United States

In this paper, we address Task 1 on adverse
drug event (ADE) detection and Task 8 on Mul-
tiClinNER at SMM4H-HeaRD 2026. ADE
detection is formulated as a multilingual bi-
nary classification problem over social me-
dia posts spanning German, French, Russian,
English, Mandarin and Japanese, with zero-
shot on Farsi. Using XLM-RoBERTa-Large
with a dual-pooling head, combined with strat-
ified sampling, language-conditioned inputs,
translation-based augmentation, and calibrated
ensembling, our model achieves a macro F1
score of 0.6088, surpassing both the competi-
tion mean (0.5465) and median (0.5798). Our
work in MultiClinNER targets clinical NER
for English text. Using GLiNER-large with
sliding-window inference, query augmentation,
and calibrated thresholds, it achieves strict F1
scores of 0.7591 (Disease), 0.7263 (Procedure),
and 0.6733 (Symptom), outperforming a Pub-
MedBERT baseline across all entities.

ACL 2026

Vinland_Vector at #SMM4H-HeaRD 2026: Multilingual ADE Detection
and Query-Augmented Clinical NER for English

adverse drug event detection

clinical named entity recognition

gliner

xlm-roberta

multilingual nlp

clinical nlp

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>
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workshop paper

We present our systems for Task 1 and Task 3
of the #SMM4H-HeaRD 2026 shared tasks.
Task 1 focuses on binary classification of ad-
verse drug event (ADE) mentions across seven
languages, including a zero-shot Persian set-
ting without labeled training data. We fine-tune
XLM-RoBERTa-large using weighted cross-
entropy loss and augment low-resource settings
with additional CADEC data and machine-
translation-based Persian augmentation. Our
system achieves a macro F1 score of 0.582, out-
performing the shared task average of 0.547.
Task 3 addresses influenza vaccine effective-
ness estimation through classification of vac-
cination status and flu-test results from X
posts. We fine-tune twitter-roberta-large,
achieving micro F1 scores of 0.845 for vaccina-
tion status and 0.883 for flu-test classification
on the official test set. Post-evaluation experi-
ments with focal loss, test-time augmentation,
and head-tail truncation further improve per-
formance. These results highlight the effec-
tiveness of robust transformer adaptation for
health-related social media classification.

Cuet_Data_Wizards at #SMM4H-HeaRD 2026:
Multilingual ADE Detection and Influenza Vaccine Effectiveness
Estimation from Social Media

We introduce Output-Space Search (OS-Search), which turns LLM generation into endpoint search. An outer loop selects a target z* in a frozen encoder-defined 3D output space Z, and a retrieval-grounded policy trained with sequence-level RL generates outputs whose coordinates land near z* under standard autoregressive decoding. This enables parallel sweeps and black-box optimization in Z without path-dependent token/program search. On stories, sweeping Z (text) yields 3.1x higher LLM-scored diversity than prompt-chaining. On code, Bayesian optimization over Z (code) improves an objective withheld from the controller under matched inference budgets while preserving validity.

Output-Space Search: Targeting LLM Generations in a Frozen Encoder-Defined Output Space

Code generation is increasingly critical for real-world applications. Still, diffusion-based large language models continue to struggle with this demand. Unlike free-form text, code requires syntactic precision; even minor structural inconsistencies can render a program non-executable. Existing diffusion-based large language models rely on random token masking for corruption, leading to two key failures: they lack awareness of syntactic boundaries during the iterative denoising process, and they fail to capture the long-range hierarchical dependencies essential for program correctness.

We propose TreeDiff to address both issues. Specifically, we propose a syntax-aware diffusion framework that incorporates structural priors from Abstract Syntax Tree (AST) into the corruption process. Instead of masking individual tokens at random, we selectively mask tokens belonging to key AST nodes. By aligning the corruption process with the underlying structure of code, our method encourages the model to internalize the compositional nature of programming languages, enabling it to reconstruct programs that respect grammatical boundaries and capture long-range dependencies. Our method achieves a 13.3% relative improvement over the random masking training method, demonstrating its effectiveness in code generation task by leveraging underlying structures.

TreeDiff: AST-Guided Code Generation with Diffusion LLMs

Representing continuous time is a critical and under-explored challenge in modeling temporal event sequences with large language models (LLMs). Various strategies like byte-level representations or calendar tokens have been proposed. However, the optimal approach remains unclear, especially given the diverse statistical distributions of real-world event data, which range from smooth log-normal to discrete, spiky patterns. This paper presents a systematic empirical study of temporal tokenization for modeling event sequences with LLMs, comparing distinct encoding strategies: naive numeric strings, high-precision byte-level representations, human-semantic calendar tokens, classic uniform binning, and adaptive residual scalar quantization. We evaluate these strategies by fine-tuning LLMs on real-world datasets that exemplify these diverse distributions. Our analysis reveals that no single strategy is universally superior; instead, prediction performance depends heavily on aligning the tokenizer with the data's statistical properties, highlighting temporal tokenization as a critical yet often overlooked design dimension in LLM-based event modeling.

Temporal Tokenization Strategies for Event Sequence Modeling with Large Language Models

We introduce EconWebArena, a benchmark for evaluating autonomous agents on complex, multimodal economic tasks in realistic web environments. The benchmark comprises 360 curated tasks from 82 authoritative websites spanning domains such as macroeconomics, labor, finance, trade, and public policy. Each task challenges agents to navigate live websites, interpret structured and visual content, interact with real interfaces, and extract precise, time-sensitive data through multi-step workflows. We construct the benchmark by prompting multiple large language models (LLMs) to generate candidate tasks, followed by rigorous human curation to ensure clarity, feasibility, and source reliability. Unlike prior work, EconWebArena emphasizes fidelity to authoritative data sources and the need for grounded web-based economic reasoning. We evaluate a diverse set of state-of-the-art multimodal LLMs as web agents, analyze failure cases, and conduct ablation studies to assess the impact of visual grounding, plan-based reasoning, and interaction design. Our results reveal substantial performance gaps and highlight persistent challenges in grounding, navigation, and multimodal understanding, positioning EconWebArena as a rigorous testbed for economic web intelligence.

EconWebArena: Benchmarking Autonomous Agents on Economic Tasks in Realistic Web Environments

Accurate chart comprehension represents a critical challenge in advancing multimodal learning systems, as extensive information is compressed into structured visual representations. However, existing vision-language models (VLMs) frequently struggle to generalize on unseen charts because it requires abstract, symbolic, and quantitative reasoning over structured visual representations. In this work, we introduce Chart-RL, an effective reinforcement learning (RL) method that employs mathematically verifiable rewards to enhance chart question answering in VLMs. Our experiments demonstrate that Chart-RL consistently outperforms supervised fine-tuning (SFT) across different chart understanding benchmarks, achieving relative improvements of 16.7% on MultiChartQA, and 11.5% on ChartInsights. We conduct robustness analysis, where Chart-RL achieves enhanced performance in 18 of 25 perturbed chart categories, demonstrating strong consistency and reasoning capability across visual variations. Furthermore, we demonstrate that task difficulty and inherent complexity are more critical than data quantity in RL training. For instance, Chart-RL trained on merely 10 complex chart-query examples significantly outperforms models trained on over 6,000 simple examples. Additionally, training on challenging reasoning tasks not only improves in-domain generalization relative to simpler tasks, but also facilitate strong transfer to out-of-domain visual mathematical problems.

Chart-RL: Generalized Chart Comprehension via Reinforcement Learning with Verifiable Rewards

When a language model answers a table question, users have no way to verify which cells informed which reasoning steps. We introduce RSAT, a method that trains small language models (SLMs, 1–8B) to produce step-by-step reasoning with cell-level citations grounded in table evidence. Phase 1 (SFT) teaches a structured JSON output format from verified reasoning traces. Phase 2 (GRPO) optimizes a composite reward centered on NLI-based faithfulness, alongside citation validity and parsimony. Across six models from two families—Qwen2.5 (1.5B/3B/7B) and Llama3 (1B/3B/8B)—RSAT improves faithfulness 3.7$\times$ over SFT alone (0.224$\rightarrow$0.826), with near-perfect citation validity (0.992). Post-hoc attribution collapses below 13\% format success, confirming that attribution must be integrated into reasoning, not retrofitted. Ablations show the faithfulness reward is essential: removing it drops faithfulness from 0.97 to 0.03.

