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counter_consistent_result_cvbench3d_depth.txt

@@ -0,0 +1,312 @@
+
+======================================================================
+Model: molmo-7B-O-0924
+======================================================================
+
+Category     Type         Correct    Total      Accuracy  
+------------------------------------------------------
+Depth        consistent   338        363        93.1%
+Depth        counter      49         65         75.4%
+Depth        ambiguous    120        172        69.8%
+------------------------------------------------------
+TOTAL        consistent   338        363        93.1%
+TOTAL        counter      49         65         75.4%
+
+Accuracy Gap (Consistent - Counter): 17.7%p
+   -> Larger gap indicates stronger reliance on the 2D heuristic
+
+🔍 Counter examples wrong: 16 / 65
+
+======================================================================
+Model: molmo-7B-O-0924-data_scale_exp_80k
+======================================================================
+
+Category     Type         Correct    Total      Accuracy  
+------------------------------------------------------
+Depth        consistent   291        363        80.2%
+Depth        counter      37         65         56.9%
+Depth        ambiguous    98         172        57.0%
+------------------------------------------------------
+TOTAL        consistent   291        363        80.2%
+TOTAL        counter      37         65         56.9%
+
+Accuracy Gap (Consistent - Counter): 23.2%p
+   -> Larger gap indicates stronger reliance on the 2D heuristic
+
+🔍 Counter examples wrong: 28 / 65
+
+======================================================================
+Model: molmo-7B-O-0924-data_scale_exp_400k
+======================================================================
+
+Category     Type         Correct    Total      Accuracy  
+------------------------------------------------------
+Depth        consistent   325        363        89.5%
+Depth        counter      37         65         56.9%
+Depth        ambiguous    118        172        68.6%
+------------------------------------------------------
+TOTAL        consistent   325        363        89.5%
+TOTAL        counter      37         65         56.9%
+
+Accuracy Gap (Consistent - Counter): 32.6%p
+   -> Larger gap indicates stronger reliance on the 2D heuristic
+
+🔍 Counter examples wrong: 28 / 65
+
+======================================================================
+Model: molmo-7B-O-0924-data_scale_exp_800k
+======================================================================
+
+Category     Type         Correct    Total      Accuracy  
+------------------------------------------------------
+Depth        consistent   322        363        88.7%
+Depth        counter      46         65         70.8%
+Depth        ambiguous    124        172        72.1%
+------------------------------------------------------
+TOTAL        consistent   322        363        88.7%
+TOTAL        counter      46         65         70.8%
+
+Accuracy Gap (Consistent - Counter): 17.9%p
+   -> Larger gap indicates stronger reliance on the 2D heuristic
+
+🔍 Counter examples wrong: 19 / 65
+
+======================================================================
+Model: molmo-7B-O-0924-data_scale_exp_2m
+======================================================================
+
+Category     Type         Correct    Total      Accuracy  
+------------------------------------------------------
+Depth        consistent   329        363        90.6%
+Depth        counter      47         65         72.3%
+Depth        ambiguous    148        172        86.0%
+------------------------------------------------------
+TOTAL        consistent   329        363        90.6%
+TOTAL        counter      47         65         72.3%
+
+Accuracy Gap (Consistent - Counter): 18.3%p
+   -> Larger gap indicates stronger reliance on the 2D heuristic
+
+🔍 Counter examples wrong: 18 / 65
+
+======================================================================
+Model: NVILA-Lite-2B
+======================================================================
+
+Category     Type         Correct    Total      Accuracy  
+------------------------------------------------------
+Depth        consistent   270        363        74.4%
+Depth        counter      26         65         40.0%
+Depth        ambiguous    119        172        69.2%
+------------------------------------------------------
+TOTAL        consistent   270        363        74.4%
+TOTAL        counter      26         65         40.0%
+
+Accuracy Gap (Consistent - Counter): 34.4%p
+   -> Larger gap indicates stronger reliance on the 2D heuristic
+
+🔍 Counter examples wrong: 39 / 65
+
