Race on to establish globally recognised'AI-free' logo Organisations worldwide are racing to develop a universally recognised label for human-made products and services as part of the growing backlash against AI use. Declarations like Proudly Human, Human-made, 'No A.I and AI-free are appearing across films, marketing, books and websites. It is in response to fears that jobs or entire professions are being swept away in a wave of AI-powered automation. BBC News has counted at least eight different initiatives trying to come up with a label that could get the kind of global recognition that the Fair Trade logo has for ethically made products. But with so many competing labels - as well as confusion over the definition of AI-free - experts say consumers are in danger of being left confused unless a single standard can be agreed on.

Time series foundation models (TSFMs) pretrained on data from multiple domains have shown strong performance on diverse modeling tasks. Various efforts have been made to develop foundation models specific to electroencephalography (EEG) data, which records brain electrical activity as time series. However, no comparative analysis of EEG-specific foundation models (EEGFMs) versus general TSFMs has been performed on EEG-specific tasks. We introduce a novel Spatial-Temporal Adapter with Multi-Head Pooling (STAMP), which leverages univariate embeddings produced by a general TSFM, implicitly models spatial-temporal characteristics of EEG data, and achieves performance comparable to state-of-the-art EEGFMs. A comprehensive analysis is performed on 8 benchmark datasets of clinical tasks using EEG for classification, along with ablation studies. Our proposed adapter is lightweight in trainable parameters and flexible in the inputs it can accommodate, supporting easy modeling of EEG data using TSFMs.

Quantization is the key method for reducing inference latency, power and memory footprint of generative AI models. However, accuracy often degrades sharply when activations are quantized below eight bits. Recent work suggests that invertible linear transformations (e.g. rotations) can aid quantization, by reparameterizing feature channels and weights. In this paper, we propose \textit{Sequence Transformation and Mixed Precision} (STaMP) quantization, a novel strategy that applies linear transformations along the \textit{sequence} dimension to exploit the strong local correlation in language and visual data. By keeping a small number of tokens in each intermediate activation at higher precision, we can maintain model accuracy at lower (average) activations bit-widths. We evaluate STaMP on recent LVM and LLM architectures, demonstrating that it significantly improves low bit width activation quantization and complements established activation and weight quantization methods including recent feature transformations.

Australia has announced plans to ban apps used for stalking and creating deepfake nudes. Tech platforms will be responsible for preventing access to "nudify" and undetectable online stalking tools under the reforms announced on Tuesday by the Australian government. Minister for Communications Anika Wells said Australia would work with firms to stamp out "abhorrent technologies" while ensuring "legitimate and consent-based" artificial intelligence (AI) and online tracking services were not adversely affected. "Abusive technologies are widely and easily accessible and are causing real and irreparable damage now," Wells said in a statement. "These new, evolving, technologies require a new, proactive, approach to harm prevention – and we'll work closely with industry to achieve this." "While this move won't eliminate the problem of abusive technology in one fell swoop, alongside existing laws and our world-leading online safety reforms, it will make a real difference in protecting Australians," she added.

Given how large parts of publicly available text are crawled to pretrain large language models (LLMs), data creators increasingly worry about the inclusion of their proprietary data for model training without attribution or licensing. Their concerns are also shared by benchmark curators whose test-sets might be compromised. In this paper, we present STAMP, a framework for detecting dataset membership-i.e., determining the inclusion of a dataset in the pretraining corpora of LLMs. Given an original piece of content, our proposal involves first generating multiple rephrases, each embedding a watermark with a unique secret key. One version is to be released publicly, while others are to be kept private. Subsequently, creators can compare model likelihoods between public and private versions using paired statistical tests to prove membership. We show that our framework can successfully detect contamination across four benchmarks which appear only once in the training data and constitute less than 0.001% of the total tokens, outperforming several contamination detection and dataset inference baselines. We verify that STAMP preserves both the semantic meaning and utility of the original data. We apply STAMP to two real-world scenarios to confirm the inclusion of paper abstracts and blog articles in the pretraining corpora.

A BSTRACT Large language models (LLMs) have demonstrated strong capabilities in programming and mathematical reasoning tasks, but are constrained by limited high-quality training data. Synthetic data can be leveraged to enhance fine-tuning outcomes, but several factors influence this process, including model size, synthetic data volume, pruning strategy, and number of fine-tuning rounds. We explore these axes and investigate which conditions enable model self-improvement. We introduce the Think, Prune, Train process, a scalable framework that iteratively fine-tunes models on their own reasoning traces, using ground-truth pruning to ensure high-quality training data. This approach yields improved performance: on GSM8K, Gemma2-2B achieves a Pass@1 of 57.6% (from 41.9%), Gemma2-9B reaches 82%, matching LLaMA-3.1-70B, and LLaMA-3.1-70B One promising approach is leveraging curated synthetic data to improve reasoning, an essential part of advancing code generation and mathematical problem-solving. Recent frontier models like LLaMA 3.1 Dubey et al. (2024) and DeepSeek R1 DeepSeek AI Team (2024) demonstrate that post-training on reasoning traces coupled with supervised fine-tuning (SFT) on filtered (pruned) data works well to improve models. Their strong performance on coding and math benchmarks highlights how properly curated synthetic data can drive substantial performance gains. For smaller models such as LLaMA (1B, 3B) and Gemma (2B) (9B) Team et al. (2024b), distillation Hinton et al. (2015) coupled with fine-tuning on reasoning trace datasets has become the dominant post-training paradigm.

