United States

We present a system that uses LLMs as a tool in the development of Constructed Languages— ConLangs, which we call IASC (Interactive Agentic System for ConLangs). The system is modular in that it creates each of the components—phonology, morphology and syntax, lexicon, orthography, and grammatical handbook, using module-specific sets of prompts. The approach is agentic in that various modules allow for refining the output given automatically-generated commentary on a previous step. Our main goals are twofold. First, we aim to provide tools that facilitate an engaging and enjoyable experience in creating artificially constructed languages. Second, the focus of this paper is on using our ConLang framework as a novel way to explore what LLMs ‘know’ about language—not what they know about any particular language or encyclopedic facts, but how much they know about and understand language and linguistic concepts. In the experiments, we particularly focus on the morphosyntax module and show that there is a fairly wide gulf in capabilities both among different LLMs and among different linguistic specifications, with it being notably easier for systems to deal with more typologically common patterns than rarer ones. All code is released: https://github.com/SakanaAI/IASC.

ACL 2026

Creating ConLangs to Probe the Metalinguistic Grammatical Knowledge of LLMs

Truth and Responsibility
“Truth and Responsibility” is a call to reimagine anthropology to meet the demands of the present moment. The imperative to bear witness, take action, and be held accountable to the truths we write and circulate invites us to reflect on our responsibility in reckoning with disciplinary histories, harms, and possibilities. To whom are we giving evidence and toward what ends? For whom are we writing? To whom are we accountable, and in what ways? We are thrilled to announce the theme of the 2021 AAA Annual Meeting to be held in Baltimore, MD: Truth and Responsibility.

Many consider anthropology to be a holistic social science dedicated to cross-cultural understanding as a way to reduce bigotry and more fully comprehend “what it means to be human.” Others push against this readily circulated origin story, arguing that “human” remains a highly contested, political category—evident as the presumed set of rights and protections associated with the “human” continue to be stripped away violently in the communities in which some of us live and work. Which violent histories and epistemologies must anthropologists take responsibility for in telling truth(s) about the discipline’s development? How do the dual histories of settler colonialism and slavery continue to influence anthropological thought and practices? In these political times, how can anthropologists throughout the globe work to secure a capacious, progressive vision of the human? How might movements for anti-racism, decoloniality, queer liberation, and healing such as #BlackLivesMatter, #ProtectMaunaKea, and #MeToo push a future anthropology out of the ashes of the anthropological past? Finally, what are the limits and possibilities of the anthropological imagination?

Across subfields, we find truths in patterns of human behavior, language, evolution, and cultural worlds. Industry-positioned anthropologists do this by extending the boundaries of the discipline not as an administrative solution to job shortages but as a source of intervention and knowledge production. Increasingly, anthropologists who make use of the scientific method question how status and identity help to decide which facts come to represent truth. How might quantitative researchers use our methods to identify patterned truths, and what responsibility do we hold in challenging conventional wisdom about these patterns? What responsibility do we have to both our colleagues and larger publics, to push toward increased transparency and ethical grounding in our data collection, analysis, and presentation?

Pedagogy is a key medium for the communication of anthropological truths. Which (and whose) truths are foregrounded in our curricula? What are the possibilities for a liberatory pedagogy in a precarious, “post-truth” era? Conferences, museums, organizing spaces, and classrooms are sites where pedagogy and various forms of communication can help us interrogate the performativity of truth. How might anthropologists conceive of truth performances? Might visual, aural, and choreographic performances during AAA sessions offer anthropologists alternative genres for truth-telling and truth-making that provoke discussions about responsibility?

In accordance with the urgency of the year’s theme, the questions posed in this call for papers do not have easy answers. The hope is that those who submit proposals will take up these questions and engage in collective thinking and imagining about the truths we hold, the truths we challenge, and the responsibilities we bear in co-creating a free and more equitable world. All panel proposals should have a statement about how the panel has incorporated the goals of equity, diversity, and inclusion, and/or analysis of power.

