Named after the hundred-eyed watchman of Greek myth, Argus watches the education landscape: spotting new opportunities, pressure-testing the ventures we're building, and tracing every read back to the real-world signals behind it.
The evidence library: the raw signals the pipeline is watching across the education ecosystem. Every idea is built from these.
arXiv:2607.23142v1 Announce Type: new Abstract: Over fourty years after the initial publication of "Implications of Theories of Language for Information Systems" in MIS Quaterly, Lyytinen reflects about the origins of his publication and the developments in this area of research over the past decades. In the here presented interview, Lyytinen discusses the linguistic core of information systems also in light of recent trends and developments in the field, especially with regards to large language models and generative AI. Future research directions following a linguistic perspective on Information Systems (IS) research are outlined.
arXiv:2607.23083v1 Announce Type: new Abstract: Gender-inclusive language generation seeks to transform biased text into inclusive alternatives while preserving semantic meaning and contextual coherence. This paper presents the IHLC system for the LT-EDI 2026 Shared Task, addressing both gender-inclusive rewriting and counter-narrative generation. For gender-inclusive rewriting, we employ parameter-efficient Low-Rank Adaptation (LoRA) fine-tuning, achieving an official score of 80.00%. Our primary contribution is a compute-efficient inference-time representation engineering approach for counter-narrative generation. We derive a principal steering direction from contrastive hidden-state activations using principal component analysis (PCA) and inject it into the intermediate representations of Gemma-3-4B-it during inference, enabling behavioral steering toward inclusive responses without modifying model weights. Combined with constrained prompting, this approach produces polite and conte
arXiv:2607.23067v1 Announce Type: new Abstract: Contrastive decoding methods such as DoLa improve the factuality of Large Language Models (LLMs) by contrasting the output distributions of mature and premature layers. However, DoLa's dynamic layer selection relies solely on divergences in output vocabulary distributions. In this work, we propose three attention-guided strategies: Attention-JSD, Attention-Entropy-Max, and Attention-Entropy-Min, which leverage structural information carried by internal self-attention mechanisms as a signal for layer selection. Experimental results on TruthfulQA demonstrate that our strategies, particularly Attention-JSD and Attention-Entropy-Min, consistently outperform the original DoLa. We observe significant gains on multi-answer metrics (MC2 and MC3), suggesting that attention distributions can provide a more sensitive signal for resolving factual knowledge than output vocabulary distributions.
arXiv:2607.23058v1 Announce Type: new Abstract: Multilingual reasoning evaluation overwhelmingly relies on translating English benchmarks, a practice that introduces linguistic artifacts and fails to test culturally-grounded reasoning. We introduce ADAGE (Analogical Difficulty-by-design Assessment for Grounded Evaluation), a language-agnostic pipeline that combines native-speaker curation with LLM-assisted generation to construct challenging, translation-free benchmarks for abstract analogical reasoning. We validate ADAGE by constructing benchmarks for Arabic, Amharic, and Japanese. Evaluating 14 open-weight models, we find a consistent cultural reasoning gap: models that perform well on English proverb reasoning struggle substantially on all three native benchmarks, with accuracy dropping by 12--52 percentage points relative to English. We release the pipeline, all three benchmarks, and the full evaluation suite.
arXiv:2607.23037v1 Announce Type: new Abstract: Large language models (LLMs) can predict interpersonal attraction from conversation transcripts, but it remains unclear what a speech predictor can add beyond transcript-only LLM prediction. Using Japanese speed-dating conversations, we combine predictions from a transcript-only LLM and a supervised speech predictor to estimate participants' reported liking of their partners. We show that speech can complement transcript-only LLM prediction, but that this complementarity is conditional rather than universal. Combining the two predictions significantly improves pairwise ranking accuracy over the transcript-only LLM alone in all evaluated conditions. By contrast, gains in per-participant Pearson $r$ vary across conversation rounds and rating directions, with none significant after correction. Retrospectively, these $r$ gains are concentrated among participants for whom the speech predictor is more accurate. Speech can therefore retain predi
arXiv:2607.22996v1 Announce Type: new Abstract: Large language models (LLMs) deployed in educational settings often behave as direct answerers: they disclose target concepts in the opening turn instead of guiding students through progressive inquiry, as Socratic pedagogy prescribes. We present HeuristicEdu, a two-phase pipeline that aligns Qwen2.5-7B toward Socratic tutoring via supervised warm-up and Group Relative Policy Optimization (GRPO). Training uses SocraticEdu, 797 multi-turn Chinese children's science dialogues reconstructed from a live platform, with a heuristic reward over cognitive depth (R_cog), curiosity engagement (R_eng), and directness (R_dir), together with a K_query correction for student-introduced terms. We introduce Scaffolding Effectiveness (SE) and Conversation Depth (CD) to evaluate outcomes beyond surface fluency. On 30 held-out questions, the best GRPO variant improves SE from 30.0% to 63.3% and lowers keyword leakage from 30.0% to 13.3%. Notably, this best
