{"schema_version":"onlylabs.public_signal.v1","title":"LG AI Research (EXAONE) Model: LGAI-EXAONE/K-EXAONE-2.0-750B-A37B-DSpark","description":"LG AI Research (EXAONE) model signal with public source context, captured evidence pages, related signals, and category-scoped analysis context.","url":"https://onlylabs.fyi/signals/2f7e1ced-5646-490e-9a6b-0253b4587a1e","json_url":"https://onlylabs.fyi/signals/2f7e1ced-5646-490e-9a6b-0253b4587a1e/signal.json","generated_at":"2026-09-09T18:46:40.455Z","evidence_latest_fetched_at":"2026-07-31T07:20:21.103561+00:00","signal_first_seen_at":"2026-07-31T07:20:07.960395+00:00","org":{"slug":"lg-ai","name":"LG AI Research (EXAONE)","category":"neolab","category_label":"Neolab","dossier_url":"https://onlylabs.fyi/labs/lg-ai","dossier_json_url":"https://onlylabs.fyi/labs/lg-ai/dossier.json"},"related_urls":{"signal":"https://onlylabs.fyi/signals/2f7e1ced-5646-490e-9a6b-0253b4587a1e","signal_json":"https://onlylabs.fyi/signals/2f7e1ced-5646-490e-9a6b-0253b4587a1e/signal.json","source":"https://huggingface.co/LGAI-EXAONE/K-EXAONE-2.0-750B-A37B-DSpark","lab_dossier":"https://onlylabs.fyi/labs/lg-ai","lab_dossier_json":"https://onlylabs.fyi/labs/lg-ai/dossier.json","analysis":"https://onlylabs.fyi/analysis/lg-ai","analysis_json":"https://onlylabs.fyi/analysis/lg-ai/analysis.json","analysis_evidence_json":"https://onlylabs.fyi/analysis/lg-ai/evidence.json","category":"https://onlylabs.fyi/neolabs","category_json":"https://onlylabs.fyi/neolabs.json","category_feed":"https://onlylabs.fyi/neolabs/feed.xml","category_signals_json":"https://onlylabs.fyi/signals.json?category=neolab","topic":"https://onlylabs.fyi/topics/releases","topic_signals_json":"https://onlylabs.fyi/topics/releases/signals.json?category=neolab","topic_feed":"https://onlylabs.fyi/topics/releases/feed.xml?category=neolab","data_business":null},"answer_pack":{"answer":"LG AI Research (EXAONE) published LGAI-EXAONE/K-EXAONE-2.0-750B-A37B-DSpark. 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K-EXAONE 2.0 was scaled to more than three times the size of its predecessor through upcycling, followed by continual pretraining, difficulty-focused mid-training, and post-training. K-EXAONE 2.0 is broadly competitive with leading open-weight models, demonstrating substantial improvements over its predecessor and achieving particularly strong results in long-context retrieval and safety. Highlights - **Frontier-Class Scale** To build a large-scale foundation model with frontier-level intelligence, we upcycled the [K-EXAONE](https://huggingface.co/collections/LGAI-EXAONE/k-exaone) model by expanding both its depth and width, resulting in a more favorable scaling curve. During this process, we found that clamping after two SwiGLU branches effectively mitigates the exploding activations in deeper layers, improving both training and inference stability. - **Advanced Reasoning & Agentic Intelligence** In response to the growth of agentic AI, we focused on expanding the model's capabilities in reasoning, agentic workflows, and long-context..."},"evidence_pages":[],"related_signals":[{"id":"ccf2ac93-7091-4ddd-9f3f-8ff6508306b9","url":"https://onlylabs.fyi/signals/ccf2ac93-7091-4ddd-9f3f-8ff6508306b9","source_url":"https://huggingface.co/LGAI-EXAONE/K-EXAONE-2.0-750B-A37B","title":"LGAI-EXAONE/K-EXAONE-2.0-750B-A37B","context":null,"kind":{"key":"model_released","label":"Model"},"org":{"slug":"lg-ai","name":"LG AI Research (EXAONE)","category":"neolab"},"occurred_at":"2026-07-29T02:52:49+00:00","first_seen_at":"2026-07-31T07:20:07.960395+00:00","date_source":"source"},{"id":"568d04dd-6e72-4ba8-8e85-3a788fd371c6","url":"https://onlylabs.fyi/signals/568d04dd-6e72-4ba8-8e85-3a788fd371c6","source_url":"https://huggingface.co/LGAI-EXAONE/EXAONE-4.5-33B","title":"LGAI-EXAONE/EXAONE-4.5-33B","context":null,"kind":{"key":"model_released","label":"Model"},"org":{"slug":"lg-ai","name":"LG AI Research (EXAONE)","category":"neolab"},"occurred_at":"2026-04-04T08:34:23+00:00","first_seen_at":"2026-06-06T01:49:32.870864+00:00","date_source":"source"},{"id":"92204e0f-9a7f-4312-996b-d76305bf7a53","url":"https://onlylabs.fyi/signals/92204e0f-9a7f-4312-996b-d76305bf7a53","source_url":"https://huggingface.co/LGAI-EXAONE/EXAONE-Path-2.0-rev-EGFR","title":"LGAI-EXAONE/EXAONE-Path-2.0-rev-EGFR","context":null,"kind":{"key":"model_released","label":"Model"},"org":{"slug":"lg-ai","name":"LG AI Research (EXAONE)","category":"neolab"},"occurred_at":"2026-03-04T11:42:00+00:00","first_seen_at":"2026-06-06T01:49:32.870864+00:00","date_source":"source"},{"id":"2f090e5b-b86b-49e5-85a6-4b86fa28479e","url":"https://onlylabs.fyi/signals/2f090e5b-b86b-49e5-85a6-4b86fa28479e","source_url":"https://huggingface.co/LGAI-EXAONE/K-EXAONE-236B-A23B","title":"LGAI-EXAONE/K-EXAONE-236B-A23B","context":null,"kind":{"key":"model_released","label":"Model"},"org":{"slug":"lg-ai","name":"LG AI Research (EXAONE)","category":"neolab"},"occurred_at":"2025-12-26T11:55:04+00:00","first_seen_at":"2026-06-06T01:49:32.870864+00:00","date_source":"source"},{"id":"5be8317e-2787-4b9c-bdc0-1a8f73f3e40c","url":"https://onlylabs.fyi/signals/5be8317e-2787-4b9c-bdc0-1a8f73f3e40c","source_url":"https://huggingface.co/LGAI-EXAONE/EXAONE-Path-2.5","title":"LGAI-EXAONE/EXAONE-Path-2.5","context":null,"kind":{"key":"model_released","label":"Model"},"org":{"slug":"lg-ai","name":"LG AI Research (EXAONE)","category":"neolab"},"occurred_at":"2025-12-15T16:34:54+00:00","first_seen_at":"2026-06-06T01:49:32.870864+00:00","date_source":"source"},{"id":"470a48e0-b893-4962-b281-5501d34af45a","url":"https://onlylabs.fyi/signals/470a48e0-b893-4962-b281-5501d34af45a","source_url":"https://huggingface.co/LGAI-EXAONE/EXAONE-4.0.1-32B","title":"LGAI-EXAONE/EXAONE-4.0.1-32B","context":null,"kind":{"key":"model_released","label":"Model"},"org":{"slug":"lg-ai","name":"LG AI Research (EXAONE)","category":"neolab"},"occurred_at":"2025-07-29T07:32:29+00:00","first_seen_at":"2026-06-06T01:49:32.870864+00:00","date_source":"source"}]}