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:2606.30658v1 Announce Type: new Abstract: Medical AI has shifted from reasoning to agentic AI, a new paradigm that autonomously invokes external tools during reasoning, rendering intermediate reasoning steps and tool outputs transparent to users. Although proven to outperform previous models, physician trust in agentic AI remains largely unexplored. To address this, three physicians evaluated 315 multimodal clinical cases quantifying both process-oriented cognitive trust and outcome-oriented behavioral reliance. Comparing agentic AI against non-agentic baselines, physicians exhibited significantly higher cognitive and behavioral trust for the agentic model (P < 0.001). Specifically, on treatment planning tasks, physicians trusted the agentic reasoning most, preferring it in 89.57% of cases. Furthermore, process-oriented cognitive trust is significantly associated with outcome-oriented behavioral reliance (P < 0.001). However, measurable over-reliance on incorrect agentic outputs
arXiv:2606.30657v1 Announce Type: new Abstract: Surgical outcomes depend not only on patient factors and postoperative care but are also strongly influenced by the quality of the operation itself. Yet, for much of mod-ern surgery, intraoperative quality has been assessed indirectly through outcomes and operative reports. The increase in minimally invasive procedures inherently guided by endoscopic video, together with advances in artificial intelligence, creates an unprecedented opportunity to systematically observe, measure, and improve surgi-cal care. This chapter introduces AI-enabled Surgical Quality Assurance as a frame-work for using surgical data to support continuous assessment and improvement in the operating room. We first review existing approaches to surgical safety, from sys-tem-level interventions to procedure-specific standards. We then describe how AI can transform intraoperative video into clinically meaningful information, including recog-nition of anatomy, instrument
arXiv:2606.30656v1 Announce Type: new Abstract: Artificial Intelligence (AI) has the potential to be transformative for development, but Africa is currently facing a fragmented and challenging "AI divide". This paper provides an empirical analysis of the current state of the AI landscape and how it compares with Africa's technological preparedness for the future. In our analysis, we approach the "AI Divide" from three angles: infrastructure, accessibility, and human capacity. First, we look at the physical constraints that prevent Africa from integrating digitally. We then evaluate the human-centred factors that limit the development of AI technology on the continent. Finally, we examine the human capacity to develop AI systems on the continent and provide three focused case studies. Our investigation shows that the physical infrastructure needed to build an AI economy on the continent is lagging, with only 38% internet penetration, poor broadband coverage and less than 1% of all data
arXiv:2606.30655v1 Announce Type: new Abstract: AI-native course assessments in senior computer science courses and related fields should grade students by \emph{AI-resilient skill}: the ability to achieve outcomes beyond a strong AI baseline. Such assessments should allow students to use AI freely, while reducing the extent to which greater private AI budget or more intensive AI use, by itself, becomes a grading advantage. This paper proposes a minimal formal framework for this goal. The framework specifies a real task, an executable evaluator, a declared AI-native Pareto frontier, and a grading rule based on Pareto surplus. The central claim is simple: Pareto surplus provides a measurable, protocol-relative certificate that a submitted artifact achieves a tradeoff not already supplied by the declared AI baseline, and grading by this surplus is AI-resilient with respect to that baseline. Interpreting surplus as evidence of student skill requires the surrounding assessment protocol--fo
arXiv:2606.30653v1 Announce Type: new Abstract: Large language models are increasingly deployed in agentic pipelines that depend on the model evaluating its own outputs without external verification. The reliability of these pipelines depends on an implicit assumption: that the model applies relevant concepts the same way when it generates an output and later evaluates that output. We propose a new measure, generator-evaluator self-consistency, to test this assumption directly and apply it to 10 frontier models across 491 concepts. We find, first, that there is substantial variation in self-consistency. Second, we find that in a clinical setting with physician-validated mistakes (Proniakin et al., 2025), across models, those with higher self-consistency are linked to greater vulnerability to mistakes. Thus, even when models consistently apply concepts they may not be safe to deploy. This is evidence of a consistency dilemma in LLMs: self-consistency is operationally useful, but models
arXiv:2606.30652v1 Announce Type: new Abstract: Transparency is increasingly mandated for public-sector AI systems, with organisations required to publish statements describing their AI use and oversight arrangements. However, the existence of such artefacts is often treated as equivalent to transparency itself, despite limited evidence that they proportionately serve relevant stakeholder groups. From a requirements engineering perspective, this raises a validation concern: compliance with mandated disclosure criteria does not necessarily ensure transparency adequacy for stakeholders with different levels of risk exposure, decision control, and involvement. This paper presents an empirical analysis of 92 publicly available AI transparency statements published by Australian Government agencies under the national AI governance mandate. We introduce the stakeholder Risk--Control--Involvement--Need (RCIN) framework to differentiate stakeholder classes according to their structural position