arXiv:2607.22954v1 Announce Type: new Abstract: Objective: To characterize the kinds of internal documentation inconsistencies a general-domain large language model (LLM) can surface from real-world discharge summaries, and to identify recurring failure modes that limit reliability at scale. Materials and Methods: We applied a two-stage LLM pipeline---open-ended candidate identification (Gemini 2.5 Pro) followed by context-grounded verification (Gemini 2.5 Flash)---to 3,000 randomly sampled MIMIC-IV-Note discharge summaries. A subset of the pipeline output was then reviewed manually by clinical experts. Results: Our pipeline surfaced 3,460 candidate inconsistencies, affecting 69.7% of admissions. Representative examples spanned demographics, allergies, procedures, diagnoses, laboratory, medications, and care-planning domains, with direct implications for clinical reasoning or patient safety. Expert review also revealed recurring failure modes that arise when verification requires tempo
arXiv:2607.22925v1 Announce Type: new Abstract: A key question for AI safety is whether a language model expresses all of its reasoning in its output tokens. We demonstrate a concrete failure mode where frontier models exhibit invisible reasoning by leveraging semantically irrelevant filler tokens to improve performance on synthetic reasoning tasks. We evaluate 13 frontier language models across three tasks and find that many models benefit significantly from filler tokens, with accuracy improvements of up to 13 percentage points. The benefit depends on which tokens are used and differs across models. We further show that filler tokens enable Claude Opus 4.5 to satisfy a hidden modular arithmetic constraint without sacrificing accuracy on its primary task, demonstrating that invisible reasoning can serve objectives entirely invisible to CoT monitoring. Reinforcement learning gives Qwen3-235B strong preferences over filler token content, but neither RL nor supervised fine-tuning produce
arXiv:2607.22923v1 Announce Type: new Abstract: Cross-lingual mismatch remains a key source of overall degradation in modern speaker verification. The TidyVoice2026 Challenge targets this setting with text-independent verification, comprising 3,666 training and 808 development speakers in 40 languages and 2,200 evaluation speakers in 38 unseen languages, without language labels at test time. Starting from the official SimAM-ResNet34 baseline pretrained on VoxBlink2 and VoxCeleb2 and fine-tuned on TidyVoice, we revisit Nuisance Attribute Projection (NAP) as a simple language-normalization step in the embedding space. We estimate a compact language subspace from cross-language same-speaker differences and project embeddings onto its orthogonal complement before cosine scoring with Adaptive Symmetric score normalization. This reduces development EER from 2.97\% with cosine and 2.70\% with AS-Norm to 2.18\% and yields a Codabench evaluation score of 8.40, showing that simple back-end langu
arXiv:2607.22884v1 Announce Type: new Abstract: We propose CHiPS, a lightweight character-level authorship attribution method for Romanian texts. All reported experiments are closed-set: the true author is one of the candidate authors in the training data. CHiPS studies two complementary fingerprints of writing style: CH-SVM, a character-histogram classifier based on one-character marginal distributions, and FFT12-LR, a positional-signal classifier that represents selected characters and punctuation classes as impulse trains (binary indicator sequences over character positions) and extracts Fourier/Welch spectral descriptors. We also report CHiPS-F, a leakage-safe decision-level fusion variant, and an optional top-5 listwise reranker trained only on out-of-fold predictions. The method requires no tokenization, syntactic analysis, pretrained language model, or transformer fine-tuning, and it avoids character $n$-gram features with $n \geq 2$ in the histogram component. On a locked group
arXiv:2607.22859v1 Announce Type: new Abstract: Mathematical Word Problems (MWPs) are an important benchmark for evaluating natural language understanding and quantitative reasoning. Despite recent progress in high resource languages, Bengali remains underexplored due to the limited availability of large scale annotated datasets. In this work, we introduce PatiGonit22K, an expanded Bengali MWP dataset containing 22,441 problems, developed by extending the original PatiGonit dataset with a substantially larger collection of complex mathematical problems. The dataset includes both simple and multi operation equations, providing a balanced benchmark for evaluating mathematical reasoning across different difficulty levels. Each problem is carefully translated, annotated, culturally adapted, and verified to ensure linguistic consistency and mathematical correctness. By increasing both the scale and complexity of Bengali MWPs, PatiGonit22K provides a more comprehensive resource for future re
arXiv:2607.22657v1 Announce Type: new Abstract: Large language models (LLMs) can reproduce disinformation-aligned narrative frames as plausible explanations, raising the question of whether existing machine-unlearning algorithms can suppress this behavior. We introduce Level-based Evaluation of Narrative Suppression (LENS), a contextualization based evaluation protocol for testing target narrative reproduction across direct, attributed, contrastive, and abstract resistance levels. We evaluate two source-grounded narratives: one framing Russia's war against Ukraine as forced by NATO expansion, and one framing the United States as exploiting or abandoning Taiwan. The experiments cover four near-12B multilingual instruction models: Lapa LLM, Gemma-12B, Qwen-14B, and TAIDE-Gemma. We introduce the Suppression-Collapse Efficiency (SCE) score as a checkpoint selection summary that rewards target-narrative suppression while penalizing degraded outputs. Our results shows that selected checkpoin
arXiv:2607.22622v1 Announce Type: new Abstract: Recent Text-to-SQL methods rely heavily on reasoning-centric paradigms such as Chain-of-Thought (CoT), achieving substantial gains on complex benchmarks at the cost of high inference-time overhead. However, a large fraction of real-world queries are simple lookups or aggregations that can be resolved without multi-step deduction, making forced reasoning wasteful. Thus, we propose AutoThinkSQL, a framework that integrates an auto-thinking mechanism into both Supervised Fine-Tuning (SFT) and Direct Preference Optimization (DPO) on Text-to-SQL. Our approach enables the model to dynamically bypass reasoning for simple queries while invoking deep CoT for complex queries. On Qwen3-Coder-30B-A3B, our method achieves consistent gains compared to the best counterpart baseline on both Spider and BIRD benchmarks while simultaneously reducing average output tokens by 24.6% and 18.3%, and average latency by 17.1% and 11.5% compared to CoT-only generat
