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Recurrent neural networks (RNNs), such as linear attention and state-space models, have gained popularity due to their constant per-token complexity when processing long contexts. However, these recurrent models struggle with tasks that require accurate recall of contextual information from long contexts, because all contextual information is compressed into a fixed-size recurrent state. Previous studies have shown that recall ability is positively correlated with the recurrent state size, yet directly training RNNs with large recurrent states results in high training costs. In this paper, we introduce StateX, a post-training framework that efficiently expands the states of pre-trained RNNs. For two popular classes of RNNs, linear attention and state-space models, we design post-training architectural modifications in StateX, to scale up the state size with no or negligible increase in model parameters. Experiments on models with up to 1.3B parameters demonstrate that StateX efficiently enhances the recall and in-context learning performance of RNNs without incurring high post-training costs or compromising other capabilities.

ACL 2026

 StateX: Enhancing RNN Recall via Post-training State Expansion

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ACL 2024

The 62nd Annual Meeting of the Association for Computational Linguistics (ACL 2024) will take place in Bangkok, Thailand from August 11th to 16th, 2024. More information will be announced soon.

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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. 
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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.

Safety-critical traffic reasoning requires contrastive consistency: models must detect true hazards when an accident occurs, and reliably reject plausible-but-false hypotheses under near-identical counterfactual scenes. We present CCTVBench, a Contrastive Consistency Traffic VideoQA Benchmark built on paired real accident videos and world-model-generated counterfactual counterparts, together with minimally different, mutually exclusive hypothesis questions. CCTVBench enforces a single structured decision pattern over each video question quadruple and provides actionable diagnostics that decompose failures into positive omission, positive swap, negative hallucination, and mutual-exclusivity violation, while separating video versus question consistency. Experiments across open-source and proprietary video LLMs reveal a large and persistent gap between standard per-instance QA metrics and quadruple-level contrastive consistency, with unreliable none-of-the-above rejection as a key bottleneck. Finally, we introduce C-TCD, which leverages the semantically exclusive counterpart video as the contrast input at inference time, improving both instance-level QA and contrastive consistency.

CCTVBench: Contrastive Consistency Traffic VideoQA Benchmark for Multimodal LLMs

Generic sentences express generalizations that tolerate exceptions without explicitly communicating information about quantities. For example, the sentence Ravens are black is true even though there are albino ravens. The sentence doesn’t explicitly communicate the number or frequency of black ravens. Whether generics semantically encode information about quantities implicitly is controversial. This work takes a large-scale distributional approach to the semantic debate. It compares thousands of naturally occurring generics and quantificational sentences using language-model probabilities. It shows that language models recover many semantic facts about quantifiers. It also shows that they recover semantic facts about surface distributional differences between generics and their “quantificational counterparts”. Accordingly, and contrary to dominant views in other fields, we formulate an empirical argument to the effect that generics are not quantificational.

Generics are not quantificational: A new path from language models to semantic theory

Numerical precision is critical in financial NLP, yet embedding-based semantic similarity metrics exhibit numerical blindness---failing to distinguish contradictory values within similar contexts. We introduce NASH (Numerically Aware Scoring Hueristic), a model-agnostic metric that decouples numerical verification from textual semantic evaluation through a three-stage pipeline: (1) modal separation via numeric masking, (2) dual-channel similarity estimation through masked-text similarity and context-aware numeric alignment, and (3) IDF-weighted aggregation. NASH functions as a drop-in enhancement to existing embedding-based metrics. Validated on our proposed NumFinE financial numerical evaluation benchmark and established semantic similarity datasets (STS-B, Financial-STS), NASH achieves substantial improvements in numerical sensitivity (up to +159.6% on listwise ranking) while preserving general semantic performance, establishing a reliable standard for numeracy-aware evaluation.

NASH: Numerically Aware Scoring Heuristic for Robust Semantic Similarity

Automatic evaluation metrics are central to the development of machine translation systems, yet their robustness under domain shift remains unclear. Most metrics are developed on the Workshop on Machine Translation (WMT) benchmarks, raising concerns about their robustness to unseen domains. Prior studies that analyze unseen domains vary translation systems, annotators, or evaluation conditions, confounding domain effects with human annotation noise.

To address these biases, we introduce a systematic multi-annotator **C**ross-**D**omain **E**rror-**S**pan-**A**nnotation dataset (CD-ESA), comprising 18.8k human error span annotations across three language pairs, where we fix annotators within each language pair and evaluate translations of the same six translation systems across one seen news domain and two unseen technical domains. 
Using this dataset, we first find that automatic metrics appear surprisingly robust to domain-shifts at the segment level (up to 0.69 agreement), but this robustness largely disappears once we account for human label variation. Averaging annotations increases inter-annotator agreement by up to +0.11. Metrics struggle on the unseen chemical domain compared to humans (inter-annotator agreement of 0.78–0.83 vs. 0.96).

We recommend comparing metric–human agreement against inter-annotator agreement, rather than comparing raw metric–human agreement alone, when evaluating across different domains.

Who Watches the Watchmen? Humans Disagree With Translation Metrics on Unseen Domains

Large language models (LLMs) are increasingly used as reasoning engines in autonomous driving, yet their decision-making remains opaque. We propose to study their decision process through counterfactual explanations, which identify the minimal semantic changes to a scene description required to alter a driving plan.

We introduce DRIV-EX, a method that leverages gradient-based optimization on continuous embeddings to identify the input shifts required to flip the model's decision. Crucially, to avoid the incoherent text typical of unconstrained continuous optimization, DRIV-EX uses these optimized embeddings solely as a semantic guide: they are used to bias a controlled decoding process that re-generates the original scene description. This approach effectively steers the generation toward the counterfactual target while guaranteeing the linguistic fluency, domain validity, and proximity to the original input essential for interpretability.

