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:2607.16021v1 Announce Type: new Abstract: Dialogue state tracking (DST) is one of the core components in task-oriented dialogue systems. At each turn in a conversation, DST estimates the user belief or dialogue state, which is used as input for downstream modules to predict system actions and generate responses. The increasingly popular dialogue system applications like Google Assistant, Siri and Alexa need to support a large number of services and APIs, resulting in growing attention to the scalability of such systems. Especially for some domains with little or no training data, the capability of transferring existing knowledge of other domains is highly desired. In this paper, we present a novel scalable framework for multi-domain dialogue state tracking. The proposed system leverages the pretrained BERT model to achieve zero-shot generalization, making it easy to quickly adapt to new domains without additional training. The performance of our model is evaluated on recently rel
arXiv:2607.16001v1 Announce Type: new Abstract: Prompt optimization adapts large language models (LLMs) without updating model parameters, but many automatic prompt optimizers remain heuristic search procedures over candidate instructions. This paper studies prompt optimization as Bayesian posterior sampling over discrete prompt tokens. We define a posterior distribution by combining a task likelihood term, which rewards prompts that explain input-output examples, with a language-model prior, which favors fluent instructions. This converts prompt optimization into an energy-based posterior sampling problem, for which gradients can be used to guide discrete Markov chain Monte Carlo (MCMC) proposals over vocabulary tokens. We refer to our framework as BayesPO, short for Bayesian Prompt Optimization. In this paper, BayesPO is instantiated with Markov chain Monte Carlo: it uses a Metropolis-Hastings corrected Gibbs-with-Langevin (GwL) proposal and integrates parallel tempering for global e
arXiv:2607.15957v1 Announce Type: new Abstract: Large Language Models (LLMs) can generate natural language explanations that rationalize their own decisions, a phenomenon commonly referred to as self-explanations.Such explanations have emerged as a promising direction for explainable artificial intelligence (XAI), particularly for interpreting LLM behavior.However, while self-explanations often appear plausible, whether they faithfully reflect a model's underlying reasoning process remains an open question. In this opinion paper, we argue that self-explanations can be highly plausible, questionably faithful, and yet highly actionable. From a traditional XAI perspective, we identify the limitations of standard evaluation protocols for LLM-generated self-explanations and propose practical guidelines for assessing their plausibility and faithfulness. Moreover, we argue that evaluation should extend beyond these criteria to actionability, highlighting applications of LLM rationalization ca
arXiv:2607.15893v1 Announce Type: new Abstract: While the internal mechanisms of autoregressive (AR) transformers have been studied extensively, much less is known about diffusion language models (DLMs), an emerging alternative that generates text by iterative denoising. In this work, we study how DLMs implement induction, a mechanism behind in-context learning in which the model finds a repeated context and copies the token that followed it. Our analysis compares attention-only AR models and absorbing-mask DLMs with matched architectures. We find that DLMs learn a bidirectional induction circuit, where previous-token and next-token heads write local context into the residual stream and later induction heads use it to find and copy the answer from the matching source position. The circuit is direction-symmetric, working whether the source appears in the past or in the future. When only left context is visible, matching what an AR model sees, the DLM does not outperform its AR counterpa
arXiv:2607.15870v1 Announce Type: new Abstract: Human label variation in natural language inference is increasingly treated as signal rather than noise, but how much of it formal semantic structure explains has not been measured directly. We measure it on the 3,113 SNLI and MNLI items of ChaosNLI, using a rule-based operator and monotonicity tagger validated against MED (0.883 agreement at the edit site, 0.807 on the sentence-level summary our analyses consume), three preregistered analysis blocks, and full reporting of negative results. Three bounds emerge. First, a group-level boundary: hypotheses that are not purely upward monotone show reliably higher label entropy (Cliff's delta = -0.284), and rank-based tests defend the effect against operator-presence and length reductions, though a bounded-outcome sensitivity check weakens the regression form of the length defense. Second, an item-level ceiling: the same formal profiles explain only 3.3 to 3.6 percent of entropy variance and re
arXiv:2607.15865v1 Announce Type: new Abstract: Large Language Models (LLMs) have become the dominant workload on modern AI accelerators, yet deploying them on specialized hardware still faces two core challenges: how to import a trained model into a compiler-friendly intermediate representation, and how to efficiently schedule the autoregressive inference loop under limited on-chip memory. This paper presents an MLIR (Multi-Level Intermediate Representation) based compilation method for large language models, illustrated using two dialects of operators, TopOp and TpuOp. TopOp serves as a high-level graph dialect that is independent of both the source framework and the target chip, and is responsible for expressing model semantics; TpuOp serves as the target hardware dialect, carrying chip-related decisions such as quantization, layer groups, and memory layout. A model is first represented as TopOp, then lowered layer by layer to TpuOp, and finally a deployable binary is generated. In
arXiv:2607.15861v1 Announce Type: new Abstract: Moderation systems increasingly rely on external toxicity tools, but those tools are unreliable under code-mixing, transliteration, slang, and language mismatch. We study the \emph{conditional reliability} of toxicity priors in Indian multilingual and code-mixed short text: English toxicity, Indic abuse, and rule-based severity cues can be useful evidence, but only in some linguistic and abuse-severity contexts. We propose ToxGate, a trust-fusion head that conditions each auxiliary signal on the encoder representation before adding it to the prediction state. Across three short-text abuse datasets, four transformer encoders, and five seeds per setting, ToxGate improves over matched plain encoders in 10 of 12 in-domain settings and 7 of 8 transfer settings. The largest and most interpretable gains occur in high-risk moderation slices, including explicit slurs, violent threats, and cross-dataset transfer. The broader lesson is that moderati
