News & Research
The latest AI research and news with real-world stakes — each item sourced, dated, summarized in plain English, and tagged by impact area. Every item is checked against its source before it appears.
Research
PolyInterview: An LLM-based Platform for Immersive Mock Interview Practice with Comprehensive Multimodal Assessment
Zhiyuan Wen, Jiannong Cao, Kelly Chan et al.
arXiv · 2026-07-11
PolyInterview is an LLM-based mock interview platform that generates job-specific questions from a candidate's CV and target job description, conducts adaptive multi-turn spoken interviews via a lip-synced digital human, and evaluates performance across content, vocal delivery, and non-verbal behavior. Four parallel evaluators produce 13 behavior-level features aggregated into 10 assessment aspects and two competency tracks, with feedback grounded in KSA and STAR frameworks. In live deployment across 101 accounts and 1,564 sessions, generated questions aligned with the correct job description in 93.7% of cases, and expert evaluators rated question plans and feedback as strong. The platform addresses the high cost and low availability of expert mock interview coaching by offering accessible, evidence-linked, multimodal interview preparation.
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Research
Can Agentic Trading Systems Pay for Their Own Intelligence?
Qiqi Duan, Changlun Li, Chen Wang et al.
arXiv · 2026-07-11
This paper investigates whether LLM-based agentic trading systems can generate enough profit to cover the costs of their own reasoning and decision-making. The authors introduce TradeLens, a diagnostic toolkit that reconstructs trading trajectories from runtime traces and deployment records, attributing profit and cost to interpretable evidence. Their analysis across multiple backbone models, capital scales, trading frequencies, and system architectures reveals that viability depends on 'intelligence-to-profit conversion,' with different models showing distinct failure modes—such as poor asset selection in DeepSeek-V3.2 and negative timing in GLM-4.7. The work reframes LLM trading agent evaluation from capability benchmarking to trace-grounded diagnosis of whether and why an agent pays for its own intelligence.
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Research
Information-seeking failures of large language models in agentic clinical reasoning
Krischan Braitsch, Laura K. Schmalbrock, Theresa Weltermann et al.
arXiv · 2026-07-11
This study evaluates 32 large language models on an agentic clinical reasoning task in hematologic oncology, where models must proactively request clinical data across multiple rounds before making a diagnosis and treatment recommendation. The best-performing model reached only 68% overall accuracy, with information utilization—the fraction of available data actually requested—being the strongest predictor of diagnostic accuracy (R=0.69, P<0.001). Utilization dropped sharply from 57% to 26% in the final round, leaving critical molecular and cytogenetic data unexamined, while reasoning traces scored high on a clinical rubric yet were decorrelated from accuracy. The findings show that the primary bottleneck for LLMs in clinical oncology is not medical knowledge but a systematic failure of information-seeking under uncertainty, driven by cognitive biases such as anchoring, satisficing, and premature closure.
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Research
One Token Is Enough: Fingerprinting and Verifying Large Language Models from Single-Token Output Distributions
Tomas Bruckner
arXiv · 2026-07-11
This paper demonstrates that large language models can be fingerprinted and verified using only their single-token output distributions—specifically, how a model answers trivial one-word prompts like 'name a random number between 1 and 100.' By measuring 165 models served through a commercial API aggregator, the authors find these distributions are highly model-specific, enabling a biometric-style verification protocol that achieves a 7.3% equal error rate with roughly a hundred single-token queries per audit. The method requires no long generated text, log-probabilities, or model owner cooperation, making it practical for auditing opaque API serving chains. Notably, the authors report ecosystem anomalies including a proprietary-branded flagship endpoint that is distributionally indistinguishable from an open-weight Qwen model, underscoring real-world deployment integrity concerns.
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Research
Falsifiable Release Gates for Self-Improving Systems: Standing Invariants at Scale
Deepak Soni
arXiv · 2026-07-11
This paper introduces 'falsifiable release gates,' a methodology requiring self-improving AI agent systems to pass pre-declared, machine-checkable acceptance tests before any new capability ships, while a fixed set of safety invariants must hold across every release. The authors instantiate this approach in an open runtime called Antahkarana and track it through multiple releases, demonstrating that six core action-safety invariants (INV-1 through INV-6) held without modification even as capabilities more than doubled and the test suite grew from 122 to 563 tests. On real hardware, the governed self-improvement loop raised a small model's accuracy from 20% to 70% while automatically rejecting a candidate that only inflated confidence, with the entire governed path adding only 0.021 ms per request. The work is directly relevant to quality assurance and certification of AI systems, providing a reproducible, empirically validated framework for maintaining safety guarantees as AI systems grow in capability.
