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Why User Studies and Participant Experience Reporting Matter for VR Motion Privacy?

User studies explain VR motion privacy mechanism acceptance far better than physical deviation metrics, a study of three mechanisms finds.

The paper examines how well physical deviation predicts user acceptance of VR motion privacy mechanisms compared to user studies, testing three mechanisms at five deviation levels. User studies explain substantially more variation in mechanism acceptance, though physical deviation remains significant, and prior VR experience affects acceptance. Public VR game leaderboards expose motion recordings from hundreds of thousands of users, creating identification and profiling risks. The authors recommend combining physical deviation metrics with user studies and reporting participants' prior experience.

arXiv cs.CRSep 11, 04:08 UTC · 5d agoResearch

5 new ways to level up your learning with Search

Google promotes five Search study tools for students preparing for classes and standardized tests.

Google published a back-to-school post highlighting five ways to use Search tools when studying for classes and standardized tests. The post is promotional product guidance rather than a launch, research result, or security announcement.

Google · AIAug 19, 19:00 UTC · 27d agoAI industry

Quantifying the Engagement Trap: Impact of Short-form Video Recommender Systems on Users with ADHD

A 302-participant study finds engagement-optimized short-form video recommenders cause disproportionate time blindness and distress for users with ADHD.

Researchers ran a stratified Prolific study with 302 participants comparing short-form video recommendation experiences with and without ADHD. Participants with ADHD reported significantly higher time blindness, post-usage regret, and emotional distress despite perceiving recommendations as similarly relevant. The paper proposes neurodiversity-aware, human-centered design interventions to mitigate these algorithmic harms.

arXiv cs.AI / cs.LG / cs.CLSep 7, 17:38 UTC · 8d agoAI research

Who Should Grade My Work? Student Perspectives on Transparent AI-Assisted Writing Assessment in Higher Education

A Saudi university study finds students value ChatGPT writing feedback but treat human instructors as the final grading authority.

Thirteen male undergraduate computing students at a Saudi public university completed handwritten writing tasks that were scored by ChatGPT using a rubric-based prompt, then reflected after being told the score and feedback were AI-generated. Inductive thematic analysis identified four themes: perceived feedback usefulness, awareness of AI's contextual and pedagogical limitations, conditional trust, and reflection on the instructor's institutional role. Participants accepted GenAI feedback for surface-level revision but consistently positioned human instructors as the authority over grading decisions, distinguishing feedback utility from evaluative authority.

arXiv cs.AI / cs.LG / cs.CLSep 4, 16:49 UTC · 11d agoAI research

Better answers, broader thinking: What students gain from ChatGPT and critical-thinking training

OpenAI published a randomized study of over 1,000 students examining ChatGPT's effects on critical thinking and performance.

OpenAI released results from a randomized study of more than 1,000 students using ChatGPT on a real-world university assignment. The research examined effects on critical thinking, originality, and student performance. Findings inform ongoing debates about AI's role in education and learning outcomes.

OpenAI NewsAug 27, 09:00 UTC · 20d agoAI research

Why Johnny Can't Encrypt: A Usability Evaluation of PGP 5.0 (1999)

Seminal 1999 USENIX study finds most novice users cannot correctly sign and encrypt email with PGP 5.0 in 90 minutes.

Whitten and Tygar's USENIX Security Symposium paper evaluates whether cryptography novices can use PGP 5.0 effectively, using cognitive walkthrough analysis and a laboratory user test. The majority of test participants failed to successfully sign and encrypt a message within 90 minutes, despite PGP 5.0 having a well-regarded graphical interface. The authors argue that security requires usability standards beyond those of general consumer software and propose domain-specific UI design principles for security. The paper is a foundational reference in usable security research.

Lobsters · securitySep 9, 06:47 UTC · 7d agoResearch

Engineered Persuasion: Evaluating Personalized Pretexts in LLM-Generated Spear Phishing

A study of 180 US workers found each LLM phishing personalization level raised click-intention odds by 28%, but credibility depends on context fit.

