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AI agents

Tool use, function calling, orchestration and the protocols that let models act rather than only answer.

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arXiv Machine Learning
Sep 1

QueryGraph: Reliable Multi-Tool Query Execution Planning via LLM-Based Graph Generation

QueryGraph is a system that transforms natural language queries into structured graphs for reliable multi-tool execution. It employs a deterministic planner that uses depth-first search to resolve tool dependencies and combine results, improving reliability over traditional keyword searches. The approach works well even with smaller or locally hosted large language models, achieving high accuracy in multi-step, cross-tool queries.

By Aishwarya Chakravarthy, Vidhi Kulkarni, Duen Horng Chau
llmsagents
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arXiv Machine Learning
Sep 1

Uncertainty-Driven Replay Memory for Reinforcement Learning

The paper introduces Uncertainty-Driven Replay Memory (UDRM), a new experience replay buffer for reinforcement learning that prioritizes storing transitions with high uncertainty estimates. Unlike traditional buffers that rely on temporal difference error or transition distributions, UDRM updates its contents based on uncertainty derived from the RL model during training. Experiments show that this uncertainty-aware buffer leads to higher rewards during training compared to other uncertainty-aware RL frameworks.

By Sheeraja Rajakrishnan, Alexander G. Ororbia, Travis Desell, Daniel E. Krutz
agentsreinforcement-learning
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arXiv AI
Sep 1

Adversarial Trust Poisoning in Vehicular Collaborative Perception

The paper introduces TrustFlip, an attack that exploits consistency‑based defenses in vehicular collaborative perception by deploying physical adversarial objects to create inconsistent observations among benign vehicles. This misattribution lowers the trust score of a targeted vehicle, leading to its exclusion from the collaboration and a degradation of perception performance. The authors evaluate the attack across multiple architectures, showing it can remove a benign vehicle in up to 87.7% of scenarios and reduce Average Precision by up to 13%, and propose a mitigation called TrustReflect that reduces the attack success rate by 35–100%.

By Yutong Liu, Chenyi Wang, Ming F. Li, Qingzhao Zhang
agentsbenchmarkssafety
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arXiv AI
Sep 1

Unified-MAS: Universally Generating Domain-Specific Nodes for Empowering Automatic Multi-Agent Systems

Unified-MAS is a two-stage framework that decouples node implementation from orchestration in Automatic Multi-Agent Systems. It first searches external knowledge to synthesize domain‑specific node blueprints, then uses a perplexity‑guided reward to optimize bottleneck nodes. Experiments across four specialized domains show that adding Unified-MAS to existing baselines improves performance‑cost trade‑offs by up to 14.2% while lowering costs.

By Hehai Lin, Yu Yan, Zixuan Wang, Bo Xu, Sudong Wang, Weiquan Huang, Ruochen Zhao, Minzhi Li, Chengwei Qin
llmsagents
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arXiv Computer Vision
Sep 1

HybridWorldSim: A Scalable and Controllable High-fidelity Simulator for Autonomous Driving

arXiv:2511.22187v4 Announce Type: replace Abstract: Realistic and controllable simulation is critical for advancing end-to-end autonomous driving, yet existing approaches often struggle to support no...

By Qiang Li, Yingwenqi Jiang, Tuoxi Li, Duyu Chen, Xiang Feng, Yucheng Ao, Shangyue Liu, Xingchen Yu, Youcheng Cai, Yumeng Liu, Yuexin Ma, Xin Hu, Li Liu, Yu Zhang, Linkun Xu, Bingtao Gao, Xueyuan Wang, Shuchang Zhou, Xianming Liu, Ligang Liu
agentsroboticsbenchmarks
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arXiv Machine Learning
Sep 1

Delta-AI: Local objectives for amortized inference in sparse graphical models

arXiv:2310.02423v3 Announce Type: replace Abstract: We present a new algorithm for amortized inference in sparse probabilistic graphical models (PGMs), which we call $\Delta$-amortized inference ($\D...

By Jean-Pierre Falet, Hae Beom Lee, Esmeralda S. Whitammer, Chen Sun, Dragos Secrieru, Thomas Jiralerspong, Dinghuai Zhang, Guillaume Lajoie, Yoshua Bengio
agentsefficiency
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arXiv Machine Learning
Sep 1

Driving on Memory

arXiv:2608.31029v1 Announce Type: cross Abstract: End-to-end autonomous driving models plan future trajectories from raw sensor input. While earlier driving benchmarks often measured deviation from t...

By Christian L\"owens, Thorben Funke, Alexandru Paul Condurache
agentsroboticsbenchmarks
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