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Showing papers from Eberhard-Karls-Universität Tübingen Show all papers

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FalconPerception-HD: High Density Perception via Reinforcement Learning

Sofian Chaybouti, YASSER ABDELAZIZ DAHOU DJILALI, Ngoc D Huynh, REDA ALAMI and 1 more

Sydney Poster Session 3, Wed, Dec 9, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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Reading Positional Coupling in Transformers with Diffusion Scores

Savik Kinger, Johannes Bertram, Luciano Dyballa, Andy Keller and 1 more

Atlanta Poster Session 4, Thu, Dec 10, 4:30 PM–7:30 PM, Hall C1 · Published 2026

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57%Worth a look
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Concentