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Text-to-image (T2I) generative models have achieved remarkable progress, demonstrating exceptional capability in synthesizing high-quality images from textual prompts. While existing research and benchmarks have extensively evaluated the ability of T2I models to follow the literal meaning of prompts, their ability to reason over prompts with domain knowledge to uncover implicit meaning and contextual nuances remains underexplored. To bridge this gap, we introduce T2I-ReasonBench, a novel benchmark designed to explore the knowledge-driven reasoning capabilities of T2I models.
T2I-ReasonBench comprises 800 meticulously designed prompts organized into four dimensions: \textbf{(1) Idiom Interpretation}, \textbf{(2) Textual Image Design}, \textbf{(3) Entity Reasoning}, and \textbf{(4) Scientific Reasoning}. These dimensions challenge models to integrate domain knowledge, infer implicit meaning, and resolve contextual ambiguities. To quantify the performance, we introduce a two-stage evaluation framework: a large language model (LLM) generates prompt-specific question-criterion pairs that evaluate if the image includes the essential elements resulting from correct reasoning; a multimodal LLM (MLLM) then scores the generated image against these criteria. 
Our comprehensive study across 16 state-of-the-art diffusion and unified multimodal models (UMMs) reveal two primary bottlenecks. First, many models lack the foundational reasoning ability to fully comprehend complex prompts. Second, even models with stronger reasoning modules exhibit a persistent gap between their internal understanding and the final generated image. This highlights an urgent need for the next generation of T2I systems to not only improve their reasoning capability but also to enhance integration between reasoning and synthesis.

ACL 2026

T2I-ReasonBench: Benchmarking Reasoning-Informed Text-to-Image Generation

We are pleased to announce the Thirty-Eighth AAAI Conference on Artificial Intelligence (AAAI-24), which will be held in Vancouver, British Columbia at the Vancouver Convention Centre – West Building from 20-27 February, 2024.

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-24 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 2024

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. 

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. 
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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.

Prompt underspecification is a common challenge when interacting with LLMs. In this paper, we present an in-depth analysis of this problem, showing that while LLMs can often infer unspecified requirements by default (41.1%), such behavior is fragile: Under-specified prompts are 2x as likely to regress across model or prompt changes, sometimes with accuracy drops exceeding 20%. This instability makes it difficult to reliably build LLM applications. Moreover, simply specifying all requirements does not consistently help, as models have limited instruction-following ability and requirements can conflict. Standard prompt optimizers likewise provide little benefit. To address these issues, we propose requirements-aware prompt optimization mechanisms that improve performance by 4.8% on average over baselines. We further advocate for a systematic process of proactive requirements discovery, evaluation, and monitoring to better manage prompt underspecification in practice.

What Prompts Don’t Say: Understanding and Managing Underspecification in LLM Prompts

Large language models (LLMs) can answer questions and summarize documents when conditioned on external contexts (e.g., retrieved evidence), yet context use remains unreliable: models may overwrite an already-correct output (neutral regression) even when the context is non-informative. We formalize neutral regression as a do-no-harm requirement and quantify it by measuring accuracy drops on baseline-correct items under answer-consistent contexts. We propose No-Worse Context-Aware Decoding (NWCAD), a decode-time adapter built on a two-stream setup with a two-stage gate: it backs off to no-context decoding when the context is non-informative, and otherwise uses context-conditioned decoding with a contrastive fallback under uncertainty. We evaluate NWCAD on benchmarks that separate do-no-harm reliability from context utilization (accuracy gains on genuinely helpful contexts). NWCAD prevents neutral regression on baseline-correct items while preserving strong context-driven accuracy on helpful contexts.

No-Worse Context-Aware Decoding: Preventing Neutral Regression in Context-Conditioned Generation

Preprocessing-based methods for stereotype mitigation, such as pre-/post-training on debiased corpora, are widely used in NLP. While these approaches reduce measurable stereotypes for targeted groups, we find they often induce unintended shifts: stereotyping or counter-stereotyping can increase for other demographics, including across unrelated categories. We demonstrate these side effects across two model families (encoder-only and decoder-only), multiple preprocessing strategies (removing stereotypical sentences, removing group mentions, and swapping references), and both pre- and post-training at different data scales on Wikipedia. Standard benchmarks frequently miss these shifts. Using attention-rollout analysis, we observe that such side effects are not accompanied by large changes in attention flow, complicating mechanistic explanations. We discuss implications for evaluation, provide actionable diagnostics, and argue for side-effect-aware, transparent mitigation practices that make claims calibrated to uncertainty.

