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Long-form audio meeting understanding (LAMU) is gaining attention, but dedicated question answering (QA) datasets are lacking. Previous tailored speech QA and existing Speech LLMs suffer from acoustic information loss and poor long-term dependency capture. We construct the LongAudioQA dataset and propose the GRGA model, which models heterogeneous audio features into a multi-dimensional graph and leverages agent planning for retrieval and answer generation, effectively addressing existing limitations.

ACL 2026

Don&#39;t Just Listen, Try Planning: Graph-based Retrieval-Generation Agent for Long-form Audio Meeting Understanding

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.

Don't Just Listen, Try Planning: Graph-based Retrieval-Generation Agent for Long-form Audio Meeting Understanding

poster

Generative retrieval directly decode a document identifier (i.e., docid) in response to a query, making it impossible to provide users with explanations as an answer for ``why is this document retrieved?''. To address this limitation, we propose Hierarchical Category Path-Enhanced Generative Retrieval (HyPE), which enhances explainability by first generating hierarchical category paths step-by-step then decoding docid. By leveraging hierarchical category paths which progress from broader to more specific semantic categories, HyPE can provide detailed explanation for its retrieval decision. For training, HyPE constructs category paths with external high-quality semantic hierarchy, leverages LLM to select appropriate candidate paths for each document, and optimizes the generative retrieval model with path-augmented dataset. During inference, HyPE utilizes path-aware ranking strategy to aggregate diverse topic information, allowing the most relevant documents to be prioritized in the final ranked list of docids. Our extensive experiments demonstrate that HyPE not only offers a high level of explainability but also improves the retrieval performance. We provide the code and a live demo of HyPE at https://augustinlib.github.io/HyPE/

Why These Documents? Explainable Generative Retrieval with Hierarchical Category Paths

Text-to-SQL enables users to query databases using natural language by generating executable SQL queries. Recent methods have increasingly adopted Large Language Models based reinforcement learning (RL) to leverage execution feedback for training. However, existing RL methods assign uniform query-level rewards to all clauses in a SQL query, treating correct and incorrect clauses equally. This coarse-grained reward design leads to insufficient learning signals for correct SQL generation. To address this issue, we propose **EXPO-SQL** (**EX**ecution-based clause-level **P**olicy **O**ptimization for Text-to-**SQL**) which provides fine-grained supervision through clause-level rewards. To assign clause-level rewards, our method identifies erroneous clauses by analyzing execution results, including error messages and clause-wise incremental execution. Experiments on widely-used Text-to-SQL benchmarks demonstrate that EXPO-SQL significantly outperforms existing supervised fine-tuning, prompting, and RL-based methods through fine-grained clause-level learning. Our code is available at https://github.com/jhn25/EXPO-SQL.

EXPO-SQL: Execution-based Clause-level Policy Optimization for Text-to-SQL

The interpretation of implicit meanings is an integral aspect of human communication. However, this framework may not transfer to interactions with Large Language Models (LLMs). To investigate this, we introduce the task of Implicit Information Extraction (IIE) and propose an LLM-based IIE pipeline that builds a structured knowledge graph from a context sentence by extracting relational triplets, validating implicit inferences, and analyzing temporal relations. We evaluate two LLMs against crowdsourced human judgments on two datasets. We find that humans agree with most model triplets yet consistently propose many additions, indicating limited coverage in current LLM-based IIE. Moreover, in our experiments, models appear to be more conservative about implicit inferences than humans in socially rich contexts, whereas humans become more conservative in shorter, fact-oriented contexts. Our code is available at https://github.com/Antonio-Dee/IIE_from_LLM.

Comparing Human and Large Language Model Interpretation of Implicit Information

Multiple-choice question answering (MCQA) is easy to evaluate but adds a meta-task: models must both solve the problem and output the symbol that represents the answer, conflating reasoning errors with symbol-binding failures. We study how language models implement MCQA internally using representational analyses (PCA, linear probes) as well as causal interventions. We find that option-boundary (newline) residual states often contain strong linearly decodable signals related to per-option correctness. Winner-identity probing reveals a two-stage progression: the winning content position becomes decodable immediately after the final option is processed, while the output symbol is represented closer to the answer emission position. Tests under symbol and content permutations support a two-stage mechanism in which models first select a winner in content space and then bind or route that winner to the appropriate symbol to emit.

When Models Decide and When They Bind: A Two-Stage Computation for Multiple-Choice Question Answering

Vision-Language Models (VLMs) provide a unified framework to process both text-only tasks and vision-language tasks. However, finetuning VLMs on vision-language data has degraded language capabilities. In this paper, we prove that as the training loss declines during finetuning, the visual representation and textual representation move closer to each other, a phenomenon we term ``representation mixing.'' We prove that the representation mixing occurring within the post-representation layers causes the degradation of language capabilities. Post-representation layers refer to the first few layers in LLMs that are involved in representation learning. To preserve the language capabilities, we propose the Representation Regulation for VLM Training (RRVLM), which introduces a Representation Distribution Difference (RDD) loss to reduce the distance between these representations. Extensive experiments on various benchmarks and VLM frameworks show that our method can effectively preserve the language capabilities and achieve superior vision-language performance.

Preserving Language Capabilities in Vision-Language Models via Representation Regulation

Training data influence estimation methods quantify the contribution of training documents to a model’s output, making them a promising source of information for example-based explanations.
As humans cannot interpret thousands of documents, only a small subset of the training data can be presented as an explanation.
Although the choice of which documents to include directly affects explanation quality, previous evaluations of such systems have largely ignored any selection strategies.
To address this, we propose a novel *selection relevance score*, a retraining-free metric that quantifies how useful a set of examples is for explaining a model's output.
We validate this score through fine-tuning experiments, confirming that it can predict whether a set of examples supports or undermines the model's predictions.
Using this metric, we further show that common selection strategies often underperform random selection. 
Motivated by this finding, we propose a strategy that balances influence and representativeness, enabling better use of selection budgets than naively selecting the highest-ranking examples.

Compact Example-Based Explanations for Language Models

As the field of Multimodal Large Language Models (MLLMs) continues to evolve, their potential to handle mathematical reasoning tasks is promising, as they can handle multimodal questions via cross-modal understanding capabilities compared to text-only LLMs. Current mathematical benchmarks predominantly focus on evaluating MLLMs' problem-solving ability, yet there is a crucial gap in addressing more complex scenarios such as error detection, for enhancing reasoning capability in complicated settings. To fill this gap, we formally formulate the new task — multimodal error detection, and introduce **ErrorRadar, the first benchmark designed to assess MLLMs' capabilities in such a task. ErrorRadar evaluates two sub-tasks: error step identification and error categorization**, providing a framework for evaluating MLLMs' complex mathematical reasoning ability. It consists of 2,500 high-quality multimodal K-12 mathematical problems, collected from real-world student interactions in an educational organization, with expert-based annotation and metadata such as problem type and error category. Through extensive experiments, we evaluated both open-source and closed-source representative MLLMs, benchmarking their performance against educational expert evaluators. Results indicate challenges still remain, as GPT-4o with best model performance is still around 10% behind human evaluation

ErrorRadar: Benchmarking Complex Mathematical Reasoning of Multimodal Large Language Models Via Error Detection

Vision-Language Models (VLMs) have demonstrated remarkable performance in document understanding tasks; however, VLMs also suffer from hallucinations inherited from LLMs. While prior work has focused on reasoning-stage hallucinations, the role of visual perception remains underexplored. In this work, we define perceptual hallucination as the phenomenon where VLMs generate information as if perceived, despite absent or damaged visual evidence. To analyze this, we construct DocHallu, a benchmark of 2,671 original--damaged image pairs across three tasks, available at https://huggingface.co/datasets/IB99/DocHallu. Experiments reveal that perceptual hallucination occurs across all models, with higher rates for numerical content than textual content. Activation patching analysis suggests that hallucinations are strongly associated with errors introduced in the vision encoder, which can subsequently propagate and become amplified through the text decoding process. We also demonstrate that LLM-based post-hoc filtering can reduce hallucination exposure by 36\% on average, with reductions of up to 88\%. This work extends VLM hallucination research by defining, analyzing, and verifying perceptual hallucination in document understanding.

Perceptual Hallucination in Vision–Language Models: Definition, Analysis and Verification

Code optimization remains a core objective in software development, yet modern compilers struggle to navigate the enormous optimization spaces. While recent research has looked into employing large language models (LLMs) to optimize source code directly, these techniques can introduce semantic errors and miss fine-grained compiler-level optimization opportunities. We present HintPilot, which bridges LLM-based reasoning with traditional compiler infrastructures via synthesizing \emph{compiler hints}—annotations that steer compiler behavior. HintPilot employs retrieval-augmented synthesis over compiler documentation and applies profiling-guided iterative refinement to synthesize semantics-preserving and effective hints. Upon PolyBench and HumanEval-CPP benchmarks, HintPilot achieves up to 6.88x geometric mean speedup over \texttt{-Ofast} while preserving program correctness.

HintPilot: LLM-based Compiler Hint Synthesis for Code Optimization

Traditional topic modeling treats each document as a single, coherent unit of topic, which can cause topic contamination when documents cover multiple topics. This becomes especially problematic when stakeholders are interested in identifying documents that focus on a specific topic. We introduce segment-based topic allocation, a novel paradigm that redefines topic assignment at the level of segments, coherent textual spans conveying distinct topical content. This granularity improves topic purity, interpretability, and applicability to multi-theme corpora such as reviews or survey responses. To support this paradigm, we construct SemEval-STM, a benchmark derived from aspect-based sentiment datasets, where segments are automatically extracted using large language models (LLMs) and post-processed with human supervision. We further propose the segment intrusion task (SIT), a novel evaluation method extending word intrusion to the span level, enabling human-centric assessment of topical coherence. Empirical results across diverse metrics and models demonstrate that SBTA significantly outperforms traditional document-based methods in clustering and interpretability. Our framework provides a practical and scalable solution for fine-grained topic analysis in heterogeneous text corpora.

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Why These Documents? Explainable Generative Retrieval with Hierarchical Category Paths

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