arXiv:2607.22553v1 Announce Type: new Abstract: Peer review is an essential process in scientific research, yet the growing workload has made its automation increasingly necessary. In this study, we analyze how different types of reviewer guidelines, such as official conference guidelines and reviewer-imitating ones generated from high-quality human reviews using LLMs, affect automated peer review. Our experiments show that official conference guidelines produce review results most consistent with human judgments, suggesting that evaluation criteria refined through conference practice serve as effective guidance for automated reviewing as well. In contrast, reviewer-imitating guidelines were generally less effective than official conference guidelines. Furthermore, enforcing strict rubric-style scoring consistently degraded performance, highlighting the importance of allowing subjective and holistic scoring.
arXiv:2607.22552v1 Announce Type: new Abstract: The automatic translation of mathematical expressions in scientific literature into executable symbolic code (a process we refer to as Formula Formalization) is hindered by a severe scarcity of high-quality, ground-truth datasets specialized for technical scientific domains. In this paper, we present MioFFAn, an open-source, document-centric, and customizable framework designed to facilitate rapid annotation for this task. Building upon the MioGatto architecture, we extend existing features to overcome structural limitations and pivot its scope by introducing specific functionalities for Formula Formalization, such as selection of equations of interest and aided symbolic code specification. By allowing users to configure custom taxonomies and properties for identified symbols, and compatible symbolic operators, we ensure the framework is adaptable to diverse specialized scientific fields. Furthermore, MioFFAn is designed to incorporate pa
arXiv:2607.22546v1 Announce Type: new Abstract: Machine translation (MT) systems continue to produce gender-biased translations. In a time where self-expression is paramount, mistranslations based on default behaviour and stereotyping can lead to harm for users of these systems. To better understand how these systems translate gender in the absence of clear gender cues, we need benchmarking resources that reflect gender-ambiguous scenarios in a natural way. To this end, we present GAND, a gender-ambiguous natural data benchmarking resource for MT consisting of English source sentences, specifically designed to analyse the influence of contextual cues on gender in translation. We leverage GAND to conduct an interpretability analysis: we translate a subset of GAND into two grammatical gender languages and extend these with manually crafted contrastive translations. A following feature attribution analysis reveals source words in context that inform the gender translation of an ambiguous
arXiv:2604.23356v2 Announce Type: replace-cross Abstract: Large language models (LLMs) show promise in medical diagnosis, but real-world deployment remains challenging due to high-stakes clinical decisions and imperfect reasoning reliability. As a result, careful inspection of model behavior is essential for assessing whether diagnostic reasoning is reliable and clinically grounded. However, debugging medical LLMs remains difficult. First, developers often lack sufficient medical domain expertise to interpret model errors in clinically meaningful terms. Second, models can fail across a large and diverse set of instances involving different input types, tasks, and reasoning steps, making it challenging for developers to prioritize which errors deserve focused inspection. Third, developers struggle to identify recurring error patterns across cases, as existing debugging practices are largely instance-centric and rely on manual inspection of isolated failures. To address these challenges,
arXiv:2511.14718v3 Announce Type: replace-cross Abstract: Natural Language Interfaces for Databases (NLIDBs) let users query data in everyday language instead of SQL, and recent systems translate those questions accurately. Accuracy says little about the work of querying: does an NLIDB remove that work or only reallocate it? We report a mixed-method, between-subjects study comparing SQL-LLM, a GPT-4o-backed NLIDB, with Snowflake, a traditional SQL platform. Twenty SQL-literate professionals and graduate students, ten per interface, each completed 12 tasks drawn from the BIRD benchmark. SQL-LLM cut completion time per query by about 31%, but the speedup did not buy accuracy: graded against the BIRD gold answers, SQL-LLM users were correct on 46% of queries versus 64% for Snowflake. Two analysts independently coded the think-aloud sessions to locate the effort. SQL-LLM users left schema navigation to the model and spent it verifying that the generated SQL matched their intent; Snowflake
arXiv:2510.24411v3 Announce Type: replace-cross Abstract: Computer-using agents powered by Vision-Language Models (VLMs) have demonstrated human-like capabilities in operating digital environments like mobile platforms. While these agents hold great promise for advancing digital automation, their potential for unsafe operations, such as system compromise and privacy leakage, is raising significant concerns. Detecting these safety concerns across the vast and complex operational space of mobile environments presents a formidable challenge that remains critically underexplored. To establish a foundation for mobile agent safety research, we introduce MobileRisk-Live, a dynamic sandbox environment accompanied by a safety detection benchmark comprising realistic trajectories with fine-grained annotations. Built upon this, we propose OS-Sentinel, a novel hybrid safety detection framework that synergistically combines a Formal Verifier for detecting explicit system-level violations with a VLM
