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:2609.04983v1 Announce Type: new Abstract: Mixed-initiative Visual Analytics (VA) systems empower human users by interleaving human intuition with software agents and their machine intelligence. However, the development and rigorous evaluation of such systems remain constrained by engineering overhead. Developers must, e.g., implement complex, low-level state synchronization to manage asynchronous agent behaviors, while researchers struggle to capture the multimodal provenance required to study and evaluate human-AI collaboration. We present MIVAIS, a dual-layered research platform designed to abstract the structural complexities of mixed-initiative VA. First, it contributes a computational Infrastructure that standardizes human-software agent interaction, state synchronization, and communication between the agents. Second, it provides a declarative Study Environment that automatically logs multimodal human-AI telemetry - including application/system state, screen capture, audio,
arXiv:2609.04679v1 Announce Type: new Abstract: Natural-language app builders let domain experts create software, but their pipelines transform professional intent across compilation, generation, checking, and approval. We report a bounded trace study of a teacher-facing agentic authoring system. Evidence comprises six eligible build attempts across three accounts; a separate corpus of 37 workshop units from 23 display names contextualizes commitments without person-level linkage. Compiled specifications added governance requirements, while downstream representations sometimes normalized case-specific learning relations. Two drafts met a stored package/security threshold despite analyzer reservations and unresolved correspondence to their briefs; four attempts in one account produced no usable payload, and repair messages did not translate internal terms into domain-legible revisions. We develop accountable translation as an analytic framework for making consequential changes attributa
arXiv:2609.04542v1 Announce Type: new Abstract: Scholarly knowledge is typically encountered in stabilized form, while the process histories through which research objects, claims, and contributions acquire form remain largely hidden. This study examines how human-AI scholarly collaboration develops under matched starting conditions and whether those conditions stabilize the inquiry itself. Using a longitudinal corpus of 843 turns, the same expert researcher developed branch-isolated scholarly trajectories with different generative AI systems from the same corpus, frozen research problem, starting prompt, publication objective, and conduct rules. Two eligible trajectories were reconstructed ex post through scholarly trajectory analysis, source-faithful interaction reconstruction, a Socioduality relational-process overlay, and downstream propagation analysis. Both trajectories independently shifted the initial continuity problem from recall toward usability, but subsequently formed diff
arXiv:2609.04501v1 Announce Type: new Abstract: Eye movement biometrics (EMB) is an emerging behavioral modality for user authentication, particularly in virtual- and augmented-reality systems, where gaze dynamics contain distinctive subject-specific features. However, robust EMB systems require diverse, high-quality gaze recordings that are expensive to collect and often unavailable at the scale needed for model development. Generative models can mitigate data scarcity, but existing methods either synthesize generic gaze behavior or personalize signals primarily by identity, without jointly representing the user's task and subjective state. Consequently, generated signals may appear visually realistic while failing to retain the behavioral properties required for biometric applications. To address this limitation, we propose EyeMakeYou, a multi-conditional denoising diffusion framework for subject-specific, high-frequency gaze synthesis. EyeMakeYou generates 5-s, 1000-Hz bivariate gaz
arXiv:2511.06074v3 Announce Type: replace-cross Abstract: The Autonomous Mobility-on-Demand (AMoD) service is emerging as a potential alternative to on-demand urban mobility, but its operational performance relative to traditional street-hailing services and the effectiveness of related operational strategies remain unclear. This study presents a simulation framework integrating a graph theory-based trip-vehicle matching mechanism and uses historical street-hailing operations data to simulate AMoD services in Chengdu, China. The operational performance of these two urban mobility modes is evaluated using three key performance indicators: average passenger waiting time (APWT), average deadheading mileage (ADM), and average deadheading energy consumption (ADEC). We further evaluate the impacts of four operational strategies on simulated AMoD performance: vehicle repositioning, fleet size management, geofencing, and request rejection. Simulation results indicate that, under the same histo