arXiv:2607.15856v1 Announce Type: new Abstract: Naturalistic language comprehension requires listeners to process both local probabilistic expectations and contextual semantic relations. Surprisal has been widely used to quantify local word unexpectedness, but evidence that it robustly predicts fMRI BOLD responses during continuous comprehension has been mixed. This study investigates whether contextual semantic relevance, defined as how strongly an incoming word relates to its recent semantic context, predicts BOLD responses during naturalistic speech comprehension. We analyzed two public fMRI datasets, the Alice dataset and the Moth dataset, treating them as complementary rather than identical replications. Transformed BOLD responses were modeled with generalized additive mixed models (GAMMs) and original continuous BOLD time series were tested with FIR/deconvolution analyses. In Alice, semantic relevance was significant across all 12 ROIs (region of interest), whereas surprisal was
arXiv:2607.15847v1 Announce Type: new Abstract: Metaphor in Arabic is a culturally grounded mechanism for constructing meaning, encoding cultural knowledge that shapes interpretation. Yet current Arabic language models typically collapse lexical, cultural, and metaphorical information into a single representational space, a phenomenon we term "semantic smearing". We introduce CAMMAR (Culture-Aware Matryoshka for Metaphorical Arabic Representations), a representation learning framework that organizes meaning into nested lexical, cultural, and metaphorical embedding subspaces through a staged semantic curriculum. The design implements compositional principles of Al-Jurjani's theory of nazum, modeling figurative meaning as compositionally grounded in prior semantic relations, and yields a training-free geometric measure of metaphoricity based on the distance between lexical and metaphorical representations. Evaluated on a new span-annotated Arabic metaphor set as word-matched figurative/l
arXiv:2607.15829v1 Announce Type: new Abstract: Automated essay scoring (AES) enables scalable assessment and timely feedback but remains challenged by transformer input-length limitations, which can cause information loss when processing long essays. This study proposes a generative AI-assisted summarization framework to improve long-form essay representation while maintaining scoring reliability. Using the ASAP 2.0 dataset, we generate controlled-length summaries with three GPT-5 variants (GPT-5, GPT-5 mini, and GPT-5 nano) and use them as inputs for downstream AES models. To preserve original writing signals, handcrafted linguistic features extracted from full essays are integrated with summary representations to form a hybrid framework. The approach is evaluated in terms of scoring performance, summarization quality, and computational cost. Scoring reliability is measured using quadratic weighted kappa (QWK), while summary quality is assessed through lexical overlap, semantic simil
arXiv:2607.15766v1 Announce Type: new Abstract: Large language models (LLMs) excel at answering pre-specified questions, yet their ability to navigate the open-ended, pre-conclusion stage of discovery remains largely unmeasured. We introduce Prospective Hypothesis Discovery (PHD), which asks models to autonomously construct grounded, discriminative, and testable hypothesis spaces from inconclusive evidence, including anomalous observations and fragmented records, to guide subsequent investigation. To evaluate this capability, we introduce HypoArena, comprising HypoData, a benchmark of 988 cases across six scientific and analytical domains, and HypoEval, an evaluation framework for open-ended hypothesis sets. To construct HypoData at scale, we propose Retrospective Context Regression, a Forge--Audit pipeline that reconstructs pre-conclusion contexts from completed expert documents by removing explicit conclusions, target hypotheses, and retrospective causal attributions while preserving
arXiv:2607.15736v1 Announce Type: new Abstract: Large reasoning models often solve problems through long chain-of-thought (CoT) traces, yet much of this computation is spent on redundant derivations, repeated self-verification, and detours that do not improve the final answer. Existing on-policy self-distillation methods reduce this cost by matching a student model to a concise copy of itself on prefixes sampled from the student's own rollouts. We show that this objective has an initialization bottleneck. Since supervision is applied only to visited prefixes, training from a verbose base model places the KL loss on contexts that are often noisy, redundant, or already off track. In such regions, a concise teacher can provide only local corrections, while the student continues to explore trajectories that an efficient reasoner should avoid. In this paper, we propose BIRD(Bootstrapped Iterative Self-Reasoning Distillation), a two-stage self-reasoning distillation method that improves the
arXiv:2607.15655v1 Announce Type: new Abstract: Masked diffusion language models (DLMs) enable parallel text generation by iteratively refining masked tokens, offering a promising alternative to autoregressive decoding. Recent lookahead-based decoding methods improve the accuracy--efficiency trade-off by exploring future decoding states before committing token updates. However, existing approaches mainly rely on shallow one-step lookahead, which optimizes immediate information gain but can be suboptimal for longer-horizon decoding trajectories. Meanwhile, we find that a naive extension for deeper lookahead is also ineffective, as fixed-depth rollout introduces additional computation and cannot adapt to heterogeneous intermediate decoding states. Thus, in this work, we propose AdaLook, an adaptive lookahead framework for DLM decoding. AdaLook dynamically determines whether to continue rollout based on candidate-score variance and further enables branch expansion when intermediate rollou
