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

Jul 13

Multimodal Scenario Similarity Search for Autonomous Driving

arXiv:2607. 09428v1 Announce Type: cross Abstract: Large-scale autonomous-driving datasets contain vast numbers of recorded scenarios, creating a need for efficient retrieval methods that can identify situations similar to a given query.

By Tam\'as Matuszka, Andr\'as Tam\'asy, Bal\'azs Szol\'ar

llms rag agents robotics multimodal

arXiv AI

Jul 13

Beyond Black-Box Obfuscation: Mechanistic Analysis and Defense of White-Box Monitors

arXiv:2505. 14300v2 Announce Type: replace Abstract: White-box monitoring is increasingly adopted as an auditing tool as Large Language Models (LLMs) are deployed in daily operations to ensure safe model behavior.

By Maheep Chaudhary, Fazl Barez

llms agents robotics benchmarks

arXiv AI

Jul 13

Single-Frame Point-Pixel Registration via Supervised Cross-Modal Feature Matching

arXiv:2506. 22784v2 Announce Type: replace-cross Abstract: Point-pixel registration between LiDAR point clouds and camera images is a fundamental yet challenging task in autonomous driving and robotic perception.

By Yu Han, Zhiwei Huang, Yanting Zhang, Fangjun Ding, Shen Cai, Xiaoyu Tang, Yanchao Dong, Rui Fan

agents robotics efficiency multimodal benchmarks

arXiv AI

Jul 13

4DR360: State Reasoning for Joint 3D Detection and Occupancy Prediction in 4D Radar-Camera Full-Scene Perception

arXiv:2607. 09629v1 Announce Type: cross Abstract: Reliable autonomous driving requires full-scene perception that couples foreground objects with dense semantic layout.

By Xiaokai Bai, Lianqing Zheng, Runwei Guan, Songkai Wang, Siyuan Cao, Hui-liang Shen

agents robotics multimodal benchmarks

arXiv AI

Jul 13

ECHO: Prune To Act, Trace To Learn With Selective Turn Memory In Agentic RL

arXiv:2606. 31650v2 Announce Type: replace-cross Abstract: Long-horizon language agents must repeatedly interact with tools, accumulate evidence, and make decisions under bounded context windows.

By Zijun Xie, Binbin Zheng, Enlei Gong, Jihua Liu, Yuyang You, Lingfeng Liu, Jiayao Tang, Guanqun Zhao, Aoqi Hu, Zeyu Chen

agents reinforcement-learning benchmarks

arXiv AI

Jul 13

Beyond Embeddings: Interpretable Feature Extraction for Binary Code Similarity

arXiv:2509. 23449v2 Announce Type: replace Abstract: Binary code similarity detection is a core task in reverse engineering.

By Charles E. Gagnon, Steven H. H. Ding, Philippe Charland, Benjamin C. M. Fung

llms rag agents benchmarks safety

arXiv AI

Jul 13

KV-PRM: Efficient Process Reward Modeling via KV-Cache Transfer for Multi-Agent Test-Time Scaling

arXiv:2607. 09153v1 Announce Type: new Abstract: Process Reward Models (PRMs) have been proven to be highly effective in guiding test-time scaling (TTS) methods, which significantly boost the capabilities of LLM-based multi-agent systems.

By Peng Kuang, Haibo Jin, Xiaoyu Han, Yanli Wang, Xiaopeng Yuan, Ye Yu, Kaidi Xu, Haohan Wang

llms agents reinforcement-learning efficiency benchmarks

arXiv AI

Jul 13

Embodied Multi-Agent Coordination by Aligning World Models Through Dialogue

arXiv:2605. 12920v3 Announce Type: replace-cross Abstract: Effective collaboration between embodied agents requires more than acting in a shared environment; it demands communication grounded in each agent's evolving understanding of the world.

By Vardhan Dongre, Dilek Hakkani-T\"ur

llms agents robotics benchmarks safety

arXiv AI

Jul 13

LongMedBench: Benchmarking Medical Agents for Long-Horizon Clinical Decision-Making

arXiv:2607. 09322v1 Announce Type: new Abstract: In this work, we introduce LongMedBench, a real-world EHR-based benchmark for long-horizon clinical decision-making.

By Yanzhen Chen, Zihan Xu, Xiaocheng Zhang, Zhiting Fan, Weiqi Zhai, Hongxia Xu, Zuozhu Liu

llms rag agents benchmarks

arXiv AI

Jul 13

Improving Language Agents through BREW: Bootstrapping expeRientially-learned Environmental knoWledge

arXiv:2511. 20297v2 Announce Type: replace Abstract: Large Language Model (LLM)-based agents are increasingly capable of complex, multi-step tasks such as GUI automation, tool use, and data manipulation, yet they cannot learn from experience: each new session rediscovers solutions from scratch.

