Morocco

Retrieval-Augmented Generation (RAG) systems face efficiency bottlenecks in prefill due to attention mechanism, and traditional KV cache only accelerates decoding. In this context, reusing document-level KV cache computed for retrieved documents in previous sessions during the prefill stage appears to be a natural way to amortize computation, but it raises serious correctness challenges due to position and context misalignment across queries and sessions. This research proposes a multi-document KV cache reuse framework for multi-document RAG workloads across queries and sessions to resolve position misalignment and context misalignment, preserving accuracy while eliminating document-specific quadratic complexity in prefill. Theoretical analysis will establish conditions under which multi-document KV cache reuse remains stable and close to full recomputation, providing principled guarantees for both efficiency and accuracy. These results will enable deployment in existing RAG pipelines without architectural changes or model retraining. Crucially, to ensure robustness in real-world deployments, validation will extend beyond standard benchmarks to include noise-robustness tests and domain-specific workloads (e.g., legal). The research aims to empirically confirm these guarantees and demonstrate that substantial prefill speedups can be achieved without materially degrading task-level performance.

EACL 2026

Thesis Proposal: Efficient KV Cache Reuse for Multi-Document Retrieval-Augmented Generation

multiple document reuse

key-value cache

retrieval-augmented generation

large language models

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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AAAI 2026

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.

technical paper

#### *Message from the General Chair, Aline Villavicencio*
I’m delighted and honoured to welcome you to the 19th Conference of the European Chapter of the Association for Computational Linguistics (EACL 2026), taking place in the beautiful city of Rabat, in Morocco, in March 24-29, 2026. EACL is the flagship European conference of the Association and EACL 2026 proudly continues our field’s tradition of excellence in scholarship, innovation, and inclusivity. I am deeply grateful to the many volunteers whose dedication, generosity, and tireless efforts have made this conference possible.
For the first time EACL is being hosted in the African continent. This is an important milestone for our community, and we are grateful to our Moroccan hosts for enabling this historic moment by bringing this edition of EACL to Rabat. We are also delighted that the Second Arabic NLP School is co-located with EACL. We hope attendees enjoy this wonderful opportunity to strengthen ties with the Computational Linguistics communities across the African continent. *[Read full message](https://drive.google.com/file/d/14NlmHvwM6fPJuMmOvVh7K0vtQbEyv3SZ/view?usp=sharing)*<br><br>

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Welcome to the 19th Conference of the European Chapter of the Association for Computational Linguistics (EACL). EACL 2026 will be held in Rabat, Morocco, from March 24–29, 2026. 

This paper introduces the concept of multimodal oxymorons. Multimodal oxymorons extend the traditional oxymoron theory by constructing and communicating meaning through the interplay of multiple modalities (such as visual and textual) rather than relying solely on language. The paper argues that multimodal oxymorons are central mechanisms of meaning-making in contemporary communication, as evidenced by the use of memes as an example. While textual oxymorons have long been the subject of analysis in order to ascertain their role in shaping thought and meaning, multimodal oxymorons demonstrate how human cognitive process transcends linguistic boundaries, integrating different modalities (e.g., visual) in order to convey complex ideas. To encourage further study, we present a curated multilingual dataset of Multimodal OXYmoron (MOXY), which can be used as a foundation for further analysis and experimentation. Furthermore, we propose a methodical approach for the identification of multimodal oxymorons along with a pipeline for automated generation. Through illustrative examples and a detailed methodology, this work establishes a comprehensive framework for understanding, identifying, and generating multimodal oxymorons, paving the way for advancements in computational linguistics, artificial intelligence, and figurative language studies.

Multimodal OXYmorons: A Comprehensive Introduction and Computational Analysis Using a Dataset of Oxymoronic Memes in Italian and Spanish

Many research areas rely on data from the
web to gain insights and test their methods.
However, collecting comprehensive research
datasets often demands manually reviewing
many web pages to identify and record relevant
data points, which is labor-intensive and sus-
ceptible to error. While the emergence of large
language models (LLM)-powered web agents
has begun to automate parts of this process,
they often struggle to ensure the validity of the
data they collect. Indeed, these agents exhibit
several recurring failure modes—including hal-
lucinating or omitting values, misinterpreting
page semantics, and failing to detect invalid
information—which are subtle and difficult
to detect and correct manually. To address
this, we introduce the AI Committee, a novel
model-agnostic multi-agent system that auto-
mates the process of validating and remediating
web-sourced datasets. Each agent is special-
ized in a distinct task in the data quality assur-
ance pipeline, from source scrutiny and fact-
checking to data remediation and integrity val-
idation. The AI Committee leverages various
LLM capabilities—including in-context learn-
ing for dataset adaptation, chain-of-thought rea-
soning for complex semantic validation, and a
self-correction loop for data remediation—all
without task-specific training. We demonstrate
the effectiveness of our system by applying
it to three real-world datasets, showing that it
generalizes across LLMs and significantly out-
performs baseline approaches, achieving data
completeness up to 73.3% and precision up to
97.3%. We additionally conduct an ablation
study demonstrating the contribution of each
agent to the Committee’s performance. This
work is released as an open-source tool for the
research community

