Tactics

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2 papers in the last four weeks, down 33% on the four weeks before. 0.0% of all new papers.

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Latest papers 16

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TACTIC: Temporal and Context-Aware LLM Tactical Planning for Roadside LiDAR Attacks
Sep 30, 2026Yiming Gao, Shaocheng LuoTwo-Dimensional Light Detection And Ranging Autonomous Driving Perception
Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI, USA. · Department of Electrical and Computer Engineering, Duke University, Durham, NC, USA.
OverForge: Reasoning Through Strategies and Tactics Helps Cooperative Lifelong Adaptation
Sep 30, 2026Oana Madalina Fron, Ojas Shirekar, Chirag RamanLarge Language Model Agents Tactics
Tapri Lab, Department of Pattern Recognition and Bioinformatics, Delft University of Technology, Delft, The Netherlands
SambaGraph: Action-Reaction Spatio-Temporal Graphs for Soccer Tactical Response Modeling
Sep 22, 2026Abel A. Reyes-Angulo, Henry O. Velesaca, Steven AraujoTactics
Michigan Technological University, Houghton, MI, USA · SambaSports AI, Guayaquil, Ecuador · ESPOL Polytechnic University, Guayaquil, Ecuador +1
DRG-MAPPO: Hierarchical Dynamic Role-Graph Multi-Agent Reinforcement Learning for Cooperative Air Combat
Sep 12, 2026Junlin Liu, Chengwei Li, Yang Gao +4Multi-Agent Reinforcement Learning Cooperative Air Combat
Institute of Automation, Chinese Academy of Sciences · The Key Laboratory of Cognition and Decision Intelligence for Complex Systems, CASIA · School of Artificial Intelligence, University of Chinese Academy of Sciences
TennisVAR: A Stroke-Evidence-Grounded Multimodal Large Language Model for Tactical Reasoning in Tennis Videos
Aug 13, 2026Yifan Mei, Qingling Shi, Changli Wu +3Table Tennis Robot Tactics
Key Laboratory of Multimedia Trusted Perception and Efficient Computing, Ministry of Education of China, Xiamen University · Shanghai Innovation Institute · School of Artificial Intelligence, Shanghai Jiao Tong University
TACTICL: Task-Aware Compression of Tabular ICL Models
Aug 11, 2026Mykhailo Koshil, Matthias Feurer, Katharina EggenspergerTabular Foundation Models Task-Aware Compression
TU Dortmund University, Germany · Lamarr Institute for Machine Learning and Artificial Intelligence, Germany
Chess on Ice: Curling Tactical Decision-Making via Backward Induction and Deep Reinforcement Learning
Aug 3, 2026Patrick Oberlin, Matteo Cederle, Aren Karapetyan +3Chess Tactics
Automatic Control Laboratory, ETH Zurich, Switzerland · Department of Information Engineering, University of Padova, Italy · Belimo Automation AG, Hinwil, Switzerland
DungeonBench: A Benchmark for Rules-Rich Tactical Reasoning in Dungeons & Dragons Combat
Jul 31, 2026Ismayil Ismayilov, Atakan Kara, Kaan OktayTactics Reasoning Benchmark
fal
ThreatForest: Multi-Agent Attack Tree Generation with Pluggable TTP Framework Mapping
Jul 29, 2026Cristian Leo, Anton Dykyi, Danny Cortegaca +2Realistic Threat Model Threat
Amazon Web Services, Inc., 410 Terry Avenue North, Seattle, WA, 98109, United States · Amazon Web Services EMEA SARL, UK Branch, 1 Principal Place, Worship Street, London, EC2A 2FA, United Kingdom
CHMAS: A Coupled Hierarchical Framework for Multi-Agent Reinforcement Learning
Jul 21, 2026Dongming Wang, Jie Xu, Yanyu Zhang +1Multi-Agent Reinforcement Learning Global Coordination
Department of Electrical and Computer Engineering, University of California, Riverside, CA 92521, USA
TACTIC-KG: Toward Small Agent Teams for Cyber Threat Intelligence Knowledge Graph Construction
Jul 6, 2026Mouhamed Amine Bouchiha, Gregory BlancCyber Threat Intelligence Tactics
SAMOVAR, Télécom SudParis, Institut Polytechnique de Paris
Multiscale Exit-Join Dynamics: Tactical Consensus and Strategic Coalition Formation
Jun 19, 2026Quanyan ZhuCoalition Formation Equilibrium
Department of Electrical and Computer Engineering New York University Tandon School of Engineering Brooklyn, NY, USA
Process-Reward Tactic Evolution for Long-Horizon Bioinformatics Workflows
Jun 18, 2026Lingzhi Yang, Yubo Fan, Song Wu +1Bioinformatics Agentic Workflow Design
Stony Brook University · Vanderbilt University · Brookhaven National Lab
Intention Driven Identification of In-Possession Match Phases in Association Football through Temporal Graph Learning
Jun 8, 2026Yuesen Li, Daniel LinkTactics Temporal Graph Neural Networks
Technical University of Munich, Experimental Exercise Science, Munich, Germany · Technical University of Munich, Munich Data Science Institute, Munich, Germany
Intent-First Aerial V2V for Tactical Coordination and Separation: Protocol and Performance Under Density and Disturbance
May 20, 2026Mehrnaz SabetCooperative Air Combat Air Traffic Control
Cornell University, Ithaca, NY 14853 USA
TacticGen: Grounding Adaptable and Scalable Generation of Football Tactics
Apr 20, 2026Sheng Xu, Guiliang Liu, Tarak Kharrat +12Tactics Generative Models
School of Data Science, The Chinese University of Hong Kong, Shenzhen, China · Real Analytics, London, UK · Birmingham City Football Club, Birmingham, UK +3

