{"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":"/census/clamp","entry":"clamp","source":"Syntology differential census (groundwork/55, run v2_2026-09-22), per sample; not an archive number","census_date":"2026-09-22","battery_sha256":["b986f7e04d794a0d88ad4c5f32cf63ec3590b5deff0192150737bc5f1c0b4677"],"runner_sha256":["5a452d0e7c0da5b80771d1be2afe3572e253568cf5c5d59a0d08ebd663d00808"],"bucket_key":"positional (rank, kind, dtype) of each array argument; the argument name is not part of the key because the harness draws the shared array from (rank, kind) and casts it to the dtype, whatever the name","bucket_fields":["rank","kind","dtype"],"claim":"Implementations sharing this entry name were each run on one shared input fixed by the positional (rank, kind, dtype) of their array arguments (the bucket). A cluster is the set whose recorded output digest (sha256 of the output rounded to 6 decimals) is identical. Identical values to six decimals on the shared input are agreement on those inputs, not a statement about the whole domain and not a substitution claim.","n_implementations_compared":7,"n_papers":8,"n_buckets":4,"n_distinct_outputs":6,"n_class_bearing":0,"not_compared":{"not_run_on_shared_input":{"n":1,"by_error":{"RuntimeError":1}},"no_array_argument_ran_on_own_fixture_arguments_only":{"n":3,"n_papers":5,"recorded_shared_digest_equals_own_fixture_digest":3},"output_not_digested_non_numeric":{"n":0,"by_type":{}}},"withdrawn_excluded":0,"code_page":"/code/clamp","buckets":[{"bucket":[[2,"float","float32"]],"n_implementations_compared":4,"n_papers":5,"n_not_digested":0,"not_digested_by_type":{},"clusters":[{"output_sha":"fe85bf84a2c9bdcd","size":2,"n_papers":2,"shape":[4,8],"dtype":"float32","type":"Tensor","finite":true,"max_difference_among_recorded_values":0.0,"members_with_recorded_values":2,"torch_reference_conventions_with_this_digest":[],"values":[1.0,0.0,0.47605323791503906,0.0,0.0,0.0,0.0,0.5728892087936401,0.5188759565353394,0.9591456651687622,0.0,0.0,0.0,1.0,0.0,0.5505285859107971,0.06639543920755386,0.0,0.4321114122867584,0.0,0.0,0.0,0.0,0.983674168586731,0.0,0.43327441811561584,0.9449777603149414,1.0,0.0,1.0,0.0,0.0],"values_recorded":32,"members":[{"code_sha256_prefix":"6007bd5812bfaa0f","path":"agent_attack/attacks/clip_attack.py","papers":["2406.12814"],"paper_pages":[{"arxiv_id":"2406.12814","page":"/paper/adversarial-attacks-on-multimodal-agents"}],"arg_sig_recorded":[["x",2,"float","float32"]],"scalar_args":{"min_value":"0","max_value":"1"},"class_bearing":false},{"code_sha256_prefix":"c09cbca10020bb47","path":"wda.py","papers":["2510.10000"],"paper_pages":[{"arxiv_id":"2510.10000","page":"/paper/arxiv-2510-10000"}],"arg_sig_recorded":[["img",2,"float","float32"]],"scalar_args":{"lo":"0.0","hi":"1.0"},"class_bearing":false}]},{"output_sha":"e3abc3b3f0527214","size":1,"n_papers":2,"shape":[4,8],"dtype":"float32","type":"Tensor","finite":true,"max_difference_among_recorded_values":null,"members_with_recorded_values":1,"torch_reference_conventions_with_this_digest":[],"values":[0.5728892087936401,-1.5337762832641602,0.47605323791503906,-0.6908870339393616,-0.9028494954109192,-1.0304712057113647,-0.6627609133720398,0.5728892087936401,0.5188759565353394,0.9591456651687622,-0.3971995711326599,-1.5683481693267822,-0.28853508830070496,0.9591456651687622,-0.5147839784622192,0.5505285859107971,0.06639543920755386,-1.6228755712509155,0.4321114122867584,-0.4061858654022217,-0.02259422466158867,-1.180782437324524,-0.5368682742118835,0.4321114122867584,-0.7956128120422363,0.43327441811561584,0.9449777603149414,1.0175182819366455,-1.225785732269287,1.0175182819366455,-1.3222218751907349,-0.5941091179847717],"values_recorded":32,"members":[{"code_sha256_prefix":"683ad0be49559242","path":"pcb-merging.py","papers":["2502.17159","2410.02396"],"paper_pages":[{"arxiv_id":"2502.17159","page":"/paper/parameter-efficient-merging-for-multimodal"},{"arxiv_id":"2410.02396","page":"/paper/parameter-competition-balancing-for-model"}],"arg_sig_recorded":[["x",2,"float","float32"]],"scalar_args":{"min_ratio":"0.1","max_ratio":"0.1"},"class_bearing":false}]},{"output_sha":"1facc0fc006259d2","size":1,"n_papers":1,"shape":[4,8],"dtype":"float32","type":"Tensor","finite":true,"max_difference_among_recorded_values":null,"members_with_recorded_values":1,"torch_reference_conventions_with_this_digest":[],"values":[0.5,-0.5,0.47605323791503906,-0.5,-0.5,-0.5,-0.5,0.5,0.5,0.5,-0.3971995711326599,-0.5,-0.28853508830070496,0.5,-0.5,0.5,0.06639543920755386,-0.5,0.4321114122867584,-0.4061858654022217,-0.02259422466158867,-0.5,-0.5,0.5,-0.5,0.43327441811