Singapore

We conduct an independent, third-party audit for bias of LinkedIn&#39;s Talent Search ranking system, focusing on potential ranking bias across two attributes: \emph{gender} and \emph{race}. 
To do so, we first construct a dataset of rankings produced by the system, collecting extensive Talent Search results across a diverse set of occupational queries. We then develop a robust labeling pipeline that infers the two demographic attributes of interest for the returned users.
To evaluate potential biases in the collected dataset of real-world rankings, we utilize two exposure disparity metrics: deviation from group proportions and $\text{MinSkew}@k$. 
Our analysis reveals an under-representation of minority groups in early ranks across many queries.
We further examine potential causes of this disparity, and discuss why they may be difficult or, in some cases, impossible to fully eliminate among the early ranks of queries. 
Beyond static metrics, we also investigate the concept of subgroup fairness over time, highlighting \emph{temporal disparities} in exposure and retention, which are often more difficult to audit for in practice.
In employer recruiting platforms such as LinkedIn Talent Search, the persistence of a particular candidate over multiple days in the ranking can directly impact the probability that the given candidate is selected for opportunities. Our analysis reveals demographic disparities in this temporal stability, with some groups experiencing greater volatility in their ranked positions than others.
We contextualize all our findings alongside LinkedIn’s published self-audits of its Talent Search system and reflect on the methodological constraints of a black-box external evaluation, including limited observability and noisy demographic inference. 
Our work contributes empirical insights and practical guidance for conducting third-party audits of modern socio-technical systems which go beyond the well-studied and standard algorithmic fairness guarantees of predictors.

AAAI 2026

An External Fairness Evaluation of LinkedIn Talent Search

auditing

ranking

fairness

bias

### Welcome to The 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025)

Message from the General Chair: 
*It is my great pleasure and honor to welcome you to the 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025), held in beautiful Vienna, Austria, from July 27 to August 1, 2025. ACL2025continues our field’s tradition of excellence in scholarship, innovation, and inclusivity, and I am deeply grateful to the many volunteers who have worked tirelessly to bring this event to life.* 
[Read more](https://drive.google.com/file/d/1GI_hvOpjswAuYdUTromfeDiPpCcqidwg/view?usp=sharing)

To access this event page, you need to log in with the **email address you registered with**. Access credentials will be sent to your email from Underline - subject line "Welcome to ACL 2025". Please be sure to check your spam email folder if you do not see an email confirmation right away.

Please log in

To access this event page, you need to log in with the **email address you registered with**. 

ACL 2025

Welcome to The 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025)

We conduct an independent, third-party audit for bias of LinkedIn's Talent Search ranking system, focusing on potential ranking bias across two attributes: \emph{gender} and \emph{race}. 
To do so, we first construct a dataset of rankings produced by the system, collecting extensive Talent Search results across a diverse set of occupational queries. We then develop a robust labeling pipeline that infers the two demographic attributes of interest for the returned users.
To evaluate potential biases in the collected dataset of real-world rankings, we utilize two exposure disparity metrics: deviation from group proportions and $\text{MinSkew}@k$. 
Our analysis reveals an under-representation of minority groups in early ranks across many queries.
We further examine potential causes of this disparity, and discuss why they may be difficult or, in some cases, impossible to fully eliminate among the early ranks of queries. 
Beyond static metrics, we also investigate the concept of subgroup fairness over time, highlighting \emph{temporal disparities} in exposure and retention, which are often more difficult to audit for in practice.
In employer recruiting platforms such as LinkedIn Talent Search, the persistence of a particular candidate over multiple days in the ranking can directly impact the probability that the given candidate is selected for opportunities. Our analysis reveals demographic disparities in this temporal stability, with some groups experiencing greater volatility in their ranked positions than others.
We contextualize all our findings alongside LinkedIn’s published self-audits of its Talent Search system and reflect on the methodological constraints of a black-box external evaluation, including limited observability and noisy demographic inference. 
Our work contributes empirical insights and practical guidance for conducting third-party audits of modern socio-technical systems which go beyond the well-studied and standard algorithmic fairness guarantees of predictors.

technical paper

We are pleased to announce the Fortieth AAAI Conference on Artificial Intelligence (AAAI-26), which will be held in Singapore EXPO from January 20 to January 27, 2026.

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-26 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and a range of other activities to be announced.

To access this event page, you need to log in with the **email address you registered with**. Access credentials will be sent to your email from Underline - subject line "Welcome to AAAI 2026". Please be sure to check your spam email folder if you do not see an email confirmation right away.

To access this event page, you are required to register.
Please complete your registration to continue.

We recommend reading [**the registration information**](https://aaai.org/conference/aaai/aaai-26/registration/) first.