+======================================================================
+Model: NVILA-Lite-2B-data-scale-exp-80k
+======================================================================
+
+Category     Type         Correct    Total      Accuracy  
+------------------------------------------------------
+Depth        consistent   260        363        71.6%
+Depth        counter      33         65         50.8%
+Depth        ambiguous    104        172        60.5%
+------------------------------------------------------
+TOTAL        consistent   260        363        71.6%
+TOTAL        counter      33         65         50.8%
+
+Accuracy Gap (Consistent - Counter): 20.9%p
+   -> Larger gap indicates stronger reliance on the 2D heuristic
+
+🔍 Counter examples wrong: 32 / 65
+
+======================================================================
+Model: NVILA-Lite-2B-data-scale-exp-400k
+======================================================================
+
+Category     Type         Correct    Total      Accuracy  
+------------------------------------------------------
+Depth        consistent   295        363        81.3%
+Depth        counter      38         65         58.5%
+Depth        ambiguous    113        172        65.7%
+------------------------------------------------------
+TOTAL        consistent   295        363        81.3%
+TOTAL        counter      38         65         58.5%
+
+Accuracy Gap (Consistent - Counter): 22.8%p
+   -> Larger gap indicates stronger reliance on the 2D heuristic
+
+🔍 Counter examples wrong: 27 / 65
+
+======================================================================
+Model: NVILA-Lite-2B-data-scale-exp-800k
+======================================================================
+
+Category     Type         Correct    Total      Accuracy  
+------------------------------------------------------
+Depth        consistent   307        363        84.6%
+Depth        counter      44         65         67.7%
+Depth        ambiguous    118        172        68.6%
+------------------------------------------------------
+TOTAL        consistent   307        363        84.6%
+TOTAL        counter      44         65         67.7%
+
+Accuracy Gap (Consistent - Counter): 16.9%p
+   -> Larger gap indicates stronger reliance on the 2D heuristic
+
+🔍 Counter examples wrong: 21 / 65
+
+======================================================================
+Model: NVILA-Lite-2B-data-scale-exp-2m
+======================================================================
+
+Category     Type         Correct    Total      Accuracy  
+------------------------------------------------------
+Depth        consistent   353        363        97.2%
+Depth        counter      61         65         93.8%
+Depth        ambiguous    149        172        86.6%
+------------------------------------------------------
+TOTAL        consistent   353        363        97.2%
+TOTAL        counter      61         65         93.8%
+
+Accuracy Gap (Consistent - Counter): 3.4%p
+   -> Larger gap indicates stronger reliance on the 2D heuristic
+
+🔍 Counter examples wrong: 4 / 65
+
+======================================================================
+Model: RoboRefer-2B-SFT
+======================================================================
+
+Category     Type         Correct    Total      Accuracy  
+------------------------------------------------------
+Depth        consistent   359        363        98.9%
+Depth        counter      62         65         95.4%
+Depth        ambiguous    153        172        89.0%
+------------------------------------------------------
+TOTAL        consistent   359        363        98.9%
+TOTAL        counter      62         65         95.4%
+
+Accuracy Gap (Consistent - Counter): 3.5%p
+   -> Larger gap indicates stronger reliance on the 2D heuristic
+
+🔍 Counter examples wrong: 3 / 65
+
+======================================================================
+Model: Qwen2.5-VL-3B-Instruct
+======================================================================
+
+Category     Type         Correct    Total      Accuracy  
+------------------------------------------------------
+Depth        consistent   274        363        75.5%
+Depth        counter      36         65         55.4%
+Depth        ambiguous    112        172        65.1%
+------------------------------------------------------
+TOTAL        consistent   274        363        75.5%
+TOTAL        counter      36         65         55.4%
+
+Accuracy Gap (Consistent - Counter): 20.1%p
+   -> Larger gap indicates stronger reliance on the 2D heuristic
+
+🔍 Counter examples wrong: 29 / 65
+
+======================================================================
+Model: Qwen2.5-VL-3B-Instruct-data_scale_exp_80k
