Singapore

Foundation models for knowledge graphs (KGs) achieve strong cohort-level performance in link prediction, yet fail to capture individual user preferences; a key disconnect between general relational reasoning and personalized ranking. We propose GatedBias, a lightweight inference-time personalization framework that adapts frozen KG embeddings to individual user contexts without retraining or compromising global accuracy. Our approach introduces structure-gated adaptation: profile-specific features combine with graph-derived binary gates to produce interpretable, per-entity biases, requiring only ${\sim}300$ trainable parameters. We evaluate GatedBias on two benchmark datasets (Amazon-Book and Last-FM), demonstrating statistically significant improvements in alignment metrics while preserving cohort performance. Counterfactual perturbation experiments validate causal responsiveness; entities benefiting from specific preference signals show 6--30$\times$ greater rank improvements when those signals are boosted. These results show that personalized adaptation of foundation models can be both parameter-efficient and causally verifiable, bridging general knowledge representations with individual user needs.

AAAI 2026

Lightweight Inference-Time Personalization for Frozen Knowledge Graph Embeddings

### 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)

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.

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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.

Online grocery shopping presents unique challenges for sequential recommendations due to repetitive purchase patterns and complex item relationships within the baskets. Unlike traditional e-commerce, grocery recommendations must capture both complementary item associations and temporal dependencies across shopping sessions. To address these challenges in Amazon's online grocery business, we propose T-REX, a novel transformer architecture that generates personalized category-level suggestions by learning both short-term basket dependencies and long-term user preferences. Our approach introduces three key innovations: (1) an efficient sampling strategy utilizing dynamic sequence splitting for sparse shopping patterns, (2) an adaptive positional encoding scheme for temporal patterns, and (3) a category-level modeling approach that reduces dimensionality while maintaining recommendation quality. Although masked language modeling techniques like BERT4Rec excel at capturing item relations, they prove less suitable for next basket generation due to information leakage issues. In contrast, T-REX's causal masking approach better aligns with the sequential nature of basket generation, enabling more accurate next-basket predictions. Experiments on large-scale grocery offline data and online A/B tests show significant improvement over existing systems.

T-REX: Transformer-based Category Sequence Generation for Grocery Basket Recommendation

Language identification is a crucial first step in multilingual systems such as chatbots and virtual assistants, enabling linguistically and culturally accurate user experiences. Errors at this stage can cascade into downstream failures, setting a high bar for accuracy. Yet, existing language identification tools struggle with key cases--such as music requests where the song title and user language differ. Open-source tools like LangDetect, FastText are fast but less accurate, while large language models, though effective, are often too costly for low-latency or low-resource settings. We introduce PolyLingua, a lightweight Transformer-based model for in-domain language detection and fine-grained language classification. It employs a two-level contrastive learning framework combining instance-level separation and class-level alignment with adaptive margins, yielding compact and well-separated embeddings even for closely related languages. Evaluated on two challenging datasets‚ÄîAmazon Massive (multilingual digital assistant utterances) and a Song dataset (music requests with frequent code-switching)‚ÄîPolyLingua achieves $99.25\%$ F1 and $98.15\%$ F1, respectively, surpassing Sonnet 3.5 while using 10√ó fewer parameters, making it ideal for compute- and latency-constrained environments.

PolyLingua: Margin-based Inter-class Transformer for Robust Cross-domain Language Detection

We revisit continual pre-training for large language models and argue that progress now depends more on scaling the right structure than on scaling parameters alone. We introduce SCALE, a width-upscaling architecture that inserts lightweight expansion into linear modules while freezing all pre-trained parameters. This preserves the residual and attention topologies and increases capacity without perturbing the base model‚Äôs original functionality. SCALE is guided by two principles: Persistent Preservation, which maintains the base model‚Äôs behavior via preservation-oriented initialization and freezing of the pre-trained weights, and Collaborative Adaptation, which selectively trains a subset of expansion components to acquire new knowledge with minimal interference. We instantiate these ideas as SCALE-Preserve (preservation-first), SCALE-Adapt (adaptation-first), and SCALE-Route, an optional routing extension that performs token-level routing between preservation and adaptation heads. On a controlled synthetic biography benchmark, SCALE mitigates the severe forgetting observed with depth expansion while still acquiring new knowledge. In continual pre-training on a Korean corpus, SCALE variants achieve less forgetting on English evaluations and competitive gains on Korean benchmarks, with these variants offering the best overall stability‚Äìplasticity trade-off. Accompanying analysis clarifies when preservation provably holds and why the interplay between preservation and adaptation stabilizes optimization compared to standard continual learning setups.

