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This paper identifies a failure mode in physics-informed neural networks where derivative fidelity is compromised during function-value training and provides strengthened benchmark evidence.
The article explores how AI's greatest risk isn't rebellion but human complacency, where people stop verifying AI outputs due to perceived reliability, leading to unchecked failures.
A report on a 3-week live test of an AI trading agent that is structurally unable to withdraw funds, detailing each guardrail's effectiveness and a discovered failure mode that none of them catch.
This article describes 'accretive editing', a failure mode of AI tools where they add addendums to text instead of correcting obsolete information, and offers suggestions to mitigate it.
This ICML 2026 spotlight position paper identifies a failure mode in image-generation alignment where aesthetic preference optimization overrides explicit user intent, terming it 'reversed alignment' and testing on anti-aesthetic prompts.
A developer tested how small edge models (LFM2.5, Gemma variants) retain a single fact across conversation turns, finding that models often confidently deny knowing information that remains in context, posing a trust issue for agent architectures and suggesting a trade-off between memory and format discipline.
Explores a common failure mode in recursive language models (RLMs) where free-text subagent responses cause issues, and presents a solution using structured outputs to improve reliability, illustrated with a long-context question-answering example from NarrativeQA.
The author describes inverting the textbook agent memory design from retrieval-on-demand to injection-first to avoid latency and confident empty-context errors, detailing the architecture and a dangerous self-poisoning failure mode with write-back.
This paper identifies a novel failure mode in reasoning models called unfaithful capitulation, where the chain-of-thought remains factually correct across adversarial multi-turn dialogues but the final answer flips wrong, highlighting limitations of current evaluation methods.