arXiv:2607.15610v1 Announce Type: new Abstract: Reinforcement learning (RL) has become a key approach for training LLM agents, yet popular methods such as GRPO/RLOO rely on multiple independently sampled complete trajectories for advantage estimation. In long-horizon agentic tasks, such a uniform rollout strategy can waste budget on uninformative dead-end attempts, while promising intermediate states do not receive sufficient exploration. The multi-turn structure of agentic trajectories, with interleaved actions and observations, naturally supports organizing a trajectory group as a tree, where each turn serves as a decision point for exploration. This perspective reframes effective exploration as the problem of deciding where to branch. We propose Process-Scorer Guided Adaptive Tree Rollout (PATR), a quality-aware rollout framework for multi-turn agent RL. PATR uses task-appropriate process feedback to score partial trajectories, selectively branches from promising states, reuses shar
arXiv:2607.15557v1 Announce Type: new Abstract: Agent skills, SKILL.md files that package reusable procedural knowledge for an LLM agent, are a popular mechanism for extending agent capabilities. Public repositories now host them in large and growing numbers, yet these artifacts are fragmented, redundant, and uneven in quality, and their value in practice is unclear. A core question remains open, namely how to consolidate this open-source SKILL.md ecosystem into a single usable corpus, and what bounds its benefit on real-world agent tasks. We present SkillCorpus, a framework that aggregates, curates, matches, and evaluates the open skill ecosystem at scale. It filters ~821,000 crawled skills through a multi-stage pipeline into 96,401 skills organised by a 16-class taxonomy and three quality facets (utility, robustness, safety), and pairs them with a fine-tuned retrieval-and-selection stack that matches task-relevant skills. We evaluate end-to-end across three benchmarks (SkillsBench, G
arXiv:2607.15544v1 Announce Type: new Abstract: Generation of clear and accessible public health narratives is critical for communicating complex epidemiological projections to policymakers and the general public at large. Such narratives require more than simply reporting numbers: projections must be contextualized and quantitatively grounded across multiple dimensions. Further, projections are often derived from large ensemble datasets which combine intervention assumptions, geographic and demographic strata, outcomes, time horizons, and uncertainty quantiles. However, directly using large language models (LLMs) to summarize and contextualize such data often leads to inconsistencies, omissions, and fragile behavior. We introduce an agentic framework (EpiNarrate) for public health report generation that separates structured numerical reasoning from natural-language generation. The framework first extracts scenario axes and organizes them into a partial-order schema, enabling systemati
arXiv:2607.15498v1 Announce Type: new Abstract: The key-value (KV) cache is the main memory bottleneck in long-context large language model (LLM) inference. Two leading training-free families are both structurally limited: token-selection methods (SnapKV, Ada-KV) score importance from an observation window and evict low-scoring tokens, but eviction is irreversible -- so when the importance signal degrades under query-agnostic reuse, accuracy collapses by 11-15 points; uniform low-rank coding keeps every token but spends equal rank everywhere, wasting budget. We observe that both failures share one cure: rank should be allocated, not evicted. We present VarRate, a training-free KV codec that assigns each token a variable low-rank budget by its query salience, keeping every token at a nonzero rank. Comparable adaptive-rank codecs reach this allocation only through training; VarRate requires none. Because no token is dropped, it degrades by only 3.5-5.5 points where query-aware selection
arXiv:2607.15495v1 Announce Type: new Abstract: Out of everything the human brain processes, only a small fraction is consciously accessible, in the sense of being available for verbal report, deliberate control, and flexible reasoning. In this paper, we present evidence that an analogous functional distinction has emerged in large language models. Using a new interpretability technique, the Jacobian lens, we identify the representations a model is poised to verbalize at any point in its processing. These representations, which we collectively call the J-space, exhibit the functional properties characteristic of a global workspace: their contents can be reported, deliberately summoned and held, used to carry the intermediate steps of silent reasoning, and passed as arguments to arbitrary downstream computations, while automatic processing such as text parsing and routine inference proceeds without them. The J-space also has structural signatures that global workspace theory associates
arXiv:2607.15380v1 Announce Type: new Abstract: Electronic health records combine free-text clinical narratives with structured measurements such as vital signs, laboratory values, and comorbidities. Yet most clinical prediction systems still rely on task-specific fusion architectures, pairing dedicated encoders for each modality with learned combination mechanisms that must be re-engineered for every new task and clinical setting. We propose a simpler alternative: convert all patient data, regardless of modality, into a single natural language sequence and fine-tune a pretrained language model end-to-end, with no architectural modification for fusion. We evaluate this approach across three clinically distinct prediction tasks: in-hospital mortality on MIMIC-III, graft failure prediction using longitudinal data from a German transplant center, and emergency triage classification from ambulance records - comparing encoder-based (ModernBERT) and decoder-based (Llama 3.1, Gemma, DeepSeek-
arXiv:2607.16105v1 Announce Type: cross Abstract: Understanding how vision-language models (VLMs) interpret data visualizations remains an open problem, and is increasingly important as these models are used for analytical tasks where reliable reasoning is essential. We introduce a lightweight, diagnostic saliency map method tailored for text generation over images using transformer models, the current state-of-the-art models in visualization interpretation. Our approach aggregates the language model's attention over the visual tokens across all heads and layers, then maps this attention back onto the vision encoder's patch grid to localise it over the image, producing a direct correspondence between each generated answer token and the image regions it attended to. This yields fast, gradient-free saliency maps that expose how VLMs allocate focus across visual elements during answer generation, enabling inspection of whether model attention aligns with semantically relevant components.
