Papers with scalable pipeline
PaperScope: A Multi-Modal Multi-Document Benchmark for Agentic Deep Research Across Massive Scientific Papers (2026.findings-acl)
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| Challenge: | Existing benchmarks focus on single-document understanding, whereas real scientific workflows require integrating evidence from multiple papers. |
| Approach: | They propose a multi-modal multi-document benchmark for agentic deep research that integrates evidence from multiple documents. |
| Outcome: | Experimental results show that even advanced systems achieve limited scores on PaperScope . paper provides a rigorous benchmark alongside a pipeline for constructing large multi-modal, multi-source deep research datasets. |
From Tens of Hours to Tens of Thousands: Scaling Back-Translation for Speech Recognition (2025.emnlp-main)
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| Challenge: | Recent advances in Automatic Speech Recognition (ASR) have been fueled by massive speech corpora, but extending coverage to diverse languages with limited resources remains a formidable challenge. |
| Approach: | They propose a pipeline that converts large-scale text corpora into synthetic speech using off-the-shelf text-to-speech (TTS) models. |
| Outcome: | The proposed pipeline generates 500,000 hours of synthetic speech in ten languages and achieves transcription error reductions of over 30%. |
Visual-Aware Speech Recognition for Noisy Scenarios (2025.emnlp-main)
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| Challenge: | Existing audio-only models that use visual cues for transcription struggle in noisy environments. |
| Approach: | They propose a method that correlates visual cues with noise sources to improve transcription by filtering speech from noise and predicting noise labels in video inputs. |
| Outcome: | The proposed model improves transcription by correlating noise sources to visual cues in audio inputs. |
VLN-NF: Feasibility-Aware Vision-and-Language Navigation with False-Premise Instructions (2026.acl-long)
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| Challenge: | Existing Vision-and-Language Navigation benchmarks assume instructions are feasible and the referenced target exists. |
| Approach: | They propose a benchmark with false-premise instructions where the target is absent . they propose supervised room-level navigation with LLM/VLM-driven in-room exploration . |
| Outcome: | The proposed benchmark produces false-premise goals that are plausible but factually incorrect . ROAM achieves the best REV-SPL among compared methods, while baselines often under-explore and terminate prematurely under unreliable instructions. |
UniVocal: Unified Speech-Singing Code-Switching Synthesis (2026.acl-long)
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| Challenge: | Existing systems cannot automatically determine when to switch between modes based on text content. |
| Approach: | They propose a unified framework that implicitly infers vocal modes from text context to pioneer SCS Synthesis. |
| Outcome: | The proposed framework infers vocal modes solely from text context to pioneer SCS Synthesis. |
CaTS-Bench: Can Language Models Describe Time Series? (2026.findings-acl)
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| Challenge: | Existing time series captioning benchmarks rely on fully synthetic or generic captions . authors propose a pipeline for generating high-fidelity synthetic captions, which is validated . |
| Approach: | They propose a benchmark for Context-aware Time Series reasoning across 11 diverse domains . they evaluate leading Vision-Language Models on their benchmark . |
| Outcome: | The proposed benchmark evaluates 1746 human-rewritten captions and shows they perform better than open-source models. |
POLYCHARTQA: Benchmarking Large Vision-Language Models with Multilingual Chart Question Answering (2026.acl-long)
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| Challenge: | Existing chart understanding benchmarks are overwhelmingly English-centric, limiting their accessibility and relevance to global audiences. |
| Approach: | They propose a multilingual chart question answering benchmark that enables efficient multilingual generation via data translation and code reuse. |
| Outcome: | The proposed benchmark systematically evaluates multilingual chart understanding on state-of-the-art LVLMs and shows a significant performance gap between English and other languages. |