Executive Session proposals have already been selected and speak directly to the theme “Truth and Responsibility.” To practice the critical reflexivity this call for papers invites, we asked that Executive Session submissions explicitly address the diversity of session participants, such as organizational/institutional affiliation, career stage, race, ethnicity, disability, sexuality, class, religion, national identity, and gender. Proposals for Executive Sessions that demonstrated a commitment to equity and inclusion and/or a clear analysis of power (i.e. anti-imperialist, anti-white supremacist, decolonial, anti-ableist, feminist, anti-transphobic, anti-xenophobic, etc) were given priority. We are excited to be in conversation with residents of the Baltimore region and look forward to the discussions, performances, and actions generated by the dynamic group of anthropologists attending the 2021 AAA Annual Meeting.

AAA Annual Meeting 2021

“Truth and Responsibility” is a call to reimagine anthropology to meet the demands of the present moment. The imperative to bear witness, take action, and be held accountable to the truths we write and circulate invites us to reflect on our responsibility in reckoning with disciplinary histories, harms, and possibilities. To whom are we giving evidence and toward what ends? For whom are we writing? To whom are we accountable, and in what ways? We are thrilled to announce the theme of the 2021 AAA Annual Meeting to be held in Baltimore, MD: Truth and Responsibility.

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. <br>
[Full Message from General Chair Philipp Koehn](https://drive.google.com/file/d/1QiJFOmMjvDgsDo3i1XzlQnImxe3RU_Cy/view?usp=sharing) <br><br><br>
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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 Multimodal Models (LMMs) achieve state-of-the-art performance in high-stakes domains like healthcare, yet their reasoning remains opaque. Attention- and saliency-based methods often fail to faithfully represent the model's decision process, particularly when integrating heterogeneous modalities. We introduce Tree-of-Evidence (ToE), an inference-time search algorithm that frames interpretability as a discrete optimization problem. Rather than relying on soft attention weights, ToE employs lightweight Evidence Bottlenecks that score coarse units of data (e.g., vital-sign windows, report chunks) and performs a beam search to identify the compact evidence set required to reproduce the model's prediction. We evaluate ToE across six tasks spanning three datasets and two domains, including clinical prediction on MIMIC-IV, cross-center validation on eICU, and non-clinical fault detection on LEMMA-RCA. ToE retains over 98\% of full-model AUROC with as few as five evidence units, achieves higher decision agreement and lower fidelity error than LIME, SHAP, saliency, and concept-bottleneck baselines under sparse budgets, and outperforms LLMs up to 70B parameters. ToE therefore provides a practical mechanism for auditing multimodal models.

Tree-of-Evidence: Efficient "System 2" Search for Faithful Multimodal Grounding

While LLMs increasingly assist individual users, there is a critical need for agents that can proactively manage complex, multi-party collaboration. However, the scarcity of systematic evaluation methods for these group dynamics limits the development of AI capable of effectively supporting teams Here, we present ProMediate, the first testbed for evaluating proactive AI mediator agents in complex, multi-topic, multi-party negotiations. ProMediate consists of two core components: (i) a simulation environment based on realistic negotiation cases with a plug-and-play proactive AI mediator, capable of flexibly deciding when and how to intervene; and (ii) a socio-cognitive evaluation framework with a new suite of metrics to measure consensus changes, intervention latency, mediator effectiveness, and intelligence. These components establish a systematic framework for assessing the capability of proactive AI agents in multi-party settings. Our results show that a socially intelligent mediator agent outperforms a generic baseline, via faster, better-targeted interventions. In the ProMediate-Hard setting, our social mediator increases consensus change by 3.6 percentage points compared to the generic baseline (10.65% vs 7.01%) while being 77% faster in response (15.98s vs. 3.71s). In conclusion, ProMediate provides a rigorous, theory-grounded testbed to advance the development of proactive, socially intelligent agents.