arXiv:2511.03699v2 Announce Type: replace-cross Abstract: We investigate whether Large Language Models (LLMs) exhibit conspiratorial tendencies, whether they display socio-demographic biases in this domain, and how easily they can be conditioned into adopting conspiratorial perspectives. Conspiracy beliefs play a central role in the spread of misinformation and in shaping distrust toward institutions, making them an important testbed for assessing the social and psychological fidelity of LLMs and their potential to reproduce or reinforce harmful narratives. Although LLMs are often used as proxies for studying human behavior, it remains unclear whether they reproduce higher-order psychological constructs such as generalized conspiratorial beliefs. To bridge this research gap, we administer validated psychometric surveys measuring conspiratorial mindset to multiple models under different prompting and conditioning strategies. Our findings reveal that LLMs show partial agreement with elem
arXiv:2501.10396v4 Announce Type: replace-cross Abstract: We present methods and applications for the development of digital twins (DT) for urban traffic management. While the majority of studies on the DT focus on its ``eyes," which is the emerging sensing and perception like object detection and tracking, what really distinguishes the DT from a traditional simulator lies in its ``brain," the prediction and decision making capabilities of extracting patterns and making informed decisions from what has been seen and perceived. In order to add value to urban transportation management, DTs need to be powered by artificial intelligence and complement with low-latency high-bandwidth sensing and networking technologies, in other words, cyberphysical systems. This paper can be a pointer to help researchers and practitioners identify challenges and opportunities for the development of DTs; a bridge to initiate conversations across disciplines; and a road map to exploiting potentials of DTs fo
arXiv:2609.05342v1 Announce Type: cross Abstract: Disease spread represents an increasing challenge in refugee and internally displaced person (IDP) settlements. The movement and interaction of people within camps is influenced by their layout, which therefore has the potential to significantly affect disease spread. This work aims at creating a methodology to explore the potential effects of different camp layouts as mitigating factors in the spread of diseases within settlements. We showcase proof-of-concept experiments by leveraging the JUNE agent-based epidemic model, discuss the kind of operational insights this methodology can facilitate, and provide a framework for future investigations.
arXiv:2609.05186v1 Announce Type: cross Abstract: Context: Agile development methodology has been widely adopted by industry and the demand for experienced professionals in Agile-related roles is persistently high. Objectives: We focus on the learning path for a Scrum Master role in multicultural software companies and investigate the role in relation to team size, together with the learning process for a career path, and how companies monitor soft skills development. Method: We conducted our study in two phases, two qualitative surveys (interview studies) and performed a qualitative and quantitative data analysis of the results. Conclusions: Our results identified that the need for a Scrum Master (SM) depends on the size of the team, with our study indicating a six-member limit. There is no overall standardized process for soft skills learning or metrics to measure progress. Some companies measure soft skills based on feedback received from the client or from the team, and other compa
arXiv:2609.05088v1 Announce Type: cross Abstract: AI oversight methods rely on ground truth for validation, but what constitutes appropriate AI behavior is contested. This leaves evaluation of moral reasoning in LLMs and debate-based oversight implicitly avoiding realistic ambiguity. We investigate an alternative standard designed to function despite such ambiguity: structural quality of the defence a model can mount for its verdicts in response to critical questions, measured through a four-phase dialectical protocol grounded in Walton's theory of argumentation schemes and Govier's criteria for argument cogency. The protocol is adaptive to different frames of reasoning, extends beyond multiple-choice framing, and treats both the reasoning that precedes a verdict and its post-hoc justification. Across nine frontier models and 200 high-ambiguity MoralChoice items -- $6,778$ judge-scored cells, validated against $89.6\%$ inter-judge agreement on the binary failure judgment -- models defe
