{"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/sanitize-filename","entry":"sanitize_filename","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":12,"n_papers_ran":6,"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":14,"n_samples_ran":7,"n_samples_fingerprinted":7,"n_places":14,"n_places_pointer_only":3,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":2,"ran_fixture":0,"ran":5,"unverified":7},"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":"2608.21252","paper":"/paper/arxiv-2608-21252","title":"EnSI-RAG: Entity-Structure-Indexed Retrieval-Augmented Generation for Long-Document Question Answering","date":null,"month_inferred_from_arxiv_id":"2026-08","title_source":"syntology","repo":"RamonMeng/EnSI-RAG","path":"ensi-rag-financebench-eval/ensi_financebench/document_preparation.py","file_url":"https://github.com/RamonMeng/EnSI-RAG/blob/HEAD/ensi-rag-financebench-eval/ensi_financebench/document_preparation.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"a87ffc279c389402","mcp_get_code":{"code_sha256":"a87ffc279c389402"}},{"arxiv_id":"2605.28639","paper":"/paper/arxiv-2605-28639","title":"The Attentional White Bear Effect in Transformer Language Models","date":null,"month_inferred_from_arxiv_id":"2026-05","title_source":"syntology","repo":"rramnauth2220/representational-suppression","path":"exp1_recoverability.py","file_url":"https://github.com/rramnauth2220/representational-suppression/blob/HEAD/exp1_recoverability.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":"589d30dc5443825b","mcp_get_code":{"code_sha256":"589d30dc5443825b"}},{"arxiv_id":"2604.27685","paper":"/paper/arxiv-2604-27685","title":"VibroML: an automated toolkit for high-throughput vibrational analysis and dynamic instability remediation of crystalline materials using machine-learned potentials","date":null,"month_inferred_from_arxiv_id":"2026-04","title_source":"syntology","repo":"rogeriog/ProtoCSP","path":"lemat_database_scripts/reorganize_db.py","file_url":"https://github.com/rogeriog/ProtoCSP/blob/HEAD/lemat_database_scripts/reorganize_db.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"1b8a6c6474a17ba7","mcp_get_code":{"code_sha256":"1b8a6c6474a17ba7"}},{"arxiv_id":"2602.17200","paper":"/paper/arxiv-2602-17200","title":"GASS: Geometry-Aware Spherical Sampling for Disentangled Diversity Enhancement in Text-to-Image Generation","date":null,"month_inferred_from_arxiv_id":"2026-02","title_source":"syntology","repo":"L-YeZhu/GASS","path":"sd3_gass_sampling.py","file_url":"https://github.com/L-YeZhu/GASS/blob/HEAD/sd3_gass_sampling.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"b1d35bf5ed3fc1d7","mcp_get_code":{"code_sha256":"b1d35bf5ed3fc1d7"}},{"arxiv_id":"2602.17200","paper":"/paper/arxiv-2602-17200","title":"GASS: Geometry-Aware Spherical Sampling for Disentangled Diversity Enhancement in Text-to-Image Generation","date":null,"month_inferred_from_arxiv_id":"2026-02","title_source":"syntology","repo":"L-YeZhu/GASS","path":"sd3_run_drawbench.py","file_url":"https://github.com/L-YeZhu/GASS/blob/HEAD/sd3_run_drawbench.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"e4ca49a059f7a74a","mcp_get_code":{"code_sha256":"e4ca49a059f7a74a"}},{"arxiv_id":"2601.04537","paper":"/paper/arxiv-2601-04537","title":"Linear Dynamics in the RLVR Training of Large Language Models","date":null,"month_inferred_from_arxiv_id":"2026-01","title_source":"syntology","repo":"Miaow-Lab/RLVR-Linearity","path":"analysis/token_logprob/plot_token_logprob_linearity.py","file_url":"https://github.com/Miaow-Lab/RLVR-Linearity/blob/HEAD/analysis/token_logprob/plot_token_logprob_linearity.