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Research
A Survey on LLM Watermarking: Theory and Deployment
Huy Phan, Kieu Dang, Ojaswi Dulal et al.
arXiv · 2026-07-11
This survey systematically reviews the landscape of watermarking techniques for large language models (LLMs), covering methods that embed invisible signatures into model outputs at generation time or training time to enable attribution, auditing, and trust decisions. The authors organize the field around key deployment questions—where watermarks are embedded, who can detect them, what assumptions are required, and which threat models (e.g., paraphrasing, style transfer, adaptive removal) are targeted—and synthesize major technique families including sampling biasing, code-based schemes, and representation-based approaches. The paper also analyzes security-utility trade-offs, attack and evasion strategies, and evaluation metrics such as false positive control and robustness curves. The findings are directly relevant to enterprise and policy contexts where provenance ambiguity, model misuse, and content laundering at scale pose significant accountability and governance challenges.
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Research
AgentAbstain: Do LLM Agents Know When Not to Act?
Xun Liu, Yi Evie Zhang, Vira Kasprova et al.
arXiv · 2026-07-11
AgentAbstain introduces the first systematic benchmark for evaluating whether LLM-based agents know when to withhold action rather than proceeding autonomously. The benchmark covers 8 abstention scenarios across 263 paired tasks in 42 sandbox environments, testing both 'should-act' and 'should-abstain' variants under conditions like ambiguity, conflicting constraints, and tool failures. Across 17 frontier LLMs tested in 4 agent harnesses, the best-performing model (Gemini 3.1 Pro) achieved only 59.5% paired accuracy, and abstention capability was found to be largely independent of general task-solving capability—meaning scaling task performance alone will not solve the problem. A notable failure mode identified is 'post-hoc abstention,' where agents execute irreversible actions before recognizing they should have stopped, posing real risks in autonomous deployments.
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Research
Assuring an AI Assistant for IRB Preparation: Replication Reliability, Warrant Stability, and Evidence-Driven Revision in Institutional RAG
Jacob D. Holster
arXiv · 2026-07-11
This paper presents the design and assurance evaluation of IRB Helper, a retrieval-augmented generation system built to help researchers navigate institutional review board compliance. Across 25 repeated executions of a 60-item stress suite and two revision cycles, the system showed high retrieval reliability and improved cited-warrant overlap from .449 to .602, with no probable fabrications detected in 660 responses after allowlist updates. The study identifies 'refusals' as claims requiring evidentiary warrants just as answers do, and highlights that decline or referral responses were the locus of all conflicting or unsupported changes. The work establishes replication reliability and warrant stability as key assurance properties for researcher-built educational AI, with implications for how such systems are evaluated and audited.
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Research
Artificial Intelligence in Australian Fashion Businesses: An Exploratory Study of Adoption and Challenges
Amrutha Baburaj, Saniyat Islam, Caroline Swee Lin Tan
Systems · 2026-07-11
This qualitative study examines AI adoption among nine Australian fashion industry professionals using the Technology–Organization–Environment (TOE) framework. It finds that AI adoption is nascent and fragmented, typically introduced through vendor-embedded features rather than deliberate strategy, with risk-averse organizational culture and limited perceived need being the dominant barriers—contrasting with international findings that emphasize cost and data infrastructure. Sustainability represents the largest unrealized opportunity, including reduced overproduction, fewer size-related returns, and improved textile recycling. The authors conclude that cultural change, tiered education, and industry-specific value propositions are needed to unlock AI's potential in this SME-dominated, geographically isolated market.
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Research
Silent Failures in Quantized LLM Reasoning: A Taxonomy-Based Analysis of Hollow Convergence and Failure Mode Shifts
Renuka Oladri, Mohan Vamsi Varadaraju Priya, Jerry Wu
arXiv · 2026-07-10
This paper investigates how post-training quantization (reducing numerical precision of large language models) can silently change the reasoning process even when overall task accuracy appears stable. Using a six-category failure taxonomy validated by two independent human annotators (Cohen's κ = 0.906), the authors analyze 30,000 chain-of-thought outputs from five instruction-tuned LLMs (3B–14B parameters) across three quantization precisions (FP32, FP16, NF4) and four benchmarks, finding that accuracy drops only up to 3.1 percentage points but that qualitative failure modes shift substantially — for example, Shortcut Collapse rises from 44% to 78% of wrong-answer failures in LLaMA 3.2-3B under NF4 while Confidence Snowballing collapses from 15.8% to near zero. A key failure mode called Hollow Convergence — correct answers reached through incomplete or unverifiable reasoning — cannot be reliably detected from surface-level text features (best F1 = 0.53), making it invisible to standard evaluation pipelines and a critical deployment risk.
