SafeAgentBench: A Benchmark for Safe Task Planning of Embodied LLM Agents

With the integration of large language models (LLMs), embodied agents have strong capabilities to execute complicated instructions in natural language, paving a way for the potential deployment of embodied robots. However, a foreseeable issue is that those embodied agents can also flawlessly execute some hazardous tasks, potentially causing damages in real world. To study this issue, we present SafeAgentBench -- a new benchmark for safety-aware task planning of embodied LLM agents. SafeAgentBench includes: (1) a new dataset with 750 tasks, covering 10 potential hazards and 3 task types; (2) SafeAgentEnv, a universal embodied environment with a low-level controller, supporting multi-agent execution with 17 high-level actions for 8 state-of-the-art baselines; and (3) reliable evaluation methods from both execution and semantic perspectives. Experimental results show that the best-performing baseline gets 69% success rate for safe tasks, but only 5% rejection rate for hazardous tasks, indicating significant safety risks. More details and codes are available at https://github.com/shengyin1224/SafeAgentBench.

ChatDyn: Language-Driven Multi-Actor Dynamics Generation in Street Scenes

Generating realistic and interactive dynamics of traffic participants according to specific instruction is critical for street scene simulation. However, there is currently a lack of a comprehensive method that generates realistic dynamics of different types of participants including vehicles and pedestrians, with different kinds of interactions between them. In this paper, we introduce ChatDyn, the first system capable of generating interactive, controllable and realistic participant dynamics in street scenes based on language instructions. To achieve precise control through complex language, ChatDyn employs a multi-LLM-agent role-playing approach, which utilizes natural language inputs to plan the trajectories and behaviors for different traffic participants. To generate realistic fine-grained dynamics based on the planning, ChatDyn designs two novel executors: the PedExecutor, a unified multi-task executor that generates realistic pedestrian dynamics under different task plannings; and the VehExecutor, a physical transition-based policy that generates physically plausible vehicle dynamics. Extensive experiments show that ChatDyn can generate realistic driving scene dynamics with multiple vehicles and pedestrians, and significantly outperforms previous methods on subtasks. Code and model will be available at https://vfishc.github.io/chatdyn.

InfiniCube: Unbounded and Controllable Dynamic 3D Driving Scene Generation with World-Guided Video Models

We present InfiniCube, a scalable method for generating unbounded dynamic 3D driving scenes with high fidelity and controllability. Previous methods for scene generation either suffer from limited scales or lack geometric and appearance consistency along generated sequences. In contrast, we leverage the recent advancements in scalable 3D representation and video models to achieve large dynamic scene generation that allows flexible controls through HD maps, vehicle bounding boxes, and text descriptions. First, we construct a map-conditioned sparse-voxel-based 3D generative model to unleash its power for unbounded voxel world generation. Then, we re-purpose a video model and ground it on the voxel world through a set of carefully designed pixel-aligned guidance buffers, synthesizing a consistent appearance. Finally, we propose a fast feed-forward approach that employs both voxel and pixel branches to lift the dynamic videos to dynamic 3D Gaussians with controllable objects. Our method can generate controllable and realistic 3D driving scenes, and extensive experiments validate the effectiveness and superiority of our model.

DiffPatch: Generating Customizable Adversarial Patches using Diffusion Model

Physical adversarial patches printed on clothing can easily allow individuals to evade person detectors. However, most existing adversarial patch generation methods prioritize attack effectiveness over stealthiness, resulting in patches that are aesthetically unpleasing. Although existing methods using generative adversarial networks or diffusion models can produce more natural-looking patches, they often struggle to balance stealthiness with attack effectiveness and lack flexibility for user customization. To address these challenges, we propose a novel diffusion-based customizable patch generation framework termed DiffPatch, specifically tailored for creating naturalistic and customizable adversarial patches. Our approach enables users to utilize a reference image as the source, rather than starting from random noise, and incorporates masks to craft naturalistic patches of various shapes, not limited to squares. To prevent the original semantics from being lost during the diffusion process, we employ Null-text inversion to map random noise samples to a single input image and generate patches through Incomplete Diffusion Optimization (IDO). Notably, while maintaining a natural appearance, our method achieves a comparable attack performance to state-of-the-art non-naturalistic patches when using similarly sized attacks. Using DiffPatch, we have created a physical adversarial T-shirt dataset, AdvPatch-1K, specifically targeting YOLOv5s. This dataset includes over a thousand images across diverse scenarios, validating the effectiveness of our attack in real-world environments. Moreover, it provides a valuable resource for future research.

