MAGE explains how externalized knowledge, bounded action, independent evaluation, and retained human authority can compose into a governed engineering environment, and proposes tests of when that environment turns commodity intelligence into durable engineering progress.
LLMs are increasingly used for code generation, yet they frequently hallucinate non-existent software packages, creating exploitable entry points into the software supply chain. We make four contributions to this problem. First, we show that prior evaluation methodologies systematically inflate hallucination rates by misclassifying standard-library modules as hallucinations in some languages. For Python, the overestimation reaches 9.4 percentage points. Second, we evaluate seven inference-time defenses for mitigating package hallucinations, including five guided decoding strategies (Greedy, Contrastive, DoLa, Nudging, and Active Layer-Contrastive Decoding), an iterative self-refinement approach (Self-Refine), and a Retrieval-Augmented Generation (RAG)-based defense.. Across eight models spanning five families and four programming languages (Python, JavaScript, Ruby, Rust), RAG reduces the package hallucination rate (PHR) in 18 of 32 model--language configurations. Third, we introduce Package Utility (PU) to assess whether defenses preserve valid and task-relevant recommendations. Among strategies evaluated, Greedy decoding provides the strongest average mitigation--utility trade-off. Fourth, we stress-test all strategies under adversarial prompts seeded with fabricated package names and find that PHR surges by up to 45 percentage points relative to standard prompts, with Ruby consistently the most vulnerable language (80.9--95.2\%). Under adversarial conditions, RAG and Self-Refine outperform all decoding-only strategies, indicating that robust defense requires either external grounding or iterative self-verification when prompts are actively hostile. Our results recast package hallucination as both a measurement problem and a decoding-time control problem, and they demonstrate that the choice of defense must be matched to the threat model and recommendation utility.
An audit-and-placebo protocol is proposed that separates verifier artifacts, interaction scaffolding, and grounded feedback credit in evaluations of self-evolving test generators in evaluations of self-evolving test generators.
A diagnostic support system based on a unified web platform that classifies patients according to the risks of developing three diseases based on regularly collected clinical or audio data using classical supervised learning algorithms is presented.
This prototype MRG image translocation software was helpful to 69% of patients with binocular diplopia, but limited by large angle strabismus because of the limited instrument field of view.
OBJECTIVE To prospectively evaluate whether modifying DSL version 5-based hearing aid (HA) fittings by adjusting gain on HA fitting software so that measured functional gain (FG) approached a one-third gain (1/3G) target could provide appropriate fitting outcomes in patients with sensorineural hearing loss. METHODS Twenty-four patients (48 ears) with bilateral sensorineural hearing loss underwent initial HA fitting using the DSL version 5 prescription formula. FG was measured at 250-4000 Hz, and HA gain was adjusted on HA fitting software so that FG approached the target 1/3 G. Speech discrimination scores at 65 and 80 dB SPL were evaluated after a two-week trial period using the 67-S Japanese monosyllable word list. Based on speech discrimination test results, ears were classified as well-fitting or non-well-fitting. FG values were compared between the two groups. RESULTS Twenty-one patients (42 ears) completed the study. Thirty-one ears (73%) were classified as well-fitting. Although HA gain was adjusted toward the target 1/3 G, measured FG values at 250 and 500 Hz remained lower than the target values. In well-fitting ears, low-frequency FG values were lower than the target 1/3 G, whereas FG at 2000 Hz was close to the target value. In contrast, non-well-fitting ears showed low-frequency FG values closer to the target 1/3 G, whereas FG values at 2000 and 4000 Hz remained below the target values. CONCLUSIONS Although HAs adjusted toward a 1/3 G target did not achieve the intended FG values, particularly at low frequencies, relatively favorable fitting outcomes were obtained in approximately three-quarters of the ears. In well-fitting ears, low-frequency FG remained below the target 1/3 G, whereas FG in the mid-frequency range around 2000 Hz was close to the target value. These findings provide a basis for future prospective studies to clarify how these FG characteristics should be applied to optimize HA adjustment.
In his latest book, Professor Eugene Fitzgerald examines the forces that turn breakthroughs into value — and why innovation resists simple formulas.
MIT students designed, built, and tested a jet engine with AI copilots, assessing AI’s usefulness in developing high-performance aerospace systems.
Researchers developed an auditing technique to test generative AI models for malicious capabilities, without prompting them for illegal outputs.
A USAF cadet and a Lincoln Laboratory researcher found AI chatbots can help nontechnical service members produce viable software applications for their unique problems.