+======================================================================
+
+Category     Type         Correct    Total      Accuracy  
+------------------------------------------------------
+Depth        consistent   253        363        69.7%
+Depth        counter      39         65         60.0%
+Depth        ambiguous    96         172        55.8%
+------------------------------------------------------
+TOTAL        consistent   253        363        69.7%
+TOTAL        counter      39         65         60.0%
+
+Accuracy Gap (Consistent - Counter): 9.7%p
+   -> Larger gap indicates stronger reliance on the 2D heuristic
+
+🔍 Counter examples wrong: 26 / 65
+
+======================================================================
+Model: Qwen2.5-VL-3B-Instruct-data_scale_exp_400k
+======================================================================
+
+Category     Type         Correct    Total      Accuracy  
+------------------------------------------------------
+Depth        consistent   239        363        65.8%
+Depth        counter      38         65         58.5%
+Depth        ambiguous    95         172        55.2%
+------------------------------------------------------
+TOTAL        consistent   239        363        65.8%
+TOTAL        counter      38         65         58.5%
+
+Accuracy Gap (Consistent - Counter): 7.4%p
+   -> Larger gap indicates stronger reliance on the 2D heuristic
+
+🔍 Counter examples wrong: 27 / 65
+
+======================================================================
+Model: Qwen2.5-VL-3B-Instruct-data_scale_exp_800k
+======================================================================
+
+Category     Type         Correct    Total      Accuracy  
+------------------------------------------------------
+Depth        consistent   222        363        61.2%
+Depth        counter      38         65         58.5%
+Depth        ambiguous    92         172        53.5%
+------------------------------------------------------
+TOTAL        consistent   222        363        61.2%
+TOTAL        counter      38         65         58.5%
+
+Accuracy Gap (Consistent - Counter): 2.7%p
+   -> Larger gap indicates stronger reliance on the 2D heuristic
+
+🔍 Counter examples wrong: 27 / 65
+
+======================================================================
+Model: Qwen2.5-VL-3B-Instruct-data_scale_exp_2m
+======================================================================
+
+Category     Type         Correct    Total      Accuracy  
+------------------------------------------------------
+Depth        consistent   225        363        62.0%
+Depth        counter      35         65         53.8%
+Depth        ambiguous    91         172        52.9%
+------------------------------------------------------
+TOTAL        consistent   225        363        62.0%
+TOTAL        counter      35         65         53.8%
+
+Accuracy Gap (Consistent - Counter): 8.1%p
+   -> Larger gap indicates stronger reliance on the 2D heuristic
+
+🔍 Counter examples wrong: 30 / 65
+
+==============================================================================
+MODEL COMPARISON (CV-Bench-3D depth)
+==============================================================================
+Model                                        Consistent   Counter      Gap       
+------------------------------------------------------------------------------
+molmo-7B-O-0924                              93.1%       75.4%       +17.7%p
+molmo-7B-O-0924-data_scale_exp_80k           80.2%       56.9%       +23.2%p
+molmo-7B-O-0924-data_scale_exp_400k          89.5%       56.9%       +32.6%p
+molmo-7B-O-0924-data_scale_exp_800k          88.7%       70.8%       +17.9%p
+molmo-7B-O-0924-data_scale_exp_2m            90.6%       72.3%       +18.3%p
+------------------------------------------------------------------------------
+NVILA-Lite-2B                                74.4%       40.0%       +34.4%p
+NVILA-Lite-2B-data-scale-exp-80k             71.6%       50.8%       +20.9%p
+NVILA-Lite-2B-data-scale-exp-400k            81.3%       58.5%       +22.8%p
+NVILA-Lite-2B-data-scale-exp-800k            84.6%       67.7%       +16.9%p
+NVILA-Lite-2B-data-scale-exp-2m              97.2%       93.8%       +3.4%p
+RoboRefer-2B-SFT                             98.9%       95.4%       +3.5%p
+------------------------------------------------------------------------------
+Qwen2.5-VL-3B-Instruct                       75.5%       55.4%       +20.1%p
+Qwen2.5-VL-3B-Instruct-data_scale_exp_80k    69.7%       60.0%       +9.7%p
+Qwen2.5-VL-3B-Instruct-data_scale_exp_400k   65.8%       58.5%       +7.4%p
+Qwen2.5-VL-3B-Instruct-data_scale_exp_800k   61.2%       58.5%       +2.7%p
+Qwen2.5-VL-3B-Instruct-data_scale_exp_2m     62.0%       53.8%       +8.1%p

