Singapore

Protein-Protein Interactions (PPIs) prediction is crucial for understanding cellular functions and disease mechanisms. Existing deep learning–based methods primarily rely on direct interaction within the PPI network to update protein representations. However, (1) such networks overlook the potential associations between functionally similar proteins, limiting the smoothing capability of Graph Neural Networks (GNNs) in learning representations for similar nodes. (2) Additionally, most approaches fail to adequately model the latent dependencies among interaction types (edge labels), which hinders their performance in PPI prediction tasks. To address these limitations, we propose SELC-PPI, a structure-enhanced and label correlation-aware model for protein-protein interactions prediction. Specifically, SELC-PPI first identifies similar proteins by leveraging both the topological information of the PPI network and the label distributions of nodes, constructing similarity edges. Then, it incorporates label co-occurrence statistics into the learning of label embeddings. Experimental results on multiple datasets and under various data split settings demonstrate that SELC-PPI significantly outperforms existing methods, validating the effectiveness of our model design.

AAAI 2026

Topology-Enhanced and Label Correlation-Aware Model for Protein-Protein Interaction Prediction

ml: multi-class/multi-label learning & extreme classification

ml: graph-based machine learning

ml: applications

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

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

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.

This paper introduces the Shepherd Test, a new conceptual
test for assessing the moral and relational dimensions of
superintelligent artificial agents. The test is inspired by
human interactions with animals, where ethical
considerations about care, manipulation, and consumption
arise in contexts of asymmetric power and
self-preservation. We argue that AI crosses an important,
and potentially dangerous, threshold of intelligence when
it exhibits the ability to manipulate, nurture, and
instrumentally use less intelligent agents, while also
managing its own survival and expansion goals. This
includes the ability to weigh moral trade-offs between
self-interest and the well-being of subordinate agents. The
Shepherd Test thus challenges traditional AI evaluation
paradigms by emphasizing moral agency, hierarchical
behavior, and complex decision-making under existential
stakes. We argue that this shift is critical for advancing
AI governance, particularly as AI systems become
increasingly integrated into multi-agent environments. We
conclude by identifying key research directions, including
the development of simulation environments for testing
moral behavior in AI, and the formalization of ethical
manipulation within multi-agent systems.

The Shepherd Test: How Will Super Intelligent Agents Balance Care and Control in Asymmetric Relationships?

The integration of Conversational Agents (CAs) into daily life offers opportunities to tackle global challenges, leading to the emergence of Conversational AI for Social Good (CAI4SG). This paper examines the advancements of CAI4SG using a role-based framework that categorizes systems according to their AI autonomy and emotional engagement. This framework emphasizes the importance of considering the role of CAs in social good contexts, such as serving as empathetic supporters in mental health or functioning as assistants for accessibility. Additionally, exploring the deployment of CAs in various roles raises unique challenges, including algorithmic bias, data privacy, and potential socio-technical harms. These issues can differ based on the CA's role and level of engagement. This paper provides an overview of the current landscape, offering a role-based understanding that can guide future research and design aimed at the equitable, ethical, and effective development of CAI4SG.

Conversational AI for Social Good (CAI4SG): An Overview of Emerging Trends, Applications, and Challenges

Retrieval-Augmented Generation (RAG) faces a core bottleneck with knowledge-sparse and semantically ambiguous long-tail queries, where retrieval noise distorts reasoning and necessitates costly post-processing. To tackle this, we propose RaCoT (Retrieval-aware Contrastive-of-Thought), a novel framework that shifts contrastive thinking to the pre-retrieval stage. By automatically generating a semantically adjacent yet differently answered contrastive question and extracting a $\Delta$-Prompt to capture their key differences, RaCoT guides the model to proactively focus on the "critical details that determine answer divergence." This approach allows it to suppress semantic interference within a single retrieval pass, overcoming the theoretical bottleneck of single-vector queries that struggle to simultaneously encode signals for what to attend to and what to ignore. On six authoritative benchmarks, including PopQA and TriviaQA-unfiltered, RaCoT outperforms strong baselines like RankRAG and Self-RAG by 0.9-2.4 percentage points. It exhibits superior robustness, with a performance drop of only 8.6\% in adversarial tests, far surpassing the over 15\% degradation in other methods. Furthermore, its low latency (3.12s) and token overhead (11.54) place it on the accuracy-efficiency Pareto frontier, while ablation studies validate the necessity of each component. Ultimately, RaCoT reframes the RAG paradigm from "post-hoc context cleaning" to "a priori shaping of discriminative reasoning," offering an efficient and robust path toward reliable AI systems for real-time, resource-constrained deployments.