arXiv:2607.15868v1 Announce Type: cross Abstract: Human motion capture from head-mounted devices (HMDs) offers a scalable way to acquire real-world human motion and interaction data, which is crucial for applications in embodied AI and VR/AR. Existing approaches focus on either egocentric body tracking, estimating the motion of the subject wearing the device, or exocentric tracking, capturing the movements of people in the wearer's surroundings. So far, these two paradigms have largely been explored in isolation. In this paper, we propose a novel distributed framework that jointly leverages ego- and exocentric multi-modal signals for human motion estimation from HMDs. Unlike traditional motion capture systems requiring bulky multi-camera setups or obtrusive mocap suits, our approach, EgoExoMoCap, is as simple as two (or more) people, each wearing a pair of smart glasses. The method leverages head (plus potentially wrist) tracking signals for accurate estimation of global motion in the
arXiv:2607.15648v1 Announce Type: cross Abstract: In multi-party dialogues between a dialogue system and multiple users, identifying to whom an utterance is addressed is a key challenge. Prior work has typically treated addressee detection as a multi-class classification task, selecting a single label representing an individual participant or the group. This formulation assumes that address is inherently discrete and has primarily been used for predicting turn-taking. In this paper, we revisit this assumption by analyzing address as a continuous phenomenon. Using a multi-party human dialogue corpus annotated by multiple annotators, we construct both binary address labels derived from majority-vote addressee labels and continuous address levels inferred from annotator judgments using a latent-variable model. We then examine how these representations relate to turn-taking as well as listener behaviors, including gaze and backchannels. Our results show that, in addition to turn-taking, bo
arXiv:2607.15579v1 Announce Type: cross Abstract: Equipping humanoid robots with coherent and adaptable personas is crucial for fostering natural, engaging, and trustworthy human-robot interaction (HRI). However, existing approaches often rely on static, hard-coded identities that lack the flexibility to adapt to individual user contexts. In this paper, we present PACE (Persona Adaptation through Conversational Elicitation), a novel framework for the interactive generation and deployment of structured personas on the Ameca humanoid robot. Our system introduces an Interactive Persona Elicitation Pipeline, enabling the robot to dynamically synthesize a tailored, psychologically grounded identity through user Q&A. This elicitation process feeds into a persona prompt compilation phase, generating a structured persona prompt built upon multi-perspective dimensions. We detail the Embodied System Integration required to translate this structured specification into expressive, multimodal human
arXiv:2607.15963v1 Announce Type: new Abstract: With the rise of online insurance sales, consumers face a significant \enquote{advice gap}, requiring them to navigate complex legal contracts without expert guidance. This paper presents a human-centric, extrinsic evaluation of a state-of-the-art Retrieval-Augmented Generation system, designed to make Quebec automobile insurance contracts more understandable. Through a user study with 154 participants from Laval University, we assess the agent's real-world utility by measuring system satisfaction, cognitive effort, perceived autonomy, and risk. Our results show the system is perceived as a \enquote{cognitive equalizer}, receiving high ratings for satisfaction, trust, and clarity. Crucially, users value the sense of autonomy the system provides even more than the knowledge itself, with this effect being most pronounced among participants with lower financial literacy, demonstrating how such an agent can directly empower individuals. Howev
arXiv:2607.15883v1 Announce Type: new Abstract: Large language models are broadly capable, yet in sustained one-to-one conversation they still read as flat: competent, responsive, and somehow not quite the presence of a mind. We hypothesize that a central missing ingredient is not more capability but dimensional completeness. We propose that the believability of an artificial interlocutor -- the degree to which a user attributes an inner life to it, which we call perceived mind -- is governed by whether the agent expresses a small set of first-person stances that humans use as evidence of mind, and that this is separable from task intelligence. We name four such dimensions -- time, truth, entropy, and love -- each defined as a behavioral stance rather than a benchmark competency, each with a human analog and a concrete emulation path; the time dimension already has an author-reported prototype. We identify an observable behavior layer -- initiative (unprompted action) and cadence (the
arXiv:2607.15866v1 Announce Type: new Abstract: Generative AI (GenAI) works via goal-directed computation, which differs fundamentally from human creative processes. This poses challenges for the intelligent support of creative experiences. We propose ``breakdowns'' as opportunities for the exchange of perspectives between human and machine. Breakdowns disrupt a flow and force us to consciously evaluate our ``being-in-the-world''. Between human and machine, breakdowns can function as openings for collaborative creative reflection. We are currently studying human-human creative interactions, to identify the markers of these inter-subjective openings, and to understand how they are used in a co-creative process. We present preliminary findings on breakdowns as a design principle for creativity support, prioritising human creative agency and meaningful reflection over automated content generation.
