Singapore

We study the fair division of indivisible chores among agents with additive disutility functions. We investigate the existence of allocations satisfying the popular fairness notion of envy-freeness up to any chore (EFX), and its multiplicative approximations. The existence of $4$ EFX allocations was recently established by Garg, Murhekar, and Qin (2025). We improve this guarantee by proving the existence of $2$-EFX allocations for all instances with additive disutilities. This approximation was previously known only for restricted instances such as bivalued disutilities (Lin, Wu, and Zhou (2025)) or three agents (Afshinmehr, Ansaripour, Danaei, and Mehlhorn (2024)). 

We obtain our result by providing a general framework for achieving approximate-EFX allocations. The approach begins with a suitable initial allocation and performs a sequence of local swaps between the bundles of envious and envied agents. For our main result, we begin with an initial allocation that satisfies envy-freeness up to one chore (EF1) and Pareto-optimality (PO); the existence of such an allocation was recently established in a major breakthrough by Mahara (2025). We further demonstrate the strength and generality of our framework by giving simple and unified proofs of existing results, namely (i) $2$-EFX for bivalued instances, (ii) 2-EFX for three agents, (iii) EFX when the number of chores is at most twice the number of agents, and (iv) $4$-EFX for all instances. We expect this framework to have broader applications in approximate-EFX due to its simplicity and generality.

AAAI 2026

Existence of 2-EFX Allocations of Chores

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.

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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.

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.

An External Fairness Evaluation of LinkedIn Talent Search

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

Large-scale genomic workflows used in precision medicine can process datasets spanning tens to hundreds of gigabytes per sample, leading to high memory spikes, intensive disk I/O, and task failures due to out-of-memory errors. Simple static resource allocation methods struggle to handle the variability in per-chromosome RAM demands, resulting in poor resource utilization and long runtimes. In this work, we propose multiple mechanisms for adaptive, RAM-efficient parallelization of chromosome-level bioinformatics workflows. First, we develop a symbolic regression model that estimates per-chromosome memory consumption for a given task, and introduces an interpolating bias to conservatively minimize over-allocation. Second, we present a dynamic scheduler that adaptively predicts RAM usage with a polynomial regression model, treating task packing as a Knapsack problem to optimally batch jobs based on predicted memory requirements. Additionally, we present a static scheduler that optimizes chromosome processing order to minimize peak memory while preserving throughput. Our proposed methods, evaluated on simulations and real-world genomic pipelines, provide new mechanisms to reduce memory overruns and balance load across threads. It thereby achieves faster end-to-end execution, showcasing its potential to optimize large-scale genomic workflows.

Efficient Chromosome Parallelization for Precision Medicine Genomic Workflows

Large Language Models (LLMs) exhibit impressive capabilities, but remain susceptible to a growing spectrum of safety risks, including jailbreaks, toxic content, hallucinations, and bias. Existing defenses often address only a single threat type or resort to rigid outright rejection, sacrificing user experience and failing to generalize across diverse and novel attacks. This paper introduces Adversarial Scenario Extrapolation (ASE), a novel inference-time computation framework that leverages Chain-of-Thought (CoT) reasoning to simultaneously enhance LLM robustness and seamlessness. ASE guides the LLM through a self-generative process of contemplating potential adversarial scenarios and formulating defensive strategies before generating a response to the user query. Comprehensive evaluation on four adversarial benchmarks with four latest LLMs shows that ASE achieves near-zero jailbreak attack success rates and minimal toxicity, while slashing outright rejections to $\leq$4\%. ASE outperforms six state-of-the-art defenses in robustness-seamlessness trade-offs, with 92–99\% accuracy on adversarial Q\&A and 4–10× lower bias scores. By transforming adversarial perception into an intrinsic cognitive process, ASE sets a new paradigm for secure and natural human-AI interaction.

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