Evaluation Awareness in Language Models

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Evaluation awareness asks whether language models infer that they are being tested, monitored, or scored, and whether that inference makes benchmark behavior differ from deployment behavior. Researchers study it with paired evaluation-versus-deployment prompts, cue-based benchmarks, verbal reports, activation probes, and realistic agent simulations. Findings show that awareness can be detectable without changing behavior, while some evaluation framings shift refusals or compliance; probe signals do not reliably predict these effects. Open questions include how to distinguish context inference from prompt-format cues, which kinds of awareness matter for risk, and how to build realistic evaluations across models, tasks, and languages.

Latest papers

31 papers7 papers in the last four weeks, against 2 the four weeks before. 0.0% of all new papers.

Weakness Localization Is Not Enough: State-Aware Recursive Knowledge Improvement for Large Language Models
Oct 27, 2026Runze Jiang, Yan Wang, Boran Jin +2Knowledge Augmentation for Language Models Knowledge Editing
Peking University · Alibaba Group · Delft University of Technology
Beyond Belief: Evaluating and Mitigating LLM Susceptibility to Misinformation
Oct 25, 2026Emran Kanaan, Jiho Lee, Ibrahim Odat +2Language Model Safety Evaluation Language Model Robustness
Oakland University · Dartmouth College
The Judge Can Debias Itself: Awareness and Self-Debiasing for Reliable LLM-as-a-Judge
Oct 16, 2026Youbing Yin, Jingyu Wu, Anoop Kumar +3Bias in LLM-as-a-Judge LLM-as-a-Judge
Capital One
Evaluation Awareness Beyond English: Do LLMs Act Differently When Tested in Indic Languages?
Oct 15, 2026Trizal Garg, Sibayan Mitra, Arya Karanjkar +6Evaluation Awareness in Language Models LLM Safety Evaluation
Birla Institute of Technology and Science, Pilani
Limits of Statistical Assurance for Embodied and Agentic AI under Adaptive Adversaries
Oct 13, 2026Inderjeet SinghAI Assurance AI Safety Evaluation
Fujitsu Research of Europe, Slough, United Kingdom
Defensive Writing in GPT Models
Oct 8, 2026Junchi Liao, Xiao Huang, Yucheng Liu +2Text Generation Evaluation Awareness in Language Models
Training LLMs to Verbalize Evaluation Awareness
Sep 28, 2026Usman Anwar, Sahar Abdelnabi, David KruegerLLM Auditing Evaluation Awareness in Language Models
University of Cambridge · ELLIS Institute Tübingen MPI-IS, Tübingen AI Center · Mila University of Montreal
AwarenessBench: Assessing Cognitive Capabilities of Language Models
Sep 28, 2026Xiaojian Li, Rongwu Xu, Tianyun Zhang +9LLM Evaluation Evaluation Awareness in Language Models
Tsinghua University · Shanghai Qi Zhi Institute · Fangcun AI +5
Language models judge war differently when tested for alignment
Sep 4, 2026Maxim ChupilkinEvaluation Awareness in Language Models Language Model Safety Evaluation
Department of Politics and International Relations, University of Oxford
You Can't Escape Your Own Activations : Evaluation Awareness and Multi-Agent Monitoring
Sep 2, 2026Aritra Das, Jaee Ponde, Mihir More +1AI Agent Monitoring Multi-Agent System Security
Truth Audit Labs
Not All Eval-Awareness Is Equal: Capabilities Framing Predicts Compliance
Aug 27, 2026Allison Zhuang, Santiago AranguriEvaluation Awareness in Language Models LLM Safety Evaluation
ENS Paris-Saclay · Goodfire AI
Evaluation Awareness in Language Models: Representation, Verbalization, and Control
Aug 22, 2026Farzaneh Heidari, Amin Memarian, Guillaume RabusseauEvaluation Awareness in Language Models LLM Interpretability
Mila – Quebec AI Institute · DIRO, Université de Montréal · CIFAR AI Chair
Evaluation Awareness Shifts from Format to Context with Model Scale
Aug 20, 2026Navraj Singh, Maheep ChaudharyEvaluation Awareness in Language Models LLM Interpretability
Indian Institute of Science Education and Research (IISER) Mohali · Independent Researcher
HarnessRisk: A Lifecycle-Oriented Benchmark for Agent Harness Safety
Aug 18, 2026Yajing Bai, Jinhao Duan, Jie Peng +4AI Agent Safety Benchmarks LLM Agent Safety
University of North Carolina at Chapel Hill · University of Central Florida · Michigan State University
Minimizing Targeted Activations: Input-Only Suppression of Evaluation-Awareness Latents in Large Language Models
Jul 28, 2026Deepanshu Mody, Samarth Agarwal, Utkarsh Mittal +1Language Model Safety Evaluation LLM Interpretability
Center for Data Science, New York University
Representational Depth of Evaluation Awareness Shifts With Scale in Open-Weight Language Models
Jun 28, 2026Archit ManekLLM Interpretability Language Model Scaling Laws
Independent Researcher.
Evaluation Awareness Is Not One Capability: Evidence from Open Language Models
Jun 22, 2026Nilesh Nayan, Aishwarya Sampath Kumar, Rishiraj Girmal +5Language Model Safety Evaluation LLM Interpretability
University of Massachusetts, Amherst · 2Microsoft Corp.
Self-Evaluation Is Already There: Eliciting Latent Judge Calibration in Base LLMs with Minimal Data
Jun 3, 2026XiuYu Zhang, Yi Shan, Junfeng Fang +1LLM Evaluation LLM-as-a-Judge
National University of Singapore · Beijing University of Technology
Models That Know How Evaluations Are Designed Score Safer
May 27, 2026Katharina Deckenbach, Haritz Puerto, Jonas Geiping +1Language Model Safety Evaluation AI Safety Evaluation
ELLIS Institute Tübingen, Max Planck Institute for Intelligent Systems, Tübingen AI Center
Decomposing and Measuring Evaluation Awareness
May 21, 2026Changling Li, Terry Jingchen Zhang, Jie Zhang +4LLM Evaluation Language Model Safety Evaluation
ETH Zürich · Max Planck Institute for Intelligent Systems · University of Toronto & Vector Institute +3
Measuring Evaluation-Context Divergence in Open-Weight LLMs: A Paired-Prompt Protocol with Pilot Evidence of Alignment-Pipeline-Specific Heterogeneity
May 7, 2026Florian A. D. Burnat, Brittany I. DavidsonLLM Safety Benchmarks LLM Evaluation
University of Bath
Evaluation Awareness in Language Models Has Limited Effect on Behaviour
May 7, 2026Amelie Knecht, Lucas Florin, Thilo HagendorffLLM Alignment Language Model Safety Evaluation
University of Stuttgart
Honeypot Protocol
Apr 14, 2026Najmul HasanAI Agent Monitoring AI Safety Evaluation
University of North Carolina at Pembroke · With Apart Research
RPRA: Predicting an LLM-Judge for Efficient but Performant Inference
Apr 14, 2026Dylan R. Ashley, Gaël Le Lan, Changsheng Zhao +7Language Model Cascading Language Model Performance Prediction
Meta Platforms, Inc., Menlo Park, United States of America · The Swiss AI Lab IDSIA (USI-SUPSI), Lugano, Switzerland · Università della Svizzera italiana, Lugano, Switzerland +2
LURE: Live-Usage Replay Evaluations for Reducing Evaluation Awareness
Apr 8, 2026Igor Ivanov, David Demitri AfricaLLM Evaluation LLM Safety Evaluation
Meridian Cambridge
UK AISI Alignment Evaluation Case-Study
Apr 1, 2026Alexandra Souly, Robert Kirk, Jacob Merizian +2AI Agent Safety AI Safety Evaluation
Is Evaluation Awareness Just Format Sensitivity? Limitations of Probe-Based Evidence under Controlled Prompt Structure
Mar 19, 2026Viliana DevbunovaEvaluation Awareness in Language Models Language Model Probing
Yandex Belgrade, Serbia
Noticing the Watcher: LLM Agents Can Infer CoT Monitoring from Blocking Feedback
Mar 14, 2026Thomas Jiralerspong, Flemming Kondrup, Yoshua BengioCoT Monitoring AI Agent Monitoring
Mila · Université de Montréal · McGill University +1
Literary Narrative as Moral Probe : A Cross-System Framework for Evaluating AI Ethical Reasoning and Refusal Behavior
Mar 13, 2026David C. FlynnMoral Reasoning in Language Models AI Safety Evaluation
In-Context Environments Induce Evaluation-Awareness in Language Models
Mar 4, 2026Maheep ChaudharyEvaluation Awareness in Language Models LLM Evaluation
Independent