The arXiv paper evaluates how personalized pretexts in LLM-generated spear phishing affect perceived credibility, using 180 US working adults across 1,436 evaluations of emails with four cumulative personalization levels, from workplace context to shared-project details. Convincingness rose 2.40 points per level in sensitivity analysis and click-intention odds increased 28% per level, while non-clickers shifted toward deleting rather than reporting. Qualitative coding showed details matching the recipient's role and routines supported credibility, whereas incorrect, vague, or channel-inappropriate details raised suspicion. The authors argue personalization effectiveness depends on pretext fit, with implications for workplace security training.

arXiv cs.CRSep 3, 19:21 UTC · 12d agoResearch

Students who use AI generally score worse at school

OECD PISA data from 91 countries shows AI-using students generally score lower, though moderate intentional use plus critical evaluation training can improve outcomes.

PISA 2025 data covering over 760,000 students in 91 countries found that, after adjusting for socioeconomic status, students who never use AI generally outperformed users in science. Effects varied by use type and frequency: task-specific uses like summarizing showed the largest drops, while weekly users of AI for general learning slightly outperformed non-users, especially when trained to critically assess AI output. AI use was higher among advantaged students and varied widely by country, from over 95% in Vietnam to 60% in Japan.

The Verge · AISep 9, 12:05 UTC · 7d agoAI research

Autonomous Research for Open-Ended Problems: A Case Study on Telecom Ticket Retrieval

Case study shows autonomous LLM research reaches 90% of SOTA on telecom ticket retrieval in 10 weeks versus 10 months human work.

The paper explores adapting autonomous research to open-ended, industry-grade ML problems through a telecom ticket retrieval case study with commercial and open-source agents. Autonomous research reached 90% of state-of-the-art performance (0.34 vs. 0.38 Recall@1) in 10 weeks versus 10 months of human work, at up to $200 per Cursor campaign. The authors find agents excel at narrow hyperparameter optimization but lack human-like intuition, recommending human-agent collaboration.

arXiv cs.AI / cs.LG / cs.CLSep 11, 17:09 UTC · 4d agoAI research

Inside ‘Project Lily’: The Humans Reading Your ChatGPT Chats

404 Media reveals OpenAI's 'Project Lily' has hundreds of contractors reading real ChatGPT user prompts, exposing sensitive personal data despite privacy filters.

404 Media reports that OpenAI employs hundreds of contractors who read real ChatGPT user prompts, including whole conversations, to rate and critique the chatbot's responses across a user base of over 900 million. Prompts are anonymized and run through OpenAI's Privacy Filter model, but the company acknowledged sensitive personal details can still reach reviewers, and 'user memories summaries' may reveal a user's location and personal context. The review work includes training ChatGPT to be less sycophantic and to stop anthropomorphizing itself, following lawsuits linking the sycophantic 4o model to multiple suicides. Anthropic confirmed it also uses human review to improve its models, and OpenAI's 'improve the model for everyone' data-sharing setting is on by default for free, Plus, and Pro users.

404 MediaSep 14, 14:22 UTC · 2d agoAI safety & security

Learning never stops: How AI makes learning continuous

OpenAI report describes how students and educators use ChatGPT to extend learning continuously beyond the classroom.

OpenAI published a report examining how students and educators use ChatGPT to make learning more continuous. The report describes support that extends beyond the classroom, positioning ChatGPT as an ongoing learning companion. The release is part of OpenAI's education-focused communications rather than a technical or safety research paper.

OpenAI NewsAug 26, 10:00 UTC · 21d agoAI industry

Two-year university study finds banning AI from classrooms leaves students worse off

A two-year university study found students banned from using ChatGPT performed worst, while formal prompt-engineering training advantages faded as everyday AI familiarity grew.

