arXiv:2604.14161v1 Announce Type: new Abstract: Reliable evaluation is essential in machine learning research, yet methodological flaws-particularly data leakage-continue to undermine the validity of reported results. In this work, we investigate whether large language models (LLMs) can act as independent analytical agents capable of identifying such issues in published studies. As a case study, we analyze a gesture-recognition paper reporting near-perfect accuracy on a small, human-centered dataset. We first show that the evaluation protocol is consistent with subject-level data […]

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Fully observable Matsuzawa puzzles are grid worlds where an agent must pick up coins in a particular order, travel down a long hallway, then pick up coins in order again. The secondary chamber has the coins in exactly the locations in which they occurred in the primary. https://i.imgur.com/5nvi0oe.png coins must be picked up in the order of their face number. coins in the secondary chamber are pickable only when there are no coins remaining in the primary. reward […]

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arXiv:2602.05000v1 Announce Type: new Abstract: Reward guidance has been applied to great success in the test-time adaptation of continuous diffusion models; it updates each denoising step using the gradients from a downstream reward model. We study reward guidance for discrete diffusion language models, where one cannot differentiate through the natural outputs of the model because they are discrete tokens. Existing approaches either replace these discrete tokens with continuous relaxations, or employ techniques like the straight-through estimator. In this […]

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Deploying a deep learning model into production has always involved a painful gap between the model a researcher trains and the model that actually runs efficiently at scale. TensorRT exists, Torch-TensorRT exists, TorchAO exists — but wiring them together, deciding which backend to use for which layer, and validating that the tuned model still produces correct outputs has historically meant substantial custom engineering work. NVIDIA AI team is now open-sourcing a toolkit designed to collapse that effort into […]

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arXiv:2602.02605v1 Announce Type: new Abstract: Metacognition is a critical component of intelligence, specifically regarding the awareness of one’s own knowledge. While humans rely on shared internal memory for both answering questions and reporting their knowledge state, this dependency in LLMs remains underexplored. This study proposes a framework to measure metacognitive ability $d_{rm{type2}}’$ using a dual-prompt method, followed by the introduction of Evolution Strategy for Metacognitive Alignment (ESMA) to bind a model’s internal knowledge to its explicit behaviors. ESMA […]

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Deep reinforcement learning (RL) has been applied extensively to solve complex decision-making problems. In many real-world scenarios, tasks often have several conflicting objectives and may require multiple agents to cooperate, which are the multi-objective multi-agent decision-making problems. However, only few works have been conducted on this intersection. Existing approaches are limited to separate fields and can only handle multi-agent decision-making with a single objective, or multi-objective decision-making with a single agent. In this paper, we propose MO-MIX to […]

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