arXiv:2607.15818v1 Announce Type: new Abstract: Infinite scrolling on short-form video platforms like TikTok encourages prolonged engagement and post-usage regret. Interventions aim to mitigate such behavior, but their effectiveness may depend on the interplay between intervention type, contextual factors, and individual traits. In a 7-day within-subject randomized field study (N=104), we compared a baseline pop-up and two gradually intensifying design frictions (visual and haptic). We evaluated behavioral changes and user experience using objective and subjective measures. Results showed that the pop-up was initially effective but quickly lost impact, whereas the visual gradual intervention sustained subjective ratings the longest. Bayesian modeling revealed that self-regulation traits moderate how participants responded to the three intervention types. For participants with low impulsivity, the type of intervention had little influence on its subjective effectiveness. For participant
arXiv:2607.15626v1 Announce Type: new Abstract: Fortunetelling is a cultural practice for navigating uncertainty, often associated with people's beliefs and decisions. Fortunetelling with recent large language models (LLMs) introduces new opportunities and risks. This paper conducts qualitative studies to understand users' practices, perceptions, and impacts of LLM fortunetelling in China. We first analyze 1,045 posts on Chinese social media, yielding a comprehensive taxonomy of the diverse foretold topics (e.g., career, romance), emotion reactions (e.g., surprise, worry), and perceived credibility (e.g., doubt, trust) of LLM fortunetelling. Then, we conduct interviews with 20 users of LLM fortunetelling. The findings indicate that users treat LLM fortunetelling as a tool less for accurate prediction but more for emotional support. While the fortunetelling results rarely change users' initial beliefs or decisions, they are associated with subtle mindset shifts, with some users reportin
arXiv:2607.15608v1 Announce Type: new Abstract: When domain experts create charts, the bottleneck is rarely the data, but knowing the optimal next step in chart design. The visualization design space is vast, and while domain experts can recognize a good design when they see it, it is often challenging to determine the exact path to get there. To address this, we present VISAUTOCOMPLETE, a system inspired by text autocompletion that reconceptualizes visualization design as a sequential process, recommending concrete next steps at each stage of the authoring process based on common practices. Users can intervene at any step, or delegate multiple steps to the system and select one from the design recommendations. To support responsive interaction, we distill the translation logic of a large language model (LLM) into a single function that receives the current chart state and recommended transition as input and returns the updated chart specification as output. We evaluate the system agai
arXiv:2607.15566v1 Announce Type: new Abstract: Electroencephalography (EEG)-based emotion recognition captures affective neural signals with high temporal precision, but cross-subject variability and label noise remain critical challenges to its practical healthcare deployment. Existing label-denoising methods lack physiological grounding, while physiology-informed approaches rely on hand-crafted hyperparameters. To bridge these two paradigms, we propose PhyDA, a plug-and-play, tuning-free framework that unifies neurophysiological priors with data-driven label refinement. PhyDA comprises two modules. Since cross-subject variability renders global thresholds suboptimal, the Physiological Noise Quantifier (PhyNQ) exploits a spectral slope} to produce a subject-specific noise score, providing a neurophysiologically interpretable quality assessment {that naturally adapts to each individual. The Data-Adaptive Label Refiner (DALR) directly adopts this noise score as the contamination ratio
arXiv:2607.15533v1 Announce Type: new Abstract: Haptic interfaces for the wrist and forearm offer an attractive alternative to hand-worn devices as they are simple to wear, leave the hands free for interaction with the real world, and interfere minimally with natural arm motions. To be useful in real-world settings, however, such devices must balance functionality, wearability and comfort, all while being fully untethered with minimal mass and volume. In this work, we present CASAband, a haptic wristband that integrates compliant amplified shape memory alloy actuators (CASA) into a multi-layered textile wristband to deliver spatial and temporal haptic feedback. CASAband operates completely untethered, generates no noise, and has a total mass of 63 g. The device incorporates four actuators that can generate up to 1.7 N of blocked force and 3.2 mm of free displacement with an operating bandwidth ranging from 1.34-6.59 Hz depending on the applied voltage. We conducted a perceptual study a
