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新论文 · v1arXiv:2611.14901

Understanding the Effects of Multi-Agent Deliberation for Human-AI Decision-Making

Harry Yizhou Tian, Ming Yin

原文摘要

Multi-agent deliberation (MAD), where multiple LLMs engage in structured discussion, improves AI performance on many benchmarks, but whether it helps humans make better decisions remains untested. We conduct two user studies on LLM-response-verification and fact-checking. We first compare MAD against common AI decision-support methods that isolate its two defining dimensions --- multiple AIs and multi-round interaction. We find MAD outperformed single-round AI assistance on both tasks; multi-round interaction was the main driver of this benefit, while a second AI added value when the single AI had room to improve. Then, we study two design choices in MAD --- AI persona and human engagement level --- finding a devil's advocate persona to lower accuracy and user experience, while humans' active participation in MAD improved accuracy in one task at the cost of frustration. We conclude by discussing implications for when, why, and how multi-agent deliberation can support human decision-making.

cs.HC · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.13790

RiSE-Drive: Risk-Sensitive Long-Horizon Candidate Evaluation for End-to-End Autonomous Driving

Yu Zhao, Hongyi Duan, Ming Yin, Hongyuan Zhang, Fan Yang, Hongzhi Wang, Xiubo Liang

原文摘要

End-to-end autonomous driving systems increasingly rely on generating multiple trajectory candidates and selecting one for execution. In this setting, a central challenge is no longer only candidate generation, but how to evaluate candidates according to their long-horizon consequences under uncertainty. Existing methods still mainly use deterministic point scores, heuristic subscores, or single-outcome evaluations, which makes it difficult to capture delayed failures, tail risk, and ranking instability under plausible future perturbations. We present RiSE-Drive, a framework for risk-sensitive long-horizon candidate evaluation in end-to-end autonomous driving. RiSE-Drive first performs candidate-conditioned latent future rollout, then estimates long-horizon outcome distributions instead of single scalar scores, and finally ranks candidates using a tail-risk-aware and perturbation-robust decision criterion. This reformulates candidate scoring from deterministic oracle-style evaluation into distributional outcome assessment with explicit risk control. As a result, the evaluator is encouraged to prefer candidates that are not only strong under nominal conditions, but also reliable under long-horizon rollout and safety-critical deviations. Experiments on the NAVSIM-v1 and NAVSIM-v2 benchmarks, as well as the closed-loop HUGSIM benchmark, show that RiSE-Drive achieves state-of-the-art performance and significantly improves the robustness of candidate selection.

cs.RO

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.10611

Detent: Batch-Invariant LLM Serving and RL Rollouts at Near-Stock Speed

Peixu Xin, Runchuan Feng, Jiazhen Guo, Kan Wu, Ming Yin

原文摘要

Reproducible evaluation, verifiable inference and on-policy reinforcement learning (RL) need an LLM engine to give a request the same output bits whichever requests share its batch. Batch-invariant kernels exist, but engines still select each kernel and its parameters from the batch. Two kernels that compute the same sum can round it differently. The engines' deterministic modes remove the choice by keeping one conservative kernel per operator and pay for it in latency. Many fast kernels are batch-invariant by themselves, so it is enough to fix the few batch-dependent values that kernel selection reads. Detent expresses kernel selection as a program that the engine executes. It finds the fewest values in this program that must be fixed and tunes them for the workload before deployment. On multi-GPU deployments of Llama-3.1-70B and Qwen3-30B-A3B in vLLM and SGLang, Detent is batch-invariant. Tuned per load, it serves at 1.04-1.17x the latency per output token of Stock (each engine with its standard settings), against 1.67-10.3x for the deterministic modes. In SGLang it matches the GSM8K accuracy of SGLang's deterministic mode. Detent covers multiple deployment combinations, including multiple engines, GPU generations, tensor-parallel degrees, and dense and MoE models. Seeded RL rollouts of models up to Llama-3.1-70B replay bit for bit under other batches and cache splits. A trainer on Detent's kernels and values matches every rollout logprob bit for bit at 1.03x Stock's rollout time per token. BF16 GRPO then stays stable where Stock's run collapses.

cs.LG · cs.DC

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arXiv ↗PDF ↗
版本更新 · v4arXiv:2606.30097

CylindTrack: Depth-Aware Cylindrical Motion Modeling for Panoramic Multi-Object Tracking in Mobile Robots

Buyin Deng, Kai Luo, Lingxin Huang, Xinqi Liu, Fei Cheng, Hang Zheng, Liming Yin, Kailun Yang

原文摘要

Maintaining consistent target identities and observation histories is essential for mobile robot perception. Panoramic Multi-Object Tracking (MOT) benefits from broad visual coverage but must address coordinate discontinuities at the equirectangular image seam and fluctuations in relative-depth estimates. Such fluctuations can undermine depth-aware association, highlighting the need for temporally consistent depth cues. We propose CylindTrack, a depth-aware cylindrical tracking-by-detection framework that jointly models panoramic topology and temporal depth consistency. Depth-Temporal Trajectory Modeling (DTM) maintains instance depth and its rate of change as filtered trajectory states. Spherical Spatio-Temporal Consistency Learning (SSTC) combines a Temporal Mixer with Spherical Geometry-aware Attention to refine depth-aware representations. A Topology-Aware Cylindrical Motion Model (TCMM) represents horizontal motion in an unwrapped angular state and incorporates periodic overlap, angular consistency, and filtered depth cues into association. Experiments on QuadTrack and JRDB demonstrate improved identity consistency and trajectory continuity, with HOTA scores of 33.67 and 31.12 and IDF1 scores of 40.45 and 34.33, respectively. The framework processes 28.56 and 21.34 frames per second, respectively, on an NVIDIA GeForce RTX 3090 GPU under the reported evaluation settings. These results support persistent panoramic multi-object tracking for mobile robot perception. The source code will be released at https://github.com/warriordby/CylindTrack.

cs.CV · cs.RO · eess.IV

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arXiv ↗PDF ↗
首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.1556ACL Anthology · ACL 2026

UniDataBench: Evaluating Data Analytics Agents Across Structured and Unstructured Data

Han Weng, Zhou Liu, Yuanfeng Song ByteDance Inc., Xiaoming Yin ByteDance Inc., Xing Chen ByteDance, Wentao Zhang

原文摘要

In real-world business environments, data is stored in a variety of sources, including structured relational databases, semi-structured databases, and unstructured files. The ability to extract reasonable insights across these diverse sources is integral to data-driven decision-making. Existing benchmarks, however, are limited in assessing agents’ capabilities across these diverse data types. To address this gap, we introduce UniDataBench, a multi-source benchmark designed to evaluate the performance of data analytics agents in handling diverse data sources. Specifically, UniDataBench is constructed based on real-life industry analysis reports, employing a pipeline to synthesize data that aligns with authentic analytical trends. It encompasses diverse datasets spanning relational databases, CSV files, and NoSQL stores to reflect real-world business settings, and provides a unified framework for evaluating how effectively agents can explore multiple data formats, extract insights, and generate meaningful summaries and recommendations. Based on UniDataBench, we propose a novel LLM-based agent named ReActInsight, an autonomous agent that performs end-to-end analysis over diverse data sources by automatically discovering cross-source linkages, decomposing goals, and generating robust, self-correcting code to extract actionable insights. Our benchmark and agent together provide a framework for facilitating the development of data analytics agents in real-world applications.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表