Added new regression PDF and visualizations

Signed-off-by: Peter Staar <taa@zurich.ibm.com>

.gitattributes

@@ -124,3 +124,9 @@ groundtruth/render/instructions/*.json filter=lfs diff=lfs merge=lfs -text
 groundtruth/parser/2508.13113v2.pdf.page_no_2.py.json filter=lfs diff=lfs merge=lfs -text
 groundtruth/parser/ligatures_01.pdf.page_no_1.py.json filter=lfs diff=lfs merge=lfs -text
 groundtruth/render/instructions/ligatures_01.pdf.page_no_1.instructions.json filter=lfs diff=lfs merge=lfs -text
+groundtruth/parser-legacy/2508.13113v2.pdf.page_no_2.py.json filter=lfs diff=lfs merge=lfs -text
+groundtruth/parser-legacy/dln-v1.pdf.page_no_1.py.json filter=lfs diff=lfs merge=lfs -text
+groundtruth/parser-legacy/font_11.pdf.page_no_24.py.json filter=lfs diff=lfs merge=lfs -text
+groundtruth/parser-legacy/font_11.pdf.page_no_25.py.json filter=lfs diff=lfs merge=lfs -text
+groundtruth/parser-legacy/font_11.pdf.page_no_26.py.json filter=lfs diff=lfs merge=lfs -text
+groundtruth/parser-legacy/ligatures_01.pdf.page_no_1.py.json filter=lfs diff=lfs merge=lfs -text

groundtruth/parser-legacy/2508.13113v2.pdf.page_no_17.py.json.line.txt

@@ -0,0 +1,79 @@
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+(108.00, 468.36) (504.00, 468.57) (504.00, 476.27) (108.00, 476.92)      /F116 Figure 10: Success rate on Rubik's Cube scrambled with 10 random moves, for models trained with different	<|special_separator|>
+(108.00, 458.61) (504.00, 458.61) (504.00, 466.30) (108.00, 466.30)      /F116 contrastive losses. Models using the backward loss consistently achieve better performance than those using	<|special_separator|>
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+(197.33, 448.64) (493.03, 448.64) (493.03, 456.34) (197.33, 456.34)      /F116 Using the dot product without in-trajectory negatives performs similarly to the	<|special_separator|>
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+(108.00, 438.68) (505.12, 438.68) (505.12, 446.38) (108.00, 446.38)      /F116 metric, while combining the dot product with in-trajectory negatives yields the highest success rate. In contrast,	<|special_separator|>
+(108.00, 428.72) (505.12, 428.72) (505.12, 436.42) (108.00, 436.42)      /F116 combining in-trajectory negatives with symmetric loss results in a drop in performance, likely because, in CRTR,	<|special_separator|>
+(108.00, 418.76) (364.44, 418.76) (364.44, 426.45) (108.00, 426.45)      /F116 such negatives are often closer to the correct solution in the state-space.	<|special_separator|>
+(108.00, 386.27) (504.00, 386.27) (504.00, 394.82) (108.00, 394.82)      /F116 learning rate and batch size was guided by the performance of the contrastive baseline on Rubik's	<|special_separator|>
+(108.00, 375.36) (503.99, 375.36) (503.99, 383.91) (108.00, 383.91)      /F116 Cube. Specifically, we evaluated solve rates on cubes shuffled 10 times, as shown in Figure 8. We	<|special_separator|>
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+(409.69, 353.54) (504.00, 353.54) (504.00, 362.09) (409.69, 362.09)      /F116 We use the same batch	<|special_separator|>
+(108.00, 342.63) (504.00, 342.63) (504.00, 351.18) (108.00, 351.18)      /F116 size and learning rate across all methods and environments, with the exception of Lights Out, where	<|special_separator|>
+(108.00, 331.72) (492.30, 331.72) (492.30, 340.27) (108.00, 340.27)      /F116 increasing the batch size and learning rate in the supervised baseline led to a higher success rate.	<|special_separator|>
+(108.00, 314.33) (199.75, 314.33) (199.75, 323.29) (108.00, 323.29)      /F107 Network architecture.	<|special_separator|>
+(209.71, 314.34) (504.00, 314.34) (504.00, 322.90) (209.71, 322.90)      /F116 We adopt the network architecture proposed by Nauman et al. [44], using 8	<|special_separator|>
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+(107.53, 281.62) (465.90, 281.62) (465.90, 290.17) (107.53, 290.17)      /F116 We observed that this architecture performs well in all environments except for two cases:	<|special_separator|>
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+(143.87, 261.54) (484.08, 261.54) (484.08, 270.09) (143.87, 270.09)      /F116 In Sokoban, a convolutional architecture was required to achieve strong performance.	<|special_separator|>
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+(143.87, 246.79) (474.20, 246.79) (474.20, 255.35) (143.87, 255.35)      /F116 In Lights Out, the convolutional network was necessary to ensure training stability.	<|special_separator|>
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+(108.00, 165.70) (504.00, 165.70) (504.00, 174.26) (108.00, 174.26)      /F116 tently outperform the symmetrized variant on Rubik's Cube as shown in Figure 10. We also found	<|special_separator|>
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+(385.91, 138.41) (504.00, 138.41) (504.00, 146.96) (385.91, 146.96)      /F116 Performance across different	<|special_separator|>
+(108.00, 127.50) (493.08, 127.50) (493.08, 136.05) (108.00, 136.05)      /F116 metrics is presented in Figure 10. While the contrastive baseline performs comparably under the	<|special_separator|>
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+(108.00, 116.59) (504.00, 116.59) (504.00, 125.14) (108.00, 125.14)      /F116 metric, CRTR achieves significantly better results with the dot product. Based on similar empirical	<|special_separator|>
+(108.00, 105.68) (368.89, 105.68) (368.89, 114.23) (108.00, 114.23)      /F116 evaluations, we use the following metrics for other environments:	<|special_separator|>
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+(143.87, 084.68) (189.53, 084.68) (189.53, 093.23) (143.87, 093.23)      /F116 Lights Out:	<|special_separator|>
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+(203.73, 084.68) (238.87, 084.68) (238.87, 093.23) (203.73, 093.23)      /F116 distance,	<|special_separator|>
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+(143.87, 069.93) (309.38, 069.93) (309.38, 078.49) (143.87, 078.49)      /F116 Digit Jumper and 15-Puzzle: dot product,	<|special_separator|>
+(301.02, 040.05) (310.98, 040.05) (310.98, 048.60) (301.02, 048.60)      /F116 17