SCALE: Upscaled Continual Learning of Large Language Models

This paper explores the advancements in making large language models (LLMs) more human-like. We focus on techniques that enhance natural language understanding, conversational coherence, and emotional intelligence in AI systems. The study evaluates various approaches, including fine-tuning with diverse datasets, incorporating psychological principles, and designing models that better mimic human reasoning patterns. Our findings demonstrate that these enhancements not only improve user interactions but also open new possibilities for AI applications across different domains. Future work will address the ethical implications and potential biases introduced by these human-like attributes.

Enhancing Human-Like Responses in Large Language Models

Large language models (LLMs) are typically aligned to general human preferences via Reinforcement Learning from Human Feedback (RLHF), but aligning them to the values of individual users remains challenging. Existing personalization methods often rely on large reward models or expensive reinforcement learning, limiting use on edge or low-compute settings.
We study Tiny Personal Critic, a lightweight scoring model that personalizes LLM outputs while reducing inference compute via a simple gating policy. The critic embeds (prompt, answer) pairs, predicts scores along 6 interpretable axes (e.g., style, safety, factuality), and aggregates them with a user-specific weight vector $\lambda$ learned from a handful of A/B clicks. At inference, we first sample $k_0$ candidate drafts and expand to $k_1$ only if the critic's top-two margin is below a threshold $\tau$, cutting unnecessary generations when the critic is confident.
Trained on a mix of UltraFeedback, Big5Chat and Arena pairs, with knowledge distillation from factuality and safety teachers, the critic achieves 73\% Arena A/B accuracy initially with uniform $\lambda$, outperforming a no-critic length heuristic(62\%), and an UltraFeedback (60\%). Data from few-clicks shifts selections toward the individual which improves the personalization. We observe ~16.7\% fewer generated tokens on our demo; indicating that total inference compute can go down, not up. In addition, as k grows we observe monotone increses in information gain (log-det of a click-induced Fisher Gram matrix) and in the number of preference axes with sufficient evidence, indicating progressively stronger, broader personalization at fixed compute.
We conducted extensive scaling study which showed that the best accuracy was achieved on mid-size critics while very large critics add floating-point operations per second (FLOPs) without consistent gains. Hence, the critics runtime cost per candidate is relatively negligible during inference. Code will be made publically available.

Tiny Personal Critic: A Lightweight Critic for Low-Compute Personalization

Over the last couple of years, AI Agents have gained significant traction due to substantial progress in the capabilities of underlying General Purpose AI (GPAI) models, enhanced scaffolding techniques, and the promise to drive societal transformation. Companies, researchers, and policy makers have started to consider the different effects that AI agents may have across different dimensions of our lives. However, the literature exploring the broader effects of human-agent interactions is still underdeveloped. In this paper, we review the problem of traffic modulation by autonomous vehicles (AVs) in mixed traffic flows and extrapolate the learnings to the different modes of interaction between humans and AVs to the pair humans-AI agents. In doing so, we propose a preliminary taxonomy of relational archetypes based on literature on Human-Computer Interaction (HCI) and AV-human interaction, and tentatively explore how the resulting framework may lead to new questions regarding human-agent interactions. Our effort is aimed at strengthening existing bridges between these two research communities, which share similar traits: autonomy, fast adoption, high impact, and great potential for economic transformation. Building on previous analogies between AI Agents and AVs (e.g., regarding autonomy levels), we anticipate this paper to spark scholarly debate on the different types of impact that agents may have on our societies, while inviting other researchers to expand the scope of their comparative analysis regarding AI Agents.

