can significantly outperform continuous optimizers. These findings suggest that successful cooperation relies on the cognitive capacity to strategically constrain calculation.

cs.MA↗

arXiv2025

A Human-centric Framework for Debating the Ethics of AI Consciousness Under Uncertainty

As AI systems become increasingly sophisticated, questions about machine consciousness and its ethical implications have moved from fringe speculation to mainstream academic debate. Current ethical frameworks in this domain often implicitly rely on contested functionalist assumptions, prioritize speculative AI welfare over concrete human interests, and lack coherent theoretical foundations. We address these limitations through a structured three-level framework grounded in philosophical uncertainty. At the foundational level, we establish five factual determinations about AI consciousness alongside human-centralism as our meta-ethical stance. These foundations logically entail three operational principles: presumption of no consciousness (placing the burden of proof on consciousness claims), risk prudence (prioritizing human welfare under uncertainty), and transparent reasoning (enabling systematic evaluation and adaptation). At the application level, the third component of our framework, we derive default positions on pressing ethical questions through a transparent logical process where each position can be explicitly traced back to our foundational commitments. Our approach balances philosophical rigor with practical guidance, distinguishes consciousness from anthropomorphism, and creates pathways for responsible evolution as scientific understanding advances, providing a human-centric foundation for navigating these profound ethical challenges.

cs.CY↗

arXiv2023

Chatlaw: A Multi-Agent Legal Assistant based on a Role-Aligned Mixture-of-Experts Architecture

Artificial Intelligence (AI) holds great potential in legal services, yet Large Language Models (LLMs) face two major challenges: limited knowledge of the Chinese legal system and vulnerability to hallucinations. To address these issues, we present Chatlaw, a multi-agent legal assistant. Chatlaw's framework is designed to emulate the Standard Operating Procedures (SOP) of real law firms, where different roles (e.g., assistant, researcher, senior lawyer) collaborate on a case. To computationally mirror this collaborative structure, we developed a novel Role-Aligned Mixture-of-Experts (RA-MoE) architecture. In this system, the internal "experts" are specifically trained to align with the distinct tasks of each agent role (e.g., inquiry, analysis, drafting). These specialized agents (Legal Assistant, Researcher, etc.) then form the collaborative framework. When they interact with users, retrieve legal knowledge, analyze case details, or generate reliable consultations, the RA-MoE architecture intelligently routes their computations to the corresponding dedicated expert, ensuring each step is handled by the most qualified parameters. In evaluations, Chatlaw surpasses general-purpose AI models, including GPT-4, achieving a 7.73% improvement in accuracy on the LawBench benchmark and an 11-point higher score on the Unified Qualification Exam for Legal Professionals. Real-case studies and expert assessments further confirm its robustness. Chatlaw enhances the accessibility and reliability of legal services, advancing the provision of legal support to the public.

cs.CL↗

arXiv2015

TOPO: Improving remote homologue recognition via identifying common protein structure framework

Protein structure prediction remains a challenge in the field of computational biology. Traditional protein structure prediction approaches include template-based modelling (say, homology modelling, and threading), and ab initio. A threading algorithm takes a query protein sequence as input, recognizes the most likely fold, and finally reports the alignments of the query sequence to structure-known templates as output. The existing threading approaches mainly utilizes the information of protein sequence profile, solvent accessibility, contact probability, etc., and correctly recognize folds for some proteins. However, the existing threading approaches show poorly performance for remote homology proteins. How to improve the fold recognition for remote homology proteins remains to be a difficult task for protein structure prediction.

q-bio.OT↗

arXiv2014

Optimizing weights of protein energy function to improve ab initio protein structure prediction

Predicting protein 3D structure from amino acid sequence remains as a challenge in the field of computational biology. If protein structure homologues are not found, one has to construct 3D structural conformations from the very beginning by the so-called ab initio approach, using some empirical energy functions. A successful algorithm in this category, Rosetta, creates an ensemble of decoy conformations by assembling selected best short fragments of known protein structures and then recognizes the native state as the highly populated one with a very low energy. Typically, an energy function is a combination of a variety of terms characterizing different structural features, say hydrophobic interactions, van der Waals force, hydrogen bonding, etc. It is critical for an energy function to be capable to distinguish native-like conformations from non-native ones and to drive most initial conformations assembled from fragments to a native-like one in a conformation search process. In this paper we propose a linear programming algorithm to optimize weighting of a total of 14 energy terms used in Rosetta. We reverse the Monte Carlo process of Rosetta to approach native-like conformations to a process generating from the native state an ensemble of initial conformations most relevant to the native state. Intuitively, an ideal weighting scheme would result in a large "basin of attraction" of the native structure, which leads to an objective function for the linear programming. We have examined the proposal on several benchmark proteins, and the experimental results suggest that the optimized weights enlarge the attraction basin of the native state and improve the quality of the predicted native states as well. In addition, a comparison of optimal weighting schema for proteins of different classes indicates that in different protein classes energy terms may have different effects.

physics.bio-ph↗