We present nemotron-120b-sitrep, an end-to-end four-stage generate-and-judge pipeline submitted to the First Shared Task on Automatic Situation Report Generation from Crisis Social Media Data at LT4CPR 2026 (AACL-IJCNLP 2026). Powered by NVIDIA’s Nemotron-3 Super 120B model hosted on the NVIDIA NIM API, the pipeline tackles the acute challenges of emergency reporting (social media noise, multi-section hierarchical taxonomies, and strict tweet-to-claim evidence attribution) by decomposing generation into: (1) rule-based ingestion and noise filtering, (2) unified template-filling synthesis across an 11-section situation report (SITREP) taxonomy under strict canonical subsection and evidence-grounding constraints, (3) cross-section deduplication, granular bullet consolidation, and dual-source confidence calibration via a Senior Judge LLM, and (4) Schema v1.2 JSON assembly. On the official shared task evaluation, our system achieves an Official Primary Score of 0.5733 on the 117-cell blind test set (BERTScore F1 of 0.5984 and BLEURT of 0.5483), demonstrating consistent performance that transfers to unseen crisis types, with scores of 0.5011 on the 140-cell training benchmark and 0.5057 on the 23-cell development benchmark. We provide a detailed calamity-by-calamity analysis across disaster scenarios, full algorithmic hyperparameters, and prompt engineering specifications.

Financial question answering demands rigorous synthesis of standardized SEC filings alongside unstructured, cross-document text. This complexity is significantly amplified in multilingual environments, where terminology and reporting conventions vary across languages. We present a two-stage pipeline for the FinNLP 2026 PolyFiQA shared task, with all inference routed through the OpenRouter API under the 70B parameter limit. Stage 1 uses Qwen3.8-27B with category-calibrated few-shot prompts to extract facts and synthesize news across five languages (English, Chinese, Japanese, Spanish, Greek). Stage 2 is an LLM-as-a-Judge formatting guard that checks format compliance, strips filler, and verifies citations, without rewriting content. On our 76-instance training/development set (used for both calibration and evaluation, no held-out split), the calibrated pipeline reaches 48.19% Macro ROUGE-1 F1, an 88.3% relative gain over a zero-shot 70B baseline and a 6.32-point margin over Llama-3-70B’s own few-shot result. Gains concentrate in categories needing narrative synthesis (Revenue Trends, Cash Flow Dynamics) and are smaller for terse, formulaic ones (R D Investments). On the official blind test set, our system scores 37.02% macro ROUGE-1 F1 on the private leaderboard (52 instances) and 31.79% on the public slice (24 instances).