arXiv:2606.30651v1 Announce Type: new Abstract: Delirium is common in hospitalized patients and is often missed in routine care. We present a user-centered interactive machine learning (UC-iML) framework for delirium detection support that combines physician-guided feature refinement with interpretable modeling. Using 3,862 labeled admissions from six Toronto hospitals in the General Medicine Inpatient Initiative (GEMINI), we integrate administrative variables, laboratory results, medications, and a radiology-derived text indicator. Physicians guide feature refinement and model evaluation, and Shapley Additive exPlanations (SHAP) are used to summarize feature attribution. We evaluate standard supervised classifiers with temporally separated holdout testing and a later-phase validation cohort. Compared with automated and baseline variants, the proposed framework shows better overall discrimination and stronger temporal robustness, while the explanations highlight clinically meaningful s
arXiv:2606.30650v1 Announce Type: new Abstract: Educational support services often face a qualified-capacity problem: staff time is scarce, qualifications decay, new support needs can appear before anyone is prepared for them, and training consumes the same hours needed by current students. We introduce a synthetic benchmark and decision-support framework for qualified educational capacity planning. The model is a stylized single-institution service system with heterogeneous support-demand categories, backlog-only dynamics, continuous preparation states with hard threshold qualification and decay, and capacity-consuming training. The benchmark includes seed-controlled scenarios for announced and surprise new support categories, staff absences, and demand surges; exact feasibility discipline; declared per-policy information sets; requalification and greenfield-qualification counters; access-dispersion metrics; replay checksums; and paired statistics. We compare service-only, reactive, s
arXiv:2606.30649v1 Announce Type: new Abstract: As LLM-based agents are increasingly deployed to negotiate, delegate, or transact on a user's behalf, software pipelines need runtime mechanisms to verify that an agent's stated intentions match its actual behavior. We study whether a lightweight, real-time chain-of-thought (CoT) monitor can detect strategic deception during asymmetric negotiations, using a used-car sales scenario where a seller agent has private knowledge of an undisclosed defect and a buyer agent has only public market data. The monitor, implemented as a third agent, audits the seller's internal reasoning against its messages and alerts the buyer whenever concealment is detected, across multiple buyer-seller model pairings. Our experiments show that this monitor increases the buyer's walk-away rate, but reveal a persistent intelligence gap: lower-capability buyers often cannot translate an alert into an equitable counter-offer and still accept exploitative deals after b
Article URL: https://interwebalchemy.com/posts/building-a-chess-tutor/ Comments URL: https://news.ycombinator.com/item?id=42569222 Points: 1 # Comments: 0
THE BEAUTIFUL BURDEN OF HARD WORK: Poet and educator katie wills evans, an EdSurge Voices of Change fellow during the 2022-2023 school year, ...
Schools have been struggling for nearly a decade with stagnant or declining test scores. Some have blamed external factors like the pandemic or children’s screen use outside of school.
I’ve been a principal for 14 years, during which time I served as the leader of an alternative school, an early college, and a large middle school. Through it all I’ve seen firsthand just how anxious families get during school transitions at every stage of the game.
Judge Blocks ED’s Rule Limiting PSLF Beneficiaries sara.custer@in… Tue, 06/30/2026 - 06:39 PM Byline(s) Sara Custer
Under Secretary Nicholas Kent said Tuesday that the agency needs to make the process for college mergers, acquisitions and even closures “a lot easier.”
ETS Acquires Standardized Test Provider ACT kathryn.palmer… Tue, 06/30/2026 - 04:57 PM The acquisition comes amid growing demand for skills-based assessments and declining participation rates for traditional standardized tests. Byline(s) Kathryn Palmer
States can block transgender athletes from playing on girls’ and women’s sports teams, the U.S. Supreme Court ruled Tuesday, handing the Trump administration a victory in its effort to enforce such restrictions. In a 6-3 decision, the conservative court said that West Virginia and Idaho did not break the law when they passed legislation prohibiting […]
The Supreme Court ruled today that President Donald J. Trump exceeded his authority with his long-shot attempt to end birthright citizenship for babies born in the U.S. to undocumented parents or those without permanent status. In a 6-3 ruling, the court held Trump overreached when he tried to usurp the 14th Amendment by executive order […]
The bill would update the Children’s Online Privacy Protection Act to apply to teens up to age 17. But critics say it leaves out a crucial enforcement measure.
The move comes as more colleges are going back to requiring standardized tests for admissions.
