United States

We present LinkNav, an enhanced experience for reading academic papers which makes explicit connections between related but non-adjacent passages. To create the experience, we instruct a language model to generate questions that may arise while reading a passage and then search for answer-bearing passages elsewhere in the document, forming intra-document connections when answers are found. We confirm that these building blocks work well to power the experience, with an answer detection pipeline that works with high precision, resulting in a reasonable number of such connections being made for a document. On a dataset of academic papers, we find that connected segments are on average ten segments away from each other, making explicit connections that a reader may have otherwise missed.

ACL 2026

G-D7 LinkNav: Surfacing Interconnected Information in Scientific Articles

### Welcome to CogSci Conference 2025!

The 47th Annual Meeting of the Cognitive Science Society was a hybrid meeting held in San Francisco. 

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#### About

The Cognitive Science Society brings together researchers from around the world who hold a common goal: understanding the nature of the human mind. The mission of the Society is to promote Cognitive Science as a discipline, and to foster scientific interchange among researchers in various areas of study, including Artificial Intelligence, Linguistics, Anthropology, Psychology, Neuroscience, Philosophy, and Education.

The Society is a non-profit professional organization and its activities include sponsoring an annual conference and publishing the journals Cognitive Science and TopiCS.

#### Our History 

* **Society Creation**<br>
The Society was incorporated as a 501(c)(3) non-profit professional organization in Massachusetts in 1979. The organizing committee included Roger Schank, Allan Collins, Donald Norman, and a number of other scholars from psychology, linguistics, computer science, and philosophy. 
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* **Conference Creation**<br>
The first conference on cognitive science was held at La Jolla, California in August, 1979, and has occurred annually since then. The proceedings of each conference are published, and those from most years are available through Lawrence Erlbaum Associates, Inc. The annual proceedings of the Cognitive Science Conference represent a major source of information on new work and new ideas in the scientific study of thinking. In 1990, the Society, with help from an anonymous donor, established the David Marr Prize for the best student paper at each annual meeting.
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* **Journal Creation**<br>
The Journal, Cognitive Science, began publication in 1976, and is now published by Wiley-Blackwell. The Executive Editor is currently Richard P. Cooper of Birkbeck, University of London, and there are 18 Associate Editors and a 30-member editorial board. It serves as the premier outlet for research reports that intersect two or more disciplines. Copyrights for articles published in the journal are held by the Society. The Governing Board of the Cognitive Science Society voted in late 2006 to found a new journal, Topics in Cognitive Science (topiCS). The Editor in Chief is Wayne Gray, Cognitive Science Department, Rensselaer Polytechnic Institute. The journal seeks to fill a niche not occupied by Cognitive Science Journal or other cognitive science journals. Membership in the Society includes a subscription to Cognitive Science and TopiCS. Copyrights for articles published in the journal are held by the Society.
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#### Code of Conduct

By attending the CogSci 2025 Conference, you are required to adhere to the society’s **[Code of Conduct](https://drive.google.com/file/d/1ChPuihLy6jE_BWqfO7J2KKgX35JW2zsM/view?usp=sharing)**.
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CogSci 2025

The 47th Annual Meeting of the Cognitive Science Society presents the latest research across cognitive science and highlights the theme of Cognition in Context.

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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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The 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026) will take place in San Diego, California, from July 2 - 7, 2026.

We present rosaOS, an open source agentic operating system for embodied LLMs: interactive, LLM-driven agents coordinate various software tools and physical devices through a single embodied desktop companion, the Reachy Mini robot. Existing LLM-robotic systems are generally built as an intertwined stack, making it difficult to switch hardware, add extra capabilities, or expand to multiple devices without bespoke integration. rosaOS aims to provide an OS-inspired architecture where an agentic kernel manages all task execution and mediates device access, while worker agents invoke tools to perform actions. We adopt industry standard interfaces with MCP for agentic tool-calling and ROS for connecting robots, and demonstrate rosaOS on a multi-device setup including a quadruped robot, a mobile robot, and a smart lamp, all controlled through interactions with Reachy Mini. By incorporating MCP extensibility with ROS hardware interoperability, rosaOS enables a plug-and-play ecosystem to build and evaluate embodied agentic systems. The implementation is available at rosaos.ai.

