Oldest Papers

Date: Oldest Clear all

Advanced filtersTopic: All

Experimental

Topic

Topics are auto-detected from title, abstract, and metadata and may be imperfect.

Publication Date

Newest Oldest Clear

Average Rating

Highest Lowest

Medical Board of Australia v Zebic (Review and Regulation) [2026] VCAT 50 (23 January 2026)

[2026] VCAT 50

austlii.edu.au·2026

Review and Regulation List - Health Practitioner Regulation National Law (Victoria) Act 2009 – medical practitioner – allegations that practitioner breached conditions on her registration; provided false, inaccurate and/or incomplete information to regulator; and failed to manage her health - allegations mostly admitted - finding that practitioner failed to attend for urinary drug screening and hair testing; and that she self-referred for treatment and tests – findings that practitioner thereby breached condition that she attend for urinary drug screening and hair testing; that she breached condition that she undertake treatment with a GP and psychiatrist; and that she failed to manage her health – finding that practitioner provided false, inaccurate and/or incomplete information to regulator - allegation that she failed to comply with treatment directions of her psychiatrist not proved - findings of professional misconduct and unprofessional conduct – extensive history including previous VCAT disciplinary proceeding - numerous breaches of conditions for drug testing and suspensions of registration by Board and its predecessor - reprimand and cancellation of registration with no disqualification period.

No ratings yet

View paper →

Rescuing Socialism from Equality

Barry Maguire

Mind·2026

Abstract Karl Marx rejected the ideal of equality as bourgeois. And yet the most significant attempt in recent years to distinguish socialist theory from liberal egalitarian theory, G. A. Cohen’s critique of John Rawls, relies almost entirely on an egalitarian principle. Although Cohen’s critique often seems to have a great deal of intuitive force, a number of Rawls’s defenders have argued, quite convincingly, that Cohen’s critique is unsuccessful. For those of us attracted to broadly socialist ideals, there does seem to be something importantly right about Cohen’s criticisms of Rawls, and more substantively, something deeply problematic in the kinds of market-based leveraging of productive abilities that would be permitted in a fully just Rawlsian society. My diagnosis is that Cohen has the right target, but the wrong fundamental value. I develop an alternative to these liberal egalitarian approaches in contemporary socialist ethics, building on the famous slogan, ‘From each according to their abilities, to each according to their needs’. This alternative ideal, of Caring Solidarity, draws on rich socialist, Christian and feminist traditions, and emphasizes the importance of care, recognition and solidarity in political and economic organization. This alternative leaves a certain amount of inequality legitimately in place, whilst providing a moral framework for a radical reorganization of production.

★ 5.0 (1)

View paper →

PreviousPage 436 of 515Next

Probabilistic Graphical Models in Astronomy

Abigail Sheerin, Giuseppe Vinci

arXiv·2026

The field of astronomy is experiencing a data explosion driven by significant advances in observational instrumentation, and classical methods often fall short of addressing the complexity of modern astronomical datasets. Probabilistic graphical models offer powerful tools for uncovering the dependence structures and data-generating processes underlying a wide array of cosmic variables. By representing variables as nodes in a network, these models allow for the visualization and analysis of the intricate relationships that underpin theories of hierarchical structure formation within the universe. We highlight the value that graphical models bring to astronomical research by demonstrating their practical application to the study of exoplanets and host stars.

No ratings yet

View paper →

Scaling Laws for Moral Machine Judgment in Large Language Models

Kazuhiro Takemoto

arXiv·2026

Autonomous systems increasingly require moral judgment capabilities, yet whether these capabilities scale predictably with model size remains unexplored. We systematically evaluate 75 large language model configurations (0.27B--1000B parameters) using the Moral Machine framework, measuring alignment with human preferences in life-death dilemmas. We observe a consistent power-law relationship with distance from human preferences ($D$) decreasing as $D \propto S^{-0.10\pm0.01}$ ($R^2=0.50$, $p<0.001$) where $S$ is model size. Mixed-effects models confirm this relationship persists after controlling for model family and reasoning capabilities. Extended reasoning models show additional 16\% improvement beyond scale effects. The relationship holds across diverse architectures, while variance decreases at larger scales, indicating systematic emergence of more reliable moral judgment with computational scale. These findings extend scaling law research to value-based judgments and provide empirical foundations for artificial intelligence governance.

No ratings yet

View paper →

Self-Distillation Enables Continual Learning

Idan Shenfeld, Mehul Damani, Jonas Hübotter, Pulkit Agrawal

arXiv·2026

Continual learning, enabling models to acquire new skills and knowledge without degrading existing capabilities, remains a fundamental challenge for foundation models. While on-policy reinforcement learning can reduce forgetting, it requires explicit reward functions that are often unavailable. Learning from expert demonstrations, the primary alternative, is dominated by supervised fine-tuning (SFT), which is inherently off-policy. We introduce Self-Distillation Fine-Tuning (SDFT), a simple method that enables on-policy learning directly from demonstrations. SDFT leverages in-context learning by using a demonstration-conditioned model as its own teacher, generating on-policy training signals that preserve prior capabilities while acquiring new skills. Across skill learning and knowledge acquisition tasks, SDFT consistently outperforms SFT, achieving higher new-task accuracy while substantially reducing catastrophic forgetting. In sequential learning experiments, SDFT enables a single model to accumulate multiple skills over time without performance regression, establishing on-policy distillation as a practical path to continual learning from demonstrations.

No ratings yet

View paper →

Oldest Papers

Medical Board of Australia v Zebic (Review and Regulation) [2026] VCAT 50 (23 January 2026)

Rescuing Socialism from Equality

Probabilistic Graphical Models in Astronomy

Ain’t No New Black Panthers

Scaling Laws for Moral Machine Judgment in Large Language Models

When the Violence Comes Home to Roost

Joan Baez beyond ‘A Complete Unknown’ | The Aggie

Claude's Constitutional Structure

DPP v Ollis [2026] VCC 20 (27 January 2026)

DPP v Sutherland [2025] VCC 1974 (27 January 2026)

Self-Distillation Enables Continual Learning

Guy v Secretary to the Department of Government Services (Review and Regulation) [2026] VCAT 51 (28 January 2026)