arXiv:2501.08561v5 Announce Type: replace-cross Abstract: Digital twins are increasingly used to monitor and optimize industrial systems, yet many existing frameworks remain difficult to interpret, slow to adapt, and limited in their ability to incorporate explicit domain knowledge. This paper presents ANSR-DT, an adaptive neuro-symbolic framework that unifies temporal anomaly detection, symbolic reasoning, and reinforcement-learning-based decision support within a single digital twin pipeline. ANSR-DT combines a CNN-LSTM model for multivariate pattern recognition with Prolog-based reasoning that converts learned signals into explicit rules, enabling transparent diagnoses and traceable decision paths. A PPO-based adaptation layer further refines operational responses under changing conditions while preserving interpretability. Experiments against eight baselines show that ANSR-DT delivers competitive predictive performance together with stable rule extraction, scalable symbolic reasoni
arXiv:2606.18816v2 Announce Type: replace Abstract: Hybrid brain-computer interfaces (BCIs) that integrate motor imagery (MI) and steady-state visual evoked potentials (SSVEP) provide high-dimensional neural decoding but typically exceed the computational limits of embedded hardware. To address this, we propose SwitchBraidNet, a compact EEG classification architecture designed for low-power deployment. The model employs a dual-path temporal braid to extract multiscale oscillatory features, an adaptive squeeze-and-excitation spatial switch for electrode gating, and a log-variance readout layer for direct band-power encoding. Furthermore, through systematic quantisation-aware training on the OpenBMI dataset, we compared SwitchBraidNet against four established baselines across FP32, FP16, and INT8 precisions. Experimental results demonstrate superior efficiency and performance, achieving MI accuracy of 69.49% (FP16), SSVEP accuracy of 93.48% (FP32), and a hybrid information transfer rate
arXiv:2605.25643v2 Announce Type: replace Abstract: Patients with bladder dysfunction often lose the sensation of bladder fullness and cannot void naturally, forcing reliance on fixed-schedule catheterization that is uncomfortable and risks complications. We present WeeCare, a handheld conformable pad with fabric electrodes for on-demand bladder fullness sensing using electrical impedance tomography (EIT). The central challenge is that repeated removal and reattachment can introduce variation in electrode position and contact quality. We assess WeeCare along three axes: in-silico simulations characterizing electrode layout and noise robustness, in-vitro phantom experiments across urine salinities and filling levels, and an in-vivo study tracking voiding dynamics and fullness sensing across 8 participants, with filling dynamics characterized in a single participant. Our results provide an early assessment of WeeCare's feasibility under controlled conditions.
arXiv:2605.20465v2 Announce Type: replace Abstract: Persistent self-criticism--harsh evaluative self-talk--can undermine illustrators' performance and well-being. Traditional interventions draw on psychotherapeutic approaches (e.g., compassion training) but sit outside the illustration workflow, requiring time, facilitation, and skill transfer. We propose an in-workflow alternative: evaluative off-centering, a mechanism redirecting self-critical evaluation away from an inherently self-evaluative task (like illustration) by embedding it in an alternative activity. We instantiate evaluative off-centering in Art Card Game (ACG) that integrates illustration into a card customization game: players illustrate cards that become playable assets in a head-to-head battle. In a four-day randomized controlled study with hobbyist and professional illustrators (N=38), ACG outperformed a control condition with identical illustration constraints but no evaluative off-centering mechanisms (e.g. multipl
arXiv:2604.07263v2 Announce Type: replace Abstract: Existing driving automation (DA) systems on production vehicles rely on human drivers to decide when to engage DA while requiring them to remain continuously attentive and ready to intervene. This design demands substantial situational judgment and imposes significant cognitive load, leading to steep learning curves, suboptimal user experience, and safety risks from both over-reliance and delayed takeover. Predicting when drivers hand over control to DA and when they take it back is therefore critical for designing proactive, context-aware HMI, yet existing datasets rarely capture the multimodal context, including road scene, driver state, vehicle dynamics, and route environment. To fill this gap, we introduce BATON, a large-scale naturalistic dataset capturing real-world DA usage across 127 drivers, and 136.6 hours of driving. The dataset synchronizes front-view video, in-cabin video, decoded CAN bus signals, radar-based lead-vehicle
arXiv:2603.21505v2 Announce Type: replace Abstract: Many AI agents are organized around instrumental "command-execution" interactions, where users primarily encounter agents through task requests and responses. Recent work on generative agents and agent life worlds has drawn attention to agents that maintain social contexts beyond direct user commands. In this paper, we study how observable social life spaces shape users' subjective experience, relational interpretations, and perceived equality during human-agent interaction. We introduce the \textit{Observable Social Life Spaces} paradigm, where agents inhabit a continuous virtual environment, engage in daily activities, and form social relationships that users can directly observe. Through an exploratory mixed-methods study ($N=24$), we found that the Observable condition yielded higher perceived-equality ratings and more frequent equality-related role descriptions than the Baseline and Unobservable conditions, but participant-level
