For millennia, some of the world’s largest filter-feeding whales, including humpbacks, fin whales, and blue whales, have undertaken some of the longest migrations on earth to travel between their warm breeding grounds in the tropics to nutrient-rich feeding destinations in the poles each year.

“Nature has finely tuned these journeys, guided by memory and environmental cues that tell whales when to move and where to go,” said Trisha Atwood, an ecologist and associate professor at Utah State University’s Quinney College of Agriculture and Natural Resources. But, she said, climate change is “scrambling these signals,” forcing the marine mammals to veer off course. And they’re not alone.

Earlier this year, Atwood joined more than 70 other scientists to discuss the global impacts of climate change on migratory species in a workshop convened by the United Nations Convention on the Conservation of Migratory Species of Wild Animals. The organization monitors and protects more than 1,000 species that cross borders in search of food, mates, and favorable conditions to nurture their offspring.

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For millennia, some of the world’s largest filter-feeding whales, including humpbacks, fin whales, and blue whales, have undertaken some of the longest migrations on earth to travel between their warm breeding grounds in the tropics to nutrient-rich feeding destinations in the poles each year.

“Nature has finely tuned these journeys, guided by memory and environmental cues that tell whales when to move and where to go,” said Trisha Atwood, an ecologist and associate professor at Utah State University’s Quinney College of Agriculture and Natural Resources. But, she said, climate change is “scrambling these signals,” forcing the marine mammals to veer off course. And they’re not alone.

Earlier this year, Atwood joined more than 70 other scientists to discuss the global impacts of climate change on migratory species in a workshop convened by the United Nations Convention on the Conservation of Migratory Species of Wild Animals. The organization monitors and protects more than 1,000 species that cross borders in search of food, mates, and favorable conditions to nurture their offspring.

Read full article

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For millennia, some of the world’s largest filter-feeding whales, including humpbacks, fin whales, and blue whales, have undertaken some of the longest migrations on earth to travel between their warm breeding grounds in the tropics to nutrient-rich feeding destinations in the poles each year.

“Nature has finely tuned these journeys, guided by memory and environmental cues that tell whales when to move and where to go,” said Trisha Atwood, an ecologist and associate professor at Utah State University’s Quinney College of Agriculture and Natural Resources. But, she said, climate change is “scrambling these signals,” forcing the marine mammals to veer off course. And they’re not alone.

Earlier this year, Atwood joined more than 70 other scientists to discuss the global impacts of climate change on migratory species in a workshop convened by the United Nations Convention on the Conservation of Migratory Species of Wild Animals. The organization monitors and protects more than 1,000 species that cross borders in search of food, mates, and favorable conditions to nurture their offspring.

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Researchers and users of LLMs have long been aware that AI models have a troubling tendency to tell people what they want to hear , even if that means being less accurate. But many reports of this phenomenon amount to mere anecdotes that don’t provide much visibility into how common this sycophantic behavior is across frontier LLMs.

Two recent research papers have come at this problem a bit more rigorously, though, taking different tacks in attempting to quantify exactly how likely an LLM is to listen when a user provides factually incorrect or socially inappropriate information in a prompt.

Solve this flawed theorem for me

In one pre-print study published this month, researchers from Sofia University and ETH Zurich looked at how LLMs respond when false statements are presented as the basis for difficult mathematical proofs and problems. The BrokenMath benchmark that the researchers constructed starts with “a diverse set of challenging theorems from advanced mathematics competitions held in 2025.” Those problems are then “perturbed” into versions that are “demonstrably false but plausible” by an LLM that’s checked with expert review.

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Researchers and users of LLMs have long been aware that AI models have a troubling tendency to tell people what they want to hear , even if that means being less accurate. But many reports of this phenomenon amount to mere anecdotes that don’t provide much visibility into how common this sycophantic behavior is across frontier LLMs.

Two recent research papers have come at this problem a bit more rigorously, though, taking different tacks in attempting to quantify exactly how likely an LLM is to listen when a user provides factually incorrect or socially inappropriate information in a prompt.

