Measuring AI Ability to Complete Long Tasks

Despite rapid progress on AI benchmarks, the real-world meaning of benchmark performance remains unclear. To quantify the capabilities of AI systems in terms of human capabilities, we propose a new metric: 50%-task-completion time horizon. This is the time humans typically take to complete tasks that AI models can complete with 50% success rate. We first timed humans with relevant domain expertise on a combination of RE-Bench, HCAST, and 66 novel shorter tasks. On these tasks, current frontier AI models such as Claude 3.7 Sonnet have a 50% time horizon of around 50 minutes. Furthermore, frontier AI time horizon has been doubling approximately every seven months since 2019, though the trend may have accelerated in 2024. The increase in AI models' time horizons seems to be primarily driven by greater reliability and ability to adapt to mistakes, combined with better logical reasoning and tool use capabilities. We discuss the limitations of our results -- including their degree of external validity -- and the implications of increased autonomy for dangerous capabilities. If these results generalize to real-world software tasks, extrapolation of this trend predicts that within 5 years, AI systems will be capable of automating many software tasks that currently take humans a month.

CPath-Omni: A Unified Multimodal Foundation Model for Patch and Whole Slide Image Analysis in Computational Pathology

The emergence of large multimodal models (LMMs) has brought significant advancements to pathology. Previous research has primarily focused on separately training patch-level and whole-slide image (WSI)-level models, limiting the integration of learned knowledge across patches and WSIs, and resulting in redundant models. In this work, we introduce CPath-Omni, the first 15-billion-parameter LMM designed to unify both patch and WSI level image analysis, consolidating a variety of tasks at both levels, including classification, visual question answering, captioning, and visual referring prompting. Extensive experiments demonstrate that CPath-Omni achieves state-of-the-art (SOTA) performance across seven diverse tasks on 39 out of 42 datasets, outperforming or matching task-specific models trained for individual tasks. Additionally, we develop a specialized pathology CLIP-based visual processor for CPath-Omni, CPath-CLIP, which, for the first time, integrates different vision models and incorporates a large language model as a text encoder to build a more powerful CLIP model, which achieves SOTA performance on nine zero-shot and four few-shot datasets. Our findings highlight CPath-Omni's ability to unify diverse pathology tasks, demonstrating its potential to streamline and advance the field of foundation model in pathology.

Learn How to Query from Unlabeled Data Streams in Federated Learning

Federated learning (FL) enables collaborative learning among decentralized clients while safeguarding the privacy of their local data. Existing studies on FL typically assume offline labeled data available at each client when the training starts. Nevertheless, the training data in practice often arrive at clients in a streaming fashion without ground-truth labels. Given the expensive annotation cost, it is critical to identify a subset of informative samples for labeling on clients. However, selecting samples locally while accommodating the global training objective presents a challenge unique to FL. In this work, we tackle this conundrum by framing the data querying process in FL as a collaborative decentralized decision-making problem and proposing an effective solution named LeaDQ, which leverages multi-agent reinforcement learning algorithms. In particular, under the implicit guidance from global information, LeaDQ effectively learns the local policies for distributed clients and steers them towards selecting samples that can enhance the global model's accuracy. Extensive simulations on image and text tasks show that LeaDQ advances the model performance in various FL scenarios, outperforming the benchmarking algorithms.

Generative Modeling with Explicit Memory

Recent studies indicate that the denoising process in deep generative diffusion models implicitly learns and memorizes semantic information from the data distribution. These findings suggest that capturing more complex data distributions requires larger neural networks, leading to a substantial increase in computational demands, which in turn become the primary bottleneck in both training and inference of diffusion models. To this end, we introduce \textbf{G}enerative \textbf{M}odeling with \textbf{E}xplicit \textbf{M}emory (GMem), leveraging an external memory bank in both training and sampling phases of diffusion models. This approach preserves semantic information from data distributions, reducing reliance on neural network capacity for learning and generalizing across diverse datasets. The results are significant: our GMem enhances both training, sampling efficiency, and generation quality. For instance, on ImageNet at $256 \times 256$ resolution, GMem accelerates SiT training by over $46.7\times$, achieving the performance of a SiT model trained for $7M$ steps in fewer than $150K$ steps. Compared to the most efficient existing method, REPA, GMem still offers a $16\times$ speedup, attaining an FID score of 5.75 within $250K$ steps, whereas REPA requires over $4M$ steps. Additionally, our method achieves state-of-the-art generation quality, with an FID score of {3.56} without classifier-free guidance on ImageNet $256\times256$. Our code is available at \url{https://github.com/LINs-lab/GMem}.

RE-Bench: Evaluating frontier AI R&D capabilities of language model agents against human experts

Frontier AI safety policies highlight automation of AI research and development (R&D) by AI agents as an important capability to anticipate. However, there exist few evaluations for AI R&D capabilities, and none that are highly realistic and have a direct comparison to human performance. We introduce RE-Bench (Research Engineering Benchmark, v1), which consists of 7 challenging, open-ended ML research engineering environments and data from 71 8-hour attempts by 61 distinct human experts. We confirm that our experts make progress in the environments given 8 hours, with 82% of expert attempts achieving a non-zero score and 24% matching or exceeding our strong reference solutions. We compare humans to several public frontier models through best-of-k with varying time budgets and agent designs, and find that the best AI agents achieve a score 4x higher than human experts when both are given a total time budget of 2 hours per environment. However, humans currently display better returns to increasing time budgets, narrowly exceeding the top AI agent scores given an 8-hour budget, and achieving 2x the score of the top AI agent when both are given 32 total hours (across different attempts). Qualitatively, we find that modern AI agents possess significant expertise in many ML topics -- e.g. an agent wrote a faster custom Triton kernel than any of our human experts' -- and can generate and test solutions over ten times faster than humans, at much lower cost. We open-source the evaluation environments, human expert data, analysis code and agent trajectories to facilitate future research.

