论文检索

Social Aspects · Alignment

Bijean Ghafouri, Eun Cheol Choi, Priyanka Dey, Emilio Ferrara

RLHF assumes that annotation responses reflect genuine human preferences. We argue this assumption warrants systematic examination, and that behavioral science offers frameworks that bring clarity to when it holds and when it breaks down. Behavioral scientists have documented for sixty years that people routinely produce responses without holding genuine opinions, construct preferences on the spot based on contextual cues, and interpret identical questions differently. These phenomena are pervasive for precisely the value-laden judgments that matter most for alignment, yet this literature has not yet been systematically integrated into ML practice. In this position paper, we argue that the ML community must treat measurement validity as logically prior to preference aggregation. Specifically, our position is that measuring human preferences in RLHF is a social science problem. We present a taxonomy distinguishing genuine preferences from non-attitudes, constructed preferences, and measurement artifacts, along with diagnostic approaches for detecting each. This framework has two important implications. First, it raises the question of whether current RLHF practice may be systematically modeling noise as signal and elicitation artifacts as human values. Second, it provides a path forward by suggesting diagnostic tools that can distinguish valid preferences from artifacts before they enter the training pipeline.

Social Aspects · Accountability, Transparency, and Interpretability

André Duarte, Brian Tufts, Aditya Oke, Fei Fang, Arlindo Oliveira, Lei Li

*How can we distinguish whether a peer review was written by a human or generated by an AI model?* We argue that, in this setting, authorship should not be attributed solely from the textual features of a review, but also from the ideas, judgments, and claims it expresses. To this end, we propose Sem-Detect, an authorship detection method for peer reviews that operationalizes this principle by combining textual features with claim-level semantic analysis. Sem-Detect compares a target review against multiple AI-generated reviews of the same paper, leveraging the observation that different AI models tend to converge on similar points, while human reviewers introduce more unique and diverse ones. As a result, Sem-Detect is able to distinguish fully AI-generated reviews from authentic human-written ones, including those that have been refined using an LLM but still reflect human judgment. Across a dataset of over 20,000 peer reviews from ICLR and NeurIPS conferences, Sem-Detect improves over the strongest prior detector by 36.5% in TPR@1% FPR in the binary setting. More importantly, in the three-class scenario, we empirically show that LLM refinement preserves the semantic signals of human reviews, which remain distinct from the patterns exhibited by fully AI-generated text; as a result, fewer than 3.5% of LLM-refined human reviews are misclassified as AI-generated.

Social Aspects · Safety

Rong Feng, Haohan Zhao, Shiqin Tang, Geng Liu, Song Lai, Meng Wang, Shuxin Zhuang, Yuqi Zha, Changyi Ma, Runsheng Yu

Transferable jailbreaking attacks enable red-teaming of black-box large language models by optimizing adversarial prompts on open-source surrogates. A natural approach to improve transferability is multi-model training---optimizing against multiple source models simultaneously. Yet this approach has been largely abandoned, as it yields only marginal gains with standard optimizers. We argue the root cause is poor generalization: standard gradient descent lacks stability whenaggregating gradients from diverse models. Since GCG and its variants~\citep{zou2023universal, jia2024improved, yang2025guiding} mplicitly perform SGD in discrete token space, they inherit this instability in multi-model settings.We address this with \textbf{Lookahead-GCG}, which combines: (1) Stochastic Nesterov Accelerated Gradient (SNAG), whose lookahead mechanism reduces sensitivity to individual gradient updates, providing stability for multi-model optimization; (2) embedding-space momentum accumulation, which enables SNAG in discrete token optimization; and (3) maximally distant initialization, which exploits SNAG's improved generalization by starting from a universally beneficial point. Experiments show our method achieves 50.37\% ASR on open-source and 34.03\% on closed-source LLMs, outperforming GCG and TransferAttack with +11.78\% gains from multi-model optimization.

