{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/code/extract-problem-solution","entry":"extract_problem_solution","source":"Syntology graph, per-sample; not an archive number","read_at":"2026-09-24T18:15:14+00:00","claim":"Names are grouped by exact entry-name string. Same-named routines are NOT asserted to be equivalent; 'ran' means executed on a synthesized fixture, not correctness. n_samples_ran = sum of by_status over every status except 'unverified' (ran_draft_wrong and ran_fixture are failures of Syntology's instrument, not of the code); n_papers_ran = papers with at least one such sample.","status_vocabulary":{"ran_honours":"ran, honoured the contract we drafted","ran_violates":"ran, violated the contract we drafted","ran_draft_wrong":"ran; our contract draft was wrong, not the code","ran_fixture":"ran; our fixture could not drive it","ran":"ran on a synthesized input","unverified":"unverified (harvested, no recorded run)"},"n_papers":8,"n_papers_ran":0,"units":"n_samples, n_samples_ran, n_samples_fingerprinted and by_status count distinct code bodies (code_sha256); n_places and n_places_pointer_only count places, one per (paper, code body) pair, which is also the unit of the samples list","n_samples":1,"n_samples_ran":0,"n_samples_fingerprinted":0,"n_places":8,"n_places_pointer_only":2,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":0,"ran_fixture":0,"ran":0,"unverified":1},"syntology":{"atlas_url":null,"mcp":null,"mcp_per_sample":{"tool":"get_code","arguments_in":"samples[].mcp_get_code"},"developers":"https://syntology.ai/developers"},"samples":[{"arxiv_id":"2601.22920","paper":"/paper/arxiv-2601-22920","title":"Q-Hawkeye: Reliable Visual Policy Optimization for Image Quality Assessment","date":"2026-01-30","month_inferred_from_arxiv_id":null,"title_source":"syntology","repo":"AMAP-ML/Q-Hawkeye","path":"src/virft/local_scripts/prepare_hf_data.py","file_url":"https://github.com/AMAP-ML/Q-Hawkeye/blob/HEAD/src/virft/local_scripts/prepare_hf_data.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"41c548a25e90eb39","mcp_get_code":{"code_sha256":"41c548a25e90eb39"}},{"arxiv_id":"2601.19686","paper":"/paper/arxiv-2601-19686","title":"2 RELATED WORK Reinforcement learning has emerged as a powerful paradigm for enhancing the reasoning abilities of LLMs","date":null,"month_inferred_from_arxiv_id":"2026-01","title_source":"syntology","repo":"zywang0104/Video-KTR","path":"src/r1-v/local_scripts/prepare_hf_data.py","file_url":"https://github.com/zywang0104/Video-KTR/blob/HEAD/src/r1-v/local_scripts/prepare_hf_data.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"41c548a25e90eb39","mcp_get_code":{"code_sha256":"41c548a25e90eb39"}},{"arxiv_id":"2601.00388","paper":"/paper/arxiv-2601-00388","title":"Vision-Language Reasoning for Geolocalization: A Reinforcement Learning Approach","date":null,"month_inferred_from_arxiv_id":"2026-01","title_source":"syntology","repo":"aialt/geo-r","path":"src/open-r1-multimodal/local_scripts/prepare_hf_data.py","file_url":"https://github.com/aialt/geo-r/blob/HEAD/src/open-r1-multimodal/local_scripts/prepare_hf_data.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"41c548a25e90eb39","mcp_get_code":{"code_sha256":"41c548a25e90eb39"}},{"arxiv_id":"2511.00405","paper":"/paper/arxiv-2511-00405","title":"UME-R1: Exploring Reasoning-Driven Generative Multimodal Embeddings","date":null,"month_inferred_from_arxiv_id":"2025-11","title_source":"syntology","repo":"XMUDeepLIT/UME-R1","path":"src/r1-train/local_scripts/prepare_hf_data.py","file_url":"https://github.com/XMUDeepLIT/UME-R1/blob/HEAD/src/r1-train/local_scripts/prepare_hf_data.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"41c548a25e90eb39","mcp_get_code":{"code_sha256":"41c548a25e90eb39"}},{"arxiv_id":"2510.14605","paper":"/paper/arxiv-2510-14605","title":"Knowledge-based Visual Question Answer with Multimodal Processing, Retrieval and Filtering","date":null,"month_inferred_from_arxiv_id":"2025-10","title_source":"syntology","repo":"om-ai-lab/VLM-R1","path":"src/open-r1-multimodal/local_scripts/prepare_hf_data.py","file_url":"https://github.com/om-ai-lab/VLM-R1/blob/HEAD/src/open-r1-multimodal/local_scripts/prepare_hf_data.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"41c548a25e90eb39","mcp_get_code":{"code_sha256":"41c548a25e90eb39"}},{"arxiv_id":"2505.15810","paper":"/paper/gui-g1-understanding-r1-zero-like-training","title":"GUI-G1: Understanding R1-Zero-Like Training for Visual Grounding in GUI Agents","date":"2025-05-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yuqi-zhou/gui-g1","path":"src/open-r1-multimodal/local_scripts/prepare_hf_data.py","file_url":"https://github.com/yuqi-zhou/gui-g1/blob/HEAD/src/open-r1-multimodal/local_scripts/prepare_hf_data.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"41c548a25e90eb39","mcp_get_code":{"code_sha256":"41c548a25e90eb39"}},{"arxiv_id":"2505.14460","paper":"/paper/visualquality-r1-reasoning-induced-image","title":"VisualQuality-R1: Reasoning-Induced Image Quality Assessment via Reinforcement Learning to Rank","date":"2025-05-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"tianhewu/visualquality-r1","path":"src/open-r1-multimodal/local_scripts/prepare_hf_data.py","file_url":"https://github.com/tianhewu/visualquality-r1/blob/HEAD/src/open-r1-multimodal/local_scripts/prepare_hf_data.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"41c548a25e90eb39","mcp_get_code":{"code_sha256":"41c548a25e90eb39"}},{"arxiv_id":"2503.21620","paper":"/paper/ui-r1-enhancing-action-prediction-of-gui","title":"UI-R1: Enhancing Efficient Action Prediction of GUI Agents by Reinforcement Learning","date":"2025-03-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"lll6gg/ui-r1","path":"src/ui_r1/local_scripts/prepare_hf_data.py","file_url":"https://github.com/lll6gg/ui-r1/blob/HEAD/src/ui_r1/local_scripts/prepare_hf_data.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"41c548a25e90eb39","mcp_get_code":{"code_sha256":"41c548a25e90eb39"}}]}