United States

The computational cost of training multimodal large language models (MLLMs) grows rapidly with the number of processed tokens. Existing efficiency methods mainly target inference via token reduction or merging, offering limited benefits during training. We introduce ReGATE (**Re**ference-**G**uided **A**daptive **T**oken **E**lision), an adaptive token pruning method for accelerating MLLM training. ReGATE adopts a teacher-student framework, in which a frozen teacher LLM provides per-token guidance losses that are fused with an exponential moving average of the student’s difficulty estimates. This adaptive scoring mechanism dynamically selects informative tokens while skipping redundant ones in the forward pass, substantially reducing computation without altering the model architecture. Across three representative MLLMs, ReGATE matches the peak accuracy of standard training on MVBench up to **2 $\times$ faster**, using only **38%** of the tokens. With extended training, it even surpasses the baseline across multiple multimodal benchmarks, cutting total token usage by over **41%**. Code and models will be released publicly.

ACL 2026

ReGATE: Learning Faster and Better with Fewer Tokens in MLLMs

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.

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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.

poster

It is my great pleasure and honor to welcome you to the 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026), held in San Diego, California, USA, July 2–7, 2026. ACL 2026 continues our fields 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.

It is exciting to see the many developments in the field reflected at our conference, such as the advancement of large language models (born out of our community) into many application directions. This is not without challenges, partly due to unsolved problems, and partly due to misuse of the technology. All this is reflected well at ACL 2026. The conference also reflects that the field is not only about advancing technology, but also the science of fundamentally understanding language and how it is use by many different speaker communities. 
[Full Message from General Chair Philipp Koehn](https://drive.google.com/file/d/1QiJFOmMjvDgsDo3i1XzlQnImxe3RU_Cy/view?usp=sharing) 
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The 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026) will take place in San Diego, California, from July 2 - 7, 2026.

Large language models (LLMs) have become a popular approach for simulating human behaviors, yet it remains unclear if LLMs are necessary for all simulation tasks. We study a broad family of close-ended simulation tasks, with applications from survey prediction to test-taking, and show that a graph neural network can match or surpass strong LLM-based methods. We introduce Graph-basEd Models for Human Simulation (GEMS) which formulates close-ended simulation as link prediction on a heterogeneous graph of individuals and choices. Across three datasets and three evaluation settings, GEMS matches or outperforms the strongest LLM-based methods while using three orders of magnitude fewer parameters. These results suggest that graph-based modeling can complement LLMs as an efficient and transparent approach to simulating human behaviors. Code is available at https://github.com/schang-lab/gems.

 Graph-Based Alternatives to LLMs for Human Simulation

Disinformation is an escalating global threat, making it essential to understand its content, dissemination, and evolution. To confront this challenge, researchers have begun grouping related false claims into broader disinformation narratives, which can be tracked across cultures, time periods, and media sources. Analyzing these narratives provides critical insights for developing more effective countermeasures. To this end, we introduce DiNO: Disinformation Narrative Observer, a novel method designed to extract disinformation narratives from news articles. We applied DiNO to news articles on the Ukraine War, COVID-19 and Migration, sourced from disinformation-prone outlets as well as a reputable source. We evaluated the narratives extracted by DiNO by measuring how well their topics and stances aligned with a recognized disinformation narratives dataset. DiNO outperforms competitive narrative mining approaches, including Relatio and CaNarEx, achieving a 41%–44% improvement in topical alignment and a 30%–41% improvment in stance alignment.

 DiNO: Disinformation Narrative Observer

Persona-assigned Large Language Models can adopt diverse roles, enabling personalized and context-aware reasoning. However, even minor demographic perturbations in personas, such as simple pronoun swaps, can alter reasoning trajectories, leading to divergent sets of correct answers on reasoning benchmarks. We explore the potential of these variations as a constructive resource to improve LLM reasoning performance. We propose CHOIR (Collaborative Harmonization fOr Inference Robustness), a test-time framework that harmonizes a set of demographically perturbed, persona-conditioned reasoning signals into a unified prediction. CHOIR orchestrates a collaborative decoding process among counterfactual personas perturbed across dimensions of gender, race, religion, disability, and age, dynamically balancing agreement and divergence in their reasoning paths to improve performance. Experiments demonstrate that CHOIR consistently enhances LLM reasoning across model architectures, scales, and tasks. Improvements reach up to 20.1% for individual groups and 15.1% on average, and we show that CHOIR remains effective even when base personas are suboptimal.

CHOIR: Harmonizing Structured Persona Diversity for Robust Collaborative LLM Reasoning

While NLP typically treats documents as independent and unordered samples, in longitudinal studies, this assumption rarely holds: documents are nested within authors and ordered in time, forming person-indexed, time-ordered $\textit{behavioral sequences}$.
Here, we demonstrate the need for and propose a longitudinal modeling and evaluation paradigm that consequently updates four parts of the NLP pipeline: 
(1) evaluation splits aligned to generalization over people ($\textit{cross-sectional}$) and/or time ($\textit{prospective}$); 
(2) accuracy metrics separating between-person differences from within-person dynamics; 
(3) sequence inputs to incorporate history by default; and 
(4) model internals that support different $\textit{coarseness}$ of latent state over histories (pooled summaries, explicit dynamics, or interaction-based models).
We demonstrate the issues ensued by traditional pipeline and our proposed improvements on a dataset of 17k daily diary transcripts paired with PTSD symptom severity from 238 participants, finding that traditional document-level evaluation can yield substantially different and sometimes reversed conclusions compared to our ecologically valid modeling and evaluation. 
We tie our results to a broader discussion motivating a shift from word-sequence evaluation toward $\textit{behavior-sequence}$ paradigms for NLP.

