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新论文 · v1arXiv:2611.13919

EAGLE: Early Identification of Information Propagation Sources through Generalized Bayesian Inference in Large-scale Social Networks

Tao Wen, Yu-wang Chen, Jie Zhao, Changchun Pan, Tahir Abbas Syed, Yong-Yeol Ahn, Kang Hao Cheong

原文摘要

In the era of infodemics, rapidly pinpointing information propagation sources is essential for mitigating negative impacts and enhancing societal resilience. This study presents EAGLE (Early identificAtion of propagation sources through GeneraLized Bayesian infErence), a novel and effective framework for early source identification in large-scale social networks, which integrates evolutionary computation to strategically deploy sparse observers with generalized Bayesian inference to aggregate observational data of varying credibility. Unlike existing approaches, our method relies on limited input from human observers and explicitly incorporates the uncertainty and noise of the data. Comprehensive evaluations across diverse networks and propagation scenarios demonstrate that our approach consistently surpasses state-of-the-art models, without relying on assumptions about propagation characteristics or user attributes. Utilizing real-world datasets with empirical propagation paths from Twitter, our method is among the first capable of identifying potential sources in large-scale networks using limited observers, demonstrating its broad applicability. These findings highlight the effectiveness of generalized Bayesian inference in uncovering propagation dynamics and equip organizations and policymakers with critical tools to proactively address threats to digital trust and security.

cs.SI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2611.06682

SecureGARD: Dynamic Ontology-Guided Governance for Secure Tool-Using LLM Agents

Guoqin Xiong, Shengtao Wen, Xiang Chen, Xinyi Zeng, Zhaoxin Fan, Wei Ye, Xiaofeng Cao, Sheng-Jun Huang

原文摘要

Tool-using large language model (LLM) agents translate model outputs into consequential actions, turning indirect prompt injection into an execution-security problem. Existing defenses constrain tool calls, trajectories, or information flows, but often leave action semantics, state dependencies, and evidence relations implicit, limiting generalization across heterogeneous tools and complicating safe updates. We present SecureGARD, a dynamic ontology-guided governance framework. SecureGARD grounds heterogeneous calls in a shared semantic representation and combines an executable ontology with episode-level task state to derive deterministic ready, waiting, and forbidden decisions. A waiting decision identifies an unmet but recoverable obligation, enabling bounded replanning without expanding the agent's authorization boundary. SecureGARD evolves its ontology offline from verified training executions. An offline LLM proposes instance-independent action-state axioms, which are accepted only if they address ontology errors exposed by training failures, preserve logical consistency and protected entailments, and pass held-out validation before the ontology is frozen. We evaluate SecureGARD on AgentDojo, ASB, and AgentDyn under benign and indirect prompt-injection settings. SecureGARD achieves a 0% attack success rate (ASR) across all three benchmarks while improving task utility under attack compared with existing baselines. These results show that explicit semantic governance can strengthen execution security without indiscriminate blocking.

cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.09597

RuACT: Recovering Inactive Rubrics as Adaptive Critics and Teachers

Yijun Li, Zhengxi Lu, Zhipiao Liu, Jinzheng Yu, Xutao Weng, Jian Shao, Yongliang Shen

原文摘要

Rubric-based reinforcement learning (RL) extends RL with verifiable rewards to open-ended tasks by scoring each response against query-specific rubrics. Under group-relative optimization, however, a rubric that every sampled response fails (unreachable) or passes (saturated) gives all responses the same outcome and no learning signal; in Qwen3-4B medical training, 60.88% of query-rubric observations are inactive. We find that the two states lack different supervision: stating an unreachable rubric in the prompt raises its satisfaction from 1.13% to 91.25%, whereas responses that all pass a saturated rubric still differ in quality. We therefore propose RuACT (Rubrics as Adaptive Critics and Teachers), which assigns each inactive rubric the role that supplies what it lacks. Rubric as Teacher uses unreachable rubrics as privileged information for reward-gated on-policy self-distillation, and Rubric as Critic decomposes saturated rubrics into child rubrics that separate current responses while sharing the parent's weight. RuACT outperforms GRPO, rubric reweighting, and rubric-guided exploration on all four medical and scientific benchmarks with Qwen3-4B and achieves the best average with Qwen3-1.7B. On initially unreachable rubrics, unguided recovery rises from 35.02% to 42.86%, while child refinement improves reward-quality alignment on independent rollouts from 57.74% to 60.77%. Code is available at https://github.com/ZJU-REAL/RuACT.

cs.CL

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2609.29711

Decoupling Knowledge and Privacy: Post-Task Self-Distillation Replay for LLM Continual Learning

Shengtao Wen, Yunying Yang, Xiang Chen, Lingbing Guo, Yu Tian, Sheng-Jun Huang

原文摘要

Privacy-preserving continual learning (PPCL) must reduce the reproduction of sensitive content while retaining useful knowledge across sequential tasks. Formal privacy guarantees characterize randomized mechanisms, whereas operational output control concerns whether a trained model selectively reduces the likelihood of sensitive content in its outputs. In this work, we investigate the latter together with continual-learning utility under realistic task evolution. Retention and privacy correction operate at different granularities: task acquisition requires broad preservation of current- and old-task behavior, whereas privacy correction targets sparse annotated positions. Joint optimization leaves the current-task preservation target continually changing. We propose SPARK, a retention-correction decomposition that first freezes the learned post-task distribution and then applies selective correction around this stable reference. Self-Distillation Replay learns the current task while distilling behavior from previous tasks, and Post-Task Privacy Correction reduces annotated-PII likelihood while anchoring current- and old-task non-PII behavior to the resulting checkpoint. Extensive evaluations demonstrate that SPARK achieves effective selective PII suppression while preserving strong continual-learning utility and knowledge retention across diverse settings. Code and data will be released upon publication.

cs.LG · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.01643

CREDO: Evidence-Conditioned Credit Assignment for Temporal Reasoning Agents

Yue Leng, Shengtao Wen, Xiang Chen, Xinyi Zeng, Yu Tian, Sheng-Jun Huang

原文摘要

Temporal Knowledge Graph Question Answering (TKGQA) requires language model agents to retrieve evidence, select relevant facts, and perform multi-step temporal reasoning. However, the trajectory-level outcome signal in GRPO does not explicitly distinguish the quality of intermediate decisions: downstream failures can penalize useful evidence acquisition, while identical rewards in all-failed groups eliminate the relative outcome signal despite differences in intermediate progress. We propose Credit Redistribution with Evidence-Driven Opportunities (CREDO), which uses intermediate evidence to recover local supervision, including in all-failed groups, and determine where outcome supervision acts. CREDO assigns positive stage-level credit through comparisons among rollouts of the same question and uses retained answer overlap as an opportunity proxy to shift part of the outcome-policy weight toward reasoning and answering. The framework preserves terminal rewards and trajectory-level advantages, while redistribution conserves total outcome-policy weight per trajectory. Following the public evaluation protocol of prior work, CREDO raises Overall accuracy from 78.0% to 82.4% and Multiple accuracy from 55.0% to 65.6% on MultiTQ relative to the strongest published baseline in our comparison. On TimelineKGQA, it raises cross-KG accuracy from 30.9% to 36.4% relative to our reproduced baseline via direct transfer to ICEWS Actor under matched training and evaluation settings.

cs.LG · cs.AI · cs.CL · cs.IR

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arXiv ↗PDF ↗