Social Aspects · Accountability, Transparency, and Interpretability

Yanchen Yin, Dongqi Han, Linghui Li

Jailbreak attacks bypass LLM safety alignment, yet their mechanisms remain poorly understood. We provide evidence that attacks do not eliminate safety features but selectively suppress specific attention heads. We identify two functionally differentiated types: **Adversarially Compromised Heads (ACHs)** concentrated in early layers, which are suppressed under attacks; and **Safety-Aligned Heads (SAHs)** in mid-layers, which maintain robust activations even when attacks succeed. Ablation studies support their causal roles: suppressing a small number of ACHs is sufficient to induce jailbreak-like behavior on normally refused inputs, while removing SAHs substantially weakens mid-layer safety activations. Token-level attribution further shows that ACH suppression is driven specifically by attack-template tokens. This provides a mechanistic account of why attacks bypass refusal decisions through ACH suppression, yet may not fully eliminate the internal safety signals sustained by SAHs---a phenomenon we term **Robust Harmful Features**. To validate the practical significance of this robustness, we show that simply reading these persistent activations---without any training---yields a detection signal competitive with dedicated safety models on most benchmarks.

Social Aspects · Everything Else

Stephan Rabanser, Sayash Kapoor, Peter Kirgis, Kangheng Liu, Saiteja Utpala, Arvind Narayanan

AI agents are increasingly deployed for consequential tasks. Yet existing benchmarks evaluate only task success rates, ignoring whether agents behave consistently, remain robust to perturbations, fail predictably, or bound error severity. We propose a framework for measuring agent reliability grounded in safety-critical engineering practice, decomposing reliability into four dimensions: consistency, robustness, predictability, and safety. Applying these metrics to 12 frontier models across two complementary benchmarks, we find that recent capability gains have produced minimal improvement in reliability: agents remain inconsistent across runs, brittle to prompt rephrasings, and poorly calibrated in their self-assessments, even as accuracy improves. Our metrics complement accuracy-focused evaluation by offering tools for reasoning about how agents perform, degrade, and fail under uncertainty.

Social Aspects · Safety

Xiang Yang, Feifei Li, Mi Zhang, Geng Hong, Xiaoyu You, Mi Wen, Min Yang

Diffusion transformers (DiTs) equipped with multimodal attention (MM-Attn) have become a dominant paradigm for image generation. However, preventing the generation of harmful content remains a critical challenge, particularly in imageto-image (I2I) editing tasks. Existing safety mechanisms are primarily designed for text-toimage (T2I) synthesis or U-Net-based architectures, which limits their effectiveness for unified safety mitigation in DiT-based frameworks. To bridge this gap, we propose Unified Visual Safety Regulator ( UVR) , a training- free safe generation framework that regulates unsafe semantics in generated images. UVR is grounded in an analysis of attention dynamics from the perspective of information flow in MM-Attn. We identify a taskindependent start-up stage, during which unsafe semantics in output patches rapidly emerge and can be accuratelv localized, where unsafe semantics in output patches quickly emerge and can be precisely localized, followed by task-specific semantic amplification and interference stages, where harmful signals are further propagated and entangled with benign content. Based on these observations, UVR mitigates unsafe generation through unified, targeted attention modulation and explicit restriction of harmful information flow over the identified unsafe output patches. Experiments across various concepts show that UVR achieves state-of-the-art safety performance by achieving 91% and $77\%$ erase rate in image synthesis and editing tasks, while preserving visual quality and fidelity with minimal degradation.

Social Aspects · Fairness

Addison J. Wu, Ryan Liu, Xuechunzi Bai, Thomas Griffiths

As large language models (LLMs) are adopted into frameworks that grant them the capacity to make real decisions, it is increasingly important to ensure that they are unbiased. In this paper, we argue that the predominant approach of simply removing existing biases from models is not enough. Using a paradigm from the psychology literature, we demonstrate that LLMs can spontaneously develop novel social biases about artificial demographic groups even when no inherent differences exist. These biases result in highly stratified task allocations, which are less fair than assignments by human participants and are exacerbated by newer and larger models. In social science, emergent biases like these have been shown to result from exploration-exploitation trade-offs, where the decision-maker explores too little, allowing early observations to strongly influence impressions about entire demographic groups. To alleviate this effect, we examine a series of interventions targeting model inputs, problem structure, and explicit steering. We find that explicitly incentivizing exploration most robustly reduces stratification, highlighting the need for better multifaceted objectives to mitigate bias. These results reveal that LLMs are not merely passive mirrors of human social biases, but can actively create new ones from experience, raising urgent questions about how these systems will shape societies over time.