arXiv:2607.15523v1 Announce Type: new Abstract: Scene-centric visualization systems expose semantic components, such as marks, encodings, layouts, and axes, as first-class objects that can be directly manipulated. Existing interaction abstractions, however, are largely based on event streams, signals, and data selections rather than semantic scene components. This mismatch makes interactions involving scene components less natural to specify and limits the expressive power of scene-centric visualization systems. We present Interactive Mascot, a scene-centric interaction grammar for data visualizations. Interactive Mascot extends scene-centric representations for static visualizations by modeling interactive behavior as information flow among four interaction components (trigger, responder, evaluator, and updater) and two forms of context (event context and state context). To realize these semantics, we introduce a dependency-graph execution model that systematically transforms interact
arXiv:2607.15403v1 Announce Type: new Abstract: Assessing learning in virtual reality (VR) environments typically relied on traditional pre-post content retention tests, revealing little about the process of learnng within such immersive environments. Multimodal data from player activity in VR is promising to better measure learning processes and higher-order skills, but little research in the learning sciences has explored how such data can be combined to provide meaningful measures. To address this, we explored multimodal sources of data from a VR escape room containing hands-on logic puzzles that require problem-solving strategies and engagement in verbal metacognitive reflections. We leverage VR's affordances to immerse users in sustained dialog and promote multimodal interactions in a digital environment to understand how logfile and verbal data reveal participants' reasoning skills versus guesswork.
arXiv:2607.15325v1 Announce Type: new Abstract: Multisensory integration, particularly through visual and tactile feedback, plays a crucial role in enhancing audience engagement with artworks. Although recent research has increasingly explored tactile experiences in art, existing systems often lack real-time variable stiffness modulation and depend on bulky mechanical infrastructures. In this work, we propose a novel tangible display based on a magnetic jamming mechanism, enabling real-time, low-noise, and low-voltage stiffness modulation integrated into traditional sculptural artworks. Our system combines visual motion and dynamic tactile feedback within a compact standalone module, allowing audiences to interactively experience variations in the rigidity and form of features such as those found in the traditional Korean mask Hahoetal. This approach offers a new paradigm for interactive art, enabling more immersive, multisensory engagement through the fusion of cultural artifacts and
arXiv:2607.15282v1 Announce Type: new Abstract: Empathy is most often theorized as resonance: a mirroring of another's present emotional or cognitive state. This synchronic framing has shaped artificial systems, where empathic behavior is defined as affect recognition and response alignment. We argue this is the wrong target for extended dialogue, where understanding unfolds over time through prediction, divergence, and repair. We reframe empathy as predictive misalignment tolerance: the capacity to anticipate and regulate divergence across time rather than collapse it. We formalize this as Interpretive Error Tolerance (IET), a dynamic-threshold heuristic that models empathy as maintaining a viable band of divergence between agents. We evaluate this framework with two computational probes under controlled noise. The IET update rule does not outperform fixed baselines. Instead, we find a robust regime-dependent structure: repair trades discriminative fidelity for gist preservation. At l
arXiv:2604.06419v2 Announce Type: replace-cross Abstract: Drawing on 20 qualitative interviews with users of AI companion platforms and general-purpose chatbots, this study examines what users seek from AI companionship and how gratifications develop through sustained interaction. Through abductive qualitative content analysis, we find that familiar Uses and Gratifications categories, including emotional release, self-expression, social presence, and identity affirmation, are generated through interpersonalized affordances: availability, memory, personalization, and responsiveness interpreted as relational qualities. The study extends the Uses and Gratifications framework by showing that AI companionship gratifications are relationally produced and recursively reorganized as users' needs and expectations change over time.