The AI Committee: A Multi-Agent Framework for Automated Validation and Remediation of Web-Sourced Data (presented virtually)

This paper introduces Kahaani, a multimodal, co-creative storytelling system that leverages Generative Artificial Intelligence, designed for children to address the challenge of sustaining engagement to foster educational narrative experiences. Here we define co-creative as a collaborative creative process in which both the child and Kahaani contribute to the generation of the story. The system combines Large Language Model (LLM), Text-to-Speech (TTS), Text-to-Music (TTM), and Text-to-Video (TTV) generation to produce a rich, immersive, and accessible storytelling experience. The system grounds the co-creation process in two classical storytelling framework, Freytag’s Pyramid and Propp’s Narrative Functions. The main goals of Kahaani are: (1) to help children improve their English skills, (2) to teach important life lessons through story morals, and (3) to help them understand how stories are structured, all in a fun and engaging way. We present evaluations for each AI component used, along with a user study involving three parent–child pairs to assess the overall experience and educational value of the system.

Kahaani: A Multimodal Co-Creative Storytelling System

This study examines how credibility, trust, and bias interact within complex communication systems that shape public understanding of scientific information. It addresses two questions: 1. What are the primary factors that influence the public’s comprehension of scientific findings? 2. How do the factors influencing public understanding of climate change science interact within a complex system? A scoping literature review synthesized disparate communication models from media studies, science communication, psychology, and information science to identify a shared set of system variables. The identified variables were organized into source-, message-, channel-, and receiver-related factors and used to develop a causal loop diagram showing how credibility, trust, and information processing co-evolve through reinforcing and balancing feedback. The resulting diagram illustrates two major loops: one centered on trust in information sources, which can foster social cohesion or accelerate truth decay, and another linking individual trust dynamics to broader patterns of polarization and unity. By clarifying how well-established constructs interact to produce dynamic communication outcomes, the framework is useful for scholars developing integrative theory and for policymakers and practitioners designing interventions in misinformation-prone environments. The CLD also provides a foundation for future system dynamics modeling to examine how interventions in transparency, media literacy, or platform governance may influence public trust over time.

Communication as a Complex System: Modeling the Feedback Dynamics of Trust and Credibility

We study object hallucination in Multimodal Large Language Models (MLLMs) and improve visual contrastive decoding (VCD) by constructing an object-aligned auxiliary view. We leverage object-centric attention in self-supervised Vision Transformers. In particular, we remove the most salient visual evidence to construct an auxiliary view that disrupts unsupported tokens and produces a stronger contrast signal. Our method is prompt-agnostic, model-agnostic, and can be seamlessly plugged into the existing VCD pipeline with little computation overhead, i.e., a single cacheable forward pass. Empirically, our method demonstrates consistent gains on two popular object hallucination benchmarks across two MLLMs.

Mask What Matters: Mitigating Object Hallucinations in Multimodal Large Language Models with Object-Aligned Visual Contrastive Decoding

Lightweight Large Language Models (LLMs) have demonstrated notable safety alignment deficits, particularly outside of English. These challenges are especially acute for Traditional Chinese (TC), stemming from distinct linguistic characteristics and a scarcity of dedicated safety resources. To address this, we introduce the Prompt Assortment for Traditional Chinese Hazards (PATCH) dataset, the first large-scale adversarial dataset tailored for TC safety evaluation, aligned with standard threat taxonomies. Using PATCH, we evaluated Llama Guard, RoBERTa, and Longformer architectures with full fine-tuning, Low-Rank Adaptation (LoRA), and Chat-Vector methods. Our findings demonstrate that parameter-efficient LoRA achieves classification performance (F1 > 0.99) comparable to full fine-tuning, providing an effective and efficient method for developing TC safety classifiers. We also find initial evidence suggesting targeted LoRA tuning may offer cross-lingual safety benefits.