Social Aspects · Accountability, Transparency, and Interpretability

Zongxin Liu, Xiaoyong Xue, Sun Weidi, Shengchao Qin, Lijun Zhang

Precise, training-free editing of text-to-image diffusion models requires balancing alignment (faithful attribute manifestation), consistency (preserving non-target content), and quality (artifact-free textures). Sparse autoencoder (SAE) steering offers interpretable, smooth ``slider-like'' control by manipulating SAE feature activations derived from the text encoder; however, existing approaches rely on heuristic feature selection and manual tuning of the steering strength, leading to suboptimal trade-offs among the three objectives. We propose Sparse Relaxed-Lasso Steering (SRLS), which casts steering-vector discovery as a convex sparse recovery problem. Exploiting the affine structure of the SAE decoder, SRLS automatically identifies sparse, generalizable support sets via a Lasso objective, and then debiases the coefficients using support-restricted ridge regression. We further select the optimal steering strength using Bayesian optimization. Experiments across diverse attributes and subjects show that SRLS generally improves over other methods, yielding a better balance among alignment, consistency, and quality.

Social Aspects · Robustness

Ruipeng Wang, Yuxin Chen, Yukai Wang, Chang Wu, Junfeng Fang, Xiaodong Cai, Qi GU, Hui Su, An Zhang, Xiang Wang 等

As LLM-based agents are increasingly deployed in real-world workflows, existing agent benchmarks---often built on idealized, noise-free assumptions---fall short of characterizing agents' robustness under imperfect user instructions and unreliable tool feedback. To address this gap, we introduce **AgentNoiseBench**, a systematic evaluation framework for *interactive noise robustness* in LLM agents. AgentNoiseBench models two primary noise sources: *user-side instruction noise* arising from ambiguity and variability in human requests, and *tool-side result noise* caused by failures, partial outputs, and erroneous or distracting tool responses. The benchmark covers two representative agentic settings: (i) *multi-step tool use* with DeepSearch-style retrieval agents on multi-hop QA tasks, and (ii) *multi-turn user--agent interaction* via adaptations of $\tau^{2}$-Bench and VitaBench to support controlled noise injection. We further provide a modular noise injection pipeline with controllable location and intensity, together with multi-dimensional metrics that go beyond final success to capture degradation trends, decision instability, and compute overhead. Evaluating 25 tool-using models across reasoning and non-reasoning families, we find that tool-side noise generally induces substantially larger performance degradation and trajectory drift than user-side noise, and that some strong reasoning models exhibit a "reasoning trap", spending markedly more tokens and steps under corrupted tool feedback while still making confident errors. Overall, AgentNoiseBench provides a practical testbed for diagnosing failure modes and advancing robust agent design for real deployments.

Social Aspects · Accountability, Transparency, and Interpretability

Xi Chen, Mingyu Jin, Jingcheng (Frank) Niu, Yutong Yin, Jinman Zhao, Bangwei Guo, Dimitris Metaxas, Zhaoran Wang, Yutao Yue, Gerald Penn

In this paper, we present empirical and theoretical evidence against a central but largely implicit assumption in circuit and sheaf discovery (CSD), which we term the *Functional Anisotropy Hypothesis*: the idea that functions in large language models (LLMs) are localised to a unique or near-unique internal mechanism. We show that a single LLM task can instead be supported by multiple, structurally distinct circuits or sheaves that are simultaneously faithful, sparse, and complete. To systematically uncover such competing mechanisms, we introduce Overlap-Aware Sheaf Repulsion, a method that augments the CSD objective with an explicit penalty on structural overlap across multiple discovery runs, enabling the discovery of circuits or sheaves with strong task performance but minimal shared structure across a plethora of common CSD benchmarks. We find that this phenomenon becomes increasingly pronounced as the number of discovered sheaves grows and persists robustly across major CSD methods. We further identify an ultra-sparse three-edge sheaf and show that none of its edges is individually indispensable, undermining even weakened notions of canonical or essential components. To explain these findings, we propose a *Distributive Dense Circuit Hypothesis* and provide a theoretical analysis demonstrating that non-unique, low-overlap circuit explanations arise naturally from high-dimensional superposition under mild assumptions. Together, our results suggest that mechanistic explanations in LLMs are inherently non-canonical and call for a rethinking of how CSD results should be interpreted and evaluated.