Figure 1: RHINO has the capabilities of real-time interaction on diverse tasks. Abstract--Humanoid robots have shown success in locomotion its effectiveness, flexibility, and safety in various scenarios. We summarize related works in each tasks in real-time. Some others focus on recognizing human category and highlight the differences between our work. The robot cannot be interrupted once a task Humanoid robots need to estimate the human physical and is in progress, and further human commands can only be mental states to provide appropriate assistance [35]. Object information in the complexity of human interactions [33, 37], but they often the environment also plays an important role in predicting suffer from high latency and are not suitable for real-time human intention by combining it with human motion. These limitations hinder robots from rapid interaction, such as pointing gestures [14] and grabbing interventions and robust, multi-step interactions in humancentered objects [24], provides a broader semantic space for human tasks. Most works on human intention recognition treat human-robot interaction with real-time intention recognition the interaction as a two-stage process, where the robot first and various skills is urgently needed to tackle the above predicts the human intention and then executes the task. Our work aims to react learning framework for Reactive Humanoid-human to human signals in real time, enabling the downstream tasks INteraction and Object Manipulation. RHINO decouples the to be interrupted at any time. In human-robot interaction and object manipulation skills based on predicted intentions. To ensure unique opportunity to learn natural motion from retargeted the scalability of RHINO across a wide range of skills, we human motion data [15]. Human motion data can be collected design a pipeline for learning the interactions from humanobject-human from motion capture systems or network videos.

Perception is a crucial component of autonomous driving systems. However, single-agent setups often face limitations due to sensor constraints, especially under challenging conditions like severe occlusion, adverse weather, and long-range object detection. Multi-agent collaborative perception (CP) offers a promising solution that enables communication and information sharing between connected vehicles. Yet, the heterogeneity among agents--in terms of sensors, models, and tasks--significantly hinders effective and efficient cross-agent collaboration. To address these challenges, we propose STAMP, a scalable task-and model-agnostic collaborative perception framework tailored for heterogeneous agents. STAMP utilizes lightweight adapter-reverter pairs to transform Bird's Eye View (BEV) features between agent-specific domains and a shared protocol domain, facilitating efficient feature sharing and fusion while minimizing computational overhead. Moreover, our approach enhances scalability, preserves model security, and accommodates a diverse range of agents. Extensive experiments on both simulated (OPV2V) and real-world (V2V4Real) datasets demonstrate that STAMP achieves comparable or superior accuracy to state-of-the-art models with significantly reduced computational costs. As the first-of-its-kind task-and model-agnostic collaborative perception framework, STAMP aims to advance research in scalable and secure mobility systems, bringing us closer to Level 5 autonomy. Our project page is at https://xiangbogaobarry.github.io/STAMP Multi-agent collaborative perception (CP) (Bai et al., 2022b; Han et al., 2023; Liu et al., 2023) has emerged as a promising solution for autonomous systems by leveraging communication among multiple connected and automated agents. It enables agents--such as vehicles, infrastructure, or even pedestrians--to share sensory and perceptual information, providing a more comprehensive view of the surrounding environment to enhance overall perception capabilities. Despite its potential, CP faces significant challenges, particularly when dealing with heterogeneous agents that defer in input modalities, model parameters, architectures, or learning objectives.

Test-time adaptation (TTA) aims to address the distribution shift between the training and test data with only unlabeled data at test time. Existing TTA methods often focus on improving recognition performance specifically for test data associated with classes in the training set. However, during the open-world inference process, there are inevitably test data instances from unknown classes, commonly referred to as outliers. This paper pays attention to the problem that conducts both sample recognition and outlier rejection during inference while outliers exist. To address this problem, we propose a new approach called STAble Memory rePlay (STAMP), which performs optimization over a stable memory bank instead of the risky mini-batch. In particular, the memory bank is dynamically updated by selecting low-entropy and label-consistent samples in a class-balanced manner. In addition, we develop a self-weighted entropy minimization strategy that assigns higher weight to low-entropy samples. Extensive results demonstrate that STAMP outperforms existing TTA methods in terms of both recognition and outlier detection performance. The code is released at https://github.com/yuyongcan/STAMP.

Numerous recent techniques for text style transfer characterize their approaches as variants of reinforcement learning and preference optimization. In this work, we consider the relationship between these approaches and a class of optimization approaches developed primarily for (non-neural) statistical machine translation, formerly known as 'tuning'. Inspired by these techniques from the past, we improve upon established preference optimization approaches, incorporating multiple iterations of exploration and optimization, and choosing contrastive examples by following a 'hope' vs 'fear' sampling strategy. Cognizant of the difference between machine translation and style transfer, however, we further tailor our framework with a new pseudo-parallel generation method and a dynamic weighted reward aggregation method to tackle the lack of parallel data and the need for a multi-objective reward. We evaluate our model on two commonly used text style transfer datasets. Through automatic and human evaluation results we show the effectiveness and the superiority of our model compared to state-of-the-art baselines.