G-D8 rosaOS: Agentic Operating System for Embodied LLMs

Multi-agent systems (MAS) are increasingly used for open-ended idea generation, driven by the expectation that collective interaction will broaden the exploration diversity. However, when and why such collaboration truly expands the solution space remains unclear. We present a systematic empirical study of diversity in MAS-based ideation across three bottom-up levels: model intelligence, agent cognition, and system dynamics. At the model level, we identify a compute efficiency paradox, where stronger, highly aligned models yield diminishing marginal diversity despite higher per-sample quality. At the cognition level, authority-driven dynamics suppress semantic diversity compared to junior-dominated groups. At the system level, group-size scaling yields diminishing returns and dense communication topologies accelerate premature convergence. We characterize these outcomes as collective failures emerging from structural coupling, a process where interaction inadvertently contracts agent exploration and triggers diversity collapse. Our analysis shows that this collapse arises primarily from the interaction structure rather than inherent model insufficiency, highlighting the importance of preserving independence and disagreement when designing MAS for creative tasks. Our code is available at https://github.com/Xtra-Computing/MAS_Diversity.

Diversity Collapse in Multi-Agent LLM Systems: Structural Coupling and Collective Failure in Open-Ended Idea Generation

Despite the rapid progress of large language models (LLMs) in code generation, existing evaluations focus on functional correctness or syntactic validity, overlooking how LLMs make critical design choices such as which library or programming language to use.
To fill this gap, we perform the first empirical study of LLMs' preferences for libraries and programming languages when generating code, covering eight diverse LLMs.
We observe a strong tendency to overuse widely adopted libraries such as NumPy; in up to 45\% of cases, this usage is not required and deviates from the ground-truth solutions.
The LLMs we study also show a significant preference toward Python as their default language.
For high-performance project initialisation tasks where Python is not the optimal language, it remains the dominant choice in 58\% of cases, and Rust is not used once.
These results highlight how LLMs prioritise familiarity and popularity over suitability and task-specific optimality;
underscoring the need for targeted fine-tuning, data diversification, and evaluation benchmarks that explicitly measure language and library selection fidelity.

 A Study of LLMs' Preferences for Libraries and Programming Languages

Multilingual ASR systems often fail to generalize to low-resource and linguistically diverse languages while remaining costly to scale. We introduce PUMA, a unified multilingual ASR model that improves low-resource performance with reduced model complexity. PUMA employs a Universal Language Projection (ULP) module that integrates a learnable language token with acoustic representations, enabling language-aware processing through shared parameters. Experiments on diverse African languages show consistent word error rate reductions over strong multilingual baselines, highlighting improved robustness and generalization. Our code is available at the following GitHub URL: https://github.com/ilyes-okd/PUMA

PUMA: Projected Universal Multilingual ASR for Low-Resource Settings. Application to Diverse African Languages

The U.S.~Federal Open Market Committee (FOMC) regularly discusses and sets monetary policy, affecting the borrowing and spending decisions of hundreds of millions of people. In this work, we release \textsc{Op-Fed}, a dataset of 1044 human-annotated sentences and their contexts from FOMC transcripts that captures monetary policy stance---specifically, whether an individual FOMC member expresses support for tightening or loosening policy. We faced two major technical challenges in dataset creation: imbalanced classes---we estimate fewer than 8\% of sentences express a non-neutral stance toward monetary policy---and inter-sentence dependence---65\% of instances require context beyond the sentence-level to annotate. To address these challenges, we developed a five-stage hierarchical schema to isolate aspects of opinion, monetary policy, and stance toward monetary policy, as well as the level of context needed. Second, we selected instances to annotate using active learning, approximately doubling the number of positive instances across all schema aspects. Using \textsc{Op-Fed}, we found a top-performing, closed-weight LLM achieves 0.80 zero-shot accuracy in opinion classification but only 0.61 zero-shot accuracy classifying stance toward monetary policy---below our human baseline of 0.89. We expect \textsc{Op-Fed} to be useful for future model training, confidence calibration, and as a seed dataset for future annotation efforts.

Op-Fed: Opinion, Stance, and Monetary Policy Annotations on FOMC Transcripts Using Active Learning

Recent work suggests that LLMs "know what they don't know", positing that hallucinated and factually correct outputs arise from distinct internal processes and can therefore be distinguished using internal signals.
However, hallucinations have multifaceted causes: beyond simple knowledge gaps, they can emerge from training incentives that encourage models to exploit statistical shortcuts or spurious associations learned during pretraining.
In this paper, we argue that when LLMs rely on such learned associations to produce hallucinations, their internal processes are mechanistically similar to those of factual recall, as both stem from strong statistical correlations encoded in the model's parameters.
To verify this, we propose a novel taxonomy categorizing hallucinations into Unassociated Hallucinations (UHs), where outputs lack parametric grounding, and Associated Hallucinations (AHs), which are driven by spurious associations. Through mechanistic analysis, we compare their computational processes and hidden-state geometries with factually correct outputs.
Our results show that hidden states primarily reflect whether the model is recalling parametric knowledge rather than the truthfulness of the output itself. Consequently, AHs exhibit hidden-state geometries that largely overlap with factual outputs, rendering standard detection methods ineffective. In contrast, UHs exhibit distinctive, clustered representations that facilitate reliable detection.

