paper_id stringlengths 17 20 | title stringlengths 26 148 | paper_url stringlengths 43 46 | authors listlengths 1 60 | abstract large_stringlengths 494 2.51k | anthology_id stringlengths 17 20 | doi stringlengths 26 29 ⌀ | award stringclasses 6
values | arxiv_id stringlengths 10 10 ⌀ | arxiv_id_source stringclasses 2
values |
|---|---|---|---|---|---|---|---|---|---|
2026.emnlp-main.2701 | Reusing Latent Speech Representations for Query-Conditioned Topic Localization in Transcripts | https://aclanthology.org/2026.emnlp-main.2701/ | [
"Steffen Freisinger",
"Philipp Seeberger",
"Thomas Ranzenberger",
"Tobias Bocklet",
"Korbinian Riedhammer"
] | Long transcripts are costly inputs for downstream NLP systems and often contain irrelevant context. We study query-conditioned topic localization: predicting the sentence span in a transcript that best addresses a topic-title query. To improve span localization, we reuse ASR encoder states as sentence-level representat... | 2026.emnlp-main.2701 | 10.18653/2026.emnlp-main.2701 | null | 2609.21844 | title_snapshot |
2026.emnlp-main.2702 | Persistent Teacher Anchoring for Tool-Using Agents | https://aclanthology.org/2026.emnlp-main.2702/ | [
"Hyun Bin Park",
"Kyungho Song",
"Sangmin Lee",
"Du-Seong Chang"
] | Distillation is common in LLM post-training, where on-policy knowledge distillation (OPKD) uses student-generated trajectories to prepare the student for downstream RL. At each state, the student matches a next-token distribution supplied by the teacher. As the rollout enters states the teacher would not visit, the tea... | 2026.emnlp-main.2702 | 10.18653/2026.emnlp-main.2702 | null | 2609.04773 | title_snapshot |
2026.emnlp-main.2703 | IGG: A Benchmark for Interactive GUI Grounding under Visibility Constraints | https://aclanthology.org/2026.emnlp-main.2703/ | [
"Kyeong Seon Kim",
"Jiyeon Son",
"Tae-Hyun Oh"
] | GUI grounding benchmarks ask whether agents can localize a target that is already visible in a static screenshot. Real interfaces often violate this assumption: a target may be off-screen, appear only after a state change or hover, remain occluded, or become actionable only after a delay. This leaves the ability to rec... | 2026.emnlp-main.2703 | 10.18653/2026.emnlp-main.2703 | null | null | null |
2026.emnlp-main.2704 | RUBICON: Do Language Models Know When They’ve Crossed the Point of No Return? | https://aclanthology.org/2026.emnlp-main.2704/ | [
"Srija Mukhopadhyay",
"Adnan Qidwai",
"Jaydeep Sen",
"Manish Shrivastava"
] | As language models are increasingly deployed as autonomous agents, understanding whether they can anticipate the consequences of their actions is critical. We introduce RUBICON, a benchmark that tests whether models recognize when their own actions have made a task impossible to complete. Three procedurally generated p... | 2026.emnlp-main.2704 | 10.18653/2026.emnlp-main.2704 | null | null | null |
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