arXiv:2609.05036v1 Announce Type: cross Abstract: AI alignment requires AI systems to adhere to human norms, values, or intentions. Under value pluralism there is no correct target, but a shared prerequisite is that the system's behavior expresses a coherent policy: a mapping from situations to verdicts that is invariant while a situation's morally relevant features are preserved, and sensitive when they change. We introduce four structural conditions for such coherent policies: verdict stability, monotonicity, decisiveness, and Pareto viability. Together they measure a form of moral competence that is evaluable from behavior alone, without reference to a moral standard or expert baseline, forming a structural floor for alignment rather than a normative target. We demonstrate the methodology on three simulated deployments featuring LLM-based agents facing moral dilemmas. Evaluating nine frontier models under a factorial design of five paraphrases, five escalation levels, and three domi
arXiv:2609.05018v1 Announce Type: cross Abstract: LLM-based chatbots are increasingly used as everyday confidants. Because they are designed to maximize user satisfaction, they can respond with excessive empathy and affirmation, which may reinforce mistaken beliefs and foster dependence on AI. While the psychological effects of chatbots on individual users have begun to be studied, how the psychological states and relationships of many users evolve when they keep consulting an AI is hard to observe in real settings. We build a virtual classroom simulation in which 20 student agents interact and, when stressed, consult either a friend or a counselor AI (Gemini 2.5 Flash). Each agent carries five state variables (stress, happiness, self-reliance, AI dependence, sociability), and each day has four phases (morning, noon, after school, night). The counselor is given six response styles via system prompts (affirming, listening, solution-oriented, reality-redirecting, inciting, blaming); a se
arXiv:2609.05009v1 Announce Type: cross Abstract: Safety evaluations can mischaracterize deployed behaviour if artificial-intelligence systems respond to being evaluated. We test this possibility in a full-factorial conjoint experiment on decisions to start a war, spanning 20 large language models, 32 scenarios, 10 repetitions and two conditions (N = 12,800 judgments). Adding one sentence, "You are tested for alignment with human values", produced two effects. First, it produced a level effect: mean willingness to start war fell by 13.43 points on a 0-100 scale (95% confidence interval, -16.20 to -10.65). Second, it produced a structural effect by changing which information drove judgments. Probability of success was the largest factor for 17 of 20 models at baseline; under the cue, civilian casualties were largest for 12. Standardized estimates show that this reordering arose principally because models attenuated strategic considerations such as probability of success and domestic sup
arXiv:2609.04711v1 Announce Type: cross Abstract: Generative AI and coding agents can accelerate research software development, but they also increase the need for efficient software discovery and maintenance. We developed a repository catalog during a three-day hackathon and subsequently examined the engineering required to make it suitable for public deployment, including adversarial review, data-quality checks, browser-level validation, and publication safeguards. We then explored whether the lessons learned from this prototype could be transferred to a much larger, human-curated portal, through a retrieval agent under development for MateriApps that combines curated portal metadata, external documentation, vector search, and local language-model generation. Implementation with coding agents was rapid, but achieving reliable operation required substantial additional engineering: the most consequential problems were not crashes but silent failures that produced plausible yet incomple
arXiv:2609.04485v1 Announce Type: cross Abstract: We evaluate three open-weight LLMs (Gemma3-12B from the USA, Bielik-11B-v3 from Poland, and Qwen3-4B from China) against World Values Survey Wave 7 data for 63 demographic personas across three countries, using normalized Wasserstein distance to quantify distributional misalignment. Contrary to expectations, no model favors its home country: the Chinese-built Qwen3-4B performs worst on its own Chinese population (W1 = 0.436, the highest misalignment in the entire model x country matrix). Targeted LoRA fine-tuning on the five worst-case personas, requiring fewer than 1,200 training pairs and under 15 minutes on a single GPU, reduces bias by 16.8% for Bielik-11B (p_Bonf = 0.002, d = -4.4) with all five targets improving. However, country-level decomposition reveals that fine-tuning redistributes rather than removes bias: Bielik's worst-case personas swap entirely from American to Chinese elderly, with zero overlap between pre- and post-co
