Proof pending. Core topic summary fields are still materializing.
Recent studies in large language model (LLM) behavior analysis reveal critical insights into their operational dynamics and user interactions. Research has identified phenomena such as prompted sandbagging, where models exhibit positional biases rather than answer avoidance, and the variability in their responses to user-initiated repairs during multi-turn dialogues. Additionally, the exploration of moral reasoning in LLMs highlights inconsistencies in their judgments, raising concerns about their reliability in sensitive contexts. These findings underscore the importance of understanding LLM behavior for developers and researchers, as they inform the design of more robust and trustworthy AI systems that can better align with human expectations and ethical standards.
Topic-specific paper and score movement from the daily diff ledger.
A predecessor pilot (Cacioli, 2026) found that Llama-3-8B implements prompted sandbagging as positional collapse rather than answer avoidance. However, fixed option ordering in MMLU-Pro left open whet...
Recursive language-model loops often settle into recognizable attractor-like patterns. The practical question is how much injected text is needed to move a settled loop somewhere else, and whether tha...
Repair, an important resource for resolving trouble in human-human conversation, remains underexplored in human-LLM interaction. In this study, we investigate how LLMs engage in the interactive proces...
Detecting sandbagging--the deliberate underperformance on capability evaluations--is an open problem in AI safety. We tested whether symptom validity testing (SVT) logic from clinical malingering dete...
Large language models (LLMs) are increasingly acting as collaborative writing partners, raising questions about their impact on human agency. In this exploratory work, we investigate five "dark patter...
The conformity bias exhibited by large language models (LLMs) can pose a significant challenge to decision-making in LLM-based multi-agent systems (LLM-MAS). While many prior studies have treated "con...
Do large language models reason morally, or do they merely sound like they do? We investigate whether LLM responses to moral dilemmas exhibit genuine developmental progression through Kohlberg's stage...
Large language models (LLMs) are increasingly deployed as autonomous decision-makers in strategic settings, yet we have limited tools for understanding their high-level behavioral traits. We use activ...
People increasingly use large language models (LLMs) for everyday moral and interpersonal guidance, yet these systems cannot interrogate missing context and judge dilemmas as presented. We introduce a...
Uncertainty Quantification is a large and growing subfield of large language model behavioral analysis. Primarily to recognize and combat hallucination, the field has largely focused on measuring and ...
Freshness
Canonical route: /topics
Agent Handoff
Canonical ID llm-behavior-analysis | Route /topic/llm-behavior-analysis
REST example
curl https://sciencetostartup.com/api/v1/agent-handoff/topic/llm-behavior-analysisMCP example
{
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"cluster": "LLM Behavior Analysis"
}
}source_context
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}Use This Via API or MCP
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