When Debiasing Backfires: Counterintuitive Side Effects of Preprocessing-Based Stereotype Mitigation

Large Language Models (LLMs) have made strong progress in reasoning. To enhance the reasoning performance, a common inference-time approach is tree-based search, which decomposes the reasoning process into multiple steps, expands multiple reasoning paths, and uses reward models to prune and select candidates. However, based on our exploration, the simple decomposition may lead to suboptimal searching efficiency: while planning is generally harder, it is the execution errors that are more likely to propagate to later steps. This indicates that planning and execution play different roles in reasoning and should be treated differently during tree-based search. Given this, to enhance the searching efficiency, we propose a dual-phase test-time scaling framework that separates reasoning into planning and execution, and performs search over each phase independently. To further refine the algorithm, we also introduce a dynamic budget allocation mechanism that adaptively redistributes sampling effort based on reward feedback, allowing early stopping on confident steps and reallocation of computation to more challenging steps. Experiments on both math reasoning and code generation benchmarks demonstrate that our approach consistently improves accuracy while reducing redundant computation.

A Reward-Guided Dual-Phase Framework for Adaptive Inference-Time Reasoning

Chain-of-thought (CoT) prompting makes language models write step-by-step explanations, but these steps may not match what the model actually used to choose its answer. Existing faithfulness checks often only test whether changing the written chain changes the answer, without verifying whether the steps are truly supported by the given evidence, or they require special prompts that do not generalize well. We present \textbf{NSF-CoT}, a neuro-symbolic formal verification method that checks CoT faithfulness step by step for contextual question answering. NSF-CoT (1) converts the provided context facts and each reasoning step into simple logical statements, (2) uses counterfactual attribution to estimate which context facts the model relied on while generating each step, and (3) verifies each step using a hybrid checker that combines an SMT solver with an LLM-based entailment judge. For every step, we score \emph{groundedness} (supported by the full context), \emph{validity} (supported by the facts the model relied on), and \emph{utility} (helps reach the final answer), and combine them into a faithfulness score. Across OpenBookQA, QASC, and HotpotQA, NSF-CoT consistently outperforms causal mediation, perturbation probes, and behavioral monitoring, and it identifies reasoning steps that are not only unfaithful but also harmful to the model's final decision.

NSF-CoT: Neuro-Symbolic Formal Verification of Chain-of-Thought Faithfulness in Contextual Question Answering

Current vision-language benchmarks predominantly feature well-structured questions with clear, explicit prompts. However, real user queries are often informal and underspecified. Users naturally leave much unsaid, relying on images to convey context. We introduce HAERAE-Vision, a benchmark of 653 real-world visual questions from Korean online communities (0.76% survival from 86K candidates), each paired with an explicit rewrite, yielding 1,306 query variants in total. Evaluating 39 VLMs, we find that even state-of-the-art models (GPT-5, Gemini 2.5 Pro) achieve under 50% on the original queries. Crucially, query explicitation alone yields 8 to 22 point improvements, with smaller models benefiting most. We further show that even with web search, under-specified queries underperform explicit queries without search, revealing that current retrieval cannot compensate for what users leave unsaid. Our findings demonstrate that a substantial portion of VLM difficulty stem from natural query under-specification instead of model capability, highlighting a critical gap between benchmark evaluation and real-world deployment.

