{"schema_version":"onlylabs.public_signal.v1","title":"InclusionAI (Ant Group) Writing: M2-Reasoning: Empowering MLLMs with Unified General and Spatial Reasoning","description":"InclusionAI (Ant Group) writing signal with public source context, captured evidence pages, related signals, and category-scoped analysis context.","url":"https://onlylabs.fyi/signals/b038ba8b-9f91-4768-96c4-5bfa672a3553","json_url":"https://onlylabs.fyi/signals/b038ba8b-9f91-4768-96c4-5bfa672a3553/signal.json","generated_at":"2026-06-07T21:15:52.431864+00:00","org":{"slug":"inclusionai","name":"InclusionAI (Ant Group)","category":"neolab","category_label":"Neolab","dossier_url":"https://onlylabs.fyi/labs/inclusionai","dossier_json_url":"https://onlylabs.fyi/labs/inclusionai/dossier.json"},"related_urls":{"signal":"https://onlylabs.fyi/signals/b038ba8b-9f91-4768-96c4-5bfa672a3553","signal_json":"https://onlylabs.fyi/signals/b038ba8b-9f91-4768-96c4-5bfa672a3553/signal.json","source":"https://www.inclusion-ai.org/blog/m2-reasoning","lab_dossier":"https://onlylabs.fyi/labs/inclusionai","lab_dossier_json":"https://onlylabs.fyi/labs/inclusionai/dossier.json","analysis":"https://onlylabs.fyi/analysis/inclusionai","analysis_json":"https://onlylabs.fyi/analysis/inclusionai/analysis.json","analysis_evidence_json":"https://onlylabs.fyi/analysis/inclusionai/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/talking","topic_signals_json":"https://onlylabs.fyi/topics/talking/signals.json?category=neolab","topic_feed":"https://onlylabs.fyi/topics/talking/feed.xml?category=neolab","data_business":null},"answer_pack":{"answer":"InclusionAI (Ant Group) published M2-Reasoning: Empowering MLLMs with Unified General and Spatial Reasoning. This talking signal gives public context for research themes, product direction, policy, or launch framing. High-signal details: Research post on reasoning, no traction info · M2-Reasoning: Empowering MLLMs with Unified General and Spatial Reasoning | INCLUSION AI Skip to main content 📖 Technical Report | 🤗 Hugging Face ｜ 🤖 ModelScope.... onlylabs links this event to 1 captured evidence page and 6 related writing signals.","signal_desk":"talking","source_context":{"source_url":"https://www.inclusion-ai.org/blog/m2-reasoning","source_host":"inclusion-ai.org","occurred_at":"2025-07-11T00:00:00+00:00","first_seen_at":"2026-06-05T22:32:23.327834+00:00","date_source":"rss.item_date","context":null},"context_markers":[{"label":"Lab","value":"InclusionAI (Ant Group)","source":"signal"},{"label":"Signal desk","value":"talking","source":"signal"},{"label":"Source host","value":"inclusion-ai.org","source":"source"},{"label":"Author","value":"ospo@antgroup.com (inclusionAI)","source":"source"},{"label":"Notability","value":"Research post on reasoning, no traction 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This talking signal gives public context for research themes, product direction, policy, or launch framing. High-signal details: Research post on reasoning, no traction info · M2-Reasoning: Empowering MLLMs with Unified General and Spatial Reasoning | INCLUSION AI Skip to main content 📖 Technical Report | 🤗 Hugging Face ｜ 🤖 ModelScope.... onlylabs links this event to 1 captured evidence page and 6 related writing signals.","semantic_triples":[{"subject":"InclusionAI (Ant Group)","predicate":"published","object":"M2-Reasoning: Empowering MLLMs with Unified General and Spatial Reasoning","text":"InclusionAI (Ant Group) published M2-Reasoning: Empowering MLLMs with Unified General and Spatial Reasoning."},{"subject":"M2-Reasoning: Empowering MLLMs with Unified General and Spatial Reasoning","predicate":"is classified as","object":"writing signal","text":"M2-Reasoning: Empowering MLLMs with Unified General and Spatial Reasoning is classified as writing signal."},{"subject":"M2-Reasoning: Empowering MLLMs with Unified General and Spatial Reasoning","predicate":"belongs to","object":"talking desk","text":"M2-Reasoning: Empowering MLLMs with Unified General and Spatial Reasoning belongs to talking desk."},{"subject":"M2-Reasoning: Empowering MLLMs with Unified General and Spatial Reasoning","predicate":"has evidence coverage","object":"1 captured evidence page","text":"M2-Reasoning: Empowering MLLMs with Unified General and Spatial Reasoning has evidence coverage 1 captured evidence page."