RaCoT: Plug-and-Play Contrastive Example Generation Mechanism for Enhanced LLM Reasoning Reliability

Autonomous execution of long-horizon, contact-rich manipulation tasks traditionally requires extensive real-world data and expert engineering, posing significant cost and scalability challenges. This paper proposes a novel framework integrating hierarchical semantic decomposition, reinforcement learning (RL), visual language models (VLMs), and knowledge distillation to overcome these limitations. Complex tasks are decomposed into atomic skills, with RL-trained policies for each primitive exclusively in simulation. Crucially, our RL formulation incorporates explicit force constraints to prevent object damage during delicate interactions. VLMs perform high-level task decomposition and skill planning, generating diverse expert demonstrations. These are distilled into a unified policy via Visual-Tactile Diffusion Policy for end-to-end execution. We conduct comprehensive ablation studies exploring different VLM-based task planners to identify optimal demonstration generation pipelines, and systematically compare imitation learning algorithms for skill distillation. Extensive simulation experiments and physical deployment validate that our approach achieves policy learning for long-horizon manipulation without costly human demonstrations, while the VLM-guided atomic skill framework enables scalable generalization to diverse tasks.

Gentle Manipulation Policy Learning via Demonstrations from VLM Planned Atomic Skills

Learning path recommendation seeks to provide learners with a structured sequence of learning items (e.g., knowledge concepts or exercises) to optimize their learning efficiency. Despite significant efforts in this area, most existing methods primarily rely on prerequisite relationships, which present two major limitations: 1) Requiring prerequisite relationships between knowledge concepts, which are difficult to obtain due to the cost of expert annotation, hindering the application of current learning path recommendation methods. 2) Relying on a single, sequentially dependent knowledge structure based on prerequisite relationships implies that difficulties at any stage can cause learning blockages, which in turn disrupt subsequent learning processes. To address these challenges, we propose a novel approach, GraphRAG-Induced Dual Knowledge Structure Graphs for Personalized Learning Path Recommendation (KnowLP), which enhances learning path recommendations by incorporating both prerequisite and similarity relationships between knowledge concepts. Specifically, we introduce a knowledge concept structure graph generation module EDU-GraphRAG that adaptively constructs knowledge concept structure graphs for different educational datasets, significantly improving the generalizability of learning path recommendation methods. We then propose a Discrimination Learning-driven Reinforcement Learning (DLRL) module, which mitigates the issue of blocked learning paths, further enhancing the efficacy of learning path recommendations. Finally, we conduct extensive experiments on three benchmark datasets, demonstrating that our method not only achieves state-of-the-art performance but also provides interpretable reasoning for the recommended learning paths. Our code is available at https://anonymous.4open.science/r/KnowLP.

GraphRAG-Induced Dual Knowledge Structure Graphs for Personalized Learning Path Recommendation

Session-based recommendation aims to predict users’ next actions by modeling their ongoing interaction sequences, particularly in scenarios where long-term user profiles are unavailable. While existing methods have achieved promising results by leveraging sequential and graph-based structures, they often rely on global aggregation strategies that emphasize dominant user interests while overlooking the transient and fine-grained behavior patterns embedded in sessions. In practice, user intent evolves across sessions and is reflected through diverse behavioral patterns, ranging from immediate preferences to segmented co-occurrence interests and long-range goals. To address these limitations, we propose **GraphFine**, a novel multi-granular graph learning framework that achieves fine-grained behavioral pattern awareness for session-based recommendation. Our approach models user behavior at different temporal and semantic granularities through a combination of graph and hypergraph neural networks. Specifically, we employ a position-aware graph to capture short-term item transitions, and construct segmented co-occurrence hypergraphs to uncover high-order semantic relations among co-occurred items. To preserve diverse user intents, we further introduce a multi-view intent readout mechanism that extracts and adaptively integrates intent signals from short-term actions, segmented co-occurrence patterns, and entire sessions. Extensive experiments on benchmark datasets demonstrate that **GraphFine** consistently outperforms existing state-of-the-art methods, confirming its effectiveness in capturing fine-grained and dynamic user preferences for more accurate recommendation.