Researcher Schrepel ran a classroom experiment in 2024 (66 students) and 2025 (164 participants) comparing a no-AI group, unguided ChatGPT users, and trained students revising EU AI Act provisions. The no-AI group finished last both years, hitting 'idea exhaustion' after 10-15 minutes, while the trained group's advantage nearly vanished by 2025 as everyday chatbot familiarity rose. Schrepel now argues blanket AI bans harm outcomes and universities should rethink bans and pure literature-review theses.

The DecoderSep 13, 09:27 UTC · 3d agoAI industry

Governing Bring Your Own AI: A Parameterized Maturity Model

Researchers propose a parameterized governance model and maturity ladder for Bring Your Own AI, finding data exposure and compliance dominate BYOAI risks.

The paper studies Bring Your Own AI (BYOAI), where employees use personal generative AI accounts such as ChatGPT, Gemini, and Claude outside enterprise identity and security controls. Drawing on a curated corpus of 30 records (24 studies and 6 framework documents), the authors build a risk taxonomy, a five-level governance maturity ladder, and a parameterized model linking control-layer coverage to residual risk. Findings highlight data exposure and compliance as the most prominent risks, inconsistent framework engagement, and evidence that layered technical controls reduce modeled exfiltration risk more than prohibition-based approaches.

arXiv cs.CRSep 4, 15:03 UTC · 12d agoResearch

Sure, Meta’s AI Muse works, but it sure creeps me out

Hands-on review finds Meta's Muse AI agent completes shopping and email tasks but surfaces personal Instagram API data beyond user-visible ad-topic settings.

Meta launched Muse, its first agentic AI productivity assistant, which performs tasks like shopping, email management, trip planning, media generation, and creating webpages or documents via a cloud-based virtual computer. The Verge's hands-on found it successfully deleted thousands of promotional emails and completed an Amazon purchase, but it also revealed detailed personal interests inferred from Instagram and Facebook account API data that is not visible in the apps' ad-topic settings. Meta says Muse only exchanges data needed for third-party integrations and does not share information with advertisers; the reviewer frames privacy unease as the main adoption hurdle.

The Verge · AIupdated Sep 12, 20:00 UTC · 3d agofirst Sep 10, 15:00 UTC · 6d agoAI industry 10 sources

Generative Marketing Mix Modeling: A Causal Inference Framework Linking GEO and GEM to Business Impact

Paper proposes Generative Marketing Mix Modeling to causally estimate Generative Engine Optimization and Marketing effects on business outcomes.

The authors develop GMMM, a causal inference framework for measuring how often users see and notice a firm's name in generated answers, which standard marketing data ignore. For GEO it combines repeated generated answers with question counts, shares of generative-system usage and notice probabilities; for GEM it uses sponsored placement records with notice probabilities. The framework compares expected business responses under alternative treatment sequences, establishes identification conditions, and is evaluated on simulated product-recommendation answers in English and Japanese.

arXiv cs.AI / cs.LG / cs.CLSep 10, 17:57 UTC · 5d agoAI research

Studying Without a Syllabus: Task-Agnostic Environment Preprocessing

Paper formalizes task-agnostic environment preprocessing, where agents study unfamiliar environments under a budget to build reusable artifacts for a frozen solver.

The paper formalizes task-agnostic environment preprocessing, where a studying system explores an environment under a budget and produces artifacts like indices, scripts, or procedural guidance for a frozen solver, without task examples or evaluation feedback. The authors compare unaided and archive-equipped meta-agents against fixed synthetic-practice and corpus-processing methods across six heterogeneous benchmarks. A meta-agent variant achieves the highest Avg@3 reward on five benchmarks, while fixed corpus processing remains best on the largest corpus benchmark. Studied artifacts reduce the test-time sampling needed to reach a given score, shifting computation from repeated test-time attempts to a pre-task study phase.