By Shashank Kirtania, Param Biyani, Priyanshu Gupta, Yasharth Bajpai, Roshni Iyer, Sumit Gulwani, Gustavo Soares

llms rag agents robotics benchmarks

arXiv AI

Jul 13

Fictional Worldbuilding: Multi-Agent LLM Collaboration with Hierarchical Context Compression and Iterative Review

arXiv:2607. 09403v1 Announce Type: new Abstract: Worldbuilding, the construction of coherent fictional worlds, is a foundational task in game design and literary creation.

By Jingbo Chen, He Wang, Wei Yuan, Yuqiao Lai, Zhenyan Lu

llms agents

arXiv AI

Jul 13

Preference Conditioned Multi-Objective Reinforcement Learning: Decomposed, Diversity-Driven Policy Optimization

arXiv:2602. 07764v2 Announce Type: replace-cross Abstract: Multi-objective reinforcement learning (MORL) seeks to train agents capable of balancing conflicting objectives.

By Tanmay Ambadkar, Sourav Panda, Shreyash Kale, Jonathan Dodge, Abhinav Verma

agents reinforcement-learning benchmarks

arXiv AI

Jul 13

SLIDERS: Systematic Reviews via Automated Evidence Synthesis and Reconciliation

arXiv:2604. 22294v2 Announce Type: replace-cross Abstract: Systematic reviews -- which requires comprehensive evidence collection and synthesis from large document corpora in response to targeted research questions -- are foundational in finance, social sciences, and other technical fields.

By Harshit Joshi, Priyank Shethia, Jadelynn Dao, Monica S. Lam

llms rag agents benchmarks

arXiv AI

Jul 13

Evolutionary Intelligence for Scientific Discovery: From Evolutionary Computation to Cumulative Discovery Systems

arXiv:2607. 09025v1 Announce Type: cross Abstract: Artificial intelligence (AI) is shifting scientific discovery from task-specific workflows towards autonomous systems that organize exploration with experimental and human feedback in open-ended candidate spaces.

By Chao Wang, Lingling Li, Fang Liu, Licheng Jiao

agents

arXiv AI

Jul 13

Neuro-Agentic Control: A Deep Learning-based LLM-Powered Agentic AI Framework for Controlling Security Controls

arXiv:2607. 09076v1 Announce Type: new Abstract: Cyberattacks on operational technology are increasingly causing costly downtime and physical damage, exposing the limitations of traditional rule-based monitoring in industrial IoT environments.

By Saroj Gopali, Bipin Chhetri, Deepika Giri, Sima Siami-Namini, Akbar Siami Namin

llms agents diffusion safety

arXiv AI

Jul 13

ARCANA: A Reflective Multi-Agent Program Synthesis Framework for ARC-AGI-2 Reasoning

arXiv:2607. 09059v1 Announce Type: new Abstract: We present ARCANA, a collaborative multi agent framework for solving ARC AGI 2 tasks under strict test time and hardware constraints.

By Kunbo Zhang, Lei Fu, Zeyu Wang, Zijing Liu, Kejian Tong

agents

arXiv AI

Jul 13

Towards Shutdownable Agents: Generalizing Stochastic Choice in RL Agents and LLMs

arXiv:2604. 17502v4 Announce Type: replace Abstract: Misaligned artificial agents might resist shutdown.

By Carissa Cullen, Harry Garland, Alexander Roman, Louis Thomson, Christos Ziakas, Elliott Thornley

llms agents reinforcement-learning fine-tuning

arXiv AI

Jul 13

Task-Specific Multimodal Question Answering Agents via Confidence Calibration and Incremental Reasoning for QANTA 2026

arXiv:2607. 09623v1 Announce Type: cross Abstract: We present our submission to the QANTA 2026 shared challenge at the ICML 2026 Workshop on Efficient Multimodal Question Answering (EMM-QA).

By Nirjhar Das, Md. Al-Mamun Provath

llms agents nlp multimodal benchmarks

arXiv AI

Jul 13

MultiView-Bench: A Diagnostic Benchmark for World-Centric Multi-View Integration in VLMs

arXiv:2607. 08970v1 Announce Type: cross Abstract: Recent benchmarks for VLMs largely assess single- or limited-view perception, leaving untested the core cognitive ability to integrate observations across viewpoints into a coherent, world-centric (allocentric) 3D mental model.

By Hantao Zhang, Jinru Sui, Ed Li, Dirk Bergemann, Zhuoran Yang

agents benchmarks

arXiv AI

Jul 13

QAgent: An LLM-based Multi-Agent System for Autonomous OpenQASM programming

arXiv:2508. 20134v2 Announce Type: replace Abstract: Programming quantum circuits at the OpenQASM level is essential for achieving hardware-aware optimization and reliable execution on noisy intermediate-scale quantum (NISQ) devices, yet it remains challenging due to the need for domain-specific planning, iterative code synthesis, and low-level calibration.

By Zhenxiao Fu, Lei Jiang, Yilun Xu, Gang Huang, Fan Chen

llms rag agents

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