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Recent work has increasingly explored neuron-level interpretation in vision-language models (VLMs) to identify neurons critical to final predictions. However, existing neuron analyses generally focus on single tasks, limiting the comparability of neuron importance across tasks. Moreover, ranking strategies tend to score neurons in isolation, overlooking how task-dependent information pathways shape the write-in effects of feed-forward network (FFN) neurons. This oversight can exacerbate neuron polysemanticity in multi-task settings, introducing noise into the identification and intervention of task-critical neurons. In this study, we propose HONES (**H**ead-**O**riented **N**euron **E**xplanation &amp; **S**teering), a gradient-free framework for task-aware neuron attribution and steering in multi-task VLMs. HONES ranks FFN neurons by their causal write-in contributions conditioned on task-relevant attention heads, and further modulates salient neurons via lightweight scaling. Experiments on four diverse multimodal tasks and two popular VLMs show that HONES outperforms existing methods in identifying task-critical neurons and improves model performance after steering. Our source code is released at: https://github.com/petergit1/HONES.

ACL 2026

From Heads to Neurons: Causal Attribution and Steering in Multi-Task Vision–Language Models

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EMNLP 2024 will take place in Miami, Florida from Nov 12th to Nov 16th, 2024, at the Hyatt Regency Miami Hote and on Underline for remote participants.

Recent work has increasingly explored neuron-level interpretation in vision-language models (VLMs) to identify neurons critical to final predictions. However, existing neuron analyses generally focus on single tasks, limiting the comparability of neuron importance across tasks. Moreover, ranking strategies tend to score neurons in isolation, overlooking how task-dependent information pathways shape the write-in effects of feed-forward network (FFN) neurons. This oversight can exacerbate neuron polysemanticity in multi-task settings, introducing noise into the identification and intervention of task-critical neurons. In this study, we propose HONES (**H**ead-**O**riented **N**euron **E**xplanation & **S**teering), a gradient-free framework for task-aware neuron attribution and steering in multi-task VLMs. HONES ranks FFN neurons by their causal write-in contributions conditioned on task-relevant attention heads, and further modulates salient neurons via lightweight scaling. Experiments on four diverse multimodal tasks and two popular VLMs show that HONES outperforms existing methods in identifying task-critical neurons and improves model performance after steering. Our source code is released at: https://github.com/petergit1/HONES.

technical paper

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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.

In task-oriented dialogue (TOD) systems, Slot Schema Induction (SSI) is essential for automatically identifying key information slots from dialogue data without manual intervention. This paper presents a novel state-of-the-art (SoTA) approach that formulates SSI as a text generation task, where a language model incrementally constructs and refines a slot schema over a stream of dialogue data. To develop this approach, we present a fully automatic LLM-based TOD simulation method that creates data with high-quality state labels for novel task domains. Furthermore, we identify issues in SSI evaluation due to data leakage and poor metric alignment with human judgment. We resolve these by creating new evaluation data using our simulation method with human guidance and correction, as well as designing improved evaluation metrics. These contributions establish a foundation for future SSI research and advance the SoTA in dialogue understanding and system development.

Generative Induction of Dialogue Task Schemas with Streaming Refinement and Simulated Interactions

Sarcasm detection remains a significant challenge due to its reliance on nuanced contextual understanding, world knowledge, and multi-faceted linguistic cues that vary substantially across different sarcastic expressions. Existing approaches, from fine-tuned transformers to large language models, apply a uniform reasoning strategy to all inputs, struggling to address the diverse analytical demands of sarcasm. These demands range from modeling contextual expectation violations to requiring external knowledge grounding or recognizing specific rhetorical patterns. To address this limitation, we introduce RAM-SD, a Retrieval-Augmented Multi-Agent framework for Sarcasm Detection. The framework operates through four stages: (1) contextual retrieval grounds the query in both sarcastic and non-sarcastic exemplars; (2) a meta-planner classifies the sarcasm type and selects an optimal reasoning plan from a predefined set; (3) an ensemble of specialized agents performs complementary, multi-view analysis; and (4) an integrator synthesizes these analyses into a final, interpretable judgment with a natural language explanation. Evaluated on four standard benchmarks, RAM-SD achieves a state-of-the-art Macro-F1 of 77.74%, outperforming the strong GPT-4o+CoC baseline by 7.01 points. Our framework not only sets a new performance benchmark but also provides transparent and interpretable reasoning traces, illuminating the cognitive processes behind sarcasm comprehension.