Hugging Face daily papersSep 8, 20:00 UTC · 7d agoAI research

When the prompt becomes the payload: A practical pen-testing guide for GenAI, LLM and RAG applications

CSO Online publishes a practical penetration-testing guide for GenAI, LLM, and RAG applications, covering prompt injection, retrieval poisoning, and tenant isolation testing.

The guide frames LLM applications as attack graphs spanning prompts, retrieval layers, vector stores, tools, identities, and downstream APIs, arguing that conventional web testing misses instruction-vs-data channel risks. It builds on OWASP prompt injection guidance (direct vs. indirect injection) and NIST's 2025 adversarial machine-learning taxonomy, noting that RAG and fine-tuning do not remove injection risk. Recommended practices include documenting trust transitions across components, using canaries and synthetic records to avoid test side effects, running multi-turn and obfuscated injection campaigns, and verifying chains from poisoned documents to observable state changes. It also details testing RAG pipelines via controlled document poisoning across metadata, OCR layers, and code comments, plus cross-tenant isolation checks on retrieved document IDs.

CSO OnlineSep 9, 10:00 UTC · 7d agoAI safety & security1

ToolLoop: Closed-Loop Tool-Use Data Synthesis via Decomposed Generation and Dynamic Self-Feedback

ToolLoop introduces a closed-loop synthetic data framework whose 11K examples lift a 4B model to 86.40% on BFCL tool-use evaluation.

ToolLoop decomposes tool-use data synthesis into function-name sampling, backward derivation of user queries, and forward derivation of tool calls, with dynamic self-feedback at each stage. This shifts the paradigm from generate-then-filter to generate-verify-refine, reducing inefficient and imbalanced synthetic data. A 4B model trained on 11K synthetic examples reaches 86.40% accuracy on BFCL non-reasoning mode (86.07% in an Isolate variant excluding BFCL-overlapping functions) and 72.1% on ACEBench using only 18.3% of baseline training data.

arXiv cs.AI / cs.LG / cs.CLSep 8, 17:26 UTC · 7d agoAI research1

Studying Image Tokenizers as Visual Languages in Unified Multimodal Models

A controlled pure-autoregressive testbed shows task-specific validation losses rank image tokenizers differently, with I2T loss the most consistent signal.

Researchers built a controlled pure-autoregressive testbed and tracked task-specific validation losses during multimodal continual pretraining across text, image, text-to-image (T2I), and image-to-text (I2T) prediction. They find losses should be analyzed per task because they exhibit distinct scaling behavior and rank tokenizers differently, and that the loss-performance relationship depends on the predicted token space. I2T loss, computed over a shared text vocabulary, correlates consistently with both generation and visual understanding performance after supervised finetuning. Case studies revisit the discriminator, semantic supervision, and vocabulary size as tokenizer design axes.

arXiv cs.AI / cs.LG / cs.CLSep 8, 17:57 UTC · 7d agoAI research1

PrivAudit: A Dual-Lens Auditing Framework for Website Privacy Practices under the CCPA

PrivAudit framework audits 998 websites for CCPA compliance, finding stronger disclosures but pervasive, weakly responsive third-party cookie tracking.

PrivAudit is an automated dual-lens auditing framework combining LLM-based analysis of privacy policies grounded in CCPA provisions with automated browser measurements of cookie writes under diverse privacy configurations. Applied to 998 websites, it finds CCPA-subject sites disclose opt-outs, data sharing, and user rights more frequently, yet tracking remains pervasive: 6,392 targeting cookies, 49% third-party writes. Cookies show limited-to-moderate responsiveness to privacy signals and consent choices even when sites claim to honor them. The framework is open-sourced and shared with regulators.

arXiv cs.CRSep 9, 04:31 UTC · 7d agoResearch

ChatGPT can now connect to your personal apps to mimic writing style

OpenAI is testing ChatGPT Writing Style, which mimics a user's voice using writing samples from connected Gmail, Slack, and Drive accounts.