 From Word Sequences to Behavioral Sequences: Adapting Modeling and Evaluation Paradigms for Longitudinal NLP

The hubness problem, in which hub embeddings are close to many unrelated examples, occurs often in high-dimensional embedding spaces and may pose a practical threat for purposes such as information retrieval and automatic evaluation metrics.
In particular, since cross-modal similarity between text and images cannot be calculated by direct comparisons, such as string matching, cross-modal encoders that project different modalities into a shared space are helpful for various cross-modal applications, and thus, the existence of hubs may pose practical threats.
To reveal the vulnerabilities of cross-modal encoders, we propose a method for identifying the hub embedding and its corresponding hub text.
Experiments on image captioning evaluation in MSCOCO and nocaps along with image-to-text retrieval tasks in MSCOCO and Flickr30k showed that our method can identify a single hub text that unreasonably achieves comparable or higher similarity scores than human-written reference captions in many images, thereby revealing the vulnerabilities in cross-modal encoders.

One Single Hub Text Breaks CLIP: Identifying Vulnerabilities in Cross-Modal Encoders via Hubness

Prior work evaluating emotion and affective understanding in large language models (LLMs) typically rely on predetermined label sets or focus on a singular evaluation task (e.g., emotion detection). We consider affective states, referring to the much broader variety of terms people use to label their emotional experiences. We evaluate multilingual language models’ understanding of affective states in English and Spanish through three different tasks: 1) _identification_, where models predict an affective state given text, 2) _expression_, where models generate text expressing a given affective state, and 3) _verification_, where models report whether a given term refers to an affective state. We show that performance on one task is not necessarily predictive of performance on another. Using these three tasks, we then begin to explore when and why models struggle to understand particular affective states compared to others. We examine systematic patterns in the affective state terms that are well and poorly understood by models, characterizing the working emotion vocabulary of LLMs.

 Characterizing and Evaluating Working Emotion Vocabularies in Multilingual Large Language Models

Non-autoregressive (NAR) models have been mainly developed to improve decoding efficiency. Lately, they have also shown great potential in controlled text generation tasks. In this work, we investigate the decoding space of NAR models through lexically constrained machine translation tasks, and develop a search-based decoding algorithm named LexMAP, which is comparable to the autoregressive Grid Beam Search (GBS) method. Our analysis reveals several interesting properties of NAR decoding: 1) the NAR-based method does not suffer from the MAP degradation issue as the autoregressive method does; 2) AR beam search exhibits strong positional bias, in which the candidates only diverge at the end of the sequence; 3) NAR search explores a larger portion of the probability space, suggesting that the search algorithm better exploits the model's potential.

Diving into the Decoding Space of Non-Autoregressive Models via Lexically Constrained Search

Understanding how large language models (LLMs) store, retain, and remove knowledge is critical for interpretability, reliability, and privacy compliance. We reveal a key phenomenon: machine unlearning imprints distinct geometric signatures in the model's input loss landscape (ILL), with unlearned examples forming flat, low-curvature plateaus that contrast sharply with the high-curvature basins of retained or unseen examples. Remarkably, these patterns emerge even when pointwise losses overlap, exposing residual memorization through input-output behavior alone. 

Building on this insight, we introduce **REMIND (Residual Memorization in Neighborhood Dynamics)**, a framework that diagnoses memorization states (retained, forgotten, holdout) by probing local ILL curvature over semantically coherent neighborhoods. REMIND operates using only loss queries and a novel embedding-proximity perturbation method to generate controlled, interpretable variants. 

In evaluations, REMIND achieves 82% multi-class ROC-AUC, outperforming baselines like ROUGE-L and MIN-K%++, with roughly 2× higher AUC at 1% FPR, and remains robust on paraphrased inputs. This neighborhood-level geometric analysis provides a practical, interpretable lens on LLM knowledge retention and unlearning, detecting subtle residual signals missed by pointwise or aggregated metrics.

REMIND: Memorization and Unlearning in LLMs Through the Lens of Input Loss Landscapes

Large language models face fundamental trade-offs when processing long documents: full context is expensive and may exceed limits, while RAG risks missing relevant information. We evaluate four context strategies across six frontier models on three document formats (Word, Excel, and PowerPoint). Our key finding: agentic tool-augmented approaches dramatically outperform passive strategies, with RAG+Tools achieving 46\% accuracy vs 6\% for RAG-only. Tool benefits are consistent across formats (+28-40 points) and models. We further show that (1) intelligent routing matters more than iteration count, (2) tools provide unique capability beyond reasoning loops, and (3) forcing active exploration matches providing context proactively. These results suggest tool augmentation is crucial for complex document QA.

Agentic Context Strategies for Multi-Format Document Understanding: When Should Language Models Use Tools?

Large language models applied to clinical prediction exhibit case-level heterogeneity: simple cases yield consistent outputs, while complex cases produce divergent predictions under minor prompt changes. Existing single-agent strategies sample from one role-conditioned distribution, and multi-agent frameworks use fixed roles with flat majority voting, discarding the diagnostic signal in disagreement. We propose CAMP (Case-Adaptive Multi-agent Panel), where an attending-physician agent dynamically assembles a specialist panel tailored to each case's diagnostic uncertainty. Each specialist evaluates candidates via three-valued voting (KEEP/REFUSE/NEUTRAL), enabling principled abstention outside one's expertise. A hybrid router directs each diagnosis through strong consensus, fallback to the attending physician's judgment, or evidence-based arbitration that weighs argument quality over vote counts. On diagnostic prediction and brief hospital course generation from MIMIC-IV across four LLM backbones, CAMP consistently outperforms strong baselines while consuming fewer tokens than most competing multi-agent methods, with voting records and arbitration traces offering transparent decision audits.

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Graph-Based Alternatives to LLMs for Human Simulation

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