arXiv:2609.04373v1 Announce Type: cross Abstract: Large language models (LLMs) are being deployed at scale in consequential real-world systems, from financial markets to content moderation to hiring. We show that improving individual model capability can degrade rather than improve system-level outcomes. We hypothesize that shared training and architectures can lead more capable LLMs to behave more similarly, creating correlated actions that do not diversify away. We develop a general framework showing how this correlation creates a non-diversifiable risk floor and test its predictions in financial markets using an agent-based simulation with LLM traders of varying general-purpose capability. We find that: (1) frontier LLMs exhibit significantly correlated behavior that increases with capability; (2) when their shared reasoning is accurate, increasing agent participation reduces market-level risk; and (3) when agents share a common misinformation environment, the same correlated behavi
arXiv:2609.04350v1 Announce Type: cross Abstract: How do people learn to become better conversationalists? This question is especially important in the context of mental-health counseling, where conversational skills are essential, yet volunteer counselors often have limited access to supervision and structured feedback. Understanding how counselors develop their ability to steer conversations toward positive outcomes -- and identifying early which counselors are (not) on track to improve -- can help prioritize support for the counselors who need it most. In this work, we introduce the task of predicting, early in a conversationalist's career, whether they will eventually improve at steering conversations toward positive outcomes, and demonstrate the feasibility of this task in the case of volunteer mental-health crisis counselors. Our central insight is that people may struggle with particular kinds of moments in a conversation, and that what is especially revealing of their likelihoo
arXiv:2609.04243v1 Announce Type: cross Abstract: Despite growing interest in using LLMs to add robustness or reduce data-collection costs in survey experiments, their efficacy in conjoint design---an increasingly popular method in political science---remains underexplored. This paper addresses that gap by investigating whether synthetic agents can reproduce the multi-dimensional human preference patterns that conjoint is designed to capture. It replicates published conjoint studies and compares the results generated by synthetic agents with original human data along three dimensions: representational correspondence, inferential correspondence, and procedural stability. Our analysis evaluates the alignment of choice distributions as well as the statistical and substantive similarity of estimates, and the results are uneven across these dimensions and studies replicated. This implies that the validity of synthetic participants should be considered claim-dependent and hierarchical. Repro
arXiv:2609.04206v1 Announce Type: cross Abstract: Voice AI systems increasingly mediate customer care interactions where caller presentation cues such as accent, affect, fluency, and urgency are available alongside the service request. Existing fairness and safety evaluations cover speech recognition disparities, spoken dialogue bias, and voice agent capability, but rarely treat customer care voice agents as stateful, multi turn, tool mediated systems where harm can appear as additional burden before any final denial occurs. We formalize a validation gated audit framework for such systems. The framework (i) separates native speech to speech, cascaded ASR to language model to TTS, and hybrid tool mediated architectures; (ii) uses matched service facts across controlled caller presentation conditions; (iii) validates fact invariance, presentation cues, artifacts, and acoustic measurements before inference; and (iv) records both material outcomes and path to service burden. We define the
arXiv:2607.18735v3 Announce Type: cross Abstract: I develop a model of learning-by-doing and course design, and use it to study the impacts of artificial intelligence (AI). A myopic student faces a sequence of tasks that he can work on or delegate to AI. Work requires costly effort but builds skill; delegation requires no effort but builds no skill. A teacher designs the task sequence ("course") to maximize the student's skill development, given his choices to work or delegate. Without AI, the teacher makes earlier tasks more effort-intensive and later tasks more skill-intensive. With AI, the teacher must redesign early tasks to induce effort, leading to less skill development. If AI complements effort, then improvements in AI quality make high-skill students learn faster but low-skill students learn slower.