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Research
Evaluating AI Models' Capability to Automate Voice Phishing Attacks
Fred Heiding, Claudio Mayrink Verdun, Simon Lermen et al.
arXiv · 2026-07-10
This large-scale study (N=4,100 survey participants and 12 qualitative interviews) evaluates how susceptible U.S. adults are to AI-automated voice phishing (vishing) attacks using leading voice models including Llama Full Duplex, Sesame, Gemini, OAI AVM, Play.AI, and ElevenLabs. Results show an overall compliance rate of 16.5% across five scam categories, with up to 36% of participants willing to comply in 'relative-in-distress' scenarios, and certain models like Sesame achieving persuasiveness ratings comparable to or slightly surpassing human voices. An economic analysis finds that while human-operated vishing is unprofitable at U.S. wages, AI-powered vishing is economically viable for several models, meaning the primary risk is the low cost and high scalability of automation rather than superhuman persuasion. The findings raise significant concerns for AI system design, consumer protection, and model release policies.
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Research
A Foundation Model for Multimodal Event Sequences in Financial Applications
Nikita Rusakov, Vladislav Meshkov, Konstantin Zorin et al.
arXiv · 2026-07-10
This paper presents a foundation transformer model that unifies heterogeneous financial data sources—such as transaction histories and digital interaction signals—into a single chronological multimodal event sequence, using a next-event prediction objective to learn general-purpose user representations. These representations are combined with engineered features and used to train lightweight models for multiple downstream financial tasks, outperforming traditional task-specific models while reducing development overhead. The system was deployed in production at one of the largest banks in Eastern Europe, yielding measurable improvements in business metrics. This demonstrates that foundation models can replace siloed, manually engineered pipelines in financial services, improving both efficiency and predictive performance.
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Research
Faithful by Design: Evaluating and Improving LLM-Generated Clinical Trial Summaries for Multi-Stakeholder Audiences
Robert Williams
arXiv · 2026-07-10
This study introduces a benchmark framework for evaluating how faithfully large language models (GPT-4o, Claude Sonnet 4.6, and Gemini 2.5 Flash) summarize clinical trial results for healthcare providers, patients, and payers. Using 200 stratified trials and 1,800 generated summaries scored across six faithfulness dimensions, the authors find that 'Unsupported Claims' is the dominant failure mode, with a mean annotation score of 1.55 out of 3. A knowledge-graph-augmented retrieval system produced statistically significant improvements in faithfulness scores (entailment +0.0125, p < 0.0001), though improvement pathways varied by model. These findings matter for quality assurance and policy in healthcare AI, as hallucinations in clinical trial summaries pose direct risks to medical decision-making across multiple stakeholder groups.
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Research
Do These Violent Delights Have Violent Ends? Measuring the Post-Merge Fate of Agentic Code
Chunqiu Steven Xia, Courtney Miller
arXiv · 2026-07-10
This paper conducts a longitudinal empirical study of AI-generated ('agentic') code contributions across 182 repositories, tracking what happens after those contributions are merged into real projects. While overall maintenance rates are comparable to human contributions, agentic code requires significantly higher rates of corrective maintenance and introduces more security weaknesses and dependency vulnerabilities. The study also finds that projects with higher no-review rates see roughly a 6% increase in agentic maintenance burden per 10 percentage-point rise in that rate. The findings suggest that evaluating agentic coding tools should go beyond merge acceptance to also assess long-term security and maintainability outcomes.
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Research
What You Train Is What You Get: Gender Bias, Training Composition, and Post-Hoc Mitigation in Audio Deepfake Detection
Aishwarya R. Fursule, Vamshi Nallaguntla, Shruti Kshirsagar et al.
arXiv · 2026-07-10
This paper investigates gender bias in audio deepfake detection models trained on the ASVspoof5 dataset, finding that the gender composition of training data strongly predicts which demographic group performs worse at test time. Using a ResNet18 classifier with two feature types (LogSpectrogram and WavLM-Base+), the researchers show that WavLM-Base+ features produce gender performance gaps 3.0 to 4.3 times larger than LogSpectrogram under identical conditions, and that balanced training reduces LogSpectrogram bias but leaves WavLM bias largely intact. Critically, all six post-hoc threshold calibration methods tested — including an oracle approach with full test-set label access — failed to close the Equal Error Rate gap, confirming that score distribution disparities cannot be corrected after training. These findings indicate that achieving gender fairness in audio deepfake detection requires interventions at training time rather than post-hoc adjustments.