**Online Registration Form**: https://aaai.getregistered.net/conference-2026 

Registration Required

We are pleased to announce the Fortieth AAAI Conference on Artificial Intelligence (AAAI-26), which will be held in Singapore EXPO from January 20 to January 27, 2026.

Light-weight neural networks for remote sensing (RS) visual analysis must overcome two inherent redundancies: spatial redundancy from vast, homogeneous backgrounds, and channel redundancy, where extreme scale variations render a single feature space inefficient. Existing models, often designed for natural images, fail to address this dual challenge in RS scenarios. To bridge this gap, we propose LWGANet, a light-weight backbone engineered for RS-specific properties. LWGANet introduces two core innovations: a Top-K Global Feature Interaction (TGFI) module that mitigates spatial redundancy by focusing computation on salient regions, and a Light-Weight Grouped Attention (LWGA) module that resolves channel redundancy by partitioning channels into specialized, scale-specific pathways. By synergistically resolving these core inefficiencies, LWGANet achieves a superior trade-off between feature representation quality and computational cost. Extensive experiments on twelve diverse datasets across four major RS tasks—scene classification, oriented object detection, semantic segmentation, and change detection—demonstrate that LWGANet consistently outperforms state-of-the-art light-weight backbones in both accuracy and efficiency. Our work establishes a new, robust baseline for efficient visual analysis in RS images.

LWGANet: Addressing Spatial and Channel Redundancy in Remote Sensing Visual Tasks with Light-Weight Grouped Attention

We present Autoregressive Representation Alignment (ARRA), a new training framework that unlocks global-coherent text-to-image generation in autoregressive LLMs without architectural modifications. Different from prior works that require complex architectural redesigns, ARRA aligns LLM's hidden states with visual representations from external visual foundational models via a global visual alignment loss and a hybrid token, <HYBNEXT>. This token enforces dual constraints: local next-token prediction and global semantic distillation, enabling LLMs to implicitly learn spatial and contextual coherence while retaining their original autoregressive paradigm. Extensive experiments validate ARRA's plug-and-play versatility. When training T2I LLMs from scratch, ARRA reduces FID by 16.6% (ImageNet), 12.0% (LAION-COCO) for autoregressive LLMs like LlamaGen, without modifying original architecture and inference mechanism. For training from text-generation-only LLMs, ARRA reduces FID by 25.5% (MIMIC-CXR), 8.8% (DeepEyeNet) for advanced LLMs like Chameleon. For domain adaptation, ARRA aligns general-purpose LLMs with specialized models (e.g., BioMedCLIP), achieving an 18.6% FID reduction over direct fine-tuning on medical imaging (MIMIC-CXR). These results demonstrate that training objective redesign, rather than architectural modifications, can resolve cross-modal global coherence challenges. ARRA offers a complementary paradigm for advancing autoregressive models. Code and models will be released to advance autoregressive image generation.

Unleashing the Potential of Large Language Models for Text-to-Image Generation Through Autoregressive Representation Alignment

Large Language Models (LLMs) have recently emerged as powerful reasoning engines in recommender systems, generating natural-language explanations that foster user engagement.
However, their recommendation performance remains limited, as they lack exposure to collaborative user-item interaction patterns.
In contrast, collaborative filtering (CF) models achieve strong performance by learning from these behavioral patterns at scale.
To unify the strengths of both paradigms, we propose TWiCE-Rec (Think Wise, Collaborate Effectively), a rationale-aware LLM-based recommender that incorporates collaborative user-item interactions.
In the first stage, we construct a rationale dataset by applying in-context learning with self-annotated curation. 
A state-of-the-art LLM is guided to generate persuasive rationales that explain the causal relationship between the user’s interaction sequence and the ground-truth next item, resulting in a curated post-hoc training dataset.
In the second stage, we perform multi-task instruction-tuned adaptation—based on the rationale-augmented training dataset—comprising item description generation and both non-reasoning and reasoning-based sequential recommendation, to equip the LLM with the ability to generate rationales that reflect how user preferences align with item characteristics.
Finally, we aim to enhance the LLM’s recommendation performance by incorporating user-item interaction patterns derived from the CF-Rec model.
To achieve this, we propose a confidence-weighted reinforcement learning strategy that adjusts rewards in proportion to both the LLM’s prediction alignment with the ground-truth and the confidence from the pretrained CF-Rec model.
Our method outperforms both CF- and LLM-Rec models on Amazon datasets in terms of recommendation performance and rationale quality. 
In an online A/B test, it achieved about 8% higher click-through rate than existing models, demonstrating practical value.
The code is available at https://anonymous.4open.science/r/TWiCE-Rec.

