| [ | |
| "Table data exhibits periodic non-vanishing mutual information at lags d = km (k = 1,2,...) for m-column tables, in contrast to the power-law decay of mutual information in natural language (Theorem 2.2).", | |
| "Table mutual information asymptotically dominates natural-language mutual information at these periodic lags (Corollary 2.4), and the effective dependency distance is infinite for tables versus finite for text (Theorem 2.6).", | |
| "TableLong's SQL-based pipeline, built from 10,000+ real-world tables via consistency-based filtration, improves average long-context benchmark performance by +8.24% for the DS-R1-Distill-32B model (Table 1).", | |
| "On the out-of-domain LiveCodeBench benchmark, the 32B model improves by +11.97% after TableLong training, part of an average +8.06% out-of-domain gain (Table 4).", | |
| "On Needle-in-a-Haystack retrieval, the 32B model improves from 87.95% to 99.40% (+13.02%) after training with TableLong data (Figure 3).", | |
| "Ablations show multi-hop reasoning performance scales with table cell count (46.30% with ~30 cells to 48.36% with ~300+ cells, Table 2), and using multiple tables versus a single table improves grounding from 46.66% to 48.36% average (Table 3)." | |
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