The move comes as more colleges are going back to requiring standardized tests for admissions.
After a power failure knocked out electricity in parts of Lānaʻi High and Elementary School in 2021, the state set up temporary electrical lines to get the lights back on. Five years later, the so-called temporary lines are still there. Four portable classrooms and a building housing the boys’ locker room were affected when the […]
ED Issues New List of Professional Degrees After Court Order Katherine Knott Tue, 06/30/2026 - 12:15 PM Byline(s) Katherine Knott
Supreme Court Upholds State Laws Banning Trans Athletes Ryan Quinn Tue, 06/30/2026 - 11:42 AM Education Secretary Linda McMahon called it a “tremendous victory.” How it will impact transgender inclusion in sports and other areas nationwide is yet to be seen. Byline(s) Ryan Quinn
Has your kid ever used artificial intelligence to answer homework questions that their teacher used artificial intelligence to write? It’s possible, according to a report from the Georgia Department of Audits and Accounts that found a majority of teachers in the state are using generative AI for planning or in the classroom, but also express concern that […]
Higher education has spent the last decade optimizing for the student experience with everything from enrollment funnels to retention analytics and personalized dashboards, while largely overlooking the people responsible for keeping it all running. And all of that optimization matters. But there’s a conversation that can’t wait any longer: the employee experience — specifically, what happens when the higher ed IT staff who keep institutions running don’t feel like institutions are keeping them. This isn’t a soft HR concern. It’s an operational risk. EDUCAUSE’s 2026 Workforce Report found…
As I climbed the steps to the stage on the morning of my junior high graduation, I felt my heart racing. Just a few feet away stood a microphone and hundreds of eyes waiting for me to begin. As the commencement speaker, I had rehearsed my speech countless times, yet I had no idea this […]
Across West Virginia, public schools are rapidly closing. Political leaders are cutting taxes and funneling hundreds of millions of taxpayer money into a school voucher program, while wringing their hands about local school financial struggles. The post Across West Virginia, public schools are closing. Communities are feeling the loss. appeared first on District Administration .
The state's school administrative units will soon be required to craft their own budgets and voters will be empowered to vote down those budgets, under a law that Republicans hope will help drive down K12 costs. The post New law allows NH voters to veto school administrator pay increases appeared first on District Administration .
Colleges and K-12 schools can determine eligibility for women's and girls' sports teams based on "biological sex," the court ruled.
The percentage of 3- and 4-year-olds enrolled in school dropped during the most recently available five-year lookback window of federal data, though that picture is likely clouded by COVID-era school closures. The decline was cited in the Kids Count Data Book, an annual report released by The Annie E. Casey Foundation, which uses federal data […]
The ruling addresses a long controversial divide on how schools approach transgender student inclusion in athletics.
The answer to stagnating test scores is not adopting technology that has never demonstrated any ability to move test scores. Educators have to finally recognize that hope is not a strategy and cease rewarding vendors who show up without RCTs and rigorous validation.
Conversations with Kevin Hogan: Peter Youngs of the University of Virginia and Edthena CEO Adam Geller on the future promise of personalized learning for teachers.
As more teachers share classroom technology across rooms and class periods, setup time can take away from instruction. Samsung’s Account Management Solution and AI Assistant are designed to help educators personalize compatible Interactive Displays and access tools for transcripts, search and quizzes. Read more:
San José State Prof Sues Over ‘Gross Violation’ of Rights Emma Whitford Tue, 06/30/2026 - 03:00 AM Byline(s) Emma Whitford
Faculty Unions Slam 3-Year Degrees Sara Weissman Tue, 06/30/2026 - 03:00 AM The American Association of University Professors and American Federation of Teachers came out against three-year degrees after Massachusetts announced plans to adopt them. Byline(s) Sara Weissman
Inside a University’s ‘AI Kitchen’ Joshua.Bay Tue, 06/30/2026 - 03:00 AM Santa Clara University’s weekly workshop fosters belonging while helping students, faculty and staff build practical AI skills through hands-on learning. Byline(s) Joshua Bay
Former Chancellor Delivers Partly AI-Generated Report to CSCU System Sara Weissman Tue, 06/30/2026 - 03:00 AM Byline(s) Sara Weissman
NEH, Beaten in Court, Asks Grant Recipients if They Still Want Awards Ryan Quinn Tue, 06/30/2026 - 03:00 AM A judge ruled that the termination of more than 1,400 National Endowment for the Humanities grants was unconstitutional. Now comes the process of reinstating these projects. Byline(s) Ryan Quinn
DOJ Sues 2 More States Over In-State Tuition for Undocumented Students Johanna Alonso Tue, 06/30/2026 - 03:00 AM Byline(s) Johanna Alonso
The three largest skill gaps in the younger workforce represent “the very skills most essential to humans in the AI era,” per a report from Cangrade.
