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The popular path query - identifying the most frequented routes between locations from historical trajectory data - has important applications in urban planning, navigation optimization, and travel recommendations. While traditional algorithms and machine learning approaches have achieved success in this domain, they typically require model training, parameter tuning, and retraining when accommodating data updates. As Large Language Models (LLMs) demonstrate increasing capabilities in spatial and graph-based reasoning, there is growing interest in exploring how these models can be applied to geo-spatial problems.

We introduce CompassLLM, a novel multi-agent framework that intelligently leverages the reasoning capabilities of LLMs into the geo-spatial domain to solve the popular path query. CompassLLM employs its agents in a two-stage pipeline: the SEARCH stage that identifies popular paths, and a GENERATE stage that synthesizes novel paths in the absence of an existing one in the historical trajectory data. Experiments on real and synthetic datasets show that CompassLLM demonstrates superior accuracy in SEARCH and competitive performance in GENERATE while being cost-effective.

ACL 2026

CompassLLM: A Multi-Agent Approach toward Geo-Spatial Reasoning for Popular Path Query

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.

poster

With the rapid progress of LLMs, high quality generative text has become widely available as a cover for text steganography. However, prevailing methods rely on hand-crafted or pre-specified strategies and struggle to balance efficiency, imperceptibility, and security, particularly at high embedding rates. Accordingly, we propose Auto-Stega, an agent-driven self-evolving framework that is the first to realize self-evolving steganographic strategies by automatically discovering, composing, and adapting strategies at inference time; the framework operates as a closed loop of generating, evaluating, summarizing, and updating that continually curates a structured strategy library and adapts across corpora, styles, and task constraints. A decoding LLM recovers the information under the shared strategy. To handle high embedding rates, we introduce PC-DNTE, a plug-and-play algorithm that maintains alignment with the base model's conditional distribution at high embedding rates, preserving imperceptibility while enhancing security. Experimental results demonstrate that at higher embedding rates Auto-Stega achieves superior performance with gains of 42.2\% in perplexity and 1.6\% in anti-steganalysis performance over SOTA methods.

Auto-Stega: An Agent-Driven System for Lifelong Strategy Evolution in LLM-Based Text Steganography

Large language models (LLMs) often encode word-form variation (e.g., *walk* vs. *walk**ed***) as linear directions in the embedding space. However, standard tokenization algorithms treat such variants as distinct words with different vocabulary entries—quickly filling the size-capped token vocabulary with surface-form variation (e.g., *walk*, *walk**ing***, ***W**alk*), at the expense of diversity and multilingual coverage. We show that many of these variations can be captured by *transformation* vectors—additive offsets that yield the appropriate word representation when applied to a *base form* embedding, in both the input and output spaces. Building on this, we propose a compact reshaping of the vocabulary: instead of assigning unique tokens to each surface form, we compose them from shared *base form* and *transformation* vectors (e.g., *walked* is *walk*+*past tense*). Our approach is lightweight—keeping the pretrained backbone frozen and only training small adaptation modules. We apply it across five languages and multiple LLMs in both pretraining and post-hoc adaptation, freeing 10-40% of vocabulary slots to be reallocated where tokenization is inefficient. Importantly, we do so while also expanding vocabulary coverage to out-of-vocabulary words, and with minimal impact on downstream performance. Our findings motivate a rethinking of vocabulary design, towards a representation that better matches the underlying structure of language and the practical needs of multilingual coverage.

Vocab Diet: Reshaping the Vocabulary of LLMs via Vector Arithmetic

Data marketplaces analyze strategic data exchanges among users, platforms, and buyers. However, most existing studies model static equilibria and complete information, which limits their realism. In this work, we study whether large language model (LLM)-driven agents can make equilibrium-consistent decisions in analytically tractable data marketplaces with evolving and incomplete-information. Specifically, we introduce EvoDM, an agent-based modeling framework that extends the static data marketplace to dynamic and incomplete-information settings while providing tractable equilibrium benchmarks for evaluating agent decisions. Building upon EvoDM, we propose Datamart-Agent, an LLM-driven game-theoretic agent that improves equilibrium-consistent decision execution through dynamic game tree memory and mechanism-guided reflection, without requiring parameter updates. Experiments demonstrate that Datamart-Agent closely matches equilibrium-consistent decision-making, achieving the lowest utility gap and over 20\% higher Pass@$\epsilon$ than strong baselines. After validating its effectiveness, we employ EvoDM with Datamart-Agent to analyze competition and regulation in assumption-relaxed settings where closed-form ground truth is unavailable, providing exploratory simulation-based insights into market dynamics and regulatory effects.

