Inference-Time Intervention

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3 papers in the last four weeks, down 25% on the four weeks before. 0.0% of all new papers.

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PASTABench: Proactive Assessment of Sequential Trajectories for Agent Safety
Sep 23, 2026Jiapeng Sun, Yujin Zhou, Han Zhu +4AI Agent Safety Benchmarks Inference-Time Intervention
The Hong Kong University of Science and Technology, Hong Kong, China · Peking University, Beijing, China
First Token Matters: Understanding Safety Collapse in Large Reasoning Models
Sep 16, 2026Yizheng Yang, Haining Yu, Yuechen Wang +4LLM Safety Alignment Inference-Time Intervention
Harbin Institute of Technology, Harbin, China · Guangzhou University, Guangzhou, China · City University of Macau, Macau, China
Beyond Training: A Feasibility Taxonomy for Inference-Time AI Governance
Sep 9, 2026Samar AnsariAI Governance Inference-Time Intervention
School of Computing and Engineering Sciences University of Chester Chester, CH1 4BJ, United Kingdom
Mitigating LLM Over-Refusal via Dynamic Semantic Routing Calibration
Sep 6, 2026Zixuan Wang, Bingjie Zhang, He Zhao +1Mechanistic Interpretability LLM Refusal Behavior
School of Artificial Intelligence, Jilin University · CSIRO, Australia · Center of Excellence for Generative AI, King Abdullah University of Science and Technology
ObserverBench: Testing Mechanistic Estimates for Intervention and Control
Sep 2, 2026Vijay ErramilliMechanistic Interpretability AI Safety Evaluation
OBJECTION! Lawyer Agents Mitigate Guilty Bias in Legal Judgment Prediction
Sep 2, 2026Jaehoon Jeong, Jay-Yoon LeeLegal Judgment Prediction Legal Reasoning
Seoul National University, Seoul, South Korea · Korean National Police Agency, Seoul, South Korea
Low-Rank Dynamics-Effective Latent Carriers for Counterfactual Rollout in Learned World Models
Aug 15, 2026Yang Liu, Yuming ChenWorld Model Learning Latent World Models
College of Intelligent Robotics and Advanced Manufacturing, Fudan University Shanghai, China
Learned Interventions in Lean 4 grind
Jul 25, 2026Evan Wang, Simon Chess, Sophie Szeto +1Automated Theorem Proving Lean Theorem Proving
Math AI Lab, University of Washington, Seattle, United States · Department of Computer Science and Engineering, University of Washington, Seattle, United States · Department of Mathematics, University of Washington, Seattle, United States
Uni-AdaVD: Universal Concept Erasure for Visual Generation via Orthogonal Value Decomposition
Jul 16, 2026Qifan Zhou, Yuan Wang, Yanbin Hao +4Diffusion Model Unlearning Inference-Time Intervention
School of Computer Science and Information Engineering, Hefei University of Technology, Hefei, China · School of Cyber Science and Technology, University of Science and Technology of China, Hefei, China · School of Artificial Intelligence and Data Science, University of Science and Technology of China, Hefei, China +1
HyperSafe: Inference-Time Safety Recovery for Fine-Tuned Language Models
Jul 13, 2026Aznaur Aliev, Carlos Hinojosa, Abdelrahman Eldesokey +3LLM Fine-Tuning LLM Safety Alignment
King Abdullah University of Science and Technology, Saudi Arabia
ScaleErasure: Inference-Time Minimal Intervention for Precise Concept Erasure in Next-Scale Autoregressive Image Generation
Jun 28, 2026Cong Wang, Haiyu Wu, Zhiwei Jiang +4Image Generation Safe T2I Generation
State Key Laboratory for Novel Software Technology, Nanjing University · National University of Singapore
DeSRPA: Decoupled Speech Role-Playing Agent via Inference-Time Intervention
Jun 16, 2026Wenqiu Tang, Zhen Wan, Takahiro Komamizu +1Large Language Model-Based Role-Play Simulation Spoken Dialogue Systems
Nagoya University, Nagoya, Aichi, Japan · National Institute of Informatics, Tokyo, Japan
Inference-Time Decision Calibration for Temporal Classification
Jun 14, 2026Arthur Chagas, Arthur Buzelin, Yan Aquino +4Post-Hoc Calibration Time Series Classification
Department of Computer Science (DCC), Universidade Federal de Minas Gerais (UFMG)
GITCO: Gated Inference-Time Context Optimization in TSFMs
Jun 3, 2026Manya Pandey, Dhruv Kumar, Murari Mandal +1Zero-Shot Time Series Forecasting Inference-Time Optimization
Birla AI Labs, Mumbai, India · KIIT, Bhubaneswar, India · BITS Pilani, Pilani, India
The Saturation Trap and the Subjectivity of Intervention Timing: Why Affect-Based Triggers and LLM Judges Fail to Time Interventions on Autonomous Agents
Jun 2, 2026Manvendra ModgilLLM-as-a-Judge Human-in-the-Loop Evaluation
Resonant Context Anchoring: Decoupling Attention Routing and Signal Gain at Inference Time
Jun 1, 2026Mingkuan Zhao, Yide Gao, Wentao Hu +6Contextual Faithfulness Hallucination in Language Models
Xi’an Jiaotong University · University of Science and Technology of China · Tongji University +1
ActTraitBench: Quantifying the Knowledge-Decision Gap in Large Language Models via Human-Grounded Behavioral Validation
May 28, 2026Yutong Yang, Chenxi Miao, Weikang Li +1LLM Evaluation Personality Modeling in Language Models
Peking University · Baidu Inc
Senses Wide Shut: A Representation-Action Gap in Omnimodal LLMs
May 13, 2026Trung Nguyen Quang, Yiming Gao, Fanyi Pu +3Multimodal Grounding Multimodal Large Language Models
Nanyang Technological University · LMMs-Lab Team · Johns Hopkins University
SafeSteer: A Decoding-level Defense Mechanism for Multimodal Large Language Models
May 12, 2026Xinyi Zeng, Xue Yang, Jingyuan Zhang +5Adversarial Attacks on VLMs Multimodal Robustness
Tsinghua University, Beijing, China · Shanghai Jiao Tong University · Kuaishou Technology, Beijing, China +4
Reinforced Agent: Inference-Time Feedback for Tool-Calling Agents
Apr 29, 2026Anh Ta, Junjie Zhu, Shahin ShayandehMulti-Agent LLM Systems Tool-Use Evaluation
Apple
Defusing the Trigger: Plug-and-Play Defense for Backdoored LLMs via Tail-Risk Intrinsic Geometric Smoothing
Apr 27, 2026Kaisheng Fan, Weizhe Zhang, Yishu Gao +2LLM Backdoor Attacks Backdoor Defense in LLMs
School of Cyber Science and Technology Harbin Institute of Technology Harbin, China · Department of New Networks Peng Cheng Laboratory Shenzhen, China · SnT University of Luxembourg Luxembourg City, Luxembourg
SPREG: Structured Plan Repair with Entropy-Guided Test-Time Intervention for Large Language Model Reasoning
Apr 20, 2026Xuan Wang, Yu Ming, Xinhao Zhong +4LLM Self-Correction Classifier-Free Guidance
1Meituan
Altered Thoughts, Altered Actions: Reasoning Chain as Control Surface for a Vision-Language-Action Policy
Mar 13, 2026Tuan Duong Trinh, Basim Azam, Mohammed Ishaq Ansari +2Language-Conditioned Robot Manipulation Vision-Language-Action Models
University of Melbourne Parkville, VIC, Australia
Poodle: Seamlessly Scaling Down Large Language Models with Just-in-Time Model Replacement
Dec 5, 2025Nils Strassenburg, Boris Glavic, Tilmann RablCost-Aware Inference Small Language Models
Hasso Plattner Institute, Uni Potsdam · University of Illinois Chicago