As schools grapple with a challenging landscape, Bellwether and WestEd suggest state leaders adjust policies and consider tools needed for success.
The three largest skill gaps in the younger workforce represent “the very skills most essential to humans in the AI era,” per a report from Cangrade.
arXiv:2606.26744v2 Announce Type: replace-cross Abstract: We present HyperDFlash, a block-parallel speculative decoding framework tailored to DeepSeek-V4's Hyper-Connections (HC). Despite the strong performance of DeepSeek-V4's native Multi-Token Prediction (MTP) module on initial token drafting, its draft accuracy degrades sharply at later positions, as error accumulation from unverified intermediate tokens harms draft acceptance rates. Although the original DFlash method supports efficient one-pass block drafting, it cannot be seamlessly adapted to the HC paradigm, since DeepSeek-V4's multi-path residual stream induces inherent feature misalignment with conventional drafting designs. To resolve this architectural mismatch, we propose two dedicated, model-aligned optimizations for HC residual streams. First, we adopt pre-collapse residual states as the exclusive conditioning signal, preserving complete multi-path structural information and better aligning the drafter with the target's
arXiv:2606.22248v2 Announce Type: replace-cross Abstract: Standard autoregressive Transformer decoders can often exhibit substantial forgetting under sequential fine-tuning on shifting curriculum distributions. This technical report evaluates SamatNext v0.2-B, an experimental 356M-parameter hybrid sequence decoder that alternates Differential-Attention-style layers with DeltaNet-inspired simplified linear-state mixer layers using RMS normalization and output scale calibration. We study the model under a controlled staged Python code curriculum and compare it with a parameter-matched Transformer baseline. In this setting, SamatNext v0.2-B achieves a 100.0% pass rate on the controlled Stage 5 holdout while retaining 98.8% of adjacent Stage 3 semantic behavior and reaching 12.0% on the Stage 2E early syntax holdout. The strongest Transformer baseline reaches 97.6% on Stage 5 but retains only 6.0% of Stage 3 behavior. Both architectures remain weak on long-horizon early-stage retention, so
arXiv:2606.14150v3 Announce Type: replace-cross Abstract: Pruning promises a shortcut to strong small language models. In this work, we examine this promise by pruning Llama-3.1-8B at pruning ratios of 0.5--0.8 with six methods spanning depth, width, and sparse granularities, under two controlled token-matched settings. (1) With the same training token budget, pruned initialization consistently outperforms random initialization. This shows that the parent model provides a strong starting point, although the advantage narrows as the training token budget grows and as the pruning ratio rises, nearly vanishing at the highest pruning ratio we study. (2) When training from scratch is instead given the full token budget consumed by the whole pipeline, pruning at finer granularities still retains an advantage, while coarser structured pruning can be matched or surpassed. This suggests that the parent model transfers knowledge that additional training tokens alone cannot fully recover, but onl
arXiv:2606.11270v2 Announce Type: replace-cross Abstract: Distillation of a language model intended to transfer benign behavior to a student model may also transfer undesirable characteristics, if they are present in the teacher model, a phenomenon known as subliminal learning. While qualitative evidence supports the existence of this effect, its magnitude has not been systematically characterized. This study quantifies subliminal behavioral transfer ratios by steering two teacher models (Llama-2-7B-Chat and Qwen2.5-7B-Instruct) at varying steering strengths and distilling student models using only benign data. Evaluation on 100 JailbreakBench prompts with GPT-4.1, serving as the evaluator, indicates that transfer is robust but exhibits distinct scaling behaviors. Llama-2 demonstrates a sharp threshold ($\tau = {0.25,0.32} \ \text{beyond} \ \alpha = -0.15$), whereas Qwen2.5 displays continuous and higher levels of transfer ($\tau$ up to $0.61$).
arXiv:2606.05510v2 Announce Type: replace-cross Abstract: Telehealth systems have become increasingly important for delivering accessible and timely medical information. Existing large language models often struggle to provide consistent and contextually appropriate medical responses across varying levels of case severity. This limitation highlights the need for models that can effectively adapt to the progressive complexity in medical queries. To address this challenge, we introduce a severity-aware multi-model framework that integrates curriculum training strategy with relevance-based response selection. The proposed framework employs a three-stage curriculum learning strategy, where each model is trained sequentially on mild, moderate, and critical cases to progressively acquire domain knowledge. The approach utilizes five large language models, each independently trained under the same curriculum scheme. During inference, all models generate candidate responses, and the most approp