Evaluated using the LC-LLM planner on the textual highD dataset, DRIV-EX generates valid, fluent counterfactuals more reliably than existing baselines. It successfully exposes latent biases and provides concrete insights to improve the robustness of LLM-based driving agents. The code is available at "https://github.com/Amaia-CARDIEL/DRIV_EX".

DRIV-EX: Counterfactual Explanations for Driving LLMs

Recent Omni-multimodal Large Language Models show promise in unified audio, vision, and text modeling. However, streaming audio-video understanding remains challenging, as existing approaches suffer from disjointed capabilities: they typically exhibit incomplete modality support or lack autonomous proactive monitoring. To address this, we present ROMA, **a real-time omni-multimodal assistant for unified reactive and proactive interaction**. ROMA processes continuous inputs as synchronized multimodal units, aligning dense audio with discrete video frames to handle granularity mismatches. For online decision-making, we introduce a lightweight *speak head* that decouples response initiation from generation to ensure precise triggering without task conflict. We train ROMA with a curated streaming dataset and a two-stage curriculum that progressively optimizes for streaming format adaptation and proactive responsiveness. To standardize the fragmented evaluation landscape, we reorganize diverse benchmarks into a unified suite covering both proactive (alert, narration) and reactive (QA) settings. Extensive experiments across 12 benchmarks demonstrate ROMA achieves state-of-the-art performance on proactive tasks while competitive in reactive settings, validating its robustness in unified real-time omni-multimodal understanding. Code and benchmark are available [here](https://eureka-maggie.github.io/ROMA_show/).

ROMA: Real-time Omni-Multimodal Assistant with Interactive Streaming Understanding

Log-likelihood vectors define a common space for comparing language models as probability distributions, enabling unified comparisons across heterogeneous settings. We extend this framework to training checkpoints and intermediate layers, and establish a consistent scale for KL divergence across pretraining, model size, random seeds, quantization, fine-tuning, and layers. Analysis of Pythia pretraining trajectories further shows that changes in log-likelihood space, as measured by the scaling behavior of KL divergence, are much smaller than in weight space, resulting in subdiffusive learning trajectories and early stabilization of language-model behavior despite weight drift.

Establishing a Scale for Kullback-Leibler Divergence in Language Models Across Various Settings

Financial question answering (QA) over long corporate filings requires evidence to satisfy strict constraints on entities, financial metrics, fiscal periods, and numeric values. However, existing LLM-based rerankers primarily optimize semantic relevance, leading to unstable rankings and opaque decisions on long documents. We propose FINCARDS, a structured reranking framework that reframes financial evidence selection as constraint satisfaction under a finance-aware schema. FINCARDS represents filing chunks and questions using aligned schema fields (entities, metrics, periods, and numeric spans), enabling deterministic field-level matching. Evidence is selected via a multi-stage tournament reranking with stability-aware aggregation, producing auditable decision traces. Across two corporate filing QA benchmarks, FINCARDS substantially improves early-rank retrieval over both lexical and LLM-based reranking baselines, while reducing ranking variance, without requiring model fine-tuning or unpredictable inference budgets. Our code is available at https://github.com/XanderZhou2022/FINCARDS.

FinCARDS: Card-Based Analyst Reranking for Financial Document Question Answering

Text-to-image diffusion models achieve remarkable generation quality, yet their internal mechanisms for grounding prompt semantics into visual structure remain poorly understood. We present a novel mechanistic interpretability framework for Stable Diffusion that probes how individual prompt tokens are represented and utilized during the denoising process. Given a prompt, we record cross-attention activations throughout UNet denoising and convert them into token-level spatial grounding maps that indicate where each token contributes signal during image synthesis. To establish causal faithfulness, we perform controlled prompt interventions by removing a single word at a time while keeping the sampling seed fixed, producing counterfactual generations. To quantify mechanistic sensitivity, we introduce a head-resolved spike score based on divergence between per-head token contribution distributions before and after intervention, enabling module-wise and head-wise attribution of semantic changes. Experiments on compositional prompts and challenging relational descriptions reveal systematic patterns of token grounding, semantic drift, and head specialization across denoising timesteps. Our results provide a practical and reproducible toolkit for analyzing how diffusion models encode and apply semantic information, supporting deeper transparency in text-to-image generation.

Mechanistic Interpretability of Text-to-Image Diffusion Models via Cross-Attention Interventions

Accurate estimation of item (question or task) difficulty is critical for educational assessment but suffers from the cold start problem. While Large Language Models demonstrate superhuman problem-solving capabilities, it remains an open question whether they can perceive the cognitive struggles of human learners. In this work, we present a large-scale empirical analysis of Human-AI Difficulty Alignment for over 20 models across diverse domains such as medical knowledge and mathematical reasoning. Our findings reveal a systematic misalignment where scaling up model size is not reliably helpful; instead of aligning with humans, models converge toward a shared machine consensus. We observe that high performance often impedes accurate difficulty estimation, as models struggle to simulate the capability limitations of students even when being explicitly prompted to adopt specific proficiency levels. Furthermore, we identify a critical lack of introspection, as models fail to predict their own limitations. These results suggest that general problem-solving capability does not imply an understanding of human cognitive struggles, highlighting the challenge of using current models for automated difficulty prediction.

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CCTVBench: Contrastive Consistency Traffic VideoQA Benchmark for Multimodal LLMs

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