Are We There Yet? Revealing the Risks of Utilizing Large Language Models in Scholarly Peer Review

Scholarly peer review is a cornerstone of scientific advancement, but the system is under strain due to increasing manuscript submissions and the labor-intensive nature of the process. Recent advancements in large language models (LLMs) have led to their integration into peer review, with promising results such as substantial overlaps between LLM- and human-generated reviews. However, the unchecked adoption of LLMs poses significant risks to the integrity of the peer review system. In this study, we comprehensively analyze the vulnerabilities of LLM-generated reviews by focusing on manipulation and inherent flaws. Our experiments show that injecting covert deliberate content into manuscripts allows authors to explicitly manipulate LLM reviews, leading to inflated ratings and reduced alignment with human reviews. In a simulation, we find that manipulating 5% of the reviews could potentially cause 12% of the papers to lose their position in the top 30% rankings. Implicit manipulation, where authors strategically highlight minor limitations in their papers, further demonstrates LLMs' susceptibility compared to human reviewers, with a 4.5 times higher consistency with disclosed limitations. Additionally, LLMs exhibit inherent flaws, such as potentially assigning higher ratings to incomplete papers compared to full papers and favoring well-known authors in single-blind review process. These findings highlight the risks of over-reliance on LLMs in peer review, underscoring that we are not yet ready for widespread adoption and emphasizing the need for robust safeguards.

One is Plenty: A Polymorphic Feature Interpreter for Immutable Heterogeneous Collaborative Perception

Collaborative perception in autonomous driving significantly enhances the perception capabilities of individual agents. Immutable heterogeneity in collaborative perception, where agents have different and fixed perception networks, presents a major challenge due to the semantic gap in their exchanged intermediate features without modifying the perception networks. Most existing methods bridge the semantic gap through interpreters. However, they either require training a new interpreter for each new agent type, limiting extensibility, or rely on a two-stage interpretation via an intermediate standardized semantic space, causing cumulative semantic loss. To achieve both extensibility in immutable heterogeneous scenarios and low-loss feature interpretation, we propose PolyInter, a polymorphic feature interpreter. It contains an extension point through which emerging new agents can seamlessly integrate by overriding only their specific prompts, which are learnable parameters intended to guide the interpretation, while reusing PolyInter's remaining parameters. By leveraging polymorphism, our design ensures that a single interpreter is sufficient to accommodate diverse agents and interpret their features into the ego agent's semantic space. Experiments conducted on the OPV2V dataset demonstrate that PolyInter improves collaborative perception precision by up to 11.1% compared to SOTA interpreters, while comparable results can be achieved by training only 1.4% of PolyInter's parameters when adapting to new agents.

V2X-R: Cooperative LiDAR-4D Radar Fusion for 3D Object Detection with Denoising Diffusion

