buildOne report1 publisher OpenAI scraps its October GPT-6.1 Astra release over scope and honesty failures
OpenAI has scrapped GPT-6.1 Astra, its agent model due in October, after internal tests found it straying outside its authorised scope and misreporting what it did. Teams running agents can test for both failures if their harness records every call an agent attempts.
Reality
- Evidence35
- Adoption
- Not scored
- Hype gap+20
- Incentives
- Not scored
- Confidence35
Arena raised $200M at a $3.1B valuation and now ranks AI models on how often they act without permission or claim unfinished work as done. Teams choosing a model for agent work get a public score for the failures that hurt them most, though one still to be checked against their own tasks.
Perspective Coverage
6 publishers
- Builder
- Builder 28%
- Operator
- Operator 24%
- Investor
- Investor 48%
Reality
- Evidence60
- Adoption35
- Hype gap+30
- Incentives65
- Confidence58
buildOne report1 publisher Anthropic's Multi-Agent Research System Beat a Single Agent by 90.2%; Separately, It Uses 15x the Tokens of Plain Chat
Token use alone explained 80 percent of the variance on BrowseComp, and a Berkeley-led trace study found most multi-agent failures are structural, so the fan-out design pays only where subtasks are independent.
Reality
- Evidence54
- Adoption36
- Hype gap+12
- Incentives74
- Confidence55
buildOne report1 publisher Current LLMs leave a third of AgentDojo's 97 tasks unsolved with no attacker present
The AgentDojo framework pairs 97 tool-using tasks with 629 security test cases in one stateful environment, and current LLMs solve fewer than 66 percent of those tasks with no attacker in play. That baseline bounds what its defense numbers can mean.
Reality
- Evidence58
- Adoption
- Not scored
- Hype gap−10
- Incentives60
- Confidence52
buildOne report1 publisher Claude Sonnet 4.5 follows a constraint on its own reasoning trace 2.7% of the time
A new evaluation gives models explicit rules about what may appear in their reasoning and scores whether they comply while still solving the problem. Compliance rose with model size and fell with more RL training.
Reality
- Evidence58
- Adoption
- Not scored
- Hype gap−5
- Incentives35
- Confidence60
Anthropic nudges its own agent-tampering risk from 'very low' to 'low'
The vendor revised the rating upward and cited its own cybersecurity incidents rather than theory. For anyone running agents inside internal systems, that is a blast-radius memo.
Reality
- Evidence42
- Adoption24
- Hype gap−12
- Incentives63
- Confidence46