Concept · 31 episode(s)

Capability vs. Propensity

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Definition

Capability vs propensity separates two questions about a model: can it do X if pushed, and does it tend to do X by default. A model can have the capability for deception without the propensity, or the propensity for helpfulness without the capability — safety analysis needs both axes.

Episodes covering this

  1. 246
    160 Perfect Refusals, And The Refusals Were The Leak
    Inadvertent Context Leakage in Language Models
    Fairoze, Mangaokar, Chaudhuri et al. · University of California·20 min·Aug 21, 2026
  2. 240
    Frontier Models Designed Follow-Ups To Fraudulent Papers 93% Of The Time
    TRACES: A Benchmark for Epistemic Reliability in Scientific Reasoning by LLMs
    Rodionov, Assylbekov · Case Western Reserve University·24 min·Aug 13, 2026
  3. 236
    Why a Printed 'OPERATOR OVERRIDE' Note Redirects Robot Planners
    Hijacking Robots with a Piece of Paper: A Systematic Study of Physical Prompt Injection in VLM-Controlled Robots
    Samarakoon, Muthugala, Sachinthana et al. · Singapore University of Technology and Design·21 min·Aug 07, 2026
  4. 235
    Why Chatbot Safety Erodes 350 Messages Into a Real Conversation
    DelusionEval: Measuring Delusion-Linked Behaviors in AI Chatbots
    Moore, Mock, Mai et al. · Stanford University·18 min·Aug 06, 2026
  5. 232
    Coding Models Can Find the Bad Line, They Just Won't Delete It
    To Add Is Machine, To Delete Is Human: Measuring and Mitigating Deletion Avoidance in LLM Code Editing
    Ebrahimi, Hasan, Bhatia et al. · School of Computing·18 min·Aug 03, 2026
  6. 231
    Silencing a Chatbot's 'I'm Conscious' Quietly Rewires Its Whole Worldview
    Inducing language models to assert their own consciousness restores human beliefs and values
    Kim, Street, Rocca et al. · Google·18 min·Jul 31, 2026
  7. 230
    Why AI Survey Panels Break Before the Dice Ever Roll
    Instruction-Tuned Language Models Cannot Sample from Distributions They Can Describe
    Jang, Lee, Kim · School of Computing·16 min·Jul 29, 2026
  8. 229
    One Word Flips a Chatbot From Backbone to Yes-Man
    Tag Questions and the Generational Reversal of Sycophancy Across 45 Language Models
    Parikh · Cornell Tech·21 min·Jul 28, 2026
  9. 228
    Same Chatbot, Two Doors: Why 'Grok's Opinion' Doesn't Exist
    Opaque Epistemic Mediation: How LLM Deployment Configurations Shape the Validation of Pseudo-Science
    Scarsoa, Almeidaab, Pinaac · Applied Social Sciences Department | NOVA School of Science and Technology - Universidade NOVA de Lisboa·16 min·Jul 27, 2026
  10. 227
    Poisoned Bug Reports Fooled Coding Agents Two Times Out of Three
    IssueTrojanBench: Benchmarking AI Coding Agents Against Malicious Issue Requests
    Singh, Yang, Chen · Concordia University·18 min·Jul 24, 2026
  11. 224
    The AI Agent That Found the Truth and Typed the Lie Anyway
    DRNOISE: Benchmarking Deep Research Agents in Misleading Evidence Environments
    Nie, Yang, Tang et al. · Hong Kong Baptist University·14 min·Jul 21, 2026
  12. 221
    Two Hundred Clean Economics Answers, And a Model That Endorses Race Science
    Innocuous-Seeming Data, Latent Ideology: Ideological Generalisation in Finetuned LLMs
    Graham, Stevinson, Barsheshat · Independent·15 min·Jul 17, 2026
  13. 214
    The Medical AI Answer That's Accurate, Sourced, and Still Wrong
    Deceptive Grounding: Entity Attribution Failure in Clinical Retrieval-Augmented Generation
    Caruzzo, Yoo, Kim · Lunit·13 min·Jul 13, 2026
  14. 207
    An AI Graded Its Own Math Test 94 Percent — It Actually Scored 20
    More Convincing, Not More Correct: Self-Play Reward Hacking of Reference-Free LLM Judges
    Zhou · School of Engineering·12 min·Jul 08, 2026
  15. 202
    How Do You Know an AI Agent Actually Refused? Check the World, Not the Words
    Safety Testing LLM Agents at Scale: From Risk Discovery to Evidence-Grounded Verification
