Singapore

Maintaining human oversight and broad stakeholder participation remain key challenges for trust management in human-AI teams. With the increasing deployment of agentic AI and large language models, the risk of unpredictable yet highly consequential outcomes could, if left unaddressed, undermine public and expert confidence in such teams. This study revisits that debate to articulate a framework that secures fairness, legitimacy, and trust. Drawing on theoretical and empirical analyses of citizen participation in fields adjacent or parallel to AI, it shows that the weaknesses of black-box AI have analogous counterparts in the isomorphic processes of black-box value elicitation and implementation decisions. Specifically, this study focuses on circumstances in which experts operate outside their domain of expertise to develop ad-hoc theories of reality. Examples of such “inexpert expert judgments” include situations where judges must evaluate complex statistical evidence in court, and where algorithm specialists must select features to be included in models based not on relevance to the goal but on availability or intuition. In cases like that, even something as trivial as including or excluding an interaction term in a regression-based model could lead to highly problematic outcomes. Bypassing domain specialist expertise and substituting it with stand-in knowledge is unnecessary when such expertise could be incorporated with modest effort.

AAAI 2026

The Hidden Black-Box: Expert Labor and the Case for Participatory AI;

stakeholder participation

social sciences

expertise

algorithm

expert

stages 

COLING, the International Conference on Computational Linguistics, is one of the premier conferences for natural language processing and computational linguistics. Often grouped within the field of artificial intelligence, but actually pre-dating the development of artificial intelligence, advances in computational linguistics and natural language processing are now some of the major drivers behind the use of artificial intelligence for commercial and social applications – for example, on-line search, machine translation and with voice-assisted conversational devices.

First established in 1965, the biennial COLING conference is held in diverse parts of the globe and attracts participants from both top-ranked research centers and emerging countries. Today, the most important developments in our field are taking place not only in universities and academic research institutes, but also in industrial research departments and in technological startups. COLING conferences provide opportunities for all these communities to showcase their exciting developments.

COLING 2020

COLING, the International Conference on Computational Linguistics, is one of the premier conferences for natural language processing and computational linguistics. Often grouped within the field of artificial intelligence, but actually pre-dating the development of artificial intelligence, advances in computational linguistics and natural language processing are now some of the major drivers behind the use of artificial intelligence for commercial and social applications – for example, on-line search, machine translation and with voice-assisted conversational devices.

workshop 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.<br><br>

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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.

Large Language Models (LLMs) excel at single-turn tasks such as instruction following and summarization, yet real-world deployments require sustained multi-turn interactions where user goals and conversational context persist and evolve. A recurring challenge in this setting is context drift: the gradual divergence of a model’s outputs from goal-consistent behavior across turns. Unlike single-turn errors, drift unfolds temporally and is poorly captured by static evaluation metrics. In this work, we present a study of context drift in multi-turn interactions and propose a simple dynamical framework to interpret its behavior. We formalize drift as the turn-wise KL divergence between the token-level predictive distributions of the test model and a goal-consistent reference model, and propose a recurrence model that interprets its evolution as a bounded stochastic process with restoring forces and controllable interventions. We instantiate this framework in both synthetic long-horizon rewriting tasks and realistic user–agent simulations, such as in tau-bench, measuring drift for several open-weight LLMs that are used as user simulators. Our experiments consistently reveal stable, noise-limited equilibria rather than runaway degradation, and demonstrate that simple reminder interventions reliably reduce divergence in line with theoretical predictions. Together, these results suggest that multi-turn drift can be understood as a controllable equilibrium phenomenon rather than as inevitable decay, providing a foundation for studying and mitigating context drift in extended interactions.

Drift No More? Context Equilibria in Multi-Turn LLM Interactions

Personalization of large language models (LLMs) has recently gained attention as a key approach to enhancing their usability and adaptability to individual users. However, when personalization relies on interaction histories, inconsistencies often arise between past and current user preferences, causing the model to fail to properly adapt to the user’s most recent intent. We propose a simple preference representation that encodes each preference as an intensity and polarity vector and aggregates past and recent preferences arithmetically to form a contradiction free, up to date personalized prompt. This enables the LLM to dynamically update user preferences at test time, linearly combining past and current preferences to produce a consistent, contradiction free personalized prompt. The proposed method does not require explicit ground truth data and can be applied to various downstream applications. Preliminary experiments using datasets demonstrate that our approach improves preference alignment compared to a non personalized model and achieves dynamic optimization over time.