RAM-SD: Retrieval-Augmented Multi-agent framework for Sarcasm Detection

Large Language Models (LLMs) excel at complex reasoning through search algorithms, yet current strategies often suffer from massive token consumption due to redundant exploration of semantically equivalent steps. Existing semantic similarity methods struggle to accurately identify such equivalence in domain-specific contexts like mathematical reasoning. To address this, we propose \textit{EquivPruner}, a simple yet effective approach that identifies and prunes semantically equivalent actions during LLM reasoning search. We also introduce MathEquiv, the first dataset we created for mathematical statement equivalence, which enables the training of a lightweight equivalence detector. Extensive experiments across various models and tasks demonstrate that \textit{EquivPruner} significantly reduces token consumption, improving searching efficiency and often bolstering reasoning accuracy. For instance, when applied to Qwen2.5-Math-7B-Instruct on GSM8K, \textit{EquivPruner} reduced token consumption by 48.1\% while also improving accuracy. Our code is available at \url{https://github.com/Lolo1222/EquivPruner}.

EquivPruner: Boosting Efficiency and Quality in LLM-Based Search via Action Pruning

Generalized Category Discovery (GCD) aims to identify both known and novel categories from partially labeled data, reflecting more realistic open-world learning scenarios. However, most existing methods rely solely on one-hot discriminative supervision, leading to overfitting on seen classes and poor generalization to unseen ones. Recent advances introduce large language models (LLMs) to incorporate external semantics, yet they often suffer from semantic–label misalignment and weak semantic integration during training. We propose GenDis, a Generative–Discriminative Dual-View Co-Training framework that unifies discriminative classification and semantic label generation within an LLM. Discriminative pseudo-labels guide the formation of a separable generative latent space, enabling semantically meaningful supervision for novel classes. To ensure consistency between the two views, we employ Canonical Correlation Analysis (CCA)-based alignment and a curriculum-guided, dispersion-aware pseudo-labeling strategy for iterative refinement. Extensive experiments on five GCD benchmarks demonstrate that GenDis substantially outperforms prior methods, validating the effectiveness of dual-view co-training with semantically enriched supervision. The anonymized repository is available at https://anonymous.4open.science/r/GenDis.

GenDis: Generative-Discriminative Dual-View Co-Training for Generalized Category Discovery

Vision-and-language pretraining (VLP) in medicine leverages contrastive learning on image–text pairs, often enhanced with masked modeling. However, existing methods face two challenges: difficulty reconstructing key pathological features due to limited data, and reliance on either paired or image-only datasets without combining both. To address this, we propose **MMCLIP** (**M**asked **M**edical **C**ontrastive **L**anguage–**I**mage **P**re-training), which introduces two modules: **AttMIM**: Masks image features highly correlated with text to improve reconstruction of fine medical details. **EntMLM**: Masks key medical entities in text and reconstructs them using visual cues. Furthermore, **MMCLIP** incorporates unpaired data through disease-kind prompts, achieving state-of-the-art performance in zero-shot and fine-tuning across five benchmarks.

MMCLIP: Cross-Modal Attention Masked Modelling for Medical Language-Image Pre-Training

Recent advances in speech large language models (e.g., GPT-4o) have enabled end-to-end spoken interactions, yet their robustness in real-world applications remains unclear, where systems must assist users in completing specific tasks under complex conditions such as multi-turn, ambiguous, and often spontaneous speech, as well as natural alternation between speech and text. Task-oriented dialogue (TOD) offers a realistic scenario to evaluate whether models can effectively help users accomplish such task-oriented goals, but existing benchmarks are mainly text-based, and the few speech datasets are limited to English and often neglect spontaneous disfluencies and speaker diversity. To address this gap, we introduce RealTalk-CN, the first Chinese multi-turn, multi-domain speech–text TOD dataset, containing 5.4k dialogues (60K turns, ~150 hours) of real human-to-human recordings with detailed annotations for dialogue states, disfluency types, and speaker characteristics. Based on this dataset, we propose a cross-modal interaction task supporting dynamic speech-text switching and a comprehensive evaluation protocol assessing robustness to disfluencies, sensitivity to speaker variation, and cross-domain generalization. Experiments on state-of-the-art models demonstrate the challenges posed by RealTalk-CN and establish its value as a benchmark for developing reliable and fair Speech LLMs in real-world deployments. The dataset and evaluation framework are available to encourage further research.