Distribution-Aware Compensation Design for Sustainable Data Rights in Machine Learning

Modern distributed learning systems face a critical challenge when clients request the removal of their data influence from trained models, as this process can significantly destabilize system performance and affect remaining participants. We propose an innovative mechanism that views this challenge through the lens of game theory, establishing a leader-follower framework where a central coordinator provides strategic incentives to maintain system stability during data removal operations. Our approach quantifies the ripple effects of data removal through a comprehensive analytical model that captures both system-wide and participant-specific impacts. We establish mathematical foundations for measuring participant utility and system outcomes, revealing critical insights into how data diversity influences both individual decisions and overall system stability. The framework incorporates a computationally efficient solution method that addresses the inherent complexity of optimizing participant interactions and resource allocation.

Mind the Remaining: Mechanism Design for Robust Federated Unlearning

Federated Unlearning (FU) aims to remove target clients' influence from trained models for privacy regulations. However, due to data distribution shifts, it can introduce side effects, including global model performance degradation and uneven impacts on the remaining clients. These effects potentially cause remaining clients to deviate, threatening the system's robustness. To address these challenges, we present a novel and robust mechanism modeling a Stackelberg game for FU. In this game, the server designs an optimal payment to stimulate remaining clients to participate in FU, ensuring unlearning effectiveness and stability. In response, the remaining clients strategically determine their participation level to maximize profit, accounting for offered payments and unlearning impacts. In modeling FU outcomes, we develop, for the first time, a comprehensive framework analytically capturing FU-induced side effects for both the server and clients. Based on this, we establish utility functions for the server and clients in FU, inherently determining their dynamic strategic decision-making. Our rigorous equilibrium analysis reveals how data heterogeneity affects the side effects in their utility and decision-making. Additionally, we develop a low-complexity algorithm for the non-convex optimization problem, enabling efficient computation of the equilibrium.

MorphAgent: Empowering Agents through Self-Evolving Profiles and Decentralized Collaboration

Large Language Model (LLM) based multi-agent systems (MAS) have shown promise in tackling complex tasks, but often rely on predefined roles and centralized coordination, limiting their adaptability to evolving challenges. This paper introduces MorphAgent, a novel framework for decentralized multi-agent collaboration that enables agents to dynamically evolve their roles and capabilities. Our approach employs self-evolving agent profiles, optimized through three key metrics, guiding agents in refining their individual expertise while maintaining complementary team dynamics. MorphAgent implements a two-phase process: a warm-up phase for initial profile optimization, followed by a task execution phase where agents continuously adapt their roles based on task feedback. Our experimental results show that MorphAgent outperforms traditional static-role MAS in terms of task performance and adaptability to changing requirements, paving the way for more robust and versatile multi-agent collaborative systems. Our code will be publicly available at \url{https://github.com/LINs-lab/learn2collaborate}.

Out-of-Bounding-Box Triggers: A Stealthy Approach to Cheat Object Detectors

In recent years, the study of adversarial robustness in object detection systems, particularly those based on deep neural networks (DNNs), has become a pivotal area of research. Traditional physical attacks targeting object detectors, such as adversarial patches and texture manipulations, directly manipulate the surface of the object. While these methods are effective, their overt manipulation of objects may draw attention in real-world applications. To address this, this paper introduces a more subtle approach: an inconspicuous adversarial trigger that operates outside the bounding boxes, rendering the object undetectable to the model. We further enhance this approach by proposing the Feature Guidance (FG) technique and the Universal Auto-PGD (UAPGD) optimization strategy for crafting high-quality triggers. The effectiveness of our method is validated through extensive empirical testing, demonstrating its high performance in both digital and physical environments. The code and video will be available at: https://github.com/linToTao/Out-of-bbox-attack.

CollabEdit: Towards Non-destructive Collaborative Knowledge Editing

Collaborative learning of large language models (LLMs) has emerged as a new paradigm for utilizing private data from different parties to guarantee efficiency and privacy. Meanwhile, Knowledge Editing (KE) for LLMs has also garnered increased attention due to its ability to manipulate the behaviors of LLMs explicitly, yet leaves the collaborative KE case (in which knowledge edits of multiple parties are aggregated in a privacy-preserving and continual manner) unexamined. To this end, this manuscript dives into the first investigation of collaborative KE, in which we start by carefully identifying the unique three challenges therein, including knowledge overlap, knowledge conflict, and knowledge forgetting. We then propose a non-destructive collaborative KE framework, COLLABEDIT, which employs a novel model merging mechanism to mimic the global KE behavior while preventing the severe performance drop. Extensive experiments on two canonical datasets demonstrate the superiority of COLLABEDIT compared to other destructive baselines, and results shed light on addressing three collaborative KE challenges and future applications.