}]},"signal":{"id":"b038ba8b-9f91-4768-96c4-5bfa672a3553","url":"https://onlylabs.fyi/signals/b038ba8b-9f91-4768-96c4-5bfa672a3553","json_url":"https://onlylabs.fyi/signals/b038ba8b-9f91-4768-96c4-5bfa672a3553/signal.json","source_url":"https://www.inclusion-ai.org/blog/m2-reasoning","title":"M2-Reasoning: Empowering MLLMs with Unified General and Spatial Reasoning","summary":"InclusionAI (Ant Group) published a writing signal. onlylabs watches public writing for research themes, product direction, and model-launch context.","context":null,"kind":{"key":"post_published","label":"Writing"},"org":{"slug":"inclusionai","name":"InclusionAI (Ant Group)","category":"neolab"},"occurred_at":"2025-07-11T00:00:00+00:00","first_seen_at":"2026-06-05T22:32:23.327834+00:00","date_source":"rss.item_date","evidence_coverage":{"target_pages":1,"captured_pages":1,"readable_pages":1,"capture_methods":["plain"],"missing_page_urls":[],"failed_page_urls":[],"blocked_page_urls":[],"page_urls":["https://www.inclusion-ai.org/blog/m2-reasoning"]},"facets":{},"traction":{"github_stars":null,"hn_points":null,"hn_comments":null,"hn_story_id":null,"hf_downloads":null,"hf_likes":null},"data_radar":null},"primary_evidence_page":{"url":"https://www.inclusion-ai.org/blog/m2-reasoning","final_url":"https://www.inclusion-ai.org/blog/m2-reasoning/","title":"M2-Reasoning: Empowering MLLMs with Unified General and Spatial Reasoning","http_status":200,"content_type":"text/html; charset=utf-8","capture_method":"plain","fetched_at":"2026-06-07T21:15:52.431864+00:00","bytes":96551,"raw_path":"64e2c67ad995d9d88ab414a402595aac1d6a1fc7ac1d8ae6b2ff2e3c62213abd.html","content_hash":"0e79998c2539522936b78fcbb6090540c818cfb28252710168afebd63adedff0","excerpt_chars":1200,"truncated":true,"excerpt":"M2-Reasoning: Empowering MLLMs with Unified General and Spatial Reasoning | INCLUSION AI Skip to main content 📖 Technical Report | 🤗 Hugging Face ｜ 🤖 ModelScope Introduction ​ We introduce M2-Reasoning-7B, a model designed to excel in both general and spatial reasoning. Our approach integrates two key innovations: (1) a novel data pipeline that generates 294.2K high-quality data samples (168K for cold-start fine-tuning and 126.2K for RLVR), which feature logically coherent reasoning trajectories and have undergone comprehensive assessment; and (2) a dynamic multi-task training strategy with step-wise optimization to mitigate conflicts between data, and task-specific rewards for delivering tailored incentive signals. This combination of curated data and advanced training allows M2-Reasoning-7B to set a new state-of-the-art (SOTA) across 8 benchmarks, showcasing superior performance in both general and spatial reasoning domains. 📌 Updates ​ [2025.07.14] 🔥 Our Technical Report is in public on arxiv. [2025.07.11] 🔥 We release M2-Reasoning on 🤗 Hugging Face and 🤖 ModelScope . Key Features ​ A High-quality Data Construction Pipeline: We design and implement a multi-stage data..."},"evidence_pages":[{"url":"https://www.inclusion-ai.org/blog/m2-reasoning","final_url":"https://www.inclusion-ai.org/blog/m2-reasoning/","title":"M2-Reasoning: Empowering MLLMs with Unified General and Spatial Reasoning","http_status":200,"content_type":"text/html; charset=utf-8","capture_method":"plain","fetched_at":"2026-06-07T21:15:52.431864+00:00","bytes":96551,"raw_path":"64e2c67ad995d9d88ab414a402595aac1d6a1fc7ac1d8ae6b2ff2e3c62213abd.html","content_hash":"0e79998c2539522936b78fcbb6090540c818cfb28252710168afebd63adedff0","excerpt_chars":1200,"truncated":true,"excerpt":"M2-Reasoning: Empowering MLLMs with Unified General and Spatial Reasoning | INCLUSION AI Skip to main content 📖 Technical Report | 🤗 Hugging Face ｜ 🤖 ModelScope Introduction ​ We introduce M2-Reasoning-7B, a model designed to excel in both general and spatial reasoning. Our approach integrates two key innovations: (1) a novel data pipeline that generates 294.2K high-quality data samples (168K for cold-start fine-tuning and 126.2K for RLVR), which feature logically coherent reasoning trajectories and have undergone comprehensive assessment; and (2) a dynamic multi-task training strategy with step-wise optimization to mitigate conflicts between data, and task-specific rewards for delivering tailored incentive signals. This combination of curated data and advanced training allows M2-Reasoning-7B to set a new state-of-the-art (SOTA) across 8 benchmarks, showcasing superior performance in both general and spatial reasoning domains. 📌 Updates ​ [2025.07.14] 🔥 Our Technical Report is in public on arxiv. [2025.07.11] 🔥 We release M2-Reasoning on 🤗 Hugging Face and 🤖 ModelScope . 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