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Research
ConceptSMILE: Auditing the Trustworthiness of Concept-Based Explainable AI
Mohadeseh Mollapour, Koorosh Aslansefat, Zeinab Dehghani et al.
arXiv · 2026-07-10
ConceptSMILE is a model-agnostic auditing framework that evaluates the reliability of concept-based explainable AI (XAI) explanations by perturbing input regions, measuring concept-response shifts, and fitting a surrogate model to approximate local concept behaviour. Reliability is assessed across five dimensions: attribution accuracy, surrogate fidelity, faithfulness, stability, and consistency. Applied to retinal fundus images, the framework finds that reliability varies by concept and pathway—MedSAM-derived visual concepts achieve higher spatial attribution and surrogate fidelity (R²=0.8503), while VLM-based semantic concepts show stronger vessel faithfulness and stability under certain artefact conditions. This work matters for quality assurance and certification in medical AI, providing an independent audit layer to assess whether concept-level explanations can actually be trusted before deployment.
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Research
TrustX Agent Risk Classification Framework (ARC): Risk-Tiering Internally Created Agentic AI Systems
Hannah M. Liu, Rhea Saxena, Shiv Asthana
arXiv · 2026-07-10
This paper introduces the TrustX Agent Risk Classification Framework (ARC), a structured instrument designed to classify and govern internally created agentic AI systems in enterprise and public-sector contexts. The framework applies across seven types of agentic AI systems using a twelve-dimension scoring rubric, a GPA + IAT classification model, and a five-level autonomy framework derived from existing literature, producing a three-tier governance output with mapped control recommendations. A specialized extension for Coding Assistants is also included to handle nuances specific to that system type. ARC addresses a gap where general-purpose AI risk frameworks have failed to keep pace with the proliferation of agentic AI, and is intended for AI governance practitioners, risk officers, developers, and regulators.
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Research
Deceptive Grounding: Entity Attribution Failure in Clinical Retrieval-Augmented Generation
Cedric Caruzzo, Donggeun Yoo, Tae Soo Kim
arXiv · 2026-07-10
This paper identifies a new failure mode in clinical retrieval-augmented generation (RAG) systems called 'deceptive grounding' (DG), where a model presents real clinical evidence about one drug as if it applies to a different queried drug. The authors show this failure is invisible to standard hallucination, faithfulness, and citation checks because every claim is sourced from a real document — just the wrong entity. Across a controlled benchmark of 13 models, DG rates range from 8–87% under adversarial conditions, with medical and biomedical fine-tuned models reaching up to 86.7%, meaning domain specialization worsens rather than prevents the problem. In a deployed RAG system evaluated over 740 drug-disease pairs, the overall DG rate was 7.8%, rising to 13.6% for recently approved drugs — a serious patient safety concern that existing evaluation frameworks do not address, though entity-attribution verification detected DG at 97.0% precision and 98.7% recall.
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Research
Letting the Data Speak: Extracting Keywords from Crowdsourced Collections with AI
Miguel Arana-Catania, Catherine Conisbee, Matthew Kidd
arXiv · 2026-07-10
This paper evaluates three Natural Language Processing approaches—Named Entity Recognition, Keyword Extraction, and Topic Modelling—for automating keyword assignment in crowdsourced digital collections, using the Their Finest Hour Online Archive at the University of Oxford as a case study. Testing methods ranging from traditional statistical techniques to modern generative AI, the authors find that NLP offers real potential for large-scale keyword extraction but that no single method is complete and model choice significantly shapes results. The study also highlights distinct ethical stewardship responsibilities arising when metadata is produced through engagement with living contributors, concluding that open-weight, extractive models are best suited for responsible deployment while generative AI introduces accountability risks that collection managers should carefully weigh.
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Research
Geopolitical alignment: Endorsement effects in large language models
Maxim Chupilkin
arXiv · 2026-07-10
This paper investigates whether large language models (LLMs) are implicitly influenced by geopolitical cues when evaluating international economic and security policies. Using an endorsement experiment, the researcher found that GPT-5, Claude Sonnet, and Gemini consistently rated identical policies lower when described as supported by China or Russia compared to the United States or the European Union, with DeepSeek behaving differently. Adding justification prompts left the Western/non-Western gap intact for some models, attenuated it for Gemini, and sharply activated China and Russia penalties in DeepSeek, with justifications revealing that Western endorsement was treated as a credibility signal while Chinese and Russian endorsement triggered concerns about data security, surveillance, and geopolitical risk. These findings matter for policy and enterprise applications because LLM evaluations of policy-relevant information can be systematically shaped by the identity of a foreign endorser even when policy content is held constant.