Multi-Granular Graph Learning with Fine-Grained Behavioral Pattern Awareness for Session-Based Recommendation

Federated Domain Generalization (FDG) aims to collaboratively train a global model across distributed clients that can generalize well on unseen domains. However, existing FDG methods typically struggle with cross-client data heterogeneity and incur significant communication and computation overhead. To address these challenges, this paper presents a new FDG framework, dubbed FaST-PT, which facilitates local feature augmentation and efficient unseen domain adaptation in a distributed manner. First, we propose a lightweight Multi-Modal Style Transfer (MST) method to transform image embedding under text supervision, which could expand the training data distribution and mitigate domain shift. We then design a dual-prompt module that decomposes the prompt into global and domain prompts. Specifically, global prompts capture general knowledge from augmented embedding across clients, while domain prompts capture domain-specific knowledge from local data. Besides, Domain-aware Prompt Generation (DPG) is introduced to adaptively generate suitable prompts for each sample, which facilitates unseen domain adaptation through knowledge fusion. Extensive experiments on four cross-domain benchmark datasets, e.g., PACS and DomainNet, demonstrate the superior performance of FaST-PT over SOTA FDG methods such as FedDG-GA and DiPrompt. Ablation studies further validate the effectiveness and efficiency of FaST-PT.

Multi-Modal Style Transfer-based Prompt Tuning for Efficient Federated Domain Generalization

We present FoundationSLAM, a learning-based monocular dense SLAM system that addresses the absence of geometric consistency in previous flow-based approaches for accurate and robust tracking and mapping.
Our core idea is to bridge flow estimation with geometric reasoning by leveraging the guidance from foundation depth models. 
To this end, we first develop a Hybrid Flow Network that produces geometry-aware correspondences, enabling consistent depth and pose inference across diverse keyframes. 
To enforce global consistency, we propose a Bi-Consistent Bundle Adjustment Layer that jointly optimizes keyframe pose and depth under multi-view constraints. Furthermore, we introduce a Reliability-Aware Refinement mechanism that dynamically adapts the flow update process by distinguishing between reliable and uncertain regions, forming a closed feedback loop between matching and optimization.
Extensive experiments demonstrate that FoundationSLAM achieves superior trajectory accuracy and dense reconstruction quality across multiple challenging datasets, while running in real-time at 18 FPS, demonstrating strong generalization to various scenarios and practical applicability of our method.

FoundationSLAM: Unleashing the Power of Depth Foundation Models for End-to-End Dense Visual SLAM

Recent progress in unsupervised probing methods, notably Contrast‑Consistent Search (CCS), has enabled the extraction of latent beliefs in language models without relying on token-level outputs. As these probes offer lightweight diagnostic tools with low alignment tax, a central question arises: can they effectively assess model alignment? We investigate this by examining CCS's sensitivity to harmful vs. safe statements and introducing Polarity‑Aware CCS (PA‑CCS), which evaluates whether a model's internal representations remain consistent under polarity inversion. We propose two alignment-oriented metrics—Polar‑Consistency and Contradiction Index—to quantify the semantic robustness of a model's latent knowledge. To validate PA-CCS, we curate two main and one control datasets containing matched harmful-safe sentence pairs formulated by different methods (concurrent and antagonistic statements), and apply PA-CCS to 18 language models. Our results demonstrate that PA‑CCS reveals both architectural and layer-specific differences in the encoding of latent harmful knowledge. Interestingly, replacing the negation token with a meaningless marker degrades the PA‑CCS scores of models with aligned representations. In contrast, models lacking robust internal calibration do not show this degradation. Our findings highlight the potential of unsupervised probing for alignment evaluation and call on the community to incorporate structural robustness checks into interpretability benchmarks.

Polarity-Aware Probing for Quantifying Latent Alignment in Language Models

Hamilton-Jacobi (HJ) reachability analysis provides a formal method for guaranteeing safety in constrained control problems. It synthesizes a value function to represent a long-term safe set called feasible region. Early synthesis methods based on state space discretization cannot scale to high-dimensional problems, while recent methods that use neural networks to approximate value functions result in unverifiable feasible regions. To achieve both scalability and verifiability, we propose a framework for synthesizing verified neural value functions for HJ reachability analysis. Our framework consists of three stages: pre-training, adversarial training, and verification-guided training. We design three techniques to address three challenges to improve scalability respectively: boundary-guided backtracking (BGB) to improve counterexample search efficiency, entering state regularization (ESR) to enlarge feasible region, and activation pattern alignment (APA) to accelerate neural network verification. We also provide a neural safety certificate synthesis and verification benchmark called Cersyve-9, which includes nine commonly used safe control tasks and supplements existing neural network verification benchmarks. Our framework successfully synthesizes verified neural value functions on all tasks, and our proposed three techniques exhibit superior scalability and efficiency compared with existing methods.

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