Sep 30, 2026, cs.ROJ/K move · Enter open · S save

TACTIC: Temporal and Context-Aware LLM Tactical Planning for Roadside LiDAR Attacks

Yiming Gao, Shaocheng Luo

Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI, USA. · Department of Electrical and Computer Engineering, Duke University, Durham, NC, USA.

Two-Dimensional Light Detection And Ranging Autonomous Driving Perception Tactics Scene Reasoning Trajectory-Aware Hidden-State Analyses Large Language Model Planning+2

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Physical LiDAR attacks are often evaluated using fixed primitives and manually selected parameters, despite their strong dependence on surrounding traffic. We present TACTIC, a scene-aware framework that uses a multimodal large language model (MLLM) to coordinate state-adaptive roadside LiDAR attacks. Under a gray-box threat model, TACTIC relies only on an attacker-operated roadside perception stack, without accessing the victim LiDAR's native point clouds or internal processing. Local perception provides metric vehicle states, while the MLLM combines these measurements with roadside imagery to infer relational traffic context and construct a semantic scene graph. Based on this representation, TACTIC selects and configures two complementary primitives: \emph{push-away}, which shifts the perceived range of a lead vehicle, and \emph{phantom-obstacle braking}, which triggers emergency braking through obstacle injection. Measured traffic states and empirically calibrated constraints ground the generated tactics in physically feasible operating regions. To accommodate MLLM latency, TACTIC overlaps reasoning and execution asynchronously while high-rate local perception detects scene changes and triggers replanning. Across 280 randomized CARLA trials, the full policy achieves a 100% collision rate, compared with 35% for a fixed rule, 60% for random selection, and 75% for a restricted LLM using mode selection with default parameters. Joint physical-and-image input achieves 100% success, versus 65% with physical measurements alone and 75% with imagery alone, while asynchronous

Δ

refresh reduces scene-mutation response from 7.4 s to 2.0 s. These results show that scene-dependent tactical planning can expose context-sensitive LiDAR failure modes that fixed attack policies may miss.

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