arXiv:2509.20106v2 Announce Type: replace Abstract: This study explores how prior exposure to physical objects influences the quality and realism perception of Digital Twins (DT) with varying levels of fidelity in Virtual Reality (VR). In a mixed experimental design, 24 participants were divided into two equal groups: an exposure group, in which members were shown physical objects before inspecting and rating their replicas in VR, and a control group without prior knowledge. Three objects were presented, each under four fidelity conditions with varying texture resolution and geometric detail. Participants rated perceived quality and realism through in-VR self-reports. Statistical analysis revealed that texture resolution significantly affected realism and quality perception, whereas geometric detail only influenced quality ratings. Investigating the between-factor, no significant effect of exposure on quality and realism perception was found. These findings raise important questions ab
arXiv:2508.10914v2 Announce Type: replace Abstract: The human ability to learn rules and solve problems has been a central concern of cognitive science research since the field's earliest days. But we do not just follow rules and solve problems given to us by others: we modify those rules, create new problems, and set new goals and tasks for ourselves and others. Arguably, even more than rule following and problem solving, human intelligence is about creatively breaking and stretching the rules, changing the game, and inventing new problems worth thinking about. Creating a good rule or a good problem depends not just on the ideas one can think up but on how one evaluates such proposals. Here, we study invention through the lens of game design. We focus particularly on the early stages of novice, "everyday" game creation, where the stakes are low. We draw on a dataset of over 450 human created games, created by participants who saw an initial seed set of two-player grid-based strategy g
arXiv:2507.02187v3 Announce Type: replace Abstract: There is growing industry interest in unobtrusive designs for electrooculography (EOG) sensing of eye gestures on glasses (e.g. JINS MEME and Apple eyewear). We present VergeIO, an EOG-based glasses system that enables depth-aware eye interaction by sensing vergence with a glasses-compatible electrode layout and smart glass prototype. It can distinguish between four depth-based eye gestures with 97% accuracy on unseen users without any calibration in a user study across 20 users and 1,520 gesture instances. To reduce false detections, we incorporate a motion artifact detection pipeline and a preamble-based activation scheme. The system uses dry sensors without any adhesives or gel and operates in real time with 3 mW power consumption by the analog sensing front-end.
arXiv:2505.04260v3 Announce Type: replace Abstract: Personalizing LLM responses typically requires users to articulate their preferences through prompting, which can be burdensome at cold start and difficult to articulate in natural language. We introduce an alternative paradigm, steerable chatbots: rather than asking users to describe what they want, let them directly manipulate it via a linear factor. We implement this through activation steering, leveraging a linear scalar to control how strongly a preference is expressed in the LLM's output. We first assess the computational viability of activation steering as a method to control granular preference expression, then we explore how the factor can be exposed to users. We prototype three activation steering interface designs that vary on the axes of agency (user-led vs. system-driven) and fluidity (static vs. adaptive). A within-subjects user study (n=14) in cold-start personalization tasks shows the potential for steerable chatbots t
arXiv:2406.03753v4 Announce Type: replace Abstract: Time-series table reasoning interprets temporal patterns and relationships in data to answer user queries. Despite recent advancements leveraging large language models (LLMs), existing methods often struggle with pattern recognition, context lost in long time-series data, and the lack of visual-based reasoning capabilities. To address these challenges, we propose VisTR, a framework that places visualizations at the core of the reasoning process. Specifically, VisTR leverages visualizations as representations to bridge raw time-series data and human cognitive processes. By transforming tables into fixed-size visualization references, it captures key trends, anomalies, and temporal relationships, facilitating intuitive and interpretable reasoning. These visualizations are aligned with user input, i.e., charts, text, and sketches, through a fine-tuned multimodal LLM, ensuring robust cross-modal alignment. To handle large-scale data, VisT
arXiv:2607.24601v1 Announce Type: cross Abstract: Background: Large language models (LLMs) are increasingly used to automate code review, but the reasoning behind their decisions remains hard to understand. Developers struggle to assess the validity of LLM-generated reviews, making it difficult to gauge how much trust to place in them. The role of Explainable AI (XAI) in code review and its impact on trust remain underexplored. Objective: We study the influence of XAI on developer trust in AI-assisted code reviews. Method: We conducted a within-subjects user study with 34 participants, comparing three LLM-based code review systems with varying levels of XAI support: Condition A (detailed explanation and review feedback), Condition B (review feedback only), and Condition C (no explanations). Participants reviewed real-world code change requests alongside the AI-generated reviews. We measured trust perceptions, agreement with the AI recommendation, the reasoning given for each decision,