Datamart-Agent: LLM-Driven Game-Theoretic Agent for Data Marketplace Modeling

In-context learning (ICL) is the standard method for low-resource classification, yet its efficacy in specialized domains remains largely unexplored. We address the challenge of classifying semantically complex, multi-party B2B conversations, where traditional ICL encounters significant limitations, especially as context length increases due to the concatenation of multiple few-shot examples. We introduce the Call Playbook dataset, featuring five classification tasks derived from real-world B2B conversations targeting core sales concepts. To bridge the gap between performance and practical utility, we propose novel knowledge extraction methods that distill verbose examples into compact, interpretable representations of structured classification criteria and precise task descriptions. Our approach achieves a 99% reduction in token usage and improves macro-averaged AUC by up to 7% over traditional ICL. Notably, it remains robust as context grows, unlike advanced token compression baselines which degrade by over 9 F1 points. Importantly, our framework enables direct refinement of classification logic, addressing critical needs for transparency, efficiency, and user interaction in real-world NLP applications.

Distilling Examples into Task Instructions: Enhanced In-Context Learning for Real-World B2B Conversations

Humans are able to predict each other’s actions by reasoning about the others’ underlying goals, preferences, and motives, such as greed and risk-aversion. Game theory provides a framework for studying human behaviors through incentivized games that simulate social situations. We utilized two validated games from the cognitive science literature—the Social Prediction Game (SPG) and the Inspection Game (IG)—to systematically study how well several recent open- and closed-source LLMs predict player actions and whether they can leverage and generalize the players’ motives learned from the iterated games. Our results indicate that state-of-the-art LLMs can achieve accuracy close to human levels in predicting players’ actions with underlying human motives in SPGs. However, unlike humans, who rely on reasoning about players’ motives to inform their predictions, LLMs failed to recognize statistical patterns in players’ actions. As a result, LLM prediction accuracy did not improve over multiple rounds. Our results in the IG further demonstrate that, unlike humans, LLMs were unable to recognize a player’s underlying motives and to generalize their understanding of the same player to a new context. This suggests that LLMs may lack reasoning capabilities. Our findings offer insights into differences in human and LLM reasoning mechanisms, suggesting that further research into human-AI alignment is needed before utilizing LLMs for human behavior modeling and simulation in this and related contexts.

Can Large Language Models Infer Human Actions and Motives? Evaluation in Social Prediction and Inspection Games

Large language models (LLMs) are increasingly grounded in sensor data to perceive and reason about human physiology and the physical world. However, accurately interpreting heterogeneous multimodal sensor data remains a fundamental challenge. We show that a single monolithic LLM often fails to reason coherently across modalities, leading to incomplete interpretations and prior-knowledge bias. We introduce ConSensus, a training-free multi-agent collaboration framework that decomposes multimodal sensing tasks into specialized, modality-aware agents. To aggregate agent-level interpretations, we propose a hybrid fusion mechanism that balances semantic aggregation, which enables cross-modal reasoning and contextual understanding, with statistical consensus, which provides robustness through agreement across modalities. While each approach has complementary failure modes, their combination enables reliable inference under sensor noise and missing data. We evaluate ConSensus on five diverse multimodal sensing benchmarks, demonstrating an average accuracy improvement of 7.1% over the single-agent baseline. Furthermore, ConSensus matches or exceeds the performance of iterative multi-agent debate methods while achieving a 12.7 times reduction in average fusion token cost through a single-round hybrid fusion protocol, yielding a robust and efficient solution for real-world multimodal sensing tasks. The source code is available at https://github.com/nokia/multi-agent-collaboration-for-multimodal-sensing.