Deep Learning · Generative Models and Autoencoders

Feifan Jiang, Yinan Bu, Shihao Wu, Gongjun Xu, Ji Zhu

Network data are ubiquitous across the social sciences, biology, and information systems. Generating realistic synthetic network data has broad applications from network simulation to scientific discovery. However, many existing black-box approaches for network generation tend to overfit observed data while overlooking characteristic network structure, and incur substantial computational overhead at scale. These practical challenges call for synthetic network generation methods that are both efficient and capable of capturing structural properties of networks. In this paper, we introduce Synthetic Network Generation via Latent Embedding Reconstruction (SyNGLER), a general and efficient framework for synthetic network generation that builds on latent space network models. Given an observed network, SyNGLER first learns low-dimensional latent node embeddings via a latent space network model and then reconstructs the latent space by building a distribution-free generator over these embeddings. For generation, SyNGLER first samples (or resamples) node embeddings from the generator in the latent space and then produces synthetic networks using the latent space network model. Through the latent space framework, SyNGLER preserves unique characteristics in networks such as sparsity and node degree heterogeneity, while allowing for efficient training with lower computational cost than many existing deep architectures. We provide theoretical guarantees by developing consistency results regarding the distance between the true and synthetic edge distributions. Empirical studies further demonstrate the effectiveness of SyNGLER, where SyNGLER efficiently produces networks that better preserve key network characteristics such as network moments and degree distributions compared with existing approaches.

Social Aspects · Safety

Liu Yu, Can Chen, PING KUANG, Zhikun Feng, Fan Zhou, Gillian Dobbie

Large Vision-Language Models (LVLMs) exhibit sophisticated reasoning but remain susceptible to object hallucination. Deviating from the prevailing attention intensity assumption, we reveal a deeper dynamic structural misalignment: hallucination is triggered at decision-critical steps where specific attention heads, acting as risky mediators, decouple from visual evidence to lock onto language priors. This establishes a pathological shortcut that bypasses visual grounding. To dismantle this, we propose Fox (Faithfulness and Observational-flow via eXpression-rectification), a training-free inference-time framework. Fox diagnoses structural misalignment using a visual attention entropy probe to localize risky mediators unsupervisedly. We then execute a targeted causal intervention via numerical logit saturation to physically sever the shortcut path. Finally, a conflict-gated cooperative decoding strategy reconciles interventional faithfulness with observational fluency. Extensive experiments demonstrate that Fox achieves SOTA performance, outperforming SID by 29.1% while preserving linguistic richness and general reasoning capabilities.

Applications · Social Sciences

Meng Chen, Junjie Yang, Zechen Li, Kai Zhao, Hongjun Dai, Weiming Huang

Predicting street-level socioeconomic indicators from street view imagery is fundamental to urban planning. Existing methods typically extract visual features via pretrained encoders and propagate information through graph-based learning, but they fail to fully exploit the structured, task-relevant, and label-efficient learning signals inherent in urban scenes. We propose MetaStreet, a semi-supervised multimodal framework with three components: (1) a semantic-spatial visual encoder that jointly models object co-occurrence and spatial adjacency at the semantic category level, (2) a task-aware textual encoder that steers LLMs toward prediction-relevant features via task-specific prompts, and (3) a geography-aware graph contrastive learning module that leverages spatial autocorrelation to extend contrastive supervision to unlabeled streets, enabling them to actively participate in representation learning. Experiments on two cities across three socioeconomic prediction tasks demonstrate that MetaStreet consistently outperforms state-of-the-art methods.