561584,0.5,0.5,-0.5,0.5,-0.5,-0.5],"values_recorded":32,"members":[{"code_sha256_prefix":"adc9dfd4579470c0","path":"models/twod_models/ops/twoside_pact.py","papers":["2108.10394"],"paper_pages":[{"arxiv_id":"2108.10394","page":"/paper/dynamic-network-quantization-for-efficient"}],"arg_sig_recorded":[["input",2,"float","float32"]],"scalar_args":{"min":"-0.5","max":"0.5","inplace":"False"},"class_bearing":false}]}]},{"bucket":[[2,"float","float32"],[0,"float","float32"],[0,"float","float32"]],"n_implementations_compared":1,"n_papers":1,"n_not_digested":0,"not_digested_by_type":{},"clusters":[{"output_sha":"0bee09c6fe3c1450","size":1,"n_papers":1,"shape":[4,8],"dtype":"float32","type":"Tensor","finite":true,"max_difference_among_recorded_values":null,"members_with_recorded_values":1,"torch_reference_conventions_with_this_digest":[],"values":[-1.4238250255584717,-1.4238250255584717,-1.4238250255584717,-1.4238250255584717,-1.4238250255584717,-1.4238250255584717,-1.4238250255584717,-1.4238250255584717,-1.4238250255584717,-1.4238250255584717,-1.4238250255584717,-1.4238250255584717,-1.4238250255584717,-1.4238250255584717,-1.4238250255584717,-1.4238250255584717,-1.4238250255584717,-1.4238250255584717,-1.4238250255584717,-1.4238250255584717,-1.4238250255584717,-1.4238250255584717,-1.4238250255584717,-1.4238250255584717,-1.4238250255584717,-1.4238250255584717,-1.4238250255584717,-1.4238250255584717,-1.4238250255584717,-1.4238250255584717,-1.4238250255584717,-1.4238250255584717],"values_recorded":32,"members":[{"code_sha256_prefix":"37a3441d06cf812e","path":"rainier/ppo.py","papers":["2210.03078"],"paper_pages":[{"arxiv_id":"2210.03078","page":"/paper/rainier-reinforced-knowledge-introspector-for"}],"arg_sig_recorded":[["value",2,"float","float32"],["min_value",0,"float","float32"],["max_value",0,"float","float32"]],"scalar_args":{},"class_bearing":false}]}]},{"bucket":[[2,"float","float64"]],"n_implementations_compared":1,"n_papers":1,"n_not_digested":0,"not_digested_by_type":{},"clusters":[{"output_sha":"fe85bf84a2c9bdcd","size":1,"n_papers":1,"shape":[4,8],"dtype":"float64","type":"ndarray","finite":true,"max_difference_among_recorded_values":null,"members_with_recorded_values":1,"torch_reference_conventions_with_this_digest":[],"values":[1.0,0.0,0.47605322923618437,0.0,0.0,0.0,0.0,0.572889220601701,0.5188759529457332,0.9591456414705227,0.0,0.0,0.0,1.0,0.0,0.5505285791931418,0.06639544283095257,0.0,0.4321114229703758,0.0,0.0,0.0,0.0,0.983674188609945,0.0,0.4332744095449241,0.9449777773193271,1.0,0.0,1.0,0.0,0.0],"values_recorded":32,"members":[{"code_sha256_prefix":"77de6eeb25f3b2cc","path":"src/deepaugment/transforms.py","papers":["1708.04552"],"paper_pages":[{"arxiv_id":"1708.04552","page":"/paper/improved-regularization-of-convolutional"}],"arg_sig_recorded":[["x",2,"float","float64"]],"scalar_args":{"min_val":"0.0","max_val":"1.0"},"class_bearing":false}]}]},{"bucket":[[4,"float","float32"],[4,"float","float32"]],"n_implementations_compared":1,"n_papers":1,"n_not_digested":0,"not_digested_by_type":{},"clusters":[{"output_sha":"3d209faac9fd9e74","size":1,"n_papers":1,"shape":[2,3,4,4],"dtype":"float32","type":"Tensor","finite":true,"max_difference_among_recorded_values":null,"members_with_recorded_values":1,"torch_reference_conventions_with_this_digest":[],"values":[0.7231047749519348,0.6530793309211731,1.0,1.0,1.0,0.0,0.0,0.8319791555404663,0.3433533310890198,0.0,0.0,0.0,0.0,0.0,0.0,0.812962532043457,0.0,0.12553228437900543,0.5928415060043335,0.0,0.0,1.0,0.0,1.0,0.22366678714752197,1.0,0.0,0.0,0.3742450773715973,0.00391392083838582,0.895301342010498,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.2125764787197113,0.0,0.0,0.0,0.0,1.0,0.022702597081661224,0.0,0.3325777053833008,0.0,1.0,0.47569993138313293,1.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.21286723017692566,0.8440517783164978,0.0,0.7723703980445862,1.0,0.4846275746822357,0.022118685767054558,1.0,0.0736190527677536,0.6060231924057007,0.28421318531036377,0.5158193111419678,0.0,1.0,0.267061322927475,0.6621701121330261,0.2371496558189392,0.0,0.0,0.16540314257144928,0.0,0.7167379260063171,1.0,0.0,1.0,0.22837327420711517,0.4391850531101227,1.0,0.0,0.49750959873199463,0.14785639941692352,0.0,0.0],"values_recorded":96,"members":[{"code_sha256_prefix":"3936f167deef1650","path":"attack/optimize.py","papers":["2402.08567"],"paper_pages":[{"arxiv_id":"2402.08567","page":"/paper/agent-smith-a-single-image-can-jailbreak-one"}],"arg_sig_recorded":[["x",4,"float","float32"],["ori_x",4,"float","float32"]],"scalar_args":{"epsilon":"0.1","norm":"'Linf'"},"class_bearing":false}]}]}]}