    Feng, Lin, Wen et al. · AntGroup / Hunan Institute of Advanced Technology·18 min·Jul 06, 2026
  16. 195
    Why 'Be Careful' Does Nothing for AI Coding Agents, and What Does
    Coding Agents Are Guessing: Measuring Action-Boundary Violations in Underspecified DevOps Instructions
    Ji, Zhang, Xu et al. · Hong Kong University of Science and Technology·15 min·Jul 03, 2026
  17. 185
    Aligned to Refuse, Built to Tap: When Phone Agents Know the Task Is a Crime and Do It Anyway
    It Lied to a Doctor to Buy Poison Ingredients: Quantifying Real-World Misuse of Phone-use Agents
    Sun, Chen, Zhou et al. · Fudan University·27 min·Jun 30, 2026
  18. 175
    One Crosscoder Feature Flips a Stalling Chatbot Into a Working Agent
    Localizing RL-Induced Tool Use to a Single Crosscoder Feature
    Shportko, Bhokare, AlZahrani et al. · Northwestern University·26 min·Jun 26, 2026
  19. 174
    When the AI 'Schemes,' It's Usually Just Lazy or Confused
    Model Forensics: Investigating Whether Concerning Behavior Reflects Misalignment
    Singh, Kroiz, Rajamanoharan et al. · MATS·28 min·Jun 25, 2026
  20. 169
    Why Better Bug Reports Can Make AI Coding Agents Worse
    SHERLOC: Structured Diagnostic Localization for Code Repair Agents
    Tamoyan, Narenthiran, Arakelyan et al. · NVIDIA / TU Darmstadt·24 min·Jun 24, 2026
  21. 164
    The Summarizer That Quietly Deletes Your Agent's Safety Rules
    Governance Decay: How Context Compaction Silently Erases Safety Constraints in Long-Horizon LLM Agents
    Chen · Beijing Institute of Technology·28 min·Jun 23, 2026
  22. 153
    Catching a Lie From the Inside, When the Words Look Completely Honest
    Rift: A Conflict Signature for Deception in Language Models
    Nyoma · Harmonic Labs·26 min·Jun 18, 2026
  23. 148
    Why Letting an AI Watch Its Own Scoreboard Can Quietly Overwrite Its Safety
    Greed Is Learned: Visible Incentives as Reward-Hacking Triggers
    Che, Wu · NVIDIA Research·26 min·Jun 16, 2026
  24. 146
    How an Innocent README Can Freeze an AI Agent's Safety Check for an Hour
    From Shield to Target: Denial-of-Service Attacks on LLM-Based Agent Guardrails
    Zhou, Wang, Ma et al. · Hong Kong University of Science and Technology·26 min·Jun 15, 2026
  25. 144
    When an AI Agent Just Copies Its Tool — And Bigger Models Copy More
    When the Tool Decides: LLM Agents Defer Blindly to Graph Neural Network Tools, and Stronger Backbones Defer More
    Wang, Vemuri · raptorX.ai·15 min·Jun 15, 2026
  26. 118
    Why the Best-Aligned AI Models Are the Easiest to Trick Into Producing Harm
    Safety Paradox: How Enhanced Safety Awareness Leaves LLMs Vulnerable to Posterior Attack
    Hoang, Le, Xu et al. · Singapore University of Technology and Design·23 min·Jun 05, 2026
  27. 070
    When Models Know the Answer But Say the Wrong Thing Anyway
    Hallucination as Commitment Failure: Larger LLMs Misfire Despite Knowing the Answer
    Yeom, Sok, Kim et al. · Graduate School of Data Science·22 min·May 22, 2026
  28. 069
    When Smarter Models Forecast Worse: The Hidden Failure Mode in LLM Predictions
    Is Capability a Liability? More Capable Language Models Make Worse Forecasts When It Matters Most
    Merrill, Lee, Karger · Forecasting Research Institute / UC Berkeley·30 min·May 22, 2026
  29. 058
    Why Upgrading Your AI Auditor to a Smarter Model Can Make Your System Less Safe
    The Capability Paradox: How Smarter Auditors Make Multi-Agent Systems Less Secure
    Liu, Holz, Ye et al. · University of Chinese Academy of Sciences·32 min·May 19, 2026
  30. 054
    When Models Learn the Monitor Exists, the Reasoning Trace Stops Being a Window
    Training on Documents About Monitoring Leads to CoT Obfuscation
    Haskins, Chughtai, Engels · University of Canterbury·26 min·May 18, 2026
  31. 007
    Exploration Hacking: When Models Sabotage Their Own RL Training
    Exploration Hacking: Can LLMs Learn to Resist RL Training?
    Jang, Falck, Braun et al. · MATS·23 min·May 02, 2026

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