Preference Descriptions for Dynamic Personalization of Large Language Models

As large language models (LLMs) become increasingly integrated into daily life, privacy concerns are on the rise. Driven by the appeal of convenient, universal solutions, current practices reflect a drift toward a one-size-fits-all approach to privacy. Unfortunately, we argue that a unified framework for prompt privacy is elusive and can instead mislead, creating even greater risk due to a false sense of safety. We identify five desirable properties of an effective privacy framework for prompts, namely protection guarantees, performance, efficiency, domain adaptability, and user accessibility, and highlight that existing frameworks, such as sanitization, differential privacy, cryptography, and contextual integrity, only satisfy a subset of these properties. Beyond individual frameworks, we find underlying tensions between these properties that preclude the development of a unified framework. We recommend two critical paths forward: emphasizing context-specific and application-specific evaluation of privacy frameworks, and fostering user awareness and privacy literacy.

A Unified Framework for Prompt Privacy is Elusive and Misleading

We propose a novel framework for persona-based language model system, motivated by the need for personalized AI agents that adapt to individual user preferences. In our approach, the agent embodies the user’s “persona” (e.g. user profile or taste) and is powered by a large language model (LLM). To enable the agent to leverage rich contextual information, we introduce a Knowledge-Graph-enhanced Retrieval Augmented Generation (Graph RAG) mechanism that constructs an LLM-derived graph index of relevant documents and summarizes communities of related information. Our framework generates personalized prompts by combining: (1) a summary of the user’s historical behaviors and preferences extracted from the knowledge graph, and (2) relevant global interaction patterns identified through graph-based community detection. This dynamic prompt engineering approach allows the agent to maintain consistent persona-aligned behaviors while benefiting from collective knowledge. On the LaMP benchmark, our method improves news categorization F1 by 11.1%, movie tagging F1 by 56.1%, and reduces product rating MAE by 10.4% over prior methods.

PersonaAgent with GraphRAG: Community-Aware Knowledge Graphs for Personalized LLM

Large Language Models (LLMs) have demonstrated exceptional capabilities but face two critical deployment challenges: high computational costs and scarcity of personalized domain training data. We address these dual challenges through a comprehensive framework that combines synthetic data generation with inference optimization techniques. Our approach employs LLMs for zero-shot and few-shot synthetic dataset creation while applying structural pruning, knowledge distillation, quantization, and prompt caching for computational efficiency. We evaluate three architectural paradigms: encoder-only, encoder-decoder, and decoder-only on synthetic building permit classification and assess optimization techniques on public benchmarks. Our systematic evaluation reveals that strategic architectural selection based on task characteristics is more critical than model complexity: encoder-only models provide superior efficiency-accuracy trade-offs for high-throughput scenarios, demonstrating that understanding problem requirements enables effective deployment without requiring the largest available models. Knowledge distillation emerges as the key optimization technique for personalized domains, recovering pruning-induced performance degradation with Llama3-8B+KD achieving 87.1% accuracy at 20% pruning while exceeding unpruned baselines. Complementary optimization strategies, including dynamic caching (5-18% latency reduction with zero performance loss) and hardware acceleration (up to 200× speedup) enable flexible deployment configurations for domain specific applications.

Domain-Specific LLM Adaptation: Bridging Personalization and Efficiency Through Synthetic Data and Optimization

As AI becomes more personal, e.g., Agentic AI, there is an increasing need for personalizing models for various use cases. Personalized federated learning (PFL) enables each client to collaboratively leverage other clients' knowledge for better adaptation to the task of interest, without privacy risks. Despite its potential, existing PFL methods remain confined to rather simplified scenarios where data and models are the same across clients. To move towards realistic scenarios, we propose FedMosaic, a method that jointly addresses data and model heterogeneity with a task-relevance-aware model aggregation strategy to reduce parameter interference, and a dimension-invariant module that enables knowledge sharing across heterogeneous architectures without huge computational cost. To mimic the real-world task diversity, we propose a multi-modal PFL benchmark spanning 40 distinct tasks with distribution shifts over time. The empirical study shows that FedMosaic outperforms the state-of-the-art PFL methods, excelling in both personalization and generalization capabilities under challenging, realistic scenarios.

Not All Clients Are Equal: Collaborative Model Personalization on Heterogeneous Multi-Modal Clients

Personalizing Large Language Model (LLM) agents requires conditioning them on user-specific data, creating a critical trade-off between task utility and data disclosure. While the utility of adding user data often exhibits diminishing returns (i.e., submodularity), enabling near-optimal greedy selection, real-world personalization is complicated by structural constraints. These include logical dependencies (e.g., selecting fact A requires fact B), categorical quotas (e.g., select at most one writing style), and hierarchical rules (e.g., select at most two social media preferences, of which at most one can be for a professional network). These constraints violate the assumptions of standard subset selection algorithms. We propose a principled method to formally model such constraints. We introduce a compilation process that transforms a user‚Äôs knowledge graph with dependencies into a set of abstract macro-facets. Our central result is a proof that common hierarchical and quota-based constraints over these macro-facets form a valid laminar matroid. This theoretical characterization lets us cast structured personalization as submodular maximization under a matroid constraint, enabling greedy with constant-factor guarantees (and (1‚àí1/e) via continuous greedy) for a much richer and more realistic class of problems.