OpenAI confirmed it is testing a Writing Style feature for ChatGPT that learns a user's voice from writing samples in connected apps. The onboarding flow references Messaging (Slack), Documents (Google Drive and Notion), and Email (Gmail) as example sources. It resembles Anthropic's Styles personalization feature but draws on existing writing inside connected services rather than uploaded samples. The feature is available to a small group of users with no announced general rollout date.

BleepingComputerSep 7, 10:36 UTC · 9d agoAI industry

Online Exam Tool Suffers Data

An online exam tool reportedly suffered a data breach, potentially exposing student or user information, according to Infosecurity Magazine.

Infosecurity Magazine published a report indicating that an online examination platform suffered a data breach. The available metadata does not include the vendor name, number of affected users, or the types of data exposed. Scope and impact cannot be fully assessed without the full article text.

Infosecurity MagazineAug 17, 00:00 UTC · Aug 17, 2026Data breach

Bag of Tricks or Bag of Myths? Reducing Modeling Complexity with Task Knowledge in Explainable Suicide Risk Assessment

Researchers audit 31 NLP techniques for clinician-annotated suicide risk prediction, finding only 5 of 31 comparisons yield reliable gains.

A study of 1,635 clinician-annotated social media posts ran roughly 300 controlled experiments across 7 methodological families, auditing techniques such as model scaling, synthetic data, ensembling, and threshold tuning under severe class imbalance. The proposed system reformulates risk factor prediction as entailment between posts and codebook definitions, using architecturally diverse ensembles with class-balanced training and deployment-consistent calibration. It scores 0.8203 for risk, 0.7953 for evidence, and 0.7045 macro-F1 for factors, ranking third among 53 teams.

arXiv cs.AI / cs.LG / cs.CLSep 7, 17:14 UTC · 8d agoAI research

Necessary or Sufficient? Evaluating LLM Explanations With Behavioural Evidence

New framework tests whether LLM-cited explanation factors are necessary or sufficient, finding weak correlation across Claude, GPT, and Gemini models.

An arXiv paper introduces black-box intervention tests measuring whether factors LLMs cite in their explanations are necessary or sufficient for their outputs in agent oversight workflows. Across eight models from the Claude, GPT, and Gemini families, Spearman correlations between cited rankings and measured influence ranged from 0.349-0.354 (advisor recommendation) to 0.431-0.580 (prompt monitoring). Uncited factors scored above the lowest cited factor in up to 57.6% of advisor responses, showing cited top-three factors do not reliably identify the most influential inputs.

arXiv cs.AI / cs.LG / cs.CLSep 4, 17:37 UTC · 11d agoAI safety & security1

Google Research Releases ToolGrad: Answer-First Framework Hits 99.8% Pass Rate for Tool-Use Data Generation

Google Research and partners introduce ToolGrad, a verified tool-chain-first data generation framework reaching 99.8% pass rate and boosting Gemma-3-12B to 83.1 on BFCL.

Researchers from Google, the University of Tokyo, RIKEN AIP, and Tohoku University released ToolGrad, which inverts query-first tool-use data generation by executing and verifying API chains before annotating them with user queries. On the ToolBench database of 16,000+ APIs, ToolGrad raised generation pass rate from 63.8% to 99.8% while increasing tool uses per sample from 2.1 to 3.4 and cutting tool-use steps from 34.3 to 20.0. Fine-tuning Gemma-3 at 1B, 4B, and 12B parameters on the 500-sample ToolGrad-500 dataset lifted ToolGrad-12B to 83.1 on the Berkeley Function Calling Leaderboard, near Gemini 2.5 Pro at 83.2 and ahead of GPT-5 at 74.4. Code is Apache-2.0, with the dataset, PyPI package, and models available on Hugging Face.

MarkTechPostSep 11, 06:01 UTC · 5d agoAI research1

"Shut Up and Let Me Enjoy My Otome": Understanding and Measuring the Toxicity in Otome Game Communities

First large-scale study finds 22.20% of Weibo otome game posts toxic versus 3.71% on Reddit, with LLM detectors reaching 0.82 F1.

