Apple researchers propose a lightweight method to detect code-switching directly from speech using pretrained speech encoders.
Anthropic’s internal research model traced the origins of NIST’s ECDSA curve seeds to the lrand48 pseudorandom number generator seeded with Unix timestamps between 1997 and 1998.
Apple’s ML Research team introduces the first tri-modal Masked Diffusion Models (MDM) pretrained from scratch on text, image-text, and audio-text data.
Apple’s ML Research team introduces Selection-Based Structured Reasoning (SSR), a framework that replaces freeform reasoning with selection from pre-specified candidates to improve efficiency in multimodal agents.
Apple’s ML Research proposes Plan-then-Retrieve (PtR), an alternative to the dominant Retrieve-then-Plan (RtP) paradigm for tool-augmented LLM agents.
Magnetar enables verifiable computing by attaching cryptographic proofs to program outputs, allowing anyone to verify correctness in milliseconds without rerunning the program or trusting the operator.
VISTA is a training-free method that selects guided timesteps for reward-guided discrete diffusion to reduce computational cost.
Benchmark saturation reduces the utility of traditional accuracy scores for differentiating model capabilities.
Google DeepMind describes using its agentic platform Google Antigravity to accelerate the search for novel antimicrobial peptides (AMPs).
A new benchmark called the LLM-Referenced C-test is proposed to evaluate systems that may surpass current AI and human capabilities.