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Texas State University

Academic institutionnorthamerica · us
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Representative Papers

Trust, but verify

Apr 18, 2025

To address uncontrolled service degradation in decentralized AI agent networks (e.g., Gaia), where nodes arbitrarily substitute or misexecute designated LLMs, this paper proposes a lightweight, peer-based social consensus mechanism for LLM execution verification. The method integrates an intersubjective validation paradigm implemented as an EigenLayer Actively Validated Service (AVS), combining on-chain incentive alignment and penalty enforcement. It further couples distributed reputation modeling with zero-knowledge verifiable inference sampling to achieve low-overhead, high-robustness consistency verification of model execution. Evaluated on the live Gaia network, the mechanism detects malicious or deviant LLM nodes with over 95% accuracy, substantially enhancing system trustworthiness and resilience against collusive attacks. Key contributions include: (i) the first instantiation of intersubjective validation as an AVS for LLM integrity; (ii) a novel synergy of cryptographic verification, reputation dynamics, and economic incentives; and (iii) empirical validation demonstrating scalability and robustness in production-deployed decentralized AI infrastructure.

4 citations1 influentialRead paper

TAB-PO: Preference Optimization with a Token-Level Adaptive Barrier for Token-Critical Structured Generation

Feb 03, 2026arXiv.org

This work addresses the challenge of gradient dilution in conventional sequence-level preference optimization for structured generation tasks, where preference and rejected samples often differ only at critical schema tokens. To enable precise alignment at these decisive points, the authors propose a token-level preference optimization framework that integrates a confusion-aware negative sample construction strategy with a confidence-gated adaptive margin mechanism, specifically targeting ontological decision errors. The approach is compatible with mainstream large language model architectures such as Llama and Qwen. Evaluated on the SciERC benchmark, it achieves an 11.59% absolute improvement in key semantic label and relation linking metrics over strong baselines, surpassing the current state-of-the-art by 14.71%, while also enhancing text grounding capabilities.

3 citationsRead paper

MS-SCANet: A Multiscale Transformer-Based Architecture with Dual Attention for No-Reference Image Quality Assessment

Apr 06, 2025IEEE International Conference on Acoustics, Speech, and Signal Processing

This work proposes a Transformer-based multi-scale dual-branch architecture to address the challenge of simultaneously modeling fine-grained multi-scale details and maintaining computational efficiency in no-reference image quality assessment. The method integrates spatial and channel-wise attention mechanisms and introduces cross-branch attention along with an adaptive pooling consistency loss to preserve spatial coherence of features across scale transformations, thereby overcoming the limitations of conventional single-scale approaches. Extensive evaluations on multiple benchmark datasets—including KonIQ-10k, LIVE, LIVE Challenge, and CSIQ—demonstrate that the proposed model significantly outperforms state-of-the-art methods, achieving notably higher correlation with human subjective quality ratings.

2 citationsRead paper
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