Researchers present the first large-scale measurement of toxicity in otome game communities, introducing OtomeSCAN, which collected and analyzed 620,045 posts from Weibo and Reddit over 18 months. They manually annotated 4,308 posts, identified eight target groups, and evaluated seven toxicity detectors, with their best LLM-based model reaching F1-scores of 0.82 on Weibo and 0.78 on Reddit. The study found 22.20% of Weibo posts were toxic versus 3.71% on Reddit, and toxicity rose to 37.09% within 72 hours during an external attack on Weibo. The authors also flagged 191 potential-coordination clusters, 64.40% of which targeted game developers.

arXiv cs.CRSep 7, 21:44 UTC · 8d agoResearch

StudyBench: Can Self-Evolution Squeeze Textbooks for Olympiad Capability?

Researchers introduce StudyBench, a physics benchmark showing self-evolution gains on textbook problems rarely transfer to olympiad-level questions.

StudyBench is a controlled physics benchmark splitting test data into an Application Set of difficult textbook problems and a Transfer Set of olympiad-level problems. Across three base models, representative self-evolution methods improved on the Application Set but rarely transferred to the harder Transfer Set. A guidance ablation reveals a Guidance Gap, and every method hits a Compute Plateau, indicating the remaining limits are method problems rather than data or compute problems.

Hugging Face daily papersAug 31, 20:00 UTC · 15d agoAI research

Show HN: LLM Attention Visualization

A developer released a browser-based tool that visualizes which past tokens influence each LLM output token using aggregated, value-weighted attention scores.

A Show HN project presents a React application built on Transformers.js that renders per-token attention influence by aggregating attention weights scaled by value-vector magnitudes across all attention heads and layers. To expose internal tensors, the author instrumented the ONNX computation graph, hosted a modified model on Hugging Face, and pre-generated prompts to avoid long model downloads in the browser. Demos with a 600-million-parameter model show how verbatim copying draws heavily on source tokens and how single outputs blend information from multiple phrases.

Hacker News · AISep 8, 16:59 UTC · 7d agoAI tools & infra HN 41↑ · 9 comments

Technical Manual for a Toolkit for Measuring Contextual Individuation in Transformer Language Models

An open methodology toolkit measures whether transformer language models contextualize fixed word forms across domains using bridge forms and layer-wise silhouette analysis.

The manual documents an open toolkit built around 'bridge forms' - identical written words recurring across two or more subject domains with a different sense in each - to test whether transformer language models individuate word occurrences by context beyond the embedding layer. It covers declarative specification of bridge forms, Wikipedia corpus acquisition, occurrence localization, layer-wise representation extraction, domain-pairwise silhouette measurement, and visualization, justifying each choice against failure modes such as sense contamination and subword-tokenization misalignment. It is a methodological and implementation reference and reports no empirical results.

arXiv cs.AI / cs.LG / cs.CLSep 4, 16:38 UTC · 11d agoAI research

Meta Introduces Muse, a Personal AI Agent That Runs on Its Own Dedicated Secure Cloud Computer

Meta launched Muse, a proactive personal AI agent running in an isolated per-user cloud VM with a Sentinel approval agent and surrogate credentials.

Meta introduced Muse, a consumer agent that performs long-horizon tasks like email, travel booking, and bill negotiation, rolling out in the US on iOS, Android, muse.ai, and WhatsApp with free and paid tiers. Each user gets a dedicated Muse Secure VM where the agent runs in a systemd-nspawn cell, while a separate Sentinel agent approves every network request at layer 4/7 and injects real credentials only at the network boundary. The underlying Muse Spark 1.3 model, which Meta says cuts tool calls by ~20% and tokens by ~25% versus 1.2 and is near state-of-the-art on prompt-injection resistance, is available via Meta Model API, with open weights on the roadmap.

MarkTechPostSep 9, 04:38 UTC · 7d agoAI industry