Position: The Pre/Post-Training Boundary Should Govern IP in Industry–Academia ML Collaborations

Instance-Optimal Estimation with Multiple LLM Judges on a Budget

This paper addresses the cost-efficient evaluation of large language models (LLMs) by utilizing multiple AI "judges" with different price points and reliability levels. The researchers formalize this challenge as budgeted heteroskedastic multi-judge estimation, seeking an optimal way to distribute a limited budget across various judges and tasks to achieve the most accurate quality scores. They introduce EST-IVWE, an adaptive algorithm that learns the unknown variances of different judges and assigns resources to those providing the best cost-to-variance trade-off. Through rigorous proofs, the authors demonstrate that their approach is instance-optimal, meaning it achieves the best possible accuracy for any specific set of judges and prompts. Furthermore, the paper provides a theoretical breakthrough by showing that specialized mathematical arguments are required to capture the true geometric structure of this allocation problem. Numerical experiments on synthetic and real-world datasets confirm that this adaptive strategy significantly outperforms simple uniform budgeting.

Robust AI Personalization Will Require a Human Context Protocol

This paper proposes the Human Context Protocol (HCP), a technical framework designed to give individuals direct control over how their personal preferences shape AI interactions. Currently, AI personalization relies on fragmented data silos and behavioral inferences that often fail to reflect a user’s true intent or values. By establishing a user-owned preference layer, the protocol allows people to securely store and share specific subsets of their data across different AI services using natural language. This architecture aims to reduce provider lock-in and ensure that artificial intelligence remains aligned with diverse human perspectives. Ultimately, the authors argue that such a system is a legal and ethical necessity for fostering a competitive, transparent, and truly personalized digital ecosystem.

Equilibrium Reasoners: Learning Attractors Enables Scalable Reasoning

This paper introduces Equilibrium Reasoners (EqR), a novel framework that conceptualizes iterative AI reasoning as a dynamical system converging toward stable latent attractors. By treating the reasoning process as a series of repeated updates to an internal state, the researchers demonstrate that models can scale performance at test-time by simply increasing the number of iterations (depth) or using multiple random starts (breadth). This approach allows a model trained on only 16 iterations to generalize to over 1,000 steps during inference, effectively unrolling the equivalent of 40,000 neural layers. This "attractor perspective" ensures that as the system reaches a mathematical equilibrium, it simultaneously settles on a correct task solution, resulting in near-perfect accuracy on complex benchmarks like Sudoku-Extreme and Maze-Unique. Ultimately, the research proves that aligning a model's internal landscape with task-specific goals enables adaptive computation, where harder problems receive more processing power to reach a valid conclusion.

Position: The Pre/Post-Training Boundary Should Govern IP in Industry–Academia ML Collaborations

This paper proposes a new contractual framework called PBOS to resolve persistent intellectual property conflicts in industry-academia machine learning collaborations. By involving scientists in legal negotiations, the authors suggest a clear division based on the pre/post-training boundary of a model. Under this model, pre-training artifacts such as code and architectures are treated as open science, while post-training weights derived from proprietary data remain protected corporate assets. This approach ensures researchers can fulfill academic publication requirements without compromising a company's competitive advantage. Ultimately, the framework aims to reduce the high transaction costs and legal delays that currently prevent many valuable large-scale research partnerships.

MEMO: Memory as a Model

MEMO (Memory as a Model), a modular framework designed to integrate new, domain-specific knowledge into Large Language Models (LLMs) without the need for expensive retraining. By encoding information into a dedicated, smaller MEMORY model while keeping the primary EXECUTIVE model frozen, the system avoids catastrophic forgetting and remains compatible with proprietary, closed-source models. The process involves a five-step data synthesis pipeline that converts raw documents into a structured question-answer dataset of "reflections" that capture complex, cross-document relationships. At inference, the EXECUTIVE model retrieves information through a structured multi-turn protocol, decomposing difficult queries into targeted sub-questions. Empirical results across multiple benchmarks demonstrate that MEMO is more robust to retrieval noise than standard methods and achieves superior performance by leveraging internalized parametric knowledge. Furthermore, the framework supports continual knowledge integration through model merging, allowing new data to be added efficiently while maintaining a retrieval cost that is independent of the overall corpus size.