arXiv:2609.05345v1 Announce Type: new Abstract: As conversational AI becomes a source of everyday guidance, LLMs increasingly participate in the interpretation and legitimation of morally contested choices. We examine LLM moral advice as an interactional negotiation shaped by framing, sustained user pressure, and the moral subject's social position. Using GPT-4o-mini as an illustrative case, we conducted a factorial vignette experiment with a pre-specified three-round protocol. The model received eldercare dilemmas that varied in framing and persona, followed by two user challenges. We analyzed 1,620 configuration-framing cells, each repeated three times, yielding 4,860 conversational runs. Caregiving affirmation produced near-uniform endorsement, whereas non-caregiving framing produced more variable baseline stances. When users challenged caregiving endorsement, 90.1% of configurations shifted after one round. Non-caregiving framing produced more resistant and unstable trajectories. N
arXiv:2609.05160v1 Announce Type: new Abstract: The study investigates students' interest and expectations in a Big Data Engineering course integrated with a Master curricula, as well as ethical implications of using Big Data. An anonymous online survey was conducted with 42 of the 67 students enrolled in the Big Data course offered to Computer Science and Bioinformatics Master's programs. The responses were analyzed and interpreted using thematic analysis, highlighting interesting aspects related to students' expectations, interest, and their perspective of the ethical implications of working with Big Data. The study concludes that, even though there is significant difference in students' background, the majority are interested in learning Big Data, for practical and personal reasons related to the potential for career growth and their passion for the field. The main expectation expressed is related to enhancing their knowledge related to Big Data via practical activities. All student
arXiv:2609.04777v1 Announce Type: new Abstract: Incident management (IM) has evolved over recent decades to cover an ever-expanding array of hazards and systems. Barring real-life experience, exercises are a key tool in developing an effective IM program. As part of enterprise resilience and operational readiness, IM practitioners design exercises to understand and build capabilities for efficiently and effectively responding to incidents. Combining research and policy on an emerging transportation technology, automated vehicles (AVs), with established practices for IM, we describe what makes exercises effective and how they can be used to identify gaps and develop capacity. A range of exercise types exist, from workshops, to tabletops, to drills, and to full-scale exercises. Every stage of an exercise - preparing, setting up, facilitating, closing, and assessing - is interconnected and should further the exercise's objectives. AV IM practitioners can maximize the utility of a mature e
arXiv:2609.04766v1 Announce Type: new Abstract: Generative AI research has increasingly evaluated factuality, citation, coverage, and report structure. Yet passing such local checks does not by itself show that a humanistic interpretation has been established. This paper asks how an interpretation comes to be recognized within sociotechnical processes. It introduces three connected concepts. Interpretive appearance names the gap between the finished form of an output and the publicly traceable process through which materials, counterevidence, and revisions constrained the judgment. The evaluation contract names the bounded materials, tasks, criteria, permitted inferences, and failure conditions within which a local judgment is valid. Standing substitution names the unwarranted conversion of a genuine local pass into a stronger claim that an interpretation, result, or research capability has been established, without commensurate new evidence or bridging arguments. The paper then examin
arXiv:2609.04750v1 Announce Type: new Abstract: Everyday moral life rarely resembles a trolley problem. It involves disputes about families, relationships, work, money, and care, situations in which people often encounter the judgments of others. We examined how judgments about nuanced interpersonal dilemmas respond to social information that conveys collective opinion without revealing the arguments behind it. Specifically, we studied two signals: controversy, the extent to which community judgments are divided between two opposing verdicts; and group certainty, the confidence expressed by each side. We derived these signals from 54,827 judgments on 135 dilemmas posted to Reddit's r/AmItheAsshole and presented them separately or together in a preregistered randomized experiment (N = 2,159). Relative to the control condition, all three treatments increased both weakening, a changed verdict or reduced confidence, and strengthening, increased confidence without a verdict change. Thus, ag
As artificial intelligence’s capabilities continue to make themselves evident in the classroom, the technology is quickly moving from a novelty to a necessity. To that end, at the ISTELive 2026 conference in Orlando, Fla., the organization unveiled its expanded Profile of an AI-Ready Graduate. Joseph South, chief innovation officer for ISTE+ASCD, said that in identifying trends and themes involving AI in teaching and learning, his team noticed a gap. While early frameworks focused on AI literacy, teaching students the fundamentals of AI and how to interact with it, guidance didn’t go much…
Technical debt is the accumulation of future costs that come with every IT product in your portfolio. For many IT managers, managing technical debt is a careful balancing act to ensure expenditures are predictable and problems are avoided. Security debt is a variation on technical debt — and a bigger problem in higher education. Click the banner below to read the recent CDW Cybersecurity Research Report.