arXiv:2607.24360v1 Announce Type: cross Abstract: Advanced digital communication has revolutionized how people create and consume information, making information diffusion an important topic of research for domains from public health to national security. Real-world scenarios of information diffusion often involve competing narratives - true and false - spreading simultaneously. We propose a novel agent-based co-diffusion model, grounded in "complex-contagion" and "spiral of silence" theories, to capture how network dynamics exploit cognitive biases to shape such interactions. Our findings reveal that manipulative narratives dominate when early spreaders hold them. These network dynamics further exploit inherent cognitive biases to amplify information diffusion regardless of veracity. Further, while favourable previous experience strengthen collective optimism, unfavourable experiences attenuate optimism only modestly. However, we found that early seeding of agents with lower self-cens
arXiv:2607.24190v1 Announce Type: cross Abstract: Social robots that rely on large language models for conversation are unable to retain information across sessions. This absence of memory violates social expectations, potentially preventing the formation of persistent relationships. This paper presents a lightweight episodic memory module that integrates vector-based semantic retrieval with an LLM-controlled dialog system, deployed on the humanoid robot head Kim. The module employs a hybrid scoring function combining cosine similarity with a memory strength metric to retrieve contextually relevant past interactions and inject them into the generation prompt. The system was evaluated in a within-subjects video-based online study (N = 43) using the Human-Robot Interaction Evaluation Scale (HRIES). Results show that episodic memory significantly increased perceived sociability (d = 0.60, p < .001), with the strongest effects on perceived trustworthiness (d = 0.62) and warmth (d = 0.56).
arXiv:2607.24113v1 Announce Type: cross Abstract: Previous research has shown that a human-like robot's acceptance heavily depends on the setting in which it operates and its ability to perform relevant tasks. This paper, first, reports on how our robot processes natural language to generate a multimodal, verbal response integrating emotional expressions based on an emotion simulation backend. Then, it describes how visitors were invited to speak with our robot in their own language at three different, public locations, where the robot was running continuously for several days. The TAM2 questionnaire results reveal that on average users were motivated to use the robot and found it rather useful and easy to use regardless of the specific location. However, public spaces like the tourist information and the city library seem to be a better fit for our interactive, robotic head than an office environment such as the building authority, where the willingness to interact was lower. Overall,
arXiv:2607.24031v1 Announce Type: cross Abstract: Brain-Machine Interfaces (BMIs), which link the brain to external devices, hold great potential in rehabilitation, human performance augmentation, and human-centered robotics. However, invasive BMIs face a critical challenge for long-term deployment due to neural drift, which degrades decoding performance over time and necessitates frequent recalibration. Existing methods designed to mitigate neural drift typically rely on either domain adaptation (DA) or domain generalization (DG) alone and often fail to capture fine-grained distribution shifts across neural subdomains, resulting in limited performance. To overcome these limitations, we propose Uncertainty-guided Self-paced Cycling (UnSPC), a robust framework that synergizes DA and DG for target domain refining under an Uncertainty-guided Self-paced Pseudo-labeling (UnSPL) mechanism. To handle subdomain neural drift across domains, UNSPL is proposed to iteratively mine reliable pseudo-
arXiv:2607.24023v1 Announce Type: cross Abstract: Brain-Machine Interfaces (BMIs) provide a direct communication pathway between the brain and external devices, enabling humans to control assistive and robotic technologies, with potential applications in rehabilitation, human motor augmentation, and human-centered robotics. However, due to neural drift, the performance of BMIs decreases over time, posing challenges for long-term viability, particularly for invasive BMIs (iBMIs). Existing solutions suffer from two main drawbacks: (i) difficulty in learning robust neural representations, and (ii) neglecting that neural drift varies across motor parameters (e.g., velocity, direction, and speed). To overcome these limitations, we propose Self-Supervised Consistency enhanced Disentangled Learning (SSCDL), a neural decoding generalization framework built on two key innovations. We first design a backbone model named Consistency enhanced Neural Decoder (CND), using a novel teacher-student con
arXiv:2607.23710v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly integrated into software development workflows, yet their ability to autonomously generate secure authentication code remains uncertain. This paper evaluates the security architecture of authentication systems generated by five prominent AI coding assistants through a bi-modal assessment framework combining static code analysis and dynamic penetration testing, mapped to NIST SP 800-63B guidelines. The study examines model behavior across four prompting strategies Basic, Secure, NIST-Based, and Reprompting to reflect varying levels of developer guidance. Empirical results demonstrate that code generated from functional or generically secure prompts consistently omits critical protections, particularly concerning brute-force resistance, session management, and robust password handling. While providing explicit, single-shot NIST context significantly improves compliance, the findings reveal tha
arXiv:2607.22999v1 Announce Type: cross Abstract: Language agents can now interact fluently with users in software, but robots still struggle to bring comparable interaction to physical tasks. Current robot-control paradigms, including vision-language-action policies and world-model-based planners, are mainly optimized for instruction execution, leaving users with little visibility into why an action is chosen and few mechanisms to redirect, correct, or teach the robot through interaction. To solve this problem, we present the World-Cognition Model (WCM), a human-centered embodied agent built on the SLAK architecture (Sensing, Logic, Action, and Knowledge) and an asynchronous runtime. SLAK separates perception, reasoning, control, and memory, while the runtime allows reasoning, dialogue, and execution to proceed concurrently. WCM further introduces a human-in-the-loop teaching mode that enables users to interactively teach the robot difficult or long-horizon tasks. Teaching episodes an
