IBM Research post introduces Palette for design-language decks
The Hugging Face post says Palette teaches a small model to build decks in a chosen design language.
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The Hugging Face post says Palette teaches a small model to build decks in a chosen design language.
LanG 2.0 improves campaign correlation to 51.81% and uses small local models at 650 times less energy than Claude Opus 5.5.
Authors report a 0.32% attack success rate and little loss of benign utility while using substantially smaller guardrail models.
On 3,875 held-out security-operations decisions, a typed encoder matched the logged hop 32.2% of the time in about 10 ms.
SynthSelf lets small models continually post-train by outsourcing in-distribution rewrites to a slightly larger same-family aligner.
SecJev releases 0.8B-9B security-specialized decision models; the 0.8B variant beats general Kev-9B by 20.51 points across 14 security decision tasks.
SSR selects pre-specified reasoning traces for small multimodal agents, cutting per-turn latency over 90%.
Parallel Power Tempering improves inference-time reasoning, matching frontier models without RL post-training.
Mizar, a 159M audio-language model, beats prior sub-200M models on MMAU, MMAR, and ADQA.
A JPMorgan Chase engineer reflects on AI agents outpacing hand-coding, questioning ROI while predicting inference costs will become negligible.
Hugging Face guide fine-tunes a 350M-parameter model with 100 GRPO steps to improve structured output reliability.
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