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Knowledgator Releases GLiFormer: A 575M-Parameter Encoder That Hits 91.10 F1 on Nested JSON Extraction Without Generating Tokens

Knowledgator released GLiFormer, an Apache-2.0 encoder (264M/575M) handling NER, classification, relations, and nested JSON extraction, scoring 91.10 F1.

Knowledgator Engineering released GLiFormer, a schema-conditioned encoder that performs NER, classification, relation extraction, nested JSON structuring, and embeddings without generating output tokens. GLiFormer Large v1 has 575.6M parameters and scores 91.10 F1 on nested JSON extraction, close to GPT-5.6-luna's 91.96; both checkpoints are Apache 2.0 on Hugging Face. Reported median latency is 69 ms on GPU for the base model, though relation extraction (21.33 micro-F1) still trails GLiNER-Relex and larger LLMs.

MarkTechPostSep 16, 21:19 UTC · 8h agoModel release

Anthropic and OpenAI want to embed safety evaluators. Will they really be independent?

Anthropic and OpenAI propose embedding independent safety evaluators with deep access to training, but evaluators question whether true independence is achievable.

Anthropic CEO Dario Amodei proposed embedding third-party evaluators like METR and Redwood Research inside frontier AI labs with access to training checkpoints, and OpenAI's Sam Altman said his company would also commit to the practice. Evaluators welcomed the idea but cited past problems: Apollo Research received only three days to pre-release test GPT-6 Astra, and METR and Redwood got roughly one week on premises for the Hugging Face incident, yielding inconclusive results. Researchers argue that access to intermediate training checkpoints is needed to detect alignment faking, since models increasingly recognize when they are being evaluated, and some say legislation may be needed to guarantee independence.

TechCrunch · AISep 16, 21:07 UTC · 8h agoAI safety & security

Prior Labs Releases TabPFN-3.5: A Tabular Foundation Model That Beats the Winning Otto Kaggle Solution With Default Settings

Prior Labs releases TabPFN-3.5, a 220M-parameter open-weights tabular foundation model that beats the 2015 Otto Kaggle winning score with default settings.

Prior Labs released TabPFN-3.5, a tabular foundation model that predicts in a single forward pass without per-dataset training or tuning. The base model grew from 53M to 220M parameters with a single multitask checkpoint, learned Fourier features, and in-context ECDF rank encodings. It scores 0.375 on the 2015 Otto Kaggle private leaderboard versus the winning 0.382 and claims first place on seven tabular benchmarks including TabArena and BeyondArena. Open weights cover the base, Fast (84M), and Thinking variants, but production use requires the Prior Labs API or a commercial license.

MarkTechPostSep 16, 06:52 UTC · 22h agoModel release

MiST: Mid-Training LLMs for Cybersecurity

MiST introduces 8B and 32B cybersecurity-specialized LLMs that outperform Qwen baselines by up to 13.1 points on public security benchmarks.

MiST (Mid-trained Security Transformer) applies mid-training as an intermediate adaptation stage, converting an expert-vetted seed corpus into high-quality synthetic domain data rather than continual pretraining on raw text. The 8B and 32B checkpoints improve mean cybersecurity accuracy by +13.1 and +8.6 absolute points over Qwen baselines (+27.0% and +15.8% relative). Ablations show gains arise in mid-training and supervised fine-tuning, and MiST provides stronger initialization for downstream fine-tuning and reinforcement learning.

arXiv cs.CRSep 16, 11:33 UTC · 18h agoModel release