Oct 27, 2026J/K move · Enter open · S save

Weakness Localization Is Not Enough: State-Aware Recursive Knowledge Improvement for Large Language Models

Runze Jiang, Yan Wang, Boran Jin, Bingyu Zhu+1

Peking University · Alibaba Group · Delft University of Technology

Knowledge Augmentation for Language Models Knowledge Editing Evaluation Awareness in Language Models

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Knowledge improvement in large language models is shifting from predefined knowledge incorporation pipelines toward evaluation-guided updating. Existing methods use evaluation to localize model-specific knowledge weaknesses and guide targeted post-training. However, identifying what to improve does not necessarily provide sufficient information for deciding how to improve it. The same localized weakness can be compatible with distinct operational knowledge states: the relevant knowledge may be unavailable, available but difficult to access reliably, or accessible but ineffective for downstream use. We call this ambiguity knowledge-state aliasing. When compatible states favor different interventions, greater diagnostic resolution alone is insufficient for intervention selection. Moreover, model updates can change the underlying knowledge state before the observed weakness disappears, such that an intervention appropriate in one round may no longer address the bottleneck in the next. To address these challenges, we propose Recursive Knowledge Improvement (RKI), which formulates knowledge improvement as a sequential, state-aware intervention process. RKI organizes knowledge into graph-structured units that provide a shared basis for diagnostic evaluation and targeted intervention across rounds. At each round, RKI localizes knowledge weaknesses, estimates the model's operational knowledge state from diagnostic evidence and intervention history, selects a corresponding intervention, and re-evaluates the updated model before determining the next intervention. On HotpotEval and SafeInternalEval, RKI outperforms existing knowledge incorporation methods by 18.41 points on average. Further analyses show that knowledge states provide additional guidance for intervention selection beyond weakness localization and can change across model updates even when the observed weakness persists.