exp2a_modified/results_all_layers/molmo/results_summary.csv

@@ -0,0 +1,161 @@
+model,sim_above_far,sim_under_close,sim_left_right,diff_above_far_vs_left_right,diff_under_close_vs_left_right,layer_idx
+molmo_vanilla,0.99711597,0.9970598,0.99998003,-0.0028640628,-0.0029202104,0
+molmo_vanilla,0.9863839,0.9862979,0.9999656,-0.013581693,-0.013667703,1
+molmo_vanilla,0.97197413,0.9721215,0.99993473,-0.027960598,-0.027813256,2
+molmo_vanilla,0.9656428,0.96577287,0.9999284,-0.034285605,-0.034155548,3
+molmo_vanilla,0.95720917,0.95771855,0.99992627,-0.0427171,-0.042207718,4
+molmo_vanilla,0.92606837,0.9264871,0.99990314,-0.07383478,-0.073416054,5
+molmo_vanilla,0.93186307,0.9325508,0.9999072,-0.068044126,-0.06735641,6
+molmo_vanilla,0.9344384,0.93388873,0.99989855,-0.065460145,-0.06600982,7
+molmo_vanilla,0.94040704,0.939772,0.9998911,-0.059484065,-0.060119092,8
+molmo_vanilla,0.932125,0.9318654,0.99988884,-0.067763865,-0.06802344,9
+molmo_vanilla,0.9173129,0.91820735,0.999853,-0.082540095,-0.08164567,10
+molmo_vanilla,0.91003335,0.9113646,0.99982363,-0.089790285,-0.088459015,11
+molmo_vanilla,0.9180365,0.9191305,0.9996937,-0.08165717,-0.08056319,12
+molmo_vanilla,0.9252183,0.925783,0.9996471,-0.0744288,-0.0738641,13
+molmo_vanilla,0.92186,0.922497,0.99891305,-0.07705307,-0.076416075,14
+molmo_vanilla,0.91549087,0.9141751,0.998386,-0.08289516,-0.08421093,15
+molmo_vanilla,0.89193356,0.88960046,0.99707025,-0.10513669,-0.1074698,16
+molmo_vanilla,0.8889458,0.8873432,0.9963355,-0.10738969,-0.10899228,17
+molmo_vanilla,0.8631955,0.8623247,0.994486,-0.1312905,-0.13216126,18
+molmo_vanilla,0.8514263,0.85130924,0.9945253,-0.14309901,-0.14321607,19
+molmo_vanilla,0.8323555,0.8360815,0.9953362,-0.16298068,-0.15925467,20
+molmo_vanilla,0.8207639,0.82622963,0.99536604,-0.17460215,-0.1691364,21
+molmo_vanilla,0.8120903,0.81830686,0.9954266,-0.18333632,-0.17711973,22
+molmo_vanilla,0.8032835,0.8111891,0.9958494,-0.19256586,-0.18466026,23
+molmo_vanilla,0.80584615,0.8141513,0.9958521,-0.19000596,-0.18170083,24
+molmo_vanilla,0.79999393,0.8064356,0.99561656,-0.19562262,-0.18918097,25
+molmo_vanilla,0.7811126,0.7902819,0.9955554,-0.21444279,-0.20527351,26
+molmo_vanilla,0.7848503,0.79467237,0.99569494,-0.21084464,-0.20102257,27
+molmo_vanilla,0.79593915,0.805076,0.99593407,-0.19999492,-0.19085807,28
+molmo_vanilla,0.8180108,0.8269587,0.99639994,-0.17838913,-0.16944122,29
+molmo_vanilla,0.82740706,0.8354879,0.996784,-0.16937691,-0.16129607,30
+molmo_vanilla,0.82378054,0.8320327,0.9968723,-0.17309177,-0.16483963,31