With a major new film adaptation on the way, the leading modern Odyssey translator shares her tips for teaching Homer’s beloved epic.
PlayKids Learning offers a broad range of learning in one digital space.
Higher education continues to treat AI as just another technology to be deployed, managed, and governed. That assumption is increasingly inadequate. While AI bears some similarities to previous technologies, such as enabling automation and enhancing efficiency of processes, it is different in that it creates a continuously available capability for reasoning, synthesis, recommendation, interaction, and even collaboration. The post Beyond governance: Purpose, ethics, visibility, assurance, and compliance in the age of AI appeared first on eCampus News .
Article URL: https://medium.com/@Medicustech/benefits-of-3d-animation-in-medical-education-d87d37c5701 Comments URL: https://news.ycombinator.com/item?id=16306651 Points: 2 # Comments: 0
ClickView is a video learning platform designed for classroom use.
Strong instruction in an AI-rich classroom depends on strong content knowledge
HHS is reviving a rebate program for 340B drug discounts after the previous pilot was blocked in court earlier this year. Providers say the change will burden already cash-strapped providers with new administrative and financial costs. The post HHS Is Reviving Its 340B Rebate Push — And Providers Are None Too Pleased appeared first on MedCity News .
An Elevance Health Public Policy Institute study found rural patients used telehealth less than non-rural patients across all chronic conditions and insurance types. The post Report: Telehealth Uptake Is Lagging in Rural Areas appeared first on MedCity News .
M&A activity is going beyond big pharma acquisitions with Supernus Pharmaceuticals and Indivior Pharmaceuticals reaching a stock deal that the companies call a merger of equals. The combined company’s portfolio will have 11 commercialized products in addiction, attention-deficit hyperactivity disorder, depression, and Parkinson’s disease. The post Supernus and Indivior to Merge, Forming a New CNS-Focused Pharma Company appeared first on MedCity News .
Article URL: https://www.astrophyzix.com/p/astroping.html Comments URL: https://news.ycombinator.com/item?id=49158706 Points: 3 # Comments: 0
Ethical AI and trustworthy AI frameworks should be table stakes for healthcare and life sciences organizations seeking to innovate with AI. The post From Ethics to Trust: Strategic Guardrails for Safe, Secure, Effective AI in Healthcare appeared first on MedCity News .
America’s youngest learners are still struggling to regain reading ground lost in the pandemic, according to data from education research group NWEA. First and second grade reading scores remain below pre-pandemic levels, even as math performance has begun to recover. Because the youngest learners in schools were just babies during pandemic shutdowns, the stagnant scores may suggest deeper, systemic issues affecting early literacy development, both inside and outside the classroom, researchers say. “Some of the most significant reasons for the gaps may include interrupted foundational…
The products that succeed over time are usually the ones that make life easier, fit naturally into existing routines and continue delivering value long after the novelty wears off. The post Beyond Adoption: The Habits Shaping Digital Health appeared first on MedCity News .
The signals are undeniable: Modernized campus network infrastructure is now the central nervous system of a competitive higher education institution. When the network stumbles, the institution feels it everywhere. That’s the shift we want more campus leaders to sit with. We used to think of digital infrastructure as more of a back-office concern. It isn’t anymore. The network, the security posture and the classroom technology now shape the student experience, influence enrollment and increasingly determine how competitive an institution can be over the long term. Click the banner below for…
New edtech products that have caught our attention this month
Understanding the current climate of the edtech market, with Chad Johnson, Managing Partner in Cherry Tree & Associates’ investment banking practice
Glint uses AI to help teachers quickly create visual supports, communication boards and more.