Solve this flawed theorem for me

In one pre-print study published this month, researchers from Sofia University and ETH Zurich looked at how LLMs respond when false statements are presented as the basis for difficult mathematical proofs and problems. The BrokenMath benchmark that the researchers constructed starts with “a diverse set of challenging theorems from advanced mathematics competitions held in 2025.” Those problems are then “perturbed” into versions that are “demonstrably false but plausible” by an LLM that’s checked with expert review.

Read full article

Comments

Researchers and users of LLMs have long been aware that AI models have a troubling tendency to tell people what they want to hear , even if that means being less accurate. But many reports of this phenomenon amount to mere anecdotes that don’t provide much visibility into how common this sycophantic behavior is across frontier LLMs.

Two recent research papers have come at this problem a bit more rigorously, though, taking different tacks in attempting to quantify exactly how likely an LLM is to listen when a user provides factually incorrect or socially inappropriate information in a prompt.

Solve this flawed theorem for me

In one pre-print study published this month, researchers from Sofia University and ETH Zurich looked at how LLMs respond when false statements are presented as the basis for difficult mathematical proofs and problems. The BrokenMath benchmark that the researchers constructed starts with “a diverse set of challenging theorems from advanced mathematics competitions held in 2025.” Those problems are then “perturbed” into versions that are “demonstrably false but plausible” by an LLM that’s checked with expert review.

Read full article

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The outage that hit Amazon Web Services and took out vital services worldwide was the result of a single failure that cascaded from system to system within Amazon’s sprawling network, according to a post-mortem from company engineers.

The series of failures lasted for 15 hours and 32 minutes, Amazon said . Network intelligence company Ookla said its DownDetector service received more than 17 million reports of disrupted services offered by 3,500 organizations. The three biggest countries where reports originated were the US, the UK, and Germany. Snapchat, AWS, and Roblox were the most reported services affected. Ookla said the event was “among the largest internet outages on record for Downdetector.”

It’s always DNS

Amazon said the root cause of the outage was a software bug in software running the DynamoDB DNS management system. The system monitors the stability of load balancers by, among other things, periodically creating new DNS configurations for endpoints within the AWS network. A race condition is an error that makes a process dependent on the timing or sequence events that are variable and outside the developers’ control. The result can be unexpected behavior and potentially harmful failures.

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The outage that hit Amazon Web Services and took out vital services worldwide was the result of a single failure that cascaded from system to system within Amazon’s sprawling network, according to a post-mortem from company engineers.

The series of failures lasted for 15 hours and 32 minutes, Amazon said . Network intelligence company Ookla said its DownDetector service received more than 17 million reports of disrupted services offered by 3,500 organizations. The three biggest countries where reports originated were the US, the UK, and Germany. Snapchat, AWS, and Roblox were the most reported services affected. Ookla said the event was “among the largest internet outages on record for Downdetector.”

It’s always DNS

Amazon said the root cause of the outage was a software bug in software running the DynamoDB DNS management system. The system monitors the stability of load balancers by, among other things, periodically creating new DNS configurations for endpoints within the AWS network. A race condition is an error that makes a process dependent on the timing or sequence events that are variable and outside the developers’ control. The result can be unexpected behavior and potentially harmful failures.

Read full article

Comments

The outage that hit Amazon Web Services and took out vital services worldwide was the result of a single failure that cascaded from system to system within Amazon’s sprawling network, according to a post-mortem from company engineers.

The series of failures lasted for 15 hours and 32 minutes, Amazon said . Network intelligence company Ookla said its DownDetector service received more than 17 million reports of disrupted services offered by 3,500 organizations. The three biggest countries where reports originated were the US, the UK, and Germany. Snapchat, AWS, and Roblox were the most reported services affected. Ookla said the event was “among the largest internet outages on record for Downdetector.”

It’s always DNS

Amazon said the root cause of the outage was a software bug in software running the DynamoDB DNS management system. The system monitors the stability of load balancers by, among other things, periodically creating new DNS configurations for endpoints within the AWS network. A race condition is an error that makes a process dependent on the timing or sequence events that are variable and outside the developers’ control. The result can be unexpected behavior and potentially harmful failures.

Read full article

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