+molmo_80k,0.99715066,0.99714863,0.9999824,-0.002831757,-0.0028337836,0
+molmo_80k,0.9846312,0.98448217,0.9999704,-0.015339196,-0.015488207,1
+molmo_80k,0.9727578,0.9727067,0.99995077,-0.02719295,-0.027244091,2
+molmo_80k,0.9683188,0.96818906,0.99994695,-0.031628132,-0.03175789,3
+molmo_80k,0.95952797,0.9596887,0.9999438,-0.040415823,-0.04025507,4
+molmo_80k,0.93633956,0.93644136,0.99992996,-0.06359041,-0.0634886,5
+molmo_80k,0.94482744,0.9447468,0.9999342,-0.05510676,-0.055187404,6
+molmo_80k,0.9438612,0.9431258,0.99993116,-0.05606997,-0.056805372,7
+molmo_80k,0.94557315,0.9446461,0.99991876,-0.054345608,-0.05527264,8
+molmo_80k,0.93377405,0.9338928,0.9999186,-0.066144526,-0.06602579,9
+molmo_80k,0.93515295,0.9357338,0.9998787,-0.06472576,-0.06414491,10
+molmo_80k,0.93617505,0.9365246,0.9998746,-0.06369954,-0.06334996,11
+molmo_80k,0.94417953,0.94417286,0.9998461,-0.055666566,-0.05567324,12
+molmo_80k,0.9501332,0.9501227,0.99982655,-0.049693346,-0.049703836,13
+molmo_80k,0.9439891,0.94274354,0.9992051,-0.055216014,-0.056461573,14
+molmo_80k,0.92958707,0.92956555,0.9985394,-0.06895232,-0.06897384,15
+molmo_80k,0.9103784,0.9114708,0.99791276,-0.08753437,-0.08644199,16
+molmo_80k,0.91124547,0.9130204,0.9966807,-0.08543521,-0.083660305,17
+molmo_80k,0.8689027,0.8720054,0.9947382,-0.12583554,-0.12273282,18
+molmo_80k,0.8622559,0.86525977,0.9953824,-0.13312656,-0.13012266,19
+molmo_80k,0.85302204,0.8579786,0.9964325,-0.14341044,-0.1384539,20
+molmo_80k,0.8092763,0.8199867,0.9959489,-0.18667263,-0.17596221,21
+molmo_80k,0.7860634,0.79665476,0.9961628,-0.2100994,-0.19950801,22
+molmo_80k,0.7750981,0.78600085,0.99691355,-0.22181547,-0.2109127,23
+molmo_80k,0.7744671,0.7858484,0.9969368,-0.22246969,-0.21108842,24
+molmo_80k,0.7703127,0.7813867,0.9969774,-0.22666466,-0.21559072,25
+molmo_80k,0.7678993,0.780402,0.99710876,-0.22920948,-0.21670675,26
+molmo_80k,0.77544314,0.7872103,0.9973793,-0.22193617,-0.21016902,27
+molmo_80k,0.7759789,0.787475,0.9976282,-0.22164929,-0.21015322,28
+molmo_80k,0.8282126,0.8382431,0.998172,-0.16995937,-0.15992886,29
+molmo_80k,0.86920613,0.87624425,0.99860156,-0.12939543,-0.12235731,30
+molmo_80k,0.8963089,0.9020278,0.99889964,-0.10259074,-0.09687185,31
+molmo_400k,0.99699706,0.9970093,0.99998075,-0.0029836893,-0.0029714704,0
+molmo_400k,0.9831523,0.9829251,0.9999683,-0.01681602,-0.017043173,1
+molmo_400k,0.9677031,0.96760446,0.9999541,-0.032251,-0.032349646,2
+molmo_400k,0.96679616,0.96660626,0.9999455,-0.03314936,-0.033339262,3