Social Aspects · Privacy

Xiongtao Sun, HUI LI, Jiaming Zhang, Yujie Yang, Kaili Liu, Ruxin Feng, Wen Tan, Wei Yang Bryan Lim

Modern Vision-Language Models (VLMs) pose significant individual-level privacy risks by linking fragmented multimodal data to identifiable individuals through hierarchical chain-of-thought reasoning. However, existing privacy benchmarks remain structurally insufficient for this threat, as they primarily evaluate privacy perception while failing to address the more critical risk of privacy reasoning: a VLM’s ability to infer and link distributed information to construct individual profiles. To address this gap, we propose MultiPriv, the first benchmark designed to systematically evaluate individual-level privacy reasoning in VLMs. We introduce the Privacy Perception and Reasoning (PPR) framework and construct a bilingual multimodal dataset with synthetic individual profiles, where identifiers (e.g., faces, names) are linked to sensitive attributes. This design enables nine challenging tasks spanning attribute detection, cross-image re-identification, and chained inference. We conduct a large-scale evaluation of over 50 open-source and commercial VLMs. Our analysis shows that 60\% of widely used VLMs can perform individual-level privacy reasoning with up to 80\% accuracy, posing a significant threat to personal privacy. MultiPriv provides a foundation for developing and assessing privacy-preserving VLMs.

Applications · Social Sciences

Zherui Huang, Guanjie Zheng, Hao Xue, Linghe Kong

Origin–destination (OD) flow modeling underpins urban planning and mobility analysis, but prevailing graph-based methods often neglect salient geographic attributes, limiting their ability to model long-range and multi-area dependencies. In this paper, we introduce GeoFlow, a novel framework that (i) augments area representations with geospatial attributes, including relative positions, -hop and geodesic distances, (ii) employs a specialized geometric-intrinsic fusion encoder design that combines graph attention for intrinsic area signals with coordinate-aware encoders for global structure, and (iii) adopts an axial-global attention decoder to capture OD-specific competitive dependencies. For OD flow generation, GeoFlow is paired with flow matching models to produce more authentic and diverse mobility samples. Empirically, GeoFlow achieves superior performance in predictive accuracy, while substantially improving generative fidelity and diversity. Ablation and analytical studies confirm the contribution of each component.

Social Aspects · Safety

Andrew Liu, Samira Nedungadi, Bryce Cai, Alex Kleinman, Harmon Bhasin, Seth Donoughe

For some time, LLMs have sped up life sciences research by synthesizing published literature, but now leading LLM-based tools can also perform certain \textit{in silico} tasks that had previously been the domain of experienced biologists. These emerging AI capabilities offer new opportunities for scientific discovery and biomedical advances, but they are also changing the landscape of biosecurity risks. To address this, we introduce the Agentic Bio-Capabilities Benchmark (ABC-Bench), a suite of evaluations to measure \textit{agentic} biosecurity-relevant capabilities. ABC-Bench evaluates LLM-based agents on both benign and potentially harmful biosecurity-relevant tasks: writing code to operate liquid handling robots, designing DNA fragments for \textit{in vitro} assembly, and evading DNA synthesis screening. These tasks require a combination of biology and software expertise; indeed, when PhD biologists with at least two years of coding experience attempted the tasks in ABC-Bench, they scored only 24\% on average. By contrast, the top-performing LLM, Grok 3, achieves 53\% across tasks, outperforming 60\%, 100\%, and 54\% of experts on the Liquid Handling Robot, Fragment Design, and Screening Evasion tasks, respectively. In three additional experiments, we found that OpenAI's GPT-4o-mini-high produced code that, when run on an OpenTrons robot, successfully assembled DNA with the expected sequences.

