Proof pending. Core topic summary fields are still materializing.
AI safety is a critical area of research focused on ensuring that AI systems operate reliably and ethically, particularly as they become more integrated into various applications. Recent advancements include frameworks like GAVEL, which enhances activation monitoring for harmful behaviors, and ReasAlign, which improves safety against prompt injection attacks. Tools such as StepShield evaluate when to intervene in rogue agent behaviors, while AEGIS provides a pre-execution firewall for AI agents. These innovations aim to enhance model robustness, reduce risks, and ensure that AI systems align with user intent, making them essential for developers and organizations deploying AI technologies. As AI continues to evolve, these safety mechanisms are crucial for mitigating potential harms and fostering trust in AI applications.
Topic-specific paper and score movement from the daily diff ledger.
Large language models (LLMs) are increasingly paired with activation-based monitoring to detect and prevent harmful behaviors that may not be apparent at the surface-text level. However, existing acti...
Large Language Models (LLMs) have enabled the development of powerful agentic systems capable of automating complex workflows across various fields. However, these systems are highly vulnerable to ind...
Existing agent safety benchmarks report binary accuracy, conflating early intervention with post-mortem analysis. A detector that flags a violation at step 8 enables intervention; one that reports it ...
Testing object detectors in safety-critical domains requires semantically meaningful probes beyond pixel-level corruptions. We present SemProbe, a tool for semantic robustness probing: users upload de...
AI agents increasingly act through external tools: they query databases, execute shell commands, read and write files, and send network requests. Yet in most current agent stacks, model-generated tool...
As AI systems are increasingly deployed in autonomous agentic settings at scale, it is important to ensure the actions they take are safe and aligned with user intent. Monitoring agent actions is a ke...
As AI agents become increasingly autonomous, widely deployed in consequential contexts, and efficacious in bringing about real-world impacts, ensuring that their decisions are not only instrumentally ...
Language models are increasingly capable and are being rapidly deployed on a population-level scale. As a result, the safety of these models is increasingly high-stakes. Fortunately, advances in align...
LLM agents with tool access can discover and exploit security vulnerabilities. This is known. What is not known is which features of a system prompt trigger this behaviour, and which do not. We presen...
Jailbreak attacks on audio language models (ALMs) optimize audio perturbations to elicit unsafe generations, and they typically update the entire waveform densely throughout optimization. In this work...
Freshness
Canonical route: /topics
Agent Handoff
Canonical ID ai-safety | Route /topic/ai-safety
REST example
curl https://sciencetostartup.com/api/v1/agent-handoff/topic/ai-safetyMCP example
{
"tool": "search_papers",
"arguments": {
"query": "AI Safety",
"cluster": "AI Safety"
}
}source_context
{
"surface": "topic",
"mode": "topic",
"query": "AI Safety",
"normalized_query": "ai-safety",
"route": "/topic/ai-safety",
"paper_ref": null,
"topic_slug": "ai-safety",
"benchmark_ref": null,
"dataset_ref": null
}Use This Via API or MCP
Topic pages bundle paper counts, viability trends, author concentration, and top questions into one canonical surface your agents can reference before they open Signal Canvas or create a workspace.