Structured Personalization: Modeling Constraints as Matroids for Data-Minimal LLM Agents

Large Language Models (LLMs) are typically aligned for safety during the post-training phase; however, they may still generate harmful outputs that pose risks to users. 
This challenge underscores the need for robust safeguards that operate across both model inputs and outputs. In this work, we introduce RoGuard 1.0, a state-of-the-art instruction fine-tuned LLM designed to enhance the safety of LLM systems through comprehensive input‚Äìoutput moderation, using a pipeline of LLMs to enhance moderation capability. 
Built on the Llama-3.1-8B-Instruct backbone, our model is instruction fine-tuned to generalize across previously unseen safety taxonomies and demonstrates strong performance on out-of-domain safety benchmarks. 
The instruction fine-tuning process uses a mix of synthetic and open-source safety datasets, augmented with chain-of-thought (CoT) rationales and input inversion to enhance contextual understanding and decision making. 
To support systematic evaluation, we also release RoGuard Evaluation, a new benchmark featuring an extensible safety taxonomy to assess the effectiveness of LLM guardrails and moderation frameworks.

Taxonomy-Adaptive Moderation Model with Robust Guardrails for Large Language Models

Large foundation models (LFMs) transform healthcare AI in prevention, diagnostics, and treatment. However, whether LFMs can provide truly personalized treatment recommendations remains an open question. Recent research has revealed multiple challenges for personalization, including the fundamental generalizability paradox: models achieving high accuracy in one clinical study perform at chance level in others, demonstrating that personalization and external validity exist in tension. This exemplifies broader contradictions in AI-driven healthcare: the privacy-performance paradox, scale-specificity paradox, and the automation-empathy paradox. As another challenge, the degree of causal understanding required for personalized recommendations, as opposed to mere predictive capacities of LFMs, remains an open question. N-of-1 trials -- crossover self-experiments and the gold standard for individual causal inference in personalized medicine -- resolve these tensions by providing within-person causal evidence while preserving privacy through local experimentation. Despite their impressive capabilities, this paper argues that LFMs cannot replace N-of-1 trials. We argue that LFMs and N-of-1 trials are complementary: LFMs excel at rapid hypothesis generation from population patterns using multimodal data, while N-of-1 trials excel at causal validation for a given individual. We propose a hybrid framework that combines the strengths of both to enable personalization and navigate the identified paradoxes: LFMs generate ranked intervention candidates with uncertainty estimates, which trigger subsequent N-of-1 trials. Clarifying the boundary between prediction and causation and explicitly addressing the paradoxical tensions are essential for responsible AI integration in personalized medicine.

Personalization of Large Foundation Models for Health Interventions

Real-world deployment of large vision‚Äìlanguage models (LVLMs) makes everyday photos vulnerable to unintended geolocation inference, motivating techniques that enable safe, user-controllable personalization and privacy-aware deployment. We present Adversarial Tiled Location Anonymity Shield (ATLAS), a fully user-side, zero-query, black-box image-layer adaptation module that can be inserted before any LVLM to control the visibility of geospatial and contextual cues under strict perceptual constraints. ATLAS learns such modulations through a Mixture-of-Tiles (MoT) formulation: a semantic segmenter decomposes an image into regions, and a compact tile bank is routed by learned semantic gates and assembled per region before blending with the input to yield a perceptually faithful image that misdirects LVLM geolocation inference. ATLAS is trained on multiple frozen surrogate vision encoders (e.g., LLaMA-4 vision tower, CLIP, BLIP), each paired with lightweight GeoHeads for geolocation supervision; tower-wise losses are aggregated to guide the MoT generator while keeping all backbones frozen. We further explore two complementary training domains: a pixel-space variant perturbing texture and color statistics, and a frequency-space variant modulating low-frequency phase structure to probe distribution-level fragility. On OpenStreetView-5M, ATLAS reduces country-level accuracy by over 40\% and increases mean localization error by over 3,000 km under matched perceptual fidelity. In practice, ATLAS serves as a deployable personalization-safety layer, complementing dialogue-level policies with portable, image-level control, offering a practical path toward privacy-preserving and user-controllable personalization in real-world LVLM deployments.

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