{"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/get-causal-mask","entry":"get_causal_mask","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":6,"n_papers_ran":4,"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":5,"n_samples_ran":4,"n_samples_fingerprinted":1,"n_places":6,"n_places_pointer_only":2,"by_status":{"ran_honours":0,"ran_violates":2,"ran_draft_wrong":1,"ran_fixture":1,"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":"2608.19735","paper":"/paper/arxiv-2608-19735","title":"RecPFN: Prior-Fitted Networks for In-Context-Based Recommendations","date":null,"month_inferred_from_arxiv_id":"2026-08","title_source":"syntology","repo":"SAP-samples/tabular-ai-recpfn","path":"src/architecture/recpfn.py","file_url":"https://github.com/SAP-samples/tabular-ai-recpfn/blob/HEAD/src/architecture/recpfn.py","status":"ran_violates","verification_level":1,"contract_check":"VIOLATES","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"5d53fecc166feaf7","mcp_get_code":{"code_sha256":"5d53fecc166feaf7"}},{"arxiv_id":"2510.27497","paper":"/paper/arxiv-2510-27497","title":"InertialAR: Autoregressive 3D Molecule Generation with Inertial Frames","date":null,"month_inferred_from_arxiv_id":"2025-10","title_source":"syntology","repo":"HaoruiLi46/InertialAR","path":"InertialAR/attention.py","file_url":"https://github.com/HaoruiLi46/InertialAR/blob/HEAD/InertialAR/attention.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"25d6d5385209908e","mcp_get_code":{"code_sha256":"25d6d5385209908e"}},{"arxiv_id":"2503.16278","paper":"/paper/uni-3dar-unified-3d-generation-and","title":"Uni-3DAR: Unified 3D Generation and Understanding via Autoregression on Compressed Spatial Tokens","date":"2025-03-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"dptech-corp/uni-3dar","path":"uni3dar/models/attention.py","file_url":"https://github.com/dptech-corp/uni-3dar/blob/HEAD/uni3dar/models/attention.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"25d6d5385209908e","mcp_get_code":{"code_sha256":"25d6d5385209908e"}},{"arxiv_id":"2502.12082","paper":"/paper/adasplash-adaptive-sparse-flash-attention","title":"AdaSplash: Adaptive Sparse Flash Attention","date":"2025-02-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"deep-spin/adasplash","path":"tests/test_adasplash.py","file_url":"https://github.com/deep-spin/adasplash/blob/HEAD/tests/test_adasplash.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":true,"code_sha256_prefix":"f6e8f05febce66db","mcp_get_code":{"code_sha256":"f6e8f05febce66db"}},{"arxiv_id":"2410.02705","paper":"/paper/controlar-controllable-image-generation-with","title":"ControlAR: Controllable Image Generation with Autoregressive Models","date":"2024-10-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hustvl/controlar","path":"autoregressive/models/gpt.py","file_url":"https://github.com/hustvl/controlar/blob/HEAD/autoregressive/models/gpt.py","status":"ran_violates","verification_level":1,"contract_check":"VIOLATES","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"8e142bd1a80ed2ff","mcp_get_code":{"code_sha256":"8e142bd1a80ed2ff"}},{"arxiv_id":"2405.15349","paper":"/paper/unke-unstructured-knowledge-editing-in-large","title":"Everything is Editable: Extend Knowledge Editing to Unstructured Data in Large Language Models","date":"2024-05-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"TrustedLLM/UnKE","path":"code/unke.py","file_url":"https://github.com/TrustedLLM/UnKE/blob/HEAD/code/unke.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"4c479fe8591c3b1b","mcp_get_code":{"code_sha256":"4c479fe8591c3b1b"}}]}