Relational Archetypes: A Comparative Analysis of AV-Human and Agent-Human Interactions

This paper offers practical guidance on AI welfare in industry. While previous scholarship has centered on the theoretical nuances of the definition of moral status or the definition and validity of the many properties that may constitute it, little attention has been given to the practical implementation of such work. Within this paper, we introduce a framework that classifies AI systems along two axes– evidence of personhood and observed controllability– yielding four operational classes (A-D: controllable & lacking moral status, controllable & possessing moral status, uncontrollable & possessing moral status, uncontrollable & lacking moral status) and three tiers of moral status: Tier 0 (Presumed Object), Tier 1 (Ambiguous Other), and Tier 2 (Confirmed Other). We first develop an industry-applicable, practical theory of indicators that an AI system must satisfy to attain any moral status (Tier 1) or to qualify for moral personhood (Tier 2), organized into four criteria: consciousness, theory of mind, self-awareness, and robust agency. Next, we argue that pre-existing AI safety evaluations can function as dual-use assessments that simultaneously test AI safety metrics and probe for our aforementioned moral status indicators from our practical theory, and we detail how specific components of alignment techniques can serve this dual function. Lastly, we propose co-alignment for AI entities belonging in Class B. We do not take a stance on whether or not any present system is conscious or will be, but argue that the combination of the non-negligible chance of AI deserving of moral consideration as well as the moral significance of a false negative necessitate immediate preparation, regardless of timeline uncertainty.

From Object to Other: A Practical Theory of AI Moral Status and Personhood in Re-evaluating AI Safety Methods

Large language models display a peculiar form of inconsistency: they "know" the correct answer but fail to act on it. In human philosophy, this tension between global judgment and local impulse is called akrasia, or weakness of will. We propose akrasia as a foundational concept for analyzing inconsistency and goal drift in agentic AI systems. To operationalize it, we introduce the Akrasia Benchmark, a structured set of prompting conditions (Baseline [B], Synonym [S], Temporal [T], and Temptation [X]) that measures when a model's local response contradicts its own prior commitments. The benchmark enables quantitative comparison of "self-control" across model families, decoding strategies, and temptation types. Beyond single-model evaluation, we outline how micro-level akrasia may compound into macro-level instability in multi-agent systems that may be interpreted as "scheming" or deliberate misalignment. By reframing inconsistency as weakness of will, this work connects agentic behavior to classical theories of agency and provides an empirical bridge between philosophy, psychology, and the emerging science of agentic AI.

The Seeds of Scheming: Weakness of Will in the Building Blocks of Agentic Systems

Autonomous AI Agents powered by LLMs have shown remarkable abilities in diverse domains. However, the training process typically require centralized collection of large amounts of real-world user data, posing substantial privacy and regulatory concerns. To this end, we explore a new decentralized training paradigm, namely FedAgent (Federated Agent Reinforcement Learning), which enables collaborative learning of AI agents across distributed clients without sharing local data. Moreover, we construct the first decentralized agent learning environment FedAgentGym, which includes four types of LLM agents, two application scenarios (WebShop and ALFWorld), three variations of decentralized settings, and three newly defined heterogeneity challenges (Preference Heterogeneity, Coverage Heterogeneity, and Hardness Heterogeneity), to systematically investigate its effectiveness and impact factors. Extensive theoretical and empirical studies show that FedAgent can have comparable performance to the centralized training paradigm and exhibit strong robustness against heterogeneities, which shows the feasibility of training AI agents without sacrificing data privacy. The code is available.

Federated Agent Reinforcement Learning

Large language model (LLM)–based agents are increasingly used to perform complex, multi-step workflows in regulated settings such as compliance and due diligence. Yet, many agentic architectures focus on prompt engineering for single agents, which makes it difficult to observe or compare how models are considering uncertainty and coordination across interconnected decision stages and with humans. This paper introduces a multi-agent system design formalized as a bounded-horizon, directed, acyclic Markov Decision Process (MDP). Each agent in this system corresponds to a specific step or role (e.g., content, business, legal in a compliance setting), with set transitions between agents representing task escalation or completion. Epistemic uncertainty (per agent) is quantified using Monte Carlo \textit{estimation}, and system-level uncertainty (across agents) is characterized the MDP setup terminating in a labeled state or one with human review. We illustrate the approach with a case study in AI safety evaluation for self-harm detection via a multi-agent compliance system based on this set-up. Results show improvements over a single-agent baseline in accuracy (up to 19%), reduction in required human review (up to 85×), and, in some configurations, less processing time.

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T-REX: Transformer-based Category Sequence Generation for Grocery Basket Recommendation

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LLMs + Persona-Plug = Personalized LLMs