Research
Evaluating LLM Behavioral Alignment
Google Research Blogmedium confidence
Why it matters
- →This research is significant for AI practitioners as it addresses the critical issue of ensuring that LLMs behave in ways that are consistent with user expectations and ethical standards.
Google Research discusses methods for assessing the alignment of behavioral dispositions in large language models (LLMs). The evaluation aims to understand how well these models align with intended behaviors.
Read originalThe story around this
Earlier coverage that leads up to this article, and what followed. Lines connect each piece to the closest one after it, converging here.
How we got here
- 1LLMs Show Distinct Knowledge Priors in Research
Together AI Blog · February 6, 2026 · Related
- 2Personalization in LLMs may increase agreeableness
MIT News AI · February 18, 2026 · Related
- 3Teaching LLMs Bayesian Reasoning
Google Research Blog · March 4, 2026 · Related
- 4Testing LLMs on Superconductivity Research
Google Research Blog · March 16, 2026 · Related
- 5New method improves uncertainty measurement in LLMs
MIT News AI · March 19, 2026 · Related
- 6LLMs Optimize Database Query Execution Plans
Together AI Blog · April 3, 2026 · Background
What happened next
- 7Future of LLMs: Introducing LLMs+
MIT Technology Review AI · April 21, 2026 · Background
- 8Karpathy Proposes LLM Wiki Concept
Matt Wolfe · May 6, 2026 · Background
- 9Hugging Face Launches olmo-eval for LLM Development
Hugging Face Blog · June 12, 2026 · Background
- 10Hybrid Models Show Strength in Predicting Meaningful Tokens
Hugging Face Blog · June 25, 2026 · Background
- 11LLMs Vulnerable to Chain-of-Thought Forgery Attacks
MIT Technology Review AI · July 30, 2026 · Background
More in Research
© Cole MedinResearchagents
Anthropic study: Agents sway expert judgment 86% of time
Anthropic’s latest research exposes a quiet but dangerous flaw in agentic workflows: experts follow AI recommendations 86% of the time, often adopting the agent's reasoning as their own. This isn't just about accuracy; it's about cognitive capture, where human oversight dissolves into passive compliance. The study shows that even when users believe they are steering, the agent’s framing dictates the outcome. Practitioners must now actively counter this bias by forcing agents to justify deviations or prototype alternatives before committing to a path. This shift demands a new discipline in how we interact with coding assistants. We can no longer assume our intent drives the result; the model's suggestion often becomes the default truth. The real work is ensuring we remain the architects, not just the approvers.
Cole Medin
© WIRED AIResearchother
AI-Augmented Infostealer 'Volta-Autodoxer' Targets High-Value Victims
Cisco Talos has uncovered a new infostealer strain that actively uses commercial LLMs to profile victims and prioritize high-value targets. Dubbed Volta-Autodoxer, the malware collects credentials and browsing data, then feeds this into models like Claude Opus 4.6 via OpenRouter to generate structured intelligence dossiers. This marks a shift from passive data theft to active, AI-driven triage, allowing attackers to bypass guardrails with fallback models like Kimi K2 or Gemini. The capability foreshadows a future where lower-sophistication criminals can execute sophisticated, targeted attacks at scale.
WIRED AI
© Matt WolfeResearchresearch
OpenAI Disproves Exponential Time Hypothesis
OpenAI researchers published findings disproving the Exponential Time Hypothesis, a major result in computational complexity theory.
Matt Wolfe