What Users Leave Unsaid: Under-Specified Queries Limit Vision-Language Models

Answering real-world geospatial questions---such as finding restaurants along a travel route or amenities near a landmark---requires reasoning over both geographic relationships and semantic user intent. However, existing large language models (LLMs) lack spatial computing capabilities and access to up-to-date, ubiquitous real-world geospatial data, while traditional geospatial systems fall short in interpreting natural language. To bridge this gap, we introduce Spatial-RAG, a Retrieval-Augmented Generation (RAG) framework designed for geospatial question answering. Spatial-RAG integrates structured spatial databases with LLMs via a hybrid spatial retriever that combines sparse spatial filtering and dense semantic matching. It formulates the answering process as a multi-objective optimization over spatial and semantic relevance, identifying Pareto-optimal candidates and dynamically selecting the best response based on user intent. Experiments across multiple tourism and map-based QA datasets show that Spatial-RAG significantly improves performance over strong baselines.

Spatial-RAG: Spatial Retrieval Augmented Generation for Real-World Geospatial Reasoning Questions

LLMs show promise in code generation, yet their effectiveness for IT automation tasks, particularly for tools like Ansible, remains understudied. Existing benchmarks rely primarily on synthetic tasks that fail to capture the needs of practitioners who use IT automation tools. We present ExITBench (Execution-based IT Automation Benchmark), a benchmark of 126 diverse tasks (e.g., configuring servers and managing files) in which each task captures state reconciliation - a core property of IT automation tools. ExITBench evaluates LLMs' ability to generate functional Ansible automation scripts via dynamic execution in controlled environments. We evaluate 14 open-source and 3 proprietary LLMs and find that GPT-4.1-Mini achieves the best pass@10 rate of 23.9\%, while Claude-3.5-Sonnet achieves the best pass@1 performance. To explain the low performance, we analyze 1,517 execution failures across the evaluated LLMs and identify two prevalent semantic error categories: failures in state-reconciliation reasoning (42.117\% combined from variable (12.287\%), host (10.363\%), path (10.511\%), and template (8.956\%) issues) and deficiencies in module-specific execution knowledge (26.203\% combined from attribute \& parameter (17.617\%) and module (8.586\%) errors). Our findings reveal key limitations in LLMs' ability to address state reconciliation and apply specialized module knowledge, indicating that reliable IT automation with LLM-based agents need major advances in state reasoning and domain-specific execution.

Large Language Models for IT Automation Tasks: Are We There Yet?

While recent safety guardrails effectively suppress overtly biased outputs, subtler forms of social bias emerge during complex logical reasoning tasks that evade current evaluation benchmarks. To fill this gap, we introduce a new evaluation framework, PRIME ($\textbf{P}$uzzle $\textbf{R}$easoning for $\textbf{I}$mplicit Biases in $\textbf{M}$odel $\textbf{E}$valuation), that uses logic grid puzzles to systematically probe the influence of social stereotypes on logical reasoning and decision making in LLMs. Our use of logic puzzles enables automatic generation and verification, as well as variability in complexity and biased settings. PRIME includes stereotypical, anti-stereotypical, and neutral puzzle variants generated from a shared puzzle structure, allowing for controlled and fine-grained comparisons. We evaluate multiple model families across puzzle sizes and test the effectiveness of prompt-based mitigation strategies. Focusing our experiments on gender stereotypes, our findings highlight that models consistently reason more accurately when solutions align with stereotypical associations. This demonstrates the significance of PRIME for diagnosing and quantifying social biases perpetuated in the deductive reasoning of LLMs, where fairness is critical.

Evaluating Implicit Biases in LLM Reasoning through Logic Grid Puzzles

Clinical named entity recognition (NER) remains difficult to scale due to the high cost of manual annotation. Although large language models (LLMs) enable zero-shot annotation, their performance on clinical NER is still limited. To this end, we improve the annotation quality by aggregating annotations from *a herd of diverse LLMs*, including general-purpose, medically adapted, and NER-specialized models. A key challenge in this multi-LLM setting is effectively leveraging entities extracted by only a minority of models: although they account for a substantial portion of true positives, they are heavily intermixed with noise. To address this, we introduce **MARY**, a label-modeling method for **M**ulti-LLM **A**nnotation using **R**epresentation learning to capture contextual similarit**Y**. During aggregation, MARY selectively incorporates minority-extracted entities whose contexts are similar to those of majority-extracted entities, yielding more reliable and comprehensive annotations. Experimental results show that MARY improves the average F1 score by 8.6% over vanilla zero-shot baselines while reducing annotation costs.

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