Do LLMs Really Know What They Don’t Know? Internal States Mainly Reflect Knowledge Recall Rather Than Truthfulness

Large language models (LLMs) exhibit strong reasoning capabilities but typically require expensive post-training to reach high performance. Recent test-time alignment methods offer a lightweight alternative, but have been explored mainly for preference alignment rather than reasoning. To bridge this gap, we propose Token-level Adaptive Routing (TARo), which steers frozen LLMs toward structured reasoning entirely at inference time. Specifically, we first train reward models on step-wise mathematical traces to capture fine-grained logical consistency signals, then introduce a learnable token-level router that automatically controls the guidance of the reward model to the base model. Extensive experiments show that TARo significantly improves reasoning performance by up to +22.4% over base model and +8.4% over existing token-level test-time alignment methods, while also boosting out-of-distribution clinical reasoning (MedXpertQA) and instruction following (AlpacaEval). Furthermore, TARo also generalizes from small to large backbones without retraining, extending test-time alignment from preference optimization to robust, cross-domain reasoning.

TARo: Token-level Adaptive Routing for LLM Test-time Alignment

Robust ASR under domain shift is crucial because real-world systems encounter unseen accents and domains with limited labeled data. 
Although pseudo-labeling offers a practical workaround, it often introduces systematic, accent-specific errors that filtering fails to fix. 
We ask: How can we correct these recurring biases without target ground truth? 
We propose a simple parameter-space correction: in a source domain containing both real and pseudo-labeled data, two ASR models are fine-tuned from the same initialization, one on ground-truth labels and the other on pseudo-labels, and their weight difference forms a correction vector that captures pseudo-label biases.
When applied to a pseudo-labeled target model, this vector enhances recognition, achieving up to a 35% relative Word Error Rate (WER) reduction on AfriSpeech-200 across ten African accents with the Whisper tiny model.

Pseudo2Real: Task Arithmetic for Pseudo-Label Correction in Automatic Speech Recognition

Large language models (LLMs) increasingly rely on external tools to complete complex tasks, yet their ability to recognize and correct their own tool-use mistakes remains underexplored. Existing benchmarks primarily evaluate planning and execution success, overlooking the self-reflective dimension of tool use. To address this gap, we present ReflecTool-Bench, the first benchmark designed to assess LLMs’ self-reflective reasoning in tool-augmented multi-turn dialogues. ReflecTool-Bench covers 10 domains with 88 distinct APIs and 968 annotated dialogues, systematically injecting diverse error types arising from both user and assistant behavior. The benchmark defines two complementary evaluation setups: the Critique task, where models diagnose errors in third-party dialogues, and the Self-Reflection Task, where models must detect and repair their own prior tool-use mistakes. We introduce fine-grained metrics for error detection, error classification, correction accuracy, and explanation quality, enabling a holistic assessment of reflective reasoning. Evaluations across 12 state-of-the-art models, including both API-based closed source models and open source models, reveal that while models can reliably identify user-originated errors, they struggle with assistant-originated ones, and performance drops sharply when moving from critique to self-reflection.

Do LLMs Catch Their Own Mistakes? A Comprehensive Benchmark for Reflective Tool Use LLMs

Large language models (LLMs) have driven major advances across domains, yet their massive size hinders deployment in resource-constrained settings. Low-rank factorization addresses this challenge by compressing models to effectively reduce their computation and memory consumption while maintaining accuracy. While these compressed models boast benign performance and system-level advantages, their trustworthiness implications remain poorly understood. In this paper, we present the first comprehensive study of how low-rank factorization affects LLM trustworthiness across privacy, adversarial robustness, ethics, and fairness, complemented by an explainability-driven analysis of the internal mechanisms behind these trust-related changes. 

We evaluate multiple LLMs of different sizes and architectures compressed with various low-rank factorization algorithms, revealing key insights: (1) low-rank factorization preserves training data privacy but weakens the protection of personally identifiable information during conversations; (2) adversarial robustness is generally enhanced under compression; (3) ethics degrades in zero-shot prompting but partially recovers in few-shot prompting; (4) fairness declines under compression. Beyond compression, we investigate how model scale and fine-tuning affect trustworthiness. Additionally, to move beyond black-box analysis, we employ a gradient-based attribution to identify which layers of LLMs contribute most to adversarial robustness.

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G-D8 rosaOS: Agentic Operating System for Embodied LLMs

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