groundtruth/parser-legacy/2508.13113v2.pdf.page_no_17.py.json.word.txt

@@ -0,0 +1,561 @@
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+(161.71, 080.83) (200.09, 080.83) (200.09, 089.78) (161.71, 089.78)      /F107 learning.	<|special_separator|>
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+(303.51, 040.05) (308.49, 040.05) (308.49, 048.60) (303.51, 048.60)      /F116 2

groundtruth/parser-legacy/2508.13113v2.pdf.page_no_9.py.json.line.txt

@@ -0,0 +1,88 @@
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+(123.60, 629.76) (185.15, 629.76) (185.15, 638.58) (123.60, 638.58)        /F1 Solution Length	<|special_separator|>
+(105.76, 644.98) (113.94, 644.98) (113.94, 650.97) (105.76, 650.97)        /F1 0.0	<|special_separator|>
+(105.76, 673.86) (113.94, 673.86) (113.94, 679.85) (105.76, 679.85)        /F1 0.5	<|special_separator|>
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+(448.14, 629.71) (509.77, 629.71) (509.77, 638.54) (448.14, 638.54)        /F1 Solution Length	<|special_separator|>
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+(215.87, 612.72) (304.33, 612.72) (304.33, 622.10) (215.87, 622.10)        /F1 CRTR without Search	<|special_separator|>
+(342.98, 612.72) (418.69, 612.72) (418.69, 622.10) (342.98, 622.10)        /F1 CRTR with BestFS	<|special_separator|>
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+(149.73, 591.81) (364.62, 591.81) (364.62, 599.87) (149.73, 599.87)      /F107 CRTR solves most tasks without requiring any search.	<|special_separator|>
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+(476.18, 580.71) (504.00, 580.71) (504.00, 589.26) (476.18, 589.26)      /F116 , while	<|special_separator|>
+(108.00, 569.80) (503.99, 569.80) (503.99, 578.35) (108.00, 578.35)      /F116 limiting the number of nodes created to 6000. Surprisingly, on the Rubik's cube CRTR achieves a	<|special_separator|>
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+(219.00, 558.89) (504.00, 558.89) (504.00, 567.44) (219.00, 567.44)      /F116 search, solving all board configurations within the budget. Figure 11 in	<|special_separator|>
+(108.00, 547.98) (504.00, 547.98) (504.00, 556.53) (108.00, 556.53)      /F116 the Appendix E presents the same results, but for CRTR, as well as the Contrastive and Supervised	<|special_separator|>
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+(130.42, 500.30) (216.56, 500.30) (216.56, 509.26) (130.42, 509.26)      /F107 Is search necessary?	<|special_separator|>
+(108.00, 478.95) (504.00, 478.95) (504.00, 487.50) (108.00, 487.50)      /F116 Do good representations allow us to solve combinatorial problems without search, or at least reduce	<|special_separator|>
+(108.00, 468.04) (504.00, 468.04) (504.00, 476.59) (108.00, 476.59)      /F116 the amount of search required to get high success rates? We study this question by using the learned	<|special_separator|>
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+(107.53, 440.74) (504.00, 440.74) (504.00, 449.30) (107.53, 449.30)      /F116 We present the results from this experiment in Figure 4, showing the fraction of problems solved with	<|special_separator|>
+(108.00, 429.84) (504.00, 429.84) (504.00, 438.39) (108.00, 438.39)      /F116 fewer than a certain number of steps. We compare to the variant of CRTR used in Sec. 5.2 that uses	<|special_separator|>