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"70be02ba13364d71","mcp_get_code":{"code_sha256":"70be02ba13364d71"}},{"arxiv_id":"2502.03373","paper":"/paper/demystifying-long-chain-of-thought-reasoning","title":"Demystifying Long Chain-of-Thought Reasoning in LLMs","date":"2025-02-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"eddycmu/demystify-long-cot","path":"minhash/find_similar_strings.py","file_url":"https://github.com/eddycmu/demystify-long-cot/blob/HEAD/minhash/find_similar_strings.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"edc77f8288a52828","mcp_get_code":{"code_sha256":"edc77f8288a52828"}},{"arxiv_id":"2410.14641","paper":"/paper/distance-between-relevant-information-pieces","title":"Distance between Relevant Information Pieces Causes Bias in Long-Context LLMs","date":"2024-10-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Rachum-thu/LongPiBench","path":"visual/visualize.py","file_url":"https://github.com/Rachum-thu/LongPiBench/blob/HEAD/visual/visualize.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"11bf077f72f9d4c0","mcp_get_code":{"code_sha256":"11bf077f72f9d4c0"}},{"arxiv_id":"2408.07978","paper":"/paper/coupling-without-communication-and-drafter","title":"Coupling without Communication and Drafter-Invariant Speculative Decoding","date":"2024-08-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"majid-daliri/disd","path":"generate_model_distribution.py","file_url":"https://github.com/majid-daliri/disd/blob/HEAD/generate_model_distribution.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"586b27e651101c24","mcp_get_code":{"code_sha256":"586b27e651101c24"}},{"arxiv_id":"2408.07978","paper":"/paper/coupling-without-communication-and-drafter","title":"Coupling without Communication and Drafter-Invariant Speculative Decoding","date":"2024-08-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"majid-daliri/disd","path":"plot_coupling_bounds.py","file_url":"https://github.com/majid-daliri/disd/blob/HEAD/plot_coupling_bounds.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"ea80be6ad6e47a5d","mcp_get_code":{"code_sha256":"ea80be6ad6e47a5d"}},{"arxiv_id":"2406.02465","paper":"/paper/an-empirical-study-into-clustering-of-unseen","title":"An Empirical Study into Clustering of Unseen Datasets with Self-Supervised Encoders","date":"2024-06-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"scottclowe/zs-ssl-clustering","path":"zs_ssl_clustering/io.py","file_url":"https://github.com/scottclowe/zs-ssl-clustering/blob/HEAD/zs_ssl_clustering/io.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"3a3579dedf3fab37","mcp_get_code":{"code_sha256":"3a3579dedf3fab37"}},{"arxiv_id":"2310.02239","paper":"/paper/minigpt-5-interleaved-vision-and-language","title":"MiniGPT-5: Interleaved Vision-and-Language Generation via Generative Vokens","date":"2023-10-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"eric-ai-lab/minigpt-5","path":"utils.py","file_url":"https://github.com/eric-ai-lab/minigpt-5/blob/HEAD/utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"c94b31b517694776","mcp_get_code":{"code_sha256":"c94b31b517694776"}},{"arxiv_id":"openreview_bgtUrDtKjF","paper":null,"title":"arXiv:openreview_bgtUrDtKjF","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"LAW1223/AlignVid","path":"models/FramePack/inference_f1_json.py","file_url":"https://github.com/LAW1223/AlignVid/blob/HEAD/models/FramePack/inference_f1_json.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":"e69c7890892b24e8","mcp_get_code":{"code_sha256":"e69c7890892b24e8"}},{"arxiv_id":"2025.acl-long.562","paper":null,"title":"arXiv:2025.acl-long.562","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"aeg-hit/PwnGPT","path":"benchmark.py","file_url":"https://github.com/aeg-hit/PwnGPT/blob/HEAD/benchmark.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"adcac16f80461c95","mcp_get_code":{"code_sha256":"adcac16f80461c95"}}]}