Think Wise, Collaborate Effectively: A Rationale-Aware LLM-Based Recommender with Reinforcement Learning from Collaborative Signals

Large language models (LLMs) increasingly rely on reinforcement learning (RL) to enhance their reasoning capabilities through feedback. A critical challenge is verifying the consistency of model-generated responses and reference answers, since these responses are often lengthy, diverse, and nuanced. Rule-based verifiers struggle with complexity, prompting the use of model-based verifiers. Existing research primarily focuses on building better verifiers, yet a systematic evaluation of different types of verifiers' performance across domains remains lacking, severely constraining the reliable development of Reinforcement Learning with Verifiable Reward (RLVR). To address this, we propose VerifyBench--a cross-domain comprehensive benchmark for systematically evaluating verifiers. We construct about 4,000 expert-level questions covering mathematics, physics, chemistry, and biology. Questions are equipped with reference answers and diverse responses. The reliability of the evaluation is ensured through a rigorous collection and annotation process conducted by a multidisciplinary expert team. We design a four-dimensional experimental framework to comprehensively compare the performance boundaries of specialized verifiers and general LLMs under combined conditions of extracted answers vs. complete responses, and short vs. long outputs. Our evaluation uncovers fundamental trade-offs in verifiers: while specialized verifiers achieve leading accuracy (the best model reaching 96.48\% in chemistry), they exhibit deficiencies in recall; general models show stronger inclusivity but unstable accuracy. More importantly, we discover verifiers' high sensitivity to input structure and inherent limitations in cross-domain generalization, providing critical insights into the bottlenecks of current verifier technology.

VerifyBench: A Systematic Benchmark for Evaluating Reasoning Verifiers Across Domains

Unsupervised representation learning on hypergraphs has recently drawn increasing attention due to its ability to capture high-order relationships without requiring labeled data. However, existing hypergraph contrastive learning methods predominantly follow spatial-based paradigms that rely on message-passing frameworks, which largely emphasize low-pass filtering. This restricts their ability to adapt to the diverse spectral characteristics of real-world hypergraphs. Motivated by the observation that different hypergraph datasets exhibit varied frequency energy distributions, we propose **HyperAim**, a novel contrastive learning framework that incorporates adaptive multi-frequency filtering into hypergraph representation learning. HyperAim integrates three complementary channels: a low-pass spatial channel, a high-pass spatial channel, and a spectral channel based on framelet transforms that jointly capture multi-frequency components. To fully exploit these diverse views, we introduce a frequency-aware contrastive learning strategy that constructs perturbed views via spectral and structural augmentations and enforces consistency across representations through inter- and intra-channel objectives. Extensive experiments on multiple benchmark datasets demonstrate that **HyperAim** consistently outperforms state-of-the-art baselines. Ablation studies further verify the effectiveness of adaptive frequency decomposition and frequency-aware contrastive learning in enhancing hypergraph representations.

HyperAim: Hypergraph Contrastive Learning with Adaptive Multi-frequency Filters

Pseudo-Boolean optimization (PBO) problem involves optimizing a linear objective function under linear inequality constraints defined over Boolean variables. PBO is widely used for modeling many combinational optimization problems, particularly in some real-world scenarios. 
In core-guided CDCL-based exact solvers, the way branching variables are assigned, known as phase selection, significantly affects the solving efficiency. 
This paper introduces two strategies to enhance solver performance by improving phase selection. 
Firstly, we design a new phase selection strategy that actively guides variables in the objective function toward assignments closer to the optimal solution. 
Secondly, to prevent the solver from becoming trapped in local solutions, we propose a reinforcement learning-based rephase mechanism that dynamically updates and resets variable phases, increasing search diversity and encouraging exploration of high-quality solution spaces.
We integrate two phase selection strategies into two state-of-the-art PBO solvers and compare them against top-performing solvers from the PB Competition 2024. The evaluation is conducted on benchmarks from the PB Competition 2016 and 2024. Experimental results show that our solvers outperform the PB Competition 2024 winning solver.

Improving Exact Algorithm for Pseudo Boolean Optimization with Two New Phase Selection Heuristics

Prompt tuning has shown promise for continual visual question answering (CVQA), facilitating modular and transferable knowledge across tasks. However, existing approaches often overlook the guiding role of prompts in the model’s implicit reasoning process. This oversight can lead to inconsistent reasoning paths and performance degradation across tasks. To address this issue, we propose the E Logic Prompt framework, which employs energy-based models (EBMs) to model the semantic compatibility between prompts and queries. In this framework, prompts function not only as adapters but also as reasoning guides that help maintain coherence throughout the inference process.
The framework enforces logical consistency at three levels. At the input level, it selects semantically aligned prompts by minimizing the energy between queries and prompts. Within the model, it aligns intermediate representations with prompts across layers to preserve step-by-step reasoning. Across tasks, it applies energy-based constraints to regulate prompt behavior, effectively suppressing semantic drift and enabling prompt reuse. These three levels of consistency together enhance the guiding capacity of prompts, allowing them to steer the model toward more stable and coherent reasoning. Extensive experiments show that E Logic Prompt outperforms existing methods in both accuracy and knowledge retention, while effectively maintaining balanced cross-modal reasoning throughout continual learning.