ProMediate: A Simulation Testbed for Evaluating Proactive Mediation in Multi-Party Negotiation

Larger language models become simultaneously better and worse at handling contextual information—better at ignoring false claims, worse at ignoring irrelevant tokens. We formalize this apparent paradox through the first scaling laws for contextual entrainment, the tendency of models to favor tokens that appeared in context regardless of relevance. Analyzing the Cerebras-GPT (111M–13B) and Pythia (14M–12B) model families, we find entrainment follows predictable power-law scaling, but with opposite trends depending on context type: semantic contexts show decreasing entrainment with scale, while non-semantic contexts show increasing entrainment. Concretely, the largest models are four times more resistant to counterfactual misinformation than the smallest, yet simultaneously twice as prone to copying arbitrary tokens. These diverging trends, which replicate across model families, suggest that semantic filtering and mechanical copying are functionally distinct behaviors that scale in opposition. These opposing trends suggest that scaling alone does not resolve context sensitivity—it reshapes it.

Better and Worse with Scale: How Contextual Entrainment Diverges with Model Size

Indirect speech acts (ISAs) require pragmatic reasoning over context, as directive intent cannot be inferred from surface form alone. Prior text-based studies and existing multimodal benchmarks largely overlook this requirement, focusing instead on explicitly encoded context or perceptual recognition, and thus underexplore context-dependent pragmatic understanding—particularly in high-context languages such as Korean. We introduce READI, a multimodal benchmark for evaluating ISA understanding through integrated reasoning over visual context and dialogue. READI models graded indirectness grounded in pragmatic theory and formulates the task as vision-based pragmatic question answering (V-PQA), supporting cross-lingual evaluation in English and Korean. Experiments show that even state-of-the-art multimodal models struggle with visually grounded indirect speech acts, with performance declining as indirectness increases, underscoring the need for benchmarks that explicitly target contextual pragmatic reasoning.

 Read the Room, Read the Image: Understanding Indirect Speech Acts in Multimodal Visual Contexts

Existing information extraction (IE) tasks increasingly adopt in-context learning (ICL) with large language models. However, current approaches either show inconsistent performance across model scales or lack systematic optimization and generalizability. Building on this, we propose BCL-IE (Bayesian In-Context Learning Framework for Information Extraction), the first optimization framework that uses particle filtering with Bayesian updates to systematically refine label representations across IE tasks. Through four steps—initialization, observation, weight update, and resampling, BCL-IE generalizes to both sequence labeling and relation classification paradigms. Extensive experiments demonstrate substantial improvements over existing approaches (up to 30%), achieving prior performance while other methods either fail to generalize or show limited effectiveness.

 BCL: Bayesian In-Context Learning Framework for Information Extraction

The Uniform Information Density (UID) hypothesis proposes that effective communication is achieved by maintaining a stable flow of information. In this work, we revisit this principle in the context of Large Language Model (LLM) reasoning, asking whether step-level uniformity reflects reasoning quality. To this end, we introduce a novel framework to quantify uniformity of information flow at both local and global levels, using an entropy-based stepwise density metric. Across experiments on seven reasoning benchmarks, we see a counter-intuitive pattern: while high-quality reasoning exhibit smooth step-by-step transitions (local uniformity) and structured, non-uniform information flow at the trajectory level (global non-uniformity). The results demonstrate that these uniformities outperform alternative internal signals as predictors of reasoning quality, and such divergence with human communication is not a model deficiency, but a byproduct of distinct objectives between human communication and LLM reasoning.