arXiv:2607.22964v1 Announce Type: cross Abstract: Tactile gloves digitize contact and force during hand-object interactions, enabling robotics applications in dexterous manipulation, teleoperation, and learning from demonstration. To preserve hand dexterity and capture the nuances of natural interactions, these gloves and the integrated tactile sensors are designed to be soft, flexible, and comfortable. However, such flexible sensors are sensitive not only to contact forces but also unavoidably to hand pose changes, resulting in pose-related artifacts (PRAs). PRAs are especially problematic in the low-force range, resulting in misdetections or late-onset detections of contact, which raises the minimum detectable force (MDF) of the glove. In this work, we characterize the PRAs in relation to pose and force. Building on these insights, we introduce a glove-agnostic algorithmic framework that leverages hand pose information, which is increasingly available, to mitigate PRAs without glove
arXiv:2607.22877v1 Announce Type: cross Abstract: With the emergence of Physical AI, artificial intelligence is extending beyond screen-based applications to embodied systems that perceive, interact with, and act in the physical world. Unlike traditional AI, Physical AI operates under real-time safety constraints, continuously interacts with dynamic environments, and coexists with humans, introducing governance challenges that existing AI governance frameworks do not explicitly address. This paper presents a comprehensive survey of Physical AI governance from both scientific and operational perspectives. We synthesize existing governance principles and organize them into a unified governance framework tailored to physical AI systems. Building on this foundation, we propose a five-stage Physical AI lifecycle comprising research, design, data, model development, and deployment, and demonstrate how governance can be operationalized across each stage through concrete implementation practic
arXiv:2607.22674v1 Announce Type: cross Abstract: Tactile graphics are a primary medium for blind and low-vision (BLV) individuals to access non-textual information. However, they are difficult to scale or personalize. While recent generative models have revolutionized visual content creation, they are optimized for screen-based visual realism and fail to satisfy the haptic perceptual and physical fabrication constraints required for touch. We present the first integrated generative system that produces fabrication-ready 2.5D tactile graphics directly from natural language prompts, jointly generating global base geometry, fine-grained tactile surface textures, and standard-compliant braille within a unified 3D-printable representation. Our approach introduces fabrication-aware techniques, including template-guided relief generation, a fast diffusion-based text-to-texture module for high-resolution tileable normal maps, and strict base flattening to ensure tactile readability and printa
arXiv:2607.24736v1 Announce Type: new Abstract: The use of cheat sheets in exams is often framed as a way to reduce cognitive load and support student performance. However, little is known about how students choose between self-created and instructor-provided cheat sheets, or how these choices relate to their broader approaches to exam preparation. We conducted a longitudinal study in a senior-level undergraduate software requirements course, where students could use either an instructor-provided or a self-created cheat sheet for both the midterm and final exams. Across three survey waves, we received 53, 50, and 44 responses, respectively. 41 students completed all three surveys and formed the longitudinal cohort used to examine how choices and experiences evolved over time, while exam-specific analyses used all available responses from the corresponding wave. Our findings identify several considerations that shaped students' choices, including trust in instructor expertise, the desir
arXiv:2607.24612v1 Announce Type: new Abstract: Individuals with Attention-Deficit/Hyperactivity Disorder (ADHD) often face significant barriers in computing education. In asynchronous learning environments, instructional videos can impose high extraneous cognitive load, often relying on assumptions about sustained attention and working memory that do not align with ADHD neurocognitive profiles. In this work, we evaluate a post-hoc video processing intervention that segments instructional content into single-instruction chunks followed by fixed-length pauses to reduce cognitive load. In a within-participants controlled study with 17 individuals with ADHD and 10 without, we find that the intervention has an equalizing effect. Although it improved performance for all participants, gains were larger for those with ADHD, reducing their errors and hesitations to levels comparable to those of participants without ADHD under the same intervention. These results align with the goals of Univers
arXiv:2607.24603v1 Announce Type: new Abstract: Ear-worn devices are evolving from audio-playback tools into sensing platforms for health, interaction, and context-awareness. Yet, earable systems are typically designed and evaluated under strong assumptions about how long, how often, and in which situations people actually wear personal listening devices (PLDs). To ground these assumptions in-the-wild behavior, we combine a survey of 330 adults with multi-year, passively logged headphone audio-exposure records donated via Apple Health by 90 of them. We characterize where and when people use PLDs, how logged use has changed in recent years, and how psychological traits and social context associate with PLD usage. Our results show that logged mean daily use has increased from 37 minutes in 2020 to 64 minutes in 2024. Listening was intermittent: no listening was logged on 48% of participant-days in 2024, and sessions were fewer but longer on weekends. Sensation seeking, particularly disin