E-Logic Prompt: Unified Energy-Logic Framework for Continual Visual Question Answering

This paper tackles the challenging task of achieving storage-efficient yet high-fidelity motion representation in large-scale dynamic 3D Gaussian Splatting. Our motivation stems from the truth that existing urban-scale methods, which rely on massive and unstructured individual Gaussians for scene modeling, face a critical scalability bottleneck. Inspired by recent advances in the 3DGS-based compression beyond autonomous driving, we address this challenge by leveraging the compression capability of anchor-driven methods (Lu et al. 2024; Chen et al. 2024a). However, this is non-trivial as our exploratory experiments reveal that the direct application of this paradigm to dynamic, large-scale urban scenes results in performance degradation. We attribute this phenomenon to the hierarchical anchor design that severely loses dynamic information. To this end, we propose Hierarchical Dynamic Gaussian Splatting (HDGS), a novel framework designed to adapt the anchor-based Gaussian paradigm to 4D urban environments. We first establish a local support network to reinforce inter-anchor consistency, mitigating geometric and appearance fractures caused by supervision attenuation in deep hierarchies. Then, we handle heterogeneous object motion via coarse-to-fine decomposition, where high-level anchors model coarse dynamics and low-level anchors refine them with residual deformations. Third, we introduce a hybrid supervision scheme that synergistically fuses global geometric constraints and local pixel-level cues to alleviate geometrically inconsistent reconstruction under sparse LiDAR. Extensive experiments show that HDGS reduces storage by 69.0\% while maintaining or even improving rendering fidelity compared to state-of-the-art methods. Code will be released.

HDGS: Hierarchical Dynamic Gaussian Splatting for Urban Driving Scenes

Steering Vector (SV) is a powerful technique for controlling Large Language Models (LLMs) by manipulating their activations without altering model weights. However, when constructed from sensitive data, SV poses significant privacy risks, as it may leak private information. Existing differential privacy (DP) techniques for constructing SV cannot be directly applied to training-based SV construction paradigms, which offer higher task performance.
In this work, we present **PrivSV**, a general privacy-preserving approach for constructing SV with DP guarantees, compatible with arbitrary SV construction paradigms while maintaining high utility. In PrivSV, we propose three novel methods: a Layer-wise Noise-Resilient Reduction (LNR²) method to reduce the injected noise in high-dimensional SV; a Directional Prior Compensation (DPC) method to recover utility degraded by noise perturbation; and a Privacy-Aware Optimal Parameter Determination (POPD) method to adaptively maximize the performance of the final compensated SV. 
Extensive experiments on open-source LLMs of different families (i.e., LlaMa, Qwen, Mistral and Gemma) demonstrate that PrivSV outperforms several existing techniques across various privacy budgets.

PrivSV: Differentially Private Steering Vector for Large Language Models

Diffusion Large Language Models (dLLMs) have recently emerged as a competitive non-autoregressive paradigm due to their unique training and inference approach. However, there is currently a lack of safety study on this novel architecture. In this paper, we present the first analysis of dLLMs' safety performance and propose a novel safety alignment method tailored to their unique generation characteristics. Specifically, we identify a critical asymmetry between the defender and attacker in terms of security. For the defender, we reveal that the middle tokens of the response, rather than the initial ones, are more critical to the overall safety of dLLM outputs; this seems to suggest that aligning middle tokens can be more beneficial to the defender. The attacker, on the contrary, may have limited power to manipulate middle tokens, as we find dLLMs have a strong tendency towards a sequential generation order in practice, forcing the attack to meet this distribution and diverting it from influencing the critical middle tokens. Building on this asymmetry, we introduce Middle-tOken Safety Alignment (MOSA), a novel method that directly aligns the model's middle generation with safe refusals exploiting reinforcement learning. We implement MOSA and compare its security performance against eight attack methods on two benchmarks. We also test the utility of MOSA-aligned dLLM on coding, math, and general reasoning. The results strongly prove the superiority of MOSA.

Downloads

Next from AAAI 2026

LWGANet: Addressing Spatial and Channel Redundancy in Remote Sensing Visual Tasks with Light-Weight Grouped Attention

Similar lecture

R-Fairness: Assessing Fairness of Ranking in Subjective Data