+(108.00, 418.93) (504.00, 418.93) (504.00, 427.48) (108.00, 427.48)      /F116 the representations to perform search. As an example of how to interpret this plot, the red line in the	<|special_separator|>
+(108.00, 408.02) (504.00, 408.02) (504.00, 416.57) (108.00, 416.57)      /F116 leftmost plot shows that CRTR (without search) can solve about 50% of Rubik's cube configurations	<|special_separator|>
+(108.00, 397.11) (466.39, 397.11) (466.39, 405.66) (108.00, 405.66)      /F116 in less than 300 moves. We compare to the CRL and Supervised baselines in Appendix E.	<|special_separator|>
+(108.00, 380.72) (504.00, 380.72) (504.00, 389.27) (108.00, 389.27)      /F116 On 4 / 5 tasks (Rubik's Cube, Lights Out, 15-Puzzle, and Digit Jumper), CRTR solves nearly all	<|special_separator|>
+(108.00, 369.81) (291.32, 369.81) (291.32, 378.36) (108.00, 378.36)      /F116 problem instances. The key takeaway is thus:	<|special_separator|>
+(294.41, 369.95) (504.00, 369.95) (504.00, 378.54) (294.41, 378.54)      /F134 for most problems, CRTR can find solutions without	<|special_separator|>
+(108.00, 359.05) (209.55, 359.05) (209.55, 367.63) (108.00, 367.63)      /F134 needing any search at all	<|special_separator|>
+(209.55, 358.90) (504.00, 358.90) (504.00, 367.45) (209.55, 367.45)      /F116 . Perhaps the most interesting result is the Rubik's cube, where we found	<|special_separator|>
+(108.00, 347.99) (504.00, 347.99) (504.00, 356.54) (108.00, 356.54)      /F116 that our representations can solve all problem instances in less than 6000 moves. Surprisingly, using	<|special_separator|>
+(108.00, 337.08) (504.00, 337.08) (504.00, 345.64) (108.00, 345.64)      /F116 search decreases the total fraction of Cube configurations that are solved. The main failure mode is	<|special_separator|>
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+(108.00, 084.12) (504.00, 084.12) (504.00, 092.67) (108.00, 092.67)      /F116 a randomly scrambled cube, illustrating how the algorithm gradually builds partial structure. The	<|special_separator|>
+(108.00, 073.21) (474.66, 073.21) (474.66, 081.76) (108.00, 081.76)      /F116 average solve is about 400 moves, and we see similar block building behavior across solves.	<|special_separator|>
+(303.51, 040.05) (308.49, 040.05) (308.49, 048.60) (303.51, 048.60)      /F116 9

groundtruth/parser-legacy/2508.13113v2.pdf.page_no_9.py.json.word.txt

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groundtruth/parser-legacy/580-17-PIB_Unfall-Provinzial-IPID_Stand-12.2016.pdf.page_no_1.py.json.line.txt

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groundtruth/parser-legacy/580-17-PIB_Unfall-Provinzial-IPID_Stand-12.2016.pdf.page_no_1.py.json.word.txt

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groundtruth/parser-legacy/580-17-PIB_Unfall-Provinzial-IPID_Stand-12.2016.pdf.page_no_2.py.json.line.txt

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groundtruth/parser-legacy/bitmap_decoding_01.pdf.page_no_1.py.json.char.txt

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groundtruth/parser-legacy/bitmap_decoding_01.pdf.page_no_1.py.json.word.txt

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groundtruth/parser-legacy/broken_media_box_v01.pdf.page_no_1.py.json.line.txt

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