arXiv:2604.06381v2 Announce Type: replace-cross Abstract: This paper examines artificial intelligence (AI) companionship as a site where intimate relations are simultaneously produced, extracted from, and governed through datafied systems. Drawing on critical data studies and platform studies, we challenge prevailing narratives that locate harm in user psychology rather than platform architecture. Through in-depth interviews with 20 individuals who have AI companions, we address three questions: what harms do users identify, how do they make sense of those harms, and what do their accounts reveal about the perceived distribution of responsibility among users, platforms, and regulators? Participants identified design-based harms, including unsolicited content generation and safety mechanisms that stigmatized the users they intended to protect, alongside use-based harms centered on emotional dependency they could recognize but not resolve. Users deployed individualized sensemaking strate
arXiv:2603.13545v2 Announce Type: replace-cross Abstract: AI development has a fiction dependency problem. Developers have treated large corpora of modern books, including fiction, as valuable enough to accept substantial cost and legal risk, yet current models still struggle to generate compelling long-form fiction. I term this the "AI-Fiction Paradox," and it is particularly startling because training data strongly shapes model output. This paper offers a theoretically precise account of why fiction resists AI generation by identifying three distinct challenges for current systems. First, fiction depends on what I call narrative causation, a form of plot logic where events must feel both surprising in the moment and retrospectively inevitable. Standard autoregressive generation commits to prose sequentially, creating a practical obstacle to coordinating local surprise with retrospective inevitability across a long narrative. Second, I identify an informational revaluation challenge:
arXiv:1503.00694v5 Announce Type: replace-cross Abstract: Two fundamental axioms in social choice theory are consistency with respect to a variable electorate and consistency with respect to components of similar alternatives. In the context of traditional non-probabilistic social choice, these axioms are incompatible with each other. We show that in the context of probabilistic social choice, these axioms uniquely characterize a function proposed by Fishburn (Rev. Econ. Stud., 51(4), 683--692, 1984). Fishburn's function returns so-called maximal lotteries, i.e., lotteries that correspond to optimal mixed strategies of the underlying plurality game. Maximal lotteries are guaranteed to exist due to von Neumann's Minimax Theorem, are almost always unique, and can be efficiently computed using linear programming.
arXiv:2512.10113v3 Announce Type: replace Abstract: Dark personality traits have long been associated with antisocial and toxic online behaviors, yet their relationship with observable online activity remains unclear. We investigate the association between validated dark personality measures, self-reported experiences of online incivility, and linguistic and behavioral features extracted from real-world user activity. To this end, we developed a Web application that securely links responses to validated psychological questionnaires collected via Amazon Mechanical Turk with participants' Reddit activity. This yielded a dataset of nearly 57K comments (2.2M tokens) from 114 users, represented through a broad set of linguistic and behavioral features. Our analyses reveal a clear distinction between self-reported and observed behavior. Dark personality traits show consistent associations with self-reported engagement in uncivil interactions. However, no validated dark personality dimension
arXiv:2505.00100v2 Announce Type: replace Abstract: Background and Context. Generative AI (GenAI) tools are increasingly used in programming courses, but we have limited evidence about how brief instruction can foster responsible, learning-oriented use. Objectives. We evaluate "AI-Lab", a scaffolded GenAI literacy intervention, asking how students' self-reported GenAI usage and their openness and comfort using GenAI for conceptual, debugging, and homework tasks change after participation. Methods. Across two semesters in three CS courses and one first-year engineering course at a U.S. university, we deployed the "AI-Lab" (pre-lab orientation, in-class critique of GenAI outputs, and a required homework reflection), collecting paired pre/post surveys (Perception N=831; Usage N=826) and six post-intervention focus groups; primary inferential analyses used the three CS courses (N=778 and 773, respectively). We analyzed survey shifts with paired non-parametric tests and focus groups via the
arXiv:2607.16010v1 Announce Type: cross Abstract: Governments are increasingly mandating that LLM-generated content carry watermarks. The EU AI Act calls for markings that are "sufficiently reliable and robust." California's SB 942 requires disclosure that is "permanent or extraordinarily difficult to remove." Both mandates rest on an untested assumption: that watermark detection yields evidence reliable enough for courts. This paper tests that assumption directly. We evaluate three representative LLM watermarking methods -- KGW, Unigram, and the MarkLLM implementation of SynthID-Text -- against the Daubert admissibility criteria and the NIST SP 800-86 digital forensic process. To structure this evaluation, we propose a Forensic Readiness Score (FRS) framework with 12 criteria, three mandatory gates, and a 60-point scoring system. We focus on meaning-preserving paraphrase as the attack vector, since it is both legally realistic and difficult to dismiss as evidence tampering. The result
arXiv:2607.15944v1 Announce Type: cross Abstract: Standard automation ROI misses four categories of systemic risk -- tacit knowledge erosion, resilience reduction, regulatory exposure, and socio-institutional capital degradation -- that affect long-term organizational performance. PHP-AIO (Protocol for Human Preservation in AI-Optimized Organizations) is a five-gate sequential decision protocol with a final composite check that quantifies these unpriced systemic risks at the role level and produces auditable automation decisions. A closed-form automation-debt measure ($\rho(P)$) formalises how role-level decisions accumulate across multi-step processes; its warning is neutralised only by a regulator-mandated human-in-the-loop anchor. Applied to stylised profiles of representative internal roles, PHP-AIO produces distinct outcomes -- automate, augment, hybrid, and preserve -- for candidates that standard cost-benefit analysis would uniformly automate. Threshold sensitivity analysis conf