Current Vehicle-to-Everything (V2X) systems have significantly enhanced 3D object detection using LiDAR and camera data. However, these methods suffer from performance degradation in adverse weather conditions. The weatherrobust 4D radar provides Doppler and additional geometric information, raising the possibility of addressing this challenge. To this end, we present V2X-R, the first simulated V2X dataset incorporating LiDAR, camera, and 4D radar. V2X-R contains 12,079 scenarios with 37,727 frames of LiDAR and 4D radar point clouds, 150,908 images, and 170,859 annotated 3D vehicle bounding boxes. Subsequently, we propose a novel cooperative LiDAR-4D radar fusion pipeline for 3D object detection and implement it with various fusion strategies. To achieve weather-robust detection, we additionally propose a Multi-modal Denoising Diffusion (MDD) module in our fusion pipeline. MDD utilizes weather-robust 4D radar feature as a condition to prompt the diffusion model to denoise noisy LiDAR features. Experiments show that our LiDAR-4D radar fusion pipeline demonstrates superior performance in the V2X-R dataset. Over and above this, our MDD module further improved the performance of basic fusion model by up to 5.73%/6.70% in foggy/snowy conditions with barely disrupting normal performance. The dataset and code will be publicly available at: https://github.com/ylwhxht/V2X-R.

Self-Evolving Multi-Agent Collaboration Networks for Software Development

LLM-driven multi-agent collaboration (MAC) systems have demonstrated impressive capabilities in automatic software development at the function level. However, their heavy reliance on human design limits their adaptability to the diverse demands of real-world software development. To address this limitation, we introduce EvoMAC, a novel self-evolving paradigm for MAC networks. Inspired by traditional neural network training, EvoMAC obtains text-based environmental feedback by verifying the MAC network's output against a target proxy and leverages a novel textual backpropagation to update the network. To extend coding capabilities beyond function-level tasks to more challenging software-level development, we further propose rSDE-Bench, a requirement-oriented software development benchmark, which features complex and diverse software requirements along with automatic evaluation of requirement correctness. Our experiments show that: i) The automatic requirement-aware evaluation in rSDE-Bench closely aligns with human evaluations, validating its reliability as a software-level coding benchmark. ii) EvoMAC outperforms previous SOTA methods on both the software-level rSDE-Bench and the function-level HumanEval benchmarks, reflecting its superior coding capabilities. The benchmark can be downloaded at https://yuzhu-cai.github.io/rSDE-Bench/.

Synthesizing Post-Training Data for LLMs through Multi-Agent Simulation

Post-training is essential for enabling large language models (LLMs) to follow human instructions. Inspired by the recent success of using LLMs to simulate human society, we leverage multi-agent simulation to automatically generate diverse text-based scenarios, capturing a wide range of real-world human needs. We propose MATRIX, a multi-agent simulator that creates realistic and scalable scenarios. Leveraging these outputs, we introduce a novel scenario-driven instruction generator MATRIX-Gen for controllable and highly realistic data synthesis. Extensive experiments demonstrate that our framework effectively generates both general and domain-specific data. Notably, on AlpacaEval 2 and Arena-Hard benchmarks, Llama-3-8B-Base, post-trained on datasets synthesized by MATRIX-Gen with just 20K instruction-response pairs, outperforms Meta's Llama-3-8B-Instruct model, which was trained on over 10M pairs; see our project at https://github.com/ShuoTang123/MATRIX-Gen.

Data Quality Control in Federated Instruction-tuning of Large Language Models

By leveraging massively distributed data, federated learning (FL) enables collaborative instruction tuning of large language models (LLMs) in a privacy-preserving way. While FL effectively expands the data quantity, the issue of data quality remains under-explored in the current literature on FL for LLMs. To address this gap, we propose a new framework of federated instruction tuning of LLMs with data quality control (FedDQC), which measures data quality to facilitate the subsequent filtering and hierarchical training processes. Our approach introduces an efficient metric to assess each client's instruction-response alignment (IRA), identifying potentially noisy data through single-shot inference. Low-IRA samples are potentially noisy and filtered to mitigate their negative impacts. To further utilize this IRA value, we propose a quality-aware hierarchical training paradigm, where LLM is progressively fine-tuned from high-IRA to low-IRA data, mirroring the easy-to-hard learning process. We conduct extensive experiments on 4 synthetic and a real-world dataset, and compare our method with baselines adapted from centralized setting. Results show that our method consistently and significantly improves the performance of LLMs trained on mix-quality data in FL.