+molmo_400k,0.9593648,0.95914954,0.99994797,-0.040583193,-0.040798426,4
+molmo_400k,0.93560684,0.935446,0.9999396,-0.06433278,-0.0644936,5
+molmo_400k,0.94099295,0.9413343,0.9999467,-0.058953762,-0.058612406,6
+molmo_400k,0.94024825,0.9396195,0.9999442,-0.05969596,-0.06032473,7
+molmo_400k,0.93930745,0.9382677,0.99992937,-0.060621917,-0.06166166,8
+molmo_400k,0.9368288,0.93580616,0.99992526,-0.06309646,-0.0641191,9
+molmo_400k,0.9361321,0.9352534,0.9998147,-0.063682616,-0.06456131,10
+molmo_400k,0.92720956,0.92647916,0.9998104,-0.07260084,-0.07333124,11
+molmo_400k,0.93184185,0.9311039,0.9991421,-0.06730026,-0.068038225,12
+molmo_400k,0.93268144,0.93169504,0.9983739,-0.065692484,-0.06667888,13
+molmo_400k,0.91464883,0.90864915,0.9822636,-0.067614794,-0.07361448,14
+molmo_400k,0.88848084,0.88790464,0.98625815,-0.09777731,-0.098353505,15
+molmo_400k,0.86070627,0.8611323,0.9883753,-0.12766904,-0.12724298,16
+molmo_400k,0.853808,0.853019,0.9845385,-0.13073051,-0.1315195,17
+molmo_400k,0.8070833,0.80220544,0.9794933,-0.17241001,-0.17728788,18
+molmo_400k,0.8004133,0.7915684,0.9835917,-0.18317837,-0.19202328,19
+molmo_400k,0.79914445,0.79544824,0.9867994,-0.18765497,-0.19135118,20
+molmo_400k,0.76134765,0.7631209,0.9855323,-0.22418463,-0.2224114,21
+molmo_400k,0.74007213,0.73739916,0.9865484,-0.2464763,-0.24914926,22
+molmo_400k,0.7359244,0.7323806,0.98823357,-0.25230914,-0.25585294,23
+molmo_400k,0.7402826,0.7392968,0.9888178,-0.24853522,-0.24952102,24
+molmo_400k,0.7324024,0.73250526,0.9884455,-0.25604314,-0.25594026,25
+molmo_400k,0.73278224,0.7314169,0.98859596,-0.25581372,-0.25717908,26
+molmo_400k,0.73714095,0.7402879,0.9896015,-0.25246054,-0.2493136,27
+molmo_400k,0.740321,0.7426294,0.99110836,-0.25078738,-0.24847895,28
+molmo_400k,0.81820846,0.8227976,0.99379545,-0.175587,-0.17099786,29
+molmo_400k,0.8795686,0.88406736,0.99583,-0.11626142,-0.11176264,30
+molmo_400k,0.9089592,0.911077,0.99682474,-0.08786553,-0.08574772,31
+molmo_800k,0.99722177,0.99726737,0.99998105,-0.0027592778,-0.0027136803,0
+molmo_800k,0.9810392,0.9808847,0.9999699,-0.018930674,-0.019085228,1
+molmo_800k,0.96477735,0.96468014,0.9999599,-0.035182536,-0.03527975,2
+molmo_800k,0.96731895,0.967111,0.9999558,-0.03263682,-0.032844782,3
+molmo_800k,0.959248,0.9589585,0.9999592,-0.040711164,-0.041000664,4
+molmo_800k,0.94032395,0.9403167,0.9999496,-0.059625626,-0.059632897,5
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exp2a_modified/results_all_layers/molmo/similarity_2m_L11.csv