ConSensus: Multi-Agent Collaboration for Multimodal Sensing

Knowledge components (KCs) are key to assessing student knowledge levels on fine-grained skills and driving personalization and feedback. However, crafting KCs and tagging them for problems, traditionally performed by human domain experts, is highly labor-intensive. Prior work has studied automated KC generation only for multiple-choice questions but not open-ended ones. We bridge this gap and present an automated, large language model (LLM)-based pipeline for KC generation and tagging for open-ended programming problems. We also develop an LLM-based knowledge tracing (KT) framework to leverage these LLM-generated KCs. We conduct extensive quantitative and qualitative evaluations on two real-world student code submission datasets. Results show that our KT method outperforms existing ones and LLM-generated KCs outperform human-written KCs on future student response prediction. We also investigate how these KCs enable us to analyze student learning curves and conduct human evaluation with course instructors to further verify the quality of KC-problem tagging.

Automated Knowledge Component Generation and Interpretable Knowledge Tracing in Coding Problems

Modern automatic speech recognition (ASR) systems have been observed to function better for certain speaker groups (SGs) than others, despite recent gains in overall performance. One potential impediment to progress towards fairer ASR is a more nuanced understanding of the types of modeling errors that speech encoder models make, and in particular the difference between the structure of embeddings for high-performance and low-performance SGs. This paper proposes a framework typifying two types of error that can occur in modeling phonemes in ASR systems: random error/high variance in phoneme embedding, vs systematic error/embedding bias. We find that training phoneme classification probes only on a single, typically disadvantaged SG, sometimes improves performance for that SG, which is evidence for the existence of SG-level bias in phoneme embeddings. On the other hand, we find that speakers and SGs with higher levels of phoneme variance are the same as those with worse phoneme prediction accuracy. We conclude that both types of error are present in phoneme embeddings and both are candidate causes for SG-level unfairness in ASR, though random error is likely a greater hindrance to fairness than systematic error. Furthermore, we find that finetuning encoder models using a fairness-enhancing algorithm (domain enhancing and adversarial training) changes neither the benefits of in-domain phoneme classification probe training, nor measured levels of random embedding error.

Identifying and typifying demographic unfairness in phoneme-level embeddings of self-supervised speech recognition models

Large Language Models (LLMs) exhibit systematic biases across demographic groups. Auditing is proposed as an accountability tool for black-box LLM applications, but suffers from resource-intensive query access. We conceptualise auditing as uncertainty estimation over a target fairness metric and introduce BAFA, the Bounded Active Fairness Auditor for query-efficient auditing of black-box LLMs. BAFA maintains a version space of surrogate models consistent with queried scores and computes uncertainty intervals for fairness metrics (e.g., $\Delta$ AUC) via constrained empirical risk minimisation. Active query selection narrows these intervals to reduce estimation error. We evaluate BAFA on two standard fairness dataset case studies: CivilComments and Bias-in-Bios, comparing against stratified sampling, power sampling, and ablations. BAFA achieves target error thresholds with up to 40$\times$ fewer queries than stratified sampling (e.g., 144 vs 5,956 queries at $\varepsilon=0.02$ for CivilComments) for tight thresholds, demonstrates substantially better performance over time, and shows lower variance across runs. These results suggest that active sampling can reduce resources needed for independent fairness auditing with LLMs, supporting continuous model evaluations.

Audit Me If You Can: Query-Efficient Active Fairness Auditing of Black-Box LLMs

Despite recent advances in safety alignment, large language models (LLMs) remain highly susceptible to adversarial attacks, while the internal mechanisms behind such vulnerabilities are still poorly understood. Existing gradient-based attribution methods offer valuable interpretability for analyzing information storage and processing in LLMs. However, they are inapplicable to adversarial attacks, which typically occur in open-ended generation settings without fixed ground-truth outputs. To address these challenges, we propose a novel similarity-based gradient attribution method to identify key neurons sensitive to adversarial behaviors in open-ended generation tasks. The detected neurons, termed targeted neurons, play a critical role in safety training. Building on this neuron-level perspective, we uncover two key neuronal patterns: (i) universal neurons that are consistently exploited across multiple attack strategies, and (ii) interference neurons that hinder safety improvements when fine-tuned indiscriminately, providing mechanistic insights into the interpretability of adversarial vulnerabilities. Inspired by these findings, we propose a neuron-level defense strategy, Targeted Neuron Tuning (TNT), which selectively fine-tunes the identified targeted neurons for specific attacks. Experimental evaluations across multiple LLM architectures and scales demonstrate that TNT substantially improves model robustness against a wide range of jailbreak attacks, achieving safe rates exceeding 90% and even approaching 100%, while preserving general task performance, enabling precise and robust safety interventions. Warning: This paper contains example data that may be harmful.

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