Physics-based AI fall detection runs on 50,000 parameters
A new arXiv preprint recasts fall detection as a stability-loss physics problem using liquid time-constant networks for low-power edge devices.
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A new arXiv preprint recasts fall detection as a stability-loss physics problem using liquid time-constant networks for low-power edge devices.
Bulkhead, a new arXiv preprint, uses multi-agent LLMs to automatically find and fix path traversal vulnerabilities in containers running AI workloads.
A new arXiv study finds LLM-suggested password replacements are more secure than human-created ones and equally memorable after one week.
Reinforcement fine-tuning with 30 prompts cut an open-weight model's building emissions to 61.2 kg-CO2, near the 60.8 optimum, a preprint shows.
Researchers find LLM judge bias lives in a low-dimensional subspace inside model hidden states, and steering along it can switch bias on and off.
MCP security scanners flag 96.89% of servers as risky, but a study of 64,611 servers finds fewer than half of alerts are true positives.
New arXiv preprint Prezta replaces application gateways at critical infrastructure edges with zero-knowledge proofs generated on client devices.
Researchers propose Q-DIBA, a backdoor attack generating a unique trigger for each input to quantum neural networks, evading three tested defenses.
A new arXiv preprint introduces StoryTeller, a training-free system that keeps film audio descriptions coherent for blind and low-vision viewers.
New arXiv preprint shows Transformer training on inductive reasoning can be confined to a low-dimensional manifold for automatic circuit detection.
An arXiv preprint finds sampling temperature on retrieval-augmented LLMs controls how strongly ideological framing from source documents bleeds into
AdvancedMathBench tests AI on graduate and doctoral math proofs, finding frontier models struggle with both writing and verifying advanced mathematics.