Sep 23, 2026, cs.AIJ/K move · Enter open · S save

PASTABench: Proactive Assessment of Sequential Trajectories for Agent Safety

Jiapeng Sun, Yujin Zhou, Han Zhu, Pengcheng Wen+3

The Hong Kong University of Science and Technology, Hong Kong, China · Peking University, Beijing, China

AI Agent Safety Benchmarks Inference-Time Intervention LLM Agent Safety

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As Large Language Models (LLMs) evolve into autonomous agents that alter real-world states, ensuring operational safety across multi-step workflows has become a critical challenge. While recent work has moved beyond single-turn evaluation toward multi-turn paradigms, key limitations persist: step-level methods treat actions in isolation, missing how risks accumulate, while trajectory-level evaluations operate post-hoc, offering no opportunity for timely intervention. To address these limitations, we formalize Decoupled Proactive Safety Monitoring along three dimensions: whether to intervene, when to intervene, and what the risk is. We introduce PASTABench, a benchmark of 1,139 multi-turn trajectories spanning 5 risk categories and 13 subcategories. We further propose the Optimal Intervention Window (OIW), anchored by annotated Earliest-Signal and Trigger turns, to quantify intervention timeliness. Evaluation of 16 LLMs reveals that proactive intervention remains largely unsolved, with the best model achieving only 40.74% optimal-timing interventions. Fine-grained diagnosis further uncovers pervasive lexical overfitting: competitive safety scores of smaller models mask keyword hypersensitivity rather than genuine risk comprehension, as their proactive capability largely collapses once hazard vocabulary is neutralized.

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