{"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/truncate-prompt","entry":"truncate_prompt","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":6,"n_samples_ran":4,"n_samples_fingerprinted":2,"n_places":6,"n_places_pointer_only":2,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":2,"ran_fixture":0,"ran":2,"unverified":2},"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":"2602.02258","paper":"/paper/arxiv-2602-02258","title":"Alignment-Aware Model Adaptation via Feedback-Guided Optimization","date":null,"month_inferred_from_arxiv_id":"2026-02","title_source":"syntology","repo":"facebookresearch/TruthRL","path":"training/open-r1/src/open_r1/sft.py","file_url":"https://github.com/facebookresearch/TruthRL/blob/HEAD/training/open-r1/src/open_r1/sft.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"801fe22f00cc39bd","mcp_get_code":{"code_sha256":"801fe22f00cc39bd"}},{"arxiv_id":"2411.07037","paper":"/paper/lifbench-evaluating-the-instruction-following","title":"LIFBench: Evaluating the Instruction Following Performance and Stability of Large Language Models in Long-Context Scenarios","date":"2024-11-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"sheldonwu0327/lif-bench-2024","path":"evaluation/Inference.py","file_url":"https://github.com/sheldonwu0327/lif-bench-2024/blob/HEAD/evaluation/Inference.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"d7c8ce8de070ca84","mcp_get_code":{"code_sha256":"d7c8ce8de070ca84"}},{"arxiv_id":"2407.15240","paper":"/paper/bigbench-a-unified-benchmark-for-social-bias","title":"BIGbench: A Unified Benchmark for Evaluating Multi-dimensional Social Biases in Text-to-Image Models","date":"2024-07-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"bigbench2024/bigbench2024","path":"benchmark/generate/generate.py","file_url":"https://github.com/bigbench2024/bigbench2024/blob/HEAD/benchmark/generate/generate.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"GPL-3.0","inline_ok":false,"code_sha256_prefix":"859bc0e0407bbfa7","mcp_get_code":{"code_sha256":"859bc0e0407bbfa7"}},{"arxiv_id":"2403.09040","paper":"/paper/ragged-towards-informed-design-of-retrieval","title":"RAGGED: Towards Informed Design of Retrieval Augmented Generation Systems","date":"2024-03-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"neulab/ragged","path":"reader/utils.py","file_url":"https://github.com/neulab/ragged/blob/HEAD/reader/utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"f6e6c7c81db7d3e1","mcp_get_code":{"code_sha256":"f6e6c7c81db7d3e1"}},{"arxiv_id":"2402.15938","paper":"/paper/generalization-or-memorization-data","title":"Generalization or Memorization: Data Contamination and Trustworthy Evaluation for Large Language Models","date":"2024-02-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yihongdong/cdd-ted4llms","path":"CDD.py","file_url":"https://github.com/yihongdong/cdd-ted4llms/blob/HEAD/CDD.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"c9ae5ca390f18904","mcp_get_code":{"code_sha256":"c9ae5ca390f18904"}},{"arxiv_id":"2306.09308","paper":"/paper/matching-pairs-attributing-fine-tuned-models","title":"Matching Pairs: Attributing Fine-Tuned Models to their Pre-Trained Large Language Models","date":"2023-06-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ibm/model-attribution-in-machine-learning","path":"compute_responses/compute_responses.py","file_url":"https://github.com/ibm/model-attribution-in-machine-learning/blob/HEAD/compute_responses/compute_responses.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"92d7fc1c7ab289c3","mcp_get_code":{"code_sha256":"92d7fc1c7ab289c3"}}]}