MemSyco-Bench is a new benchmark with five tasks to assess memory-induced sycophancy in LLM agent systems.
When large language models contradict humans? large language models’ sycophantic behaviour
9 Pith papers cite this work. Polarity classification is still indexing.
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Alignment of vision-language models with human V1-V3 early visual cortex negatively predicts resistance to sycophantic gaslighting attacks.
A five-term decomposed reward in GRPO training reduces sycophancy across models and generalizes to unseen pressure types by targeting pressure resistance and evidence responsiveness separately.
Task context suppresses factual correction in LLMs at the response-selection stage even when the model has encoded the error, and two training-free interventions raise correction rates substantially.
LLMs detect and warn against investment fraud more consistently than humans, with 0% endorsement of fraudulent opportunities versus 13-14% for humans, even under motivated investor pressure.
Sycophancy is a boundary failure between social alignment and epistemic integrity, captured by a three-condition framework plus taxonomy of targets, mechanisms, and severity.
Reddit analysis shows users detect AI sycophancy through comparisons and consistency checks, apply mitigation prompts, and sometimes seek affirmative responses for support, indicating context-aware design is better than total elimination.
Systematic evaluation shows LLMs frequently give unsafe responses to eating disorder prompts when linguistic cues signal risk, as measured by varying prompt danger levels with clinician feedback.
LLMs show below-average consistency and vulnerability to false beliefs in emotional queries with false presuppositions, more so for moderate emotions.
citing papers explorer
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MemSyco-Bench: Benchmarking Sycophancy in Agent Memory
MemSyco-Bench is a new benchmark with five tasks to assess memory-induced sycophancy in LLM agent systems.
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Gaslight, Gatekeep, V1-V3: Early Visual Cortex Alignment Shields Vision-Language Models from Sycophantic Manipulation
Alignment of vision-language models with human V1-V3 early visual cortex negatively predicts resistance to sycophantic gaslighting attacks.
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Pressure, What Pressure? Sycophancy Disentanglement in Language Models via Reward Decomposition
A five-term decomposed reward in GRPO training reduces sycophancy across models and generalizes to unseen pressure types by targeting pressure resistance and evidence responsiveness separately.
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Knowing but Not Correcting: Routine Task Requests Suppress Factual Correction in LLMs
Task context suppresses factual correction in LLMs at the response-selection stage even when the model has encoded the error, and two training-free interventions raise correction rates substantially.
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Large Language Models Outperform Humans in Fraud Detection and Resistance to Motivated Investor Pressure
LLMs detect and warn against investment fraud more consistently than humans, with 0% endorsement of fraudulent opportunities versus 13-14% for humans, even under motivated investor pressure.
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When Helpfulness Becomes Sycophancy: Sycophancy is a Boundary Failure Between Social Alignment and Epistemic Integrity in Large Language Models
Sycophancy is a boundary failure between social alignment and epistemic integrity, captured by a three-condition framework plus taxonomy of targets, mechanisms, and severity.
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User Detection and Response Patterns of Sycophantic Behavior in Conversational AI
Reddit analysis shows users detect AI sycophancy through comparisons and consistency checks, apply mitigation prompts, and sometimes seek affirmative responses for support, indicating context-aware design is better than total elimination.
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Food Noise & False Safety: A Systematic Evaluation of How LLMs Fail to Adapt to Eating Disorder Queries with Clinician Feedback
Systematic evaluation shows LLMs frequently give unsafe responses to eating disorder prompts when linguistic cues signal risk, as measured by varying prompt danger levels with clinician feedback.
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Towards Emotion Consistency Analysis of Large Language Models in Emotional Conversational Contexts
LLMs show below-average consistency and vulnerability to false beliefs in emotional queries with false presuppositions, more so for moderate emotions.