PATCH Dataset: Empowering Traditional Chinese Safety Classifiers for Lightweight LLM

Fine-tuning large language models can introduce misaligned behaviors, but when and how distinct misaligned behaviors emerge during training remains poorly understood. We investigate the temporal dynamics of misalignment by analyzing model behavior at every checkpoint throughout fine-tuning. Our results reveal that hallucination serves as a primary, early-onset failure mode across domains. We fine-tuned Qwen2.5-7B-Instruct on medical and math datasets with varying degrees of misalignment and evaluated hallucination and malicious behavior at each checkpoint. In the medical domain, we observe a distinct temporal lag: hallucination scores spike early in training, preceding the emergence of malicious traits by approximately 18-22 checkpoints. In contrast, the math domain exhibits rising hallucinations without the corresponding emergence of malicious behavior, suggesting that while hallucination is a pervasive early signal, the solidification of "evil" traits is highly domain-dependent. Critically, because hallucination provides an earlier and more reliable signal than malicious traits in medical fine-tuning, monitoring it could enable targeted interventions in domains where harmful behaviors subsequently emerge. Our work demonstrates that misalignment evolves gradually with detectable precursor signals, and that temporal relationships between misalignment types vary significantly by domain.

Emergent Misalignment: Tracking the Emergence and Evolution of Misaligned traits throughout Model Training

Reasoning-specialized models like OpenAI's 5.1 and DeepSeek-V3.2 allocate substantial inference compute to extended chain-of-thought (CoT) traces, yet reasoning tokens incur significant costs. How do different reasoning modalities of code, natural language, hybrid, or none do perform under token constraints?
We introduce a framework that constrains models to reason exclusively through code, comments, both, or neither, then systematically ablates token budgets to 10\%, 30\%, 50\%, and 70\% of optimal. We evaluate four frontier models (GPT-5.1, Gemini 3 Flash, DeepSeek-V3.2, Grok 4.1) across mathematical benchmarks (AIME, GSM8K, HMMT).
Our findings reveal: (1) \textbf{truncated reasoning can hurt} as DeepSeek-V3.2 achieves 53\% with no reasoning but only 17\% with truncated CoT at 50\% budget; (2) \textbf{code degrades gracefully} as Gemini's comments collapse to 0\% while code maintains 43-47\%; (3) \textbf{hybrid reasoning underperforms} single modalities; (4) \textbf{robustness is model-dependent} as Grok maintains 80-90\% at 30\% budget where OpenAI and DeepSeek collapse to 7-27\%. These results suggest incomplete reasoning chains actively mislead models, with implications for deploying reasoning-specialized systems under resource constraints.

Broken Chains: The Cost of Incomplete Reasoning in LLMs

Large language models (LLMs) are increasingly utilised for social simulation and persona generation, necessitating an understanding of how they represent geopolitical identities. In this paper, we analyse personas generated for Palestinian and Israeli identities by five popular LLMs across 640 experimental conditions, varying context (war vs non-war) and assigned roles. We observe significant distributional patterns in the generated attributes: Palestinian profiles in war contexts are frequently associated with lower socioeconomic status and survival-oriented roles, whereas Israeli profiles predominantly retain middle-class status and specialised professional attributes. When prompted with explicit instructions to avoid harmful assumptions, models exhibit diverse distributional changes, e.g., marked increases in non-binary gender inferences or a convergence toward generic occupational roles (e.g., "student"), while the underlying socioeconomic distinctions often remain. Furthermore, analysis of reasoning traces reveals an interesting dynamics between model reasoning and generation: while rationales consistently mention fairness-related concepts, the final generated personas follow the aforementioned diverse distributional changes. These findings illustrate a picture of how models interpret geopolitical contexts, while suggesting that they process fairness and adjust in varied ways; there is no consistent, direct translation of fairness concepts into representative outcomes.

Analysing LLM Persona Generation and Fairness Interpretation in Polarised Geopolitical Contexts

Stance detection on social media plays a vital role in understanding public opinion on contentious topics. While prior work leverages external knowledge sources like Wikipedia to enrich limited target information, it primarily introduces conceptual content, neglecting the interpretability potential of knowledge and often leading to the incorporation of irrelevant or redundant information that hinders stance prediction performance. To address these limitations, we introduce PiKGL, a Pruned interpretable Knowledge Graph Learning framework for explainable stance detection. Specifically, we first extract event triples and topic entities from text and query the GDELT knowledge base to retrieve real-world knowledge, which is then used to construct an interpretable knowledge graph, enriching its content with the retrieved insights. To ensure precision and minimize noise, we introduce a topology-aware retrieval pruning strategy that incorporates target-specific commonsense knowledge, effectively filtering redundant nodes and enhancing graph relevance. Finally, the pruned knowledge graph is injected into a large language model to jointly model textual, target, and commonsense signals for improved stance comprehension. Experimental results conducted on three public datasets demonstrate our PiKGL achieves state-of-the-art performance on stance detection.

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