Social Aspects · Alignment

Han Jiang, Dongyao Zhu, Xiaoyuan Yi, Ziang Xiao, Zhihua Wei, Xing Xie

In-Context Learning has shown great potential for aligning Large Language Models (LLMs) with human values, helping reduce harmful outputs and accommodate diverse preferences without costly post-training, known as *In-Context Alignment* (ICA). However, LLMs' comprehension of input prompts remains agnostic, limiting ICA's ability to address value tensions—human values are inherently *pluralistic*, often imposing conflicting demands, *e.g.*, stimulation vs. tradition. Current ICA methods therefore face the *Instruction Bottleneck* challenge, where LLMs struggle to reconcile multiple intended values within a single prompt, leading to incomplete or biased alignment. To address this, we propose **PICACO**, a novel pluralistic ICA method. Without fine-tuning, PICACO optimizes a meta-instruction that incorporates multiple values to better elicit LLMs' understanding of them and improve alignment. This is achieved by maximizing the total correlation between specified values and LLM responses, which theoretically reinforces value conformity and reduces distractive noise, resulting in more effective instructions. Extensive experiments on five value sets show that PICACO works well with both black-box and open-source LLMs, outperforms several recent strong baselines, and achieves a better balance across up to 8 distinct values.

General Machine Learning · Causality

Ayush Khot, Miruna Oprescu, Maresa Schröder, Ai Kagawa, Xihaier Luo

Causal inference in spatial domains faces two intertwined challenges: (1) unmeasured spatial factors, such as weather, air pollution, or mobility, that confound treatment and outcome, and (2) interference from nearby treatments that violate standard no-interference assumptions. While existing methods typically address one by assuming away the other, we show they are deeply connected: *interference reveals structure* in the latent confounder. Leveraging this insight, we propose the **Spatial Deconfounder**, a two-stage method that reconstructs a substitute confounder from local treatment vectors using a conditional variational autoencoder (C-VAE) with a spatial prior, then estimates causal effects via a flexible outcome model. We show that this approach enables nonparametric identification of both direct and spillover effects under weak assumptions—without requiring multiple treatment types or a known model of the latent field. Empirically, we extend ```SpaCE```, a benchmark suite for spatial confounding, to include treatment interference, and show that the Spatial Deconfounder consistently improves effect estimation across real-world datasets in environmental health and social science. By turning interference into a multi-cause signal, our framework bridges spatial and deconfounding literatures to advance robust causal inference in structured spatial data.

Applications · Social Sciences

Zengqing Wu, Run Peng, Takayuki Ito, Makoto Onizuka, Chuan Xiao

This position paper argues that **LLM-based social simulations require clear boundaries to make meaningful contributions to social science**. While Large Language Models (LLMs) offer promising capabilities for simulating human behavior, their tendency to produce homogeneous outputs, acting as an "average persona", fundamentally limits their ability to capture the behavioral diversity essential for complex social dynamics. We examine why heterogeneity matters for social simulations and how current LLMs fall short, analyzing the relationship between mean alignment and variance in LLM-generated behaviors. Through a systematic review of representative studies, we find that validation practices often fail to match the heterogeneity requirements of research questions: while most papers include ground truth comparisons, fewer than half explicitly assess behavioral variance, and most that do report lower variance than human populations. We propose that researchers should: (1) match validation depth to the heterogeneity demands of their research questions, (2) explicitly report variance alongside mean alignment, and (3) constrain claims to collective-level qualitative patterns when variance is insufficient. Rather than dismissing LLM-based simulation, we advocate for a boundary-aware approach that ensures these methods contribute genuine insights to social science.

Applications · Social Sciences

Maximilian Puelma Touzel, Sneheel Sarangi, Aurélien Bück-Kaeffer, Zachary Yang, Jean-François Godbout, Reihaneh Rabbany

LLM-based social simulations—in which many language model agents interact over multiple turns—are rapidly proliferating across policy analysis, epidemiology, and computational social science. Yet the field lacks consensus on how to validate these simulations, with evaluation methods that are sparse, inconsistent, and rarely shared across disciplinary silos. We argue this creates a serious risk: premature deployment of unvalidated simulators in high-stakes domains. Our position is that the field must pivot from expansion to consolidation, prioritizing methodological standardization—shared benchmarks, open data, and reproducible evaluation protocols grounded in social science and complex systems research. We outline a concrete research program organized around specific learning problems/benchmarks, providing a path toward answering the fundamental question: when are LLM social simulations useful modelling objects?