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exp2a_modified/results_all_layers/molmo/similarity_2m_L17.csv

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exp2a_modified/results_all_layers/molmo/similarity_2m_L2.csv

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exp2a_modified/results_all_layers/molmo/similarity_2m_L21.csv

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exp2a_modified/results_all_layers/molmo/similarity_2m_L26.csv

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exp2a_modified/results_all_layers/molmo/similarity_2m_L28.csv

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exp2a_modified/results_all_layers/molmo/similarity_2m_L30.csv

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exp2a_modified/results_all_layers/molmo/similarity_2m_L4.csv

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exp2a_modified/results_all_layers/molmo/similarity_2m_L5.csv

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exp2a_modified/results_all_layers/molmo/similarity_2m_L6.csv

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exp2a_modified/results_all_layers/molmo/similarity_2m_L7.csv

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exp2a_modified/results_all_layers/molmo/similarity_400k_L1.csv

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exp2a_modified/results_all_layers/molmo/similarity_400k_L12.csv

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exp2a_modified/results_all_layers/molmo/similarity_400k_L17.csv

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exp2a_modified/results_all_layers/molmo/similarity_400k_L25.csv

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exp2a_modified/results_all_layers/molmo/similarity_400k_L26.csv

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exp2a_modified/results_all_layers/molmo/similarity_400k_L28.csv

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exp2a_modified/results_all_layers/molmo/similarity_400k_L7.csv

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exp2a_modified/results_all_layers/molmo/similarity_400k_L9.csv

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exp2a_modified/results_all_layers/molmo/similarity_800k_L10.csv

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exp2a_modified/results_all_layers/molmo/similarity_800k_L12.csv

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exp2a_modified/results_all_layers/molmo/similarity_800k_L14.csv

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exp2a_modified/results_all_layers/molmo/similarity_800k_L15.csv

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exp2a_modified/results_all_layers/molmo/similarity_800k_L23.csv

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exp2a_modified/results_all_layers/molmo/similarity_800k_L25.csv

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exp2a_modified/results_all_layers/molmo/similarity_800k_L30.csv

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exp2a_modified/results_all_layers/molmo/similarity_800k_L31.csv

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exp2a_modified/results_all_layers/molmo/similarity_800k_L6.csv

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exp2a_modified/results_all_layers/molmo/similarity_800k_L8.csv

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exp2a_modified/results_all_layers/molmo/similarity_80k_L0.csv

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exp2a_modified/results_all_layers/molmo/similarity_80k_L13.csv

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exp2a_modified/results_all_layers/molmo/similarity_80k_L17.csv

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exp2a_modified/results_all_layers/molmo/similarity_80k_L21.csv

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exp2a_modified/results_all_layers/molmo/similarity_80k_L27.csv

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exp2a_modified/results_all_layers/molmo/similarity_80k_L31.csv

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exp2a_modified/results_all_layers/molmo/similarity_80k_L4.csv

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exp2a_modified/results_all_layers/molmo/similarity_80k_L5.csv

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exp2a_modified/results_all_layers/molmo/similarity_vanilla_L0.csv

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exp2a_modified/results_all_layers/molmo/similarity_vanilla_L18.csv

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exp2a_modified/results_all_layers/molmo/similarity_vanilla_L19.csv

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exp2a_modified/results_all_layers/molmo/similarity_vanilla_L2.csv

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exp2a_modified/results_all_layers/molmo/similarity_vanilla_L20.csv

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exp2a_modified/results_all_layers/molmo/similarity_vanilla_L21.csv

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exp2a_modified/results_all_layers/molmo/similarity_vanilla_L22.csv

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exp2a_modified/results_all_layers/molmo/similarity_vanilla_L28.csv

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exp2a_modified/results_all_layers/molmo/similarity_vanilla_L29.csv

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exp2a_modified/results_all_layers/molmo/similarity_vanilla_L30.csv

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+close,0.8483111,0.8444402,0.8221611,0.8354879,0.99702054,0.9999998

exp2a_modified/results_all_layers/molmo/similarity_vanilla_L9.csv

@@ -0,0 +1,7 @@
+,left,right,above,under,far,close
+left,0.9999999,0.99988884,0.98276126,0.98081756,0.9269093,0.9275157
+right,0.99988884,0.9999999,0.9824638,0.98055077,0.92584574,0.9264226
+above,0.98276126,0.9824638,0.99999976,0.9993683,0.932125,0.9322524
+under,0.98081756,0.98055077,0.9993683,1.0000005,0.9317689,0.9318654
+far,0.9269093,0.92584574,0.932125,0.9317689,0.99999994,0.9997995
+close,0.9275157,0.9264226,0.9322524,0.9318654,0.9997995,1.0000005