1. Writerslogic at PAN 2026: Process over Content for Robust Detection under Domain Shift

   http://arxiv.org/abs/2610.03565v1 · arxiv

   We describe the Writerslogic systems for three PAN at CLEF 2026 shared tasks (Reasoning Trajectory Detection, Voight-Kampff Generative AI Detection, and Multi-Author Writing Style Analysis), unified by a shared analytical framework: feature robustness under distribution shift is governed by support

2. TTLab at Daleel 2026: STAR-Ar, Sequence Tagging for Argument Recognition in Arabic

   http://arxiv.org/abs/2609.39385v1 · arxiv

   Argument Mining (AM) is a critical NLP task that remains significantly under-resourced in Arabic. This paper presents $\testtt{STAR-Ar}$, a BERT-BiLSTM-CRF architecture for argument discourse detection and classification, as our system for Daleel 2026, the inaugural Arabic argument mining shared tas

3. Massachusetts' 2026 Clean Peak Standard Recalibration: Adaptation and Storage Tradeoffs

   http://arxiv.org/abs/2609.36468v1 · arxiv

   Massachusetts recalibrated its Clean Peak Standard (CPS) in 2026 by lowering the minimum standards and expanding the Near-Term Resource Multiplier for qualifying storage. This paper evaluates the change using a three-zone, hourly capacity expansion model for 2026--2030. Both scenarios achieve full C

4. A Pocket Offline Model for Simultaneous Speech Translation as CUNI Submission to IWSLT 2026

   http://arxiv.org/abs/2606.03948v1 · arxiv

   We implement simultaneous translation capability with the offline direct speech-to-text translation model Canary, using the state-of-the-art policy AlignAtt, and submit it to IWSLT 2026 Simultaneous Speech Translation Shared task for Czech to English and English to German and Italian. The strength

5. LOCO 2026 Lightning Talk Abstracts: 2nd International Workshop on Low Carbon Computing

   http://arxiv.org/abs/2609.13355v1 · arxiv

   This volume contains the accepted lightning-talk contributions from the 2nd International Workshop on Low Carbon Computing (LOCO 2026), held at Lancaster University, United Kingdom, on 10-11 September 2026. The collection brings together 14 short papers presenting emerging research, early-stage resu

6. Learning to Fold: prizewinning solution at LeHome Challenge 2026 (1st place online, 2nd offline)

   http://arxiv.org/abs/2606.27163v3 · arxiv

   I describe my solution to the LeHome Challenge 2026, an ICRA 2026 competition on bimanual garment folding. The system placed 1st of 62 teams in the online (simulation) round and 2nd in the real-world final. It improves a vision-language-action (VLA) policy with a reinforcement-learning loop. The pol

7. Writerslogic at the CLEF 2026 SimpleText Track: Multi-Candidate LLM Simplification and Stacked Complexity Spotting

   http://arxiv.org/abs/2610.03567v1 · arxiv

   We describe the Writerslogic team's participation in the CLEF 2026 SimpleText shared task, addressing Task 1 (text simplification) and Task 2 (complexity spotting). For Task 1, we develop a multi-candidate generation pipeline using GPT-4o-mini that produces five simplification candidates per sentenc

8. ICASSP 2026 URGENT Speech Enhancement Challenge

   http://arxiv.org/abs/2601.13531v1 · arxiv

   The ICASSP 2026 URGENT Challenge advances the series by focusing on universal speech enhancement (SE) systems that handle diverse distortions, domains, and input conditions. This overview paper details the challenge's motivation, task definitions, datasets, baseline systems, evaluation protocols, an

9. NTIRE 2026 Challenge on Video Saliency Prediction: Methods and Results

   http://arxiv.org/abs/2604.14816v1 · arxiv

   This paper presents an overview of the NTIRE 2026 Challenge on Video Saliency Prediction. The goal of the challenge participants was to develop automatic saliency map prediction methods for the provided video sequences. The novel dataset of 2,000 diverse videos with an open license was prepared for

10. POLY-SIM: Polyglot Speaker Identification with Missing Modality Grand Challenge 2026 Evaluation Plan

    http://arxiv.org/abs/2603.24569v2 · arxiv

    Multimodal speaker identification systems typically assume the availability of complete and homogeneous audio-visual modalities during both training and testing. However, in real-world applications, such assumptions often do not hold. Visual information may be missing due to occlusions, camera failu

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