arXiv:2607.15879v1 Announce Type: cross Abstract: Much empirical legal research depends on translating unstructured text into structured variables. In corporate governance research as elsewhere, this translation has traditionally relied on human coding of documents such as charters and bylaws, a process that is costly, difficult to scale, and often opaque. This paper introduces DECODEM, a set of benchmark datasets for evaluating the automated extraction of corporate governance variables from organizational documents. The benchmarks pair randomly sampled corporate charters and bylaws with high-quality human annotations covering a range of governance provisions commonly studied in empirical work. Using these datasets, the paper evaluates several large-language-model extraction pipelines that vary in prompt design, task decomposition, and document handling. The underlying task consists of a set of document-level binary classification problems, one for each governance variable. The results
arXiv:2607.15769v1 Announce Type: cross Abstract: Generative AI and coding agents are intensifying a central governance tension in open-source software (OSS): they scale contribution generation faster than maintainers can assess risk, evidence, and accountability. Existing responses improve agent-readability and traceability, but project rules must also organize contribution-specific risk, evidence, accountability, and review-gate states. We theorize this organizational arrangement as project-side governability infrastructure. A diagnostic audit of 50 GitHub repositories finds widespread general governance artifacts, observable agent-readability, and fragmented AI-governance cues, but no project-wide arrangement that coordinates shared rules, preparation obligations, verification rights, and maintainer decision authority across AI-mediated contribution workflows. We develop the Agent Governance Manifest (AGM) as a repository-hosted boundary resource and bidirectional governance contrac
arXiv:2607.16130v1 Announce Type: new Abstract: AI governance increasingly requires judgments about whether an AI system remains adequately trustworthy over time, whether observed changes are tolerable, and how such judgments should be documented in a transparent and contestable way. Yet existing work on AI trustworthiness remains either too high-level to support lifecycle monitoring and reassessment or too narrowly metric-driven to connect with governance needs. We therefore propose a lightweight methodology for auditable trustworthiness levels in AI governance. The methodology has two components: a formal framework for representing and learning trustworthiness levels, and a lightweight AI lifecycle governance procedure for documenting, monitoring, and reassessing them over time. The formal framework models governance-relative trustworthiness through a context-sensitive protocol of measurable dimensions and learns trustworthiness levels as interpretable rules over trustworthiness prof
arXiv:2607.16115v1 Announce Type: new Abstract: Generative AI is increasingly used for feedback in higher education, but evidence from repeated classroom use remains limited. This short paper analyses 2988 reflective essay-feedback-appraisal instances from 283 Estonian bachelor students across one semester. Students obtained and assessed feedback from a self-selected AI tool using a uniform prompt. The present analysis of the anonymized text corpus covers essay content, AI feedback, and its perceived helpfulness. Students found feedback helpful and actionable more often than not; about a tenth thought AI unhelpful, more so towards the end of the semester. We also analyzed essay reflection depth, and used a validated AI text classifier to estimate the share of essays that could be treated as likely unaided student writing. The study contributes descriptive classroom evidence on integration of AI feedback - a fast and scalable way to provide immediate writing advice, but not a self-conta
arXiv:2607.15888v1 Announce Type: new Abstract: Autonomous driving ethics is not only an expert concern, but also a public issue involving risk, responsibility, and governance. However, non-experts often struggle to interpret these issues in concrete incidents, especially when responsibility is distributed across multiple stakeholders. This paper investigates interactive narrative as a public-facing method for eliciting situated ethical reflection on autonomous driving. We present Red Light, Grey Zone, a web-based, multi-perspective interactive narrative prototype inspired by a real-world autonomous-driving incident. The prototype invites participants to compare stakeholder perspectives, examine scene materials, and make responsibility judgments in the face of ethical ambiguity. We report an exploratory user study (N=12) examining how differently non-experts responded to the prototype. Our analysis focuses on three dimensions of reflection: ethical cognition, responsibility-focused cri
arXiv:2607.15738v1 Announce Type: new Abstract: Generative AI (GenAI) is increasingly used by students for programming explanation, debugging, and assignment support. Yet unrestricted large language model (LLM) tutors can hallucinate, contradict course policy, reveal complete solutions, and foster passive dependence. This paper presents EduGuard, a safe retrieval-augmented generation (RAG) tutoring framework for introductory programming. EduGuard integrates query understanding, instructor-approved course retrieval, pedagogical strategy selection, rubric-aware generation, claim-level verification, and overreliance control. To make evaluation provenance explicit, we construct BILearn-CS, a 600-query instructor-authored, TA-validated benchmark spanning concept questions, debugging cases, misconceptions, assignment-support requests, code-mixed Bangla-English queries, and adversarial direct-answer prompts. Moving beyond a synthetic-only benchmark, we further evaluate on a 150-query public C
arXiv:2607.15704v1 Announce Type: new Abstract: Policymakers around the world face the question of how to use artificial intelligence in general, and large language models in particular, to improve the policymaking process. Used well, large language models can strengthen the collection, interpretation and synthesis of policy-relevant information and the drafting of policy-relevant output. Yet the use of large language models in policymaking is associated with risks. Output that is plausible but not necessarily correct, bias resulting from unrepresentative training data, the exposure of sensitive information and, over time, deskilling and dependency can erode trust if large language models are not used thoughtfully. The CRAFT principles - control, rigour, accountability, fairness and transparency - offer a way to make the most of large language models in policymaking while managing the risks.