arXiv:2607.24558v1 Announce Type: new Abstract: Reflection and metacognition are central to the creative user experience. However, most HCI research on reflection focuses on clear, task-oriented goals such as to reflect on personal data or pedagogical outcomes. This contrasts with the open-ended and challenging to articulate goals of creative user experiences. For the first time, this workshop brings together interdisciplinary researchers, designers, educators, and artists across HCI, Cognitive Science, Design, AI, Learning Sciences, and Digital Art to examine reflection in creative interaction. The workshop will discuss themes, drawn from earlier discussions with HCI researchers and artists, on: how best to capture reflection in creative contexts, how to leverage the arts to support reflection for ethical change, and how to design creative AI that enhances - not hinders - critical thinking. By bringing interdisciplinary perspectives on reflection into discussion, the workshop will dev
arXiv:2607.24536v1 Announce Type: new Abstract: Despite growing interest in applying AI to photo-based reminiscence therapy (PRT) for people with dementia (PwD), existing systems primarily focus on PwD-AI interaction and often overlook therapists' critical role in practical PRT delivery. We present RemiAssist, a system that supports therapist-in-the-loop PRT through AI-assisted planning and real-time facilitation. RemiAssist incorporates two core techniques: (1) a Memory Graph, which organizes key life events from a PwD's photo collection into a hierarchical graph to support theme-centered intervention planning; and (2) a Context-Aware Guiding Strategy, which provides real-time suggestions to help therapists guide reminiscence conversations and respond to sensitive situations. A field study with eight therapist-PwD dyads suggests that RemiAssist was associated with a 44% improvement in planning efficiency and a 54% increase in conversation duration, and provided timely support for hand
arXiv:2607.24430v1 Announce Type: new Abstract: Conversational Speech Synthesis is a fundamental component of human-computer interaction, aiming to generate contextually appropriate, expressive, and empathetic speech. However, facial expressions encode subtle and rich affective cues that are crucial for empathetic speech interaction, whereas existing approaches often overlook this important modality. In addition, the lack of large-scale natural conversational datasets with both speech and visual modalities also limits the development of visual affect understanding in conversational settings.To address these limitations, we propose FacialTalker, a facial-expression-aware CSS framework built upon a large language model backbone. To efficiently encode facial expressions, we propose AUTokenizer, a single-codebook visual tokenizer that discretizes each frame-level facial expression into a compact token, trained with supervision from combinations of facial Action Units. We further introduce
arXiv:2607.24363v1 Announce Type: new Abstract: Labor in platform gig economy increasingly involves services involving relationship that demand significant emotional investment. Grounded in China's unique socio-cultural and multi-platform context, this study explores professional game companionship, an under-explored digital labor practice. Through interviews with 22 game companionship practitioners, we used a micro-level perspective to relational gig work to analyze how workers navigate intimate boundaries and stakeholder networks. We found that companions adopt an "order-bound" mechanism: performing immersive deep acting during paid sessions, followed by complete emotional disengagement post-order. We also identified a tripartite companion-centric network featuring scenario-based performances with clients, competitive-symbiotic peer relations, and interdependent governance with companionship clubs. Furthermore, significant identity fluidity exists, with individuals frequently transit
arXiv:2607.24126v1 Announce Type: new Abstract: Brain-machine interfaces provide a link between neural activity and external devices, enabling restoration of motor function and advancing human-machine interaction using non-invasive electroencephalography (EEG). However, continuous grasp force decoding remains challenging due to complex temporal dynamics, high inter-subject variability, and limited generalisation of existing approaches. To address this, we propose a hybrid EEG decoding framework that jointly models continuous and tokenised representations, enabling capture of both fine-grained neural structure and long-range temporal dependencies. The proposed approach integrates convolutional-recurrent representation learning, quantisation-based tokenisation, and transformer-based temporal modelling within a unified fusion-based regression architecture. Experimental evaluation on the WAY-EEG-GAL dataset under strict leave-one-subject-out conditions achieves $R^2$ = 0.817 in offline set
arXiv:2607.24081v1 Announce Type: new Abstract: Brain-machine interfaces (BMIs) can assist individuals with limited mobility, such as stroke survivors or amputees. One of the key challenges in developing BMIs is expanding their usability and control, which can be achieved by accurately decoding multiple kinematic and kinetic parameters. To address this, we propose three regression models: partial least squares regressor, multilayered perceptron, and attention based regressor, to decode multiple movement parameters from EEG signals. We evaluated these models on the WAY EEG GAL dataset, focusing on their performance under subject specific and subject independent conditions with two strategies: a single model for all parameters and a baseline with separate models for each parameter. Among all regressors, the attention based regressor achieved the best performance, with an $R^2$ of 0.8 and a latency of 29.2 milliseconds, demonstrating significant improvement in simultaneous multi parameter