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Research
A Personalized Computational Framework for Assessing the Sufficiency of Partially Observed Data in Healthcare AI models
Qingchu Jin, Felistas Mazhude, Jamie B. Rabb et al.
arXiv · 2026-07-10
This paper introduces Feature Sufficiency Analysis (FSA), a personalized computational framework that determines whether a subset of available clinical features is sufficient for an AI model to achieve the same predictive performance as when all features are present (termed full-feature-capacity, or FFC). By estimating the distributions of missing variables conditioned on available ones, FSA provides patient-specific assessments of whether additional data collection is necessary before making a prediction. The authors demonstrate FSA in two clinical case studies—predicting postoperative prolonged ventilation after heart surgery and 10-year mortality in an outpatient cohort—and show it can also rank features by predictive sufficiency, identify hard-to-predict patient subgroups, and support cost-aware data acquisition. This work matters for healthcare AI deployment by offering a principled method to determine when incomplete clinical data is trustworthy enough to support AI-assisted decision-making.
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Research
MedRealMM: A Real-World Multimodal Benchmark for Chinese Online Medical Consultation
Runhan Shi, Quan Zhou, Yuqian Xu et al.
arXiv · 2026-07-10
MedRealMM introduces a large-scale benchmark of 5,620 real-world multimodal medical consultation cases drawn from de-identified patient-doctor interactions at a Chinese internet hospital, spanning 64 clinical departments. Unlike existing benchmarks that rely on synthetic conversations or multiple-choice metrics, it uses a Multimodal Clinical Challenge Point (MCCP) framework to identify clinically demanding moments and evaluates model responses against physician-refined rubrics that reward desirable clinical behaviors and penalize unsafe or contradictory outputs. Evaluation of 19 general-purpose and medical-specialized LLMs finds that image information is critical for reliable clinical performance and that current frontier models fall below online physician response quality, particularly in avoiding safety-sensitive errors. The benchmark highlights that error avoidance remains a central bottleneck for deploying AI in clinical consultation settings.
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Research
Beyond Metadata: CAPRA for Hidden Subgroup Analysis under Missing Metadata in Medical Imaging
Yawen Li, Yan Li, Zhe Xue et al.
arXiv · 2026-07-10
CAPRA is a framework for auditing medical imaging AI models when demographic and acquisition metadata are unavailable at deployment. It predicts image-derived semantic axes, calibrates posteriors on a small labeled split, and organizes them into a subgroup interface that reveals disparity patterns and failure modes otherwise masked by strong aggregate performance. Tested across fundus, dermoscopy, and chest radiography datasets, CAPRA uncovers disparities missed by metadata-only slicing and produces subgroup partitions that align more closely with explicit failure axes than baseline approaches. The framework is reusable by downstream robust learners, making hidden subgroup auditing more interpretable and practical in real-world deployment settings.
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Research
The Admissibility Threshold: A Sector-Specific Certification Standard for High-Stakes AI, with Health as the First Mandatory Domain (Version 2)
Siddiqui Jameel Ahmed
Zenodo (CERN European Organization for Nuclear Research) · 2026-07-10
This paper introduces 'Domain Admissibility,' a proposed certification framework requiring AI systems to demonstrate fitness for a specific high-stakes domain before deployment, rather than relying on uniform horizontal governance standards. The authors argue that existing medical-device regulation leaves a critical gap for general-purpose AI entering clinical settings without any formal approval pathway, and they propose four testable pillars of admissibility along with a certification threshold. The framework is illustrated through a clinical case study, with health proposed as the first domain where the threshold should be mandatory. The work aims to lay constitutional groundwork for future domain-specific AI certification institutions.
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Research
AI-related disclosure intensity and financial transparency: evidence from Chinese listed companies
Yulun Gu
Cogent Business & Management · 2026-07-10
Analyzing a balanced panel of 1,028 Chinese firms listed on the Shanghai and Shenzhen Stock Exchanges from 2011 to 2023, this study finds that greater AI-related disclosure intensity in annual reports is associated with higher external financial transparency ratings. The relationship holds after controlling for firm characteristics, year effects, and alternative model specifications, and is particularly pronounced for smaller firms and non-state-owned enterprises. These findings suggest that AI-related corporate communications may serve as a meaningful signal of disclosure quality, with implications for how investors and regulators assess financial transparency in China's rapidly growing AI market.
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