RSAT: Structured Attribution Makes Small Language Models Faithful Table Reasoners

Prompting schemes such as Chain of Thought, Tree of Thoughts, and Graph of Thoughts can significantly enhance the reasoning capabilities of large language models. However, most existing schemes require users to define static, problem-specific reasoning structures that lack adaptability to dynamic or unseen problem types. Additionally, these schemes are often under-optimized in terms of hyperparameters, prompts, runtime, and prompting cost. To address these limitations, we introduce Framework of Thoughts (FoT) – a general-purpose foundation framework for implementing and optimizing dynamic reasoning schemes. FoT comes with built-in features for hyperparameter tuning, prompt optimization, parallel execution, and intelligent caching, unlocking the latent performance potential of reasoning schemes. We demonstrate FoT’s capabilities by implementing three popular schemes – Tree of Thoughts, Graph of Thoughts, and ProbTree – within FoT. We empirically show that FoT enables significantly faster execution, reduces costs, and achieves better task scores through optimization. We release our codebase to facilitate the development of future dynamic and efficient reasoning schemes.

Framework of Thoughts: A Foundation Framework for Dynamic and Optimized Reasoning based on Chains, Trees, and Graphs

Tabular anomaly detection is challenging because real-world tables contain heterogeneous columns, ranging from structured identifiers to free-form text. Existing methods face a fundamental trilemma: rule-based systems require extensive manual configuration and fail on novel schemas; statistical methods scale efficiently but miss semantic errors; and LLM-based approaches understand semantics but incur prohibitive per-cell inference costs. No prior method simultaneously addresses semantic heterogeneity, domain-specific validation rules, and enterprise-scale processing.

We introduce TabGuard, an agentic framework that resolves this trilemma through semantic routing. Using LLM function calling, the system analyzes a small sample of each column and dynamically selects the most effective validation strategy, routing to a regex-based validator for syntactic patterns, a code-generation validator for domain-specific rules (such as Luhn checksums for credit cards), or an embedding-based validator for distributional outliers. This architecture decouples expensive cognitive reasoning ($O(m)$ LLM calls for $m$ columns) from scalable programmatic execution, enabling deployment on enterprise datasets without per-cell inference.

TabGuard: Agentic LLM Orchestration for Adaptive Tabular Anomaly Detection via Dynamic Validator Selection and Generation

Large language models have shown impressive capabilities in code generation, yet they often produce functionally incorrect code. Uncertainty quantification (UQ) methods have emerged as a promising approach for detecting hallucinations in natural language generation, but their effectiveness for code generation tasks remains underexplored. We systematically evaluate how UQ techniques transfer to code generation across three programming languages, five LLMs, and over 1,700 problems. We find that some token-probability-based methods generalize effectively without modification, while sampling-based methods relying on natural language inference (NLI) fail because NLI models cannot distinguish functionally different code, causing most responses to collapse into a single semantic cluster. To address this, we introduce \emph{functional equivalence methods}, a family of code-specific methods that replace NLI-based semantic equivalence with an LLM-based functional equivalence assessment, including functional entropy, a code-specific analog of semantic entropy. Functional equivalence methods achieve top AUROC in 11 out of 15 model-benchmark combinations and the best calibration across most settings, consistently outperforming both NLI-based counterparts and all other methods evaluated.

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Cuet_Data_Wizards at #SMM4H-HeaRD 2026: Multilingual ADE Detection and Influenza Vaccine Effectiveness Estimation from Social Media

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Gladiators at #SMM4H–HeaRD 2026: Multi-Seed XLM-RoBERTa Ensemble with Focal Loss and Per-Language Threshold Optimization for Multilingual Adverse Drug Event Detection

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