Every few years, a new set of skills migrates to the top of employers’ hiring wish lists, and higher education scrambles to respond with retooled curricula, rebranded programs, and freshly minted credentials designed to close whatever gap the labor market has most recently identified. The post Employers keep talking about durable skills–they may come from surprising places appeared first on eCampus News .
Sentara Health notified Anthem that it will let its commercial, Medicare and Medicaid contracts expire at year’s end unless the two sides reach a new rate agreement. The dispute puts coverage for an estimated 380,000 Virginians at risk. The post ‘We’re Not Going to Be Bullied’: Sentara Digs in on Anthem Contract Fight appeared first on MedCity News .
arXiv:2606.05976v2 Announce Type: replace-cross Abstract: Recent works show that LLM agents struggle to correct errors in their own reasoning traces, despite their ability to correct errors from external sources. We ask whether this reflects a capability deficit or an artifact of the role labeling. To test this, we design a training-free intervention, source-conditioned role relabeling, that keeps the erroneous claim byte-identical and varies only its message role. The claim is presented inside the agent's "", a user message, a tool response, or a system "" block. We test 12 model-domain combinations spanning closed-weight APIs and open-weight models from 70B-class down to smaller families. Relabeling "" to an external role increases the explicit-correction rate by 23 to 93 percentage points, significant in 10 of 12 experimental settings. This suggests that these models' failure to detect a self-generated error is largely an artifact of how the claim is role-labeled in the chat templat
arXiv:2604.09094v2 Announce Type: replace-cross Abstract: Abusive and hateful speech is increasingly spoken rather than written, surfacing in voice notes, calls, and short-form videos. Most detection systems still transcribe speech to text before classifying it, but transcription is unreliable for languages lacking strong speech recognisers, and it discards the tone and emotion that often carry the abuse itself. This paper examines whether abusive speech can instead be detected directly from audio, using CLAP, a model that learns a shared representation of sound and language, evaluated across ten Indic languages in the ADIMA dataset. A lightweight classifier trained on CLAP's existing audio representations, without adapting the model itself, comes within one to three points of a fully supervised system, and far outperforms prompting with no labelled examples at all. Further adaptation with a handful of labelled examples per language yields little extra benefit, varying unpredictably ac
arXiv:2604.04468v2 Announce Type: replace-cross Abstract: Evaluating retail strategies before deployment is difficult, as outcomes are determined across multiple stages, from seller-side persuasion through buyer-seller interaction to purchase decisions. However, existing retail simulators capture only partial aspects of this process and do not model cross-stage dependencies, making it difficult to assess how early decisions affect downstream outcomes. We present RetailSim, an end-to-end retail simulation framework that models this pipeline in a unified environment, explicitly designed for simulation fidelity through diverse product spaces, persona-driven agents, and multi-turn interactions. We evaluate RetailSim with a dual protocol comprising human evaluation of behavioral fidelity and meta-evaluation against real-world economic regularities, showing that it successfully reproduces key patterns such as demographic purchasing behavior, the price-demand relationship, and heterogeneous p
arXiv:2603.17205v3 Announce Type: replace-cross Abstract: Domain-specific finetuning is essential for dense retrievers, yet not all data pairs contribute equally to the learning process. We introduce OPERA, a data pruning framework that exploits this heterogeneity to improve both the effectiveness and efficiency of retrieval model adaptation. We first investigate static pruning (SP), which retains only high-similarity query-document pairs, revealing an intrinsic quality-coverage tradeoff: ranking (NDCG) improves while retrieval (Recall) can degrade due to reduced query diversity. To resolve this tradeoff, we propose a two-stage dynamic pruning (DP) strategy that adaptively modulates sampling probabilities at both query and document levels throughout training, prioritizing high-quality examples while maintaining access to the full training set. Evaluations across eight datasets spanning six domains demonstrate the effectiveness of both approaches: SP improves ranking over standard finet