 Revisiting the Uniform Information Density Hypothesis in LLM Reasoning

Enhancing the task-specific capabilities of Large Language Models (LLMs) primarily requires substantial instruction-tuning datasets. However, the sheer volume of such data imposes a considerable annotation cost, and a lack of optimization methods for tailoring LLMs to specific tasks persists. To address the above issues, we propose a Planning framework for constructing Extractive-based LLMs called PlanE, which includes data decomposition, instruction tuning, and prompt inference. Additionally, we introduce a Data-Tuning-Inference (DTI) planner, aimed at selecting the optimal base-LLM and its DTI combinations for specific datasets to improve construction efficiency. The experimental results demonstrate the effectiveness of our PlanE from two views: (1) across different datasets using the same base-LLM, and (2) on the same dataset using different base-LLMs. Furthermore, we validate the generalizability of the proposed DTI planner under different optimization objectives. The codes are publicly available at https://github.com/gugugu-469/PlanE.

 PlanE: Meta Planning of Data, Tuning, and Inference for Extractive-based LLMs

Reasoning language models (RLMs) achieve strong performance on complex reasoning tasks, yet they still exhibit a multilingual reasoning gap, performing better in high-resource languages than in low-resource ones. While recent efforts have been made to address this gap, its underlying causes remain largely unexplored. In this work, we show that this gap primarily stems from failures in language understanding—specifically, the model’s inability to translate multilingual inputs into the language dominating its reasoning traces (typically English). As identifying understanding failures can enable targeted mitigation of the gap, we evaluate a range of detection methods and find that understanding failures are detectable to a meaningful extent, with supervised approaches performing best. Building on this, we propose Selective Translation, a strategy that incorporates an English translation into the initial reasoning trace when an understanding failure is detected. Experimental results using Qwen3-4B show that Selective Translation substantially bridges the multilingual reasoning gap, achieving near full-translation performance while translating only about 20% of inputs. Together, our results show that failures in language understanding are the primary driver of the multilingual reasoning gap and can be detected and selectively mitigated, clarifying its origin and suggesting a path toward more equitable multilingual reasoning.

 Why Do Multilingual Reasoning Gaps Emerge in Reasoning Language Models?

A key challenge for Large Language Models (LLMs) is improving their Multilingual instruction-following ability over time without deteriorating their ability in languages they already excel at, typically English. In this paper, we study a two-phase \emph{Continual Fine-tuning (CFT)} setup toward improving a model's Multilingual adaptability. Concretely, we consider a two-phase CFT process in which an English-only end-to-end instruction fine-tuned LLM (Phase 1) is sequentially fine-tuned on a multilingual instruction dataset (Phase 2). Across MISTRAL-7B and LLAMA-3-8B and multiple dataset pairs, we show that instructional similarity between phases is critical: aligned datasets preserve or improve English while boosting multilingual ability, whereas misaligned datasets cause English degradation. We show that this degradation arises from representation shift during CFT, and that targeted mitigation strategies, including generative replay and heuristic-based layer freezing, reduce this shift and improve multilingual adaptation.

Exploring Two-Phase Continual Instruction Fine-tuning for Multilingual Adaptation in Large Language Models

The popular path query - identifying the most frequented routes between locations from historical trajectory data - has important applications in urban planning, navigation optimization, and travel recommendations. While traditional algorithms and machine learning approaches have achieved success in this domain, they typically require model training, parameter tuning, and retraining when accommodating data updates. As Large Language Models (LLMs) demonstrate increasing capabilities in spatial and graph-based reasoning, there is growing interest in exploring how these models can be applied to geo-spatial problems.

We introduce CompassLLM, a novel multi-agent framework that intelligently leverages the reasoning capabilities of LLMs into the geo-spatial domain to solve the popular path query. CompassLLM employs its agents in a two-stage pipeline: the SEARCH stage that identifies popular paths, and a GENERATE stage that synthesizes novel paths in the absence of an existing one in the historical trajectory data. Experiments on real and synthetic datasets show that CompassLLM demonstrates superior accuracy in SEARCH and competitive performance in GENERATE while being cost-effective.

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Tree-of-Evidence: Efficient "System 2" Search for Faithful Multimodal Grounding

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