👥 Social Computing

🤖 AAAI2026 · 10 paper notes

📌 Same area in other venues: 📷 CVPR2026 (3) · 🔬 ICLR2026 (17) · 💬 ACL2026 (45) · 🧪 ICML2026 (9) · 🧠 NeurIPS2025 (20) · 📹 ICCV2025 (4)

🔥 Top topics: Reasoning ×2

Argumentative Debates for Transparent Bias Detection

This paper proposes ABIDE (Argumentative BIas Detection by DEbate), which constructs Quantitative Bipolar Argumentation Frameworks (QBAFs) via neighborhood-based argument schemes, models the bias detection process as a structured debate, enables transparent bias reasoning from individual neighborhoods to the global level, and formally proves the correspondence between QBAF semantics and the expected behavior of bias detection.

Bias Association Discovery Framework for Open-Ended LLM Generations

This paper proposes the Bias Association Discovery Framework (BADF), which systematically extracts both known and unknown bias associations between demographic identities and descriptive concepts from LLM open-ended story generation, overcoming the limitation of prior methods that rely on predefined bias concepts.

Cross-modal Prompting for Balanced Incomplete Multi-modal Emotion Recognition

This paper proposes Cross-modal Prompting (ComP), which addresses the modality imbalance problem in incomplete multi-modal emotion recognition (IMER) via progressive prompt generation, cross-modal knowledge propagation, and a dynamic scheduler, achieving state-of-the-art performance across 4 datasets and 7 missing rates.

Fact2Fiction: Targeted Poisoning Attack to Agentic Fact-checking System

This paper proposes Fact2Fiction, the first poisoning attack framework targeting agentic fact-checking systems (e.g., DEFAME, InFact). It employs a Planner Agent to simulate claim decomposition and generate sub-questions, reverse-engineers key reasoning points from system justifications to craft targeted malicious evidence, and allocates the poisoning budget according to sub-claim importance. At a poisoning rate of only 1%, Fact2Fiction achieves 8.9%–21.2% higher attack success rate (ASR) than the state-of-the-art PoisonedRAG.

FactGuard: Event-Centric and Commonsense-Guided Fake News Detection

This paper proposes FactGuard, a framework that leverages LLMs to extract event-centric content (with style removed) and generate commonsense rationales. A Rationale Usability Evaluator dynamically assesses the reliability of LLM suggestions. Knowledge distillation yields a lightweight variant, FactGuard-D, that operates without LLM inference, achieving both robustness and efficiency in fake news detection.

From Imitation to Discrimination: Toward A Generalized Curriculum Advantage Mechanism Enhancing Cross-Domain Reasoning Tasks

This paper proposes CAPO (Curriculum Advantage Policy Optimization), an adaptive curriculum mechanism based on advantage signals. Through a two-phase strategy — first imitation (using only positive-advantage samples) then discrimination (introducing negative signals) — CAPO stably and significantly improves LLM performance on mathematical reasoning and multimodal GUI reasoning tasks.

Multi-modal Dynamic Proxy Learning for Personalized Multiple Clustering

This paper proposes the Multi-DProxy framework, which leverages learnable textual proxies for personalized multiple clustering through three key innovations: gated cross-modal fusion, dual-constraint proxy optimization, and dynamic candidate management, achieving state-of-the-art performance on all public benchmarks.

Reasoning About the Unsaid: Misinformation Detection with Omission-Aware Graph Inference

This paper proposes OmiGraph, the first omission-aware misinformation detection framework. By constructing omission-aware graphs, leveraging LLMs to reason about omission intent, and employing omission-guided message passing and aggregation mechanisms, OmiGraph extracts deception patterns from "what is unsaid," achieving average gains of +5.4% F1 and +5.3% ACC on bilingual datasets.

SceneJailEval: A Scenario-Adaptive Multi-Dimensional Framework for Jailbreak Evaluation

This paper proposes SceneJailEval, a scenario-adaptive multi-dimensional jailbreak evaluation framework that defines 14 jailbreak scenarios and 10 evaluation dimensions. Through a pipeline of scenario classification → dynamic dimension selection → multi-dimensional detection → weighted harm scoring, it achieves F1 of 0.917 on a self-constructed dataset (surpassing SOTA by 6%) and 0.995 on JBB (surpassing SOTA by 3%), while supporting harm severity quantification beyond binary classification.

T2Agent: A Tool-augmented Multimodal Misinformation Detection Agent with Monte Carlo Tree Search

This paper proposes T2Agent, a misinformation detection agent integrating an extensible toolset with Monte Carlo Tree Search (MCTS). By decomposing detection into sub-tasks targeting distinct forgery sources via a multi-source verification mechanism, T2Agent achieves a new state of the art on MMfakebench, improving the accuracy of the baseline MMDAgent by 28.7% using GPT-4o as the backbone.