RealTalk-CN: A Realistic Chinese Speech Task-Oriented Dialogue Benchmark with Cross-Modal Analysis

Learning unknown knowledge through ICL and RAG can enhance LLM capabilities in specialized fields. While most research focuses on how to identify and utilize such knowledge, little work examines what factors lead LLMs to trust and adopt it, leaving models prone to errors and harmful content. Grounded in extensive pre-experiments, we design five pairs of trust‑enhancing and trust‑diminishing transformations on unknown knowledge to experimentally identify the key trust factors. These findings are further substantiated through a detailed theoretical analysis grounded in the epistemological framework of evidentialism. Based on these insights, we challengingly propose a completely unrestricted and fully randomized jailbreak attack that embeds malicious queries within trust‑enhanced unknown knowledge. In both defended and undefended scenarios, our method achieves 99\% to 100\% ASR on all tested LLMs, including the latest GPT‑5.1, and becomes SOTA. This attack confirms the trust mechanism and exposes a critical and hard-to-defend security risk. Our conclusions provide valuable guidance for understanding trust mechanism of unknown knowledge and for future research.

How Do LLMs "Trust" Unknown Knowledge? An Unknown Knowledge Based Jailbreak Attack

Adapting large language models (LLMs) to domain-specific tasks via fine-tuning is often infeasible: models are protected by intellectual property, while sensitive data cannot be shared due to privacy regulations. A promising paradigm, Offsite Tuning (OT), addresses this challenge by constructing an emulator of the original model. Data owners leverage the emulator to train an adapter on downstream data, which is then plugged back into the original model, enabling knowledge transfer without transmitting either the original model or the raw data. However, emulators constructed by existing OT-based methods often retain substantial inference capabilities, thereby exposing model capability privacy and posing risks of misuse. To address this, we propose Loss Landscape Elevation Offsite Tuning (LLEOT), a framework that secures data privacy as well as model parameter and capability privacy. At its core, Loss Landscape Elevation (LLE) enforces a fixed margin between the loss landscapes of the emulator and the original model. We theoretically demonstrate that LLE simultaneously (i) degrades emulator inference via perplexity amplification and (ii) preserves gradient alignment, ensuring consistent convergence for adapter training. Extensive experiments confirm that LLEOT achieves strong adaptation performance while effectively mitigating emulator misuse. Code is available at https://github.com/Z-eloto/LLEOT.

LLEOT: A Privacy-Enhancing Offsite Tuning Framework via Loss Landscape Elevation

While Multimodal Large Language Models (MLLMs) have demonstrated the capacity for multi-modal reasoning, current Referring Expression Comprehension (REC) benchmarks lag behind, predominantly relying on intra-image cues and neglecting the integration of external world knowledge, which significantly impedes the evolution of REC towards real-world applications. This limitation obscures a model's true capability to conduct textual reasoning (entity resolution), resolve spatial location (visual grounding), and verify reference validity (hallucination rejection). To address this, we introduce KnowDR-REC, a targeted audit benchmark comprising 1,042 positive triplets derived from real-world knowledge, along with rigorously matched negative samples. Unlike traditional datasets, we implement a controllable counterfactual evaluation mechanism that subjects textual expressions to single-factor perturbations (entity, relation, or time) to test sensitivity to fine-grained factual changes. Extensive evaluation of 18 state-of-the-art LMMs exposes a critical ``binding hallucination,'' revealing that current high performance is often built on fragile visual shortcuts rather than true understanding. KnowDR-REC thus serves as a pivotal diagnostic instrument, steering future research toward the genuine integration of perception and reasoning.

KnowDR-REC: Auditing Knowledge-Conditioned Visual Grounding in Referring Expression Comprehension

Language evolution is cognitively motivated by the reduction of communicative effort. Current research exploring this reported tendency has been constrained by the heavy reliance on manually annotated resources (e.g., dependency parsing) as well as a narrow focus (e.g., syntax as the single metric). To transcend these limitations, we propose two measures: Attention-based Structural Distance (ASD) and Semantic Space Distance (SSD). ASD is a parser-free measure of syntactic locality derived from the attention mechanism of pretrained large language models (LLM), while SSD is a measure of lexical distances that quantify the degree of separation between different parts of speech in the word vector space. Based on multiple diachronic and multilingual corpora, our experiments show a significant decrease of ASD while an increase of SSD, which implies a language developmental trend towards structural compactness and semantic divergence. Our research pioneers a novel lens grounded in LLM for studying language evolution, which has two major contributions. Linguistically, our study corroborates the hypothesized law of human language evolution by demonstrating that its development optimizes syntactic locality as well as functional semantic discriminability. Cognitively, our study shows that human and LLMs share common characteristics in language processing, lending support to the potential of employing LLMs in the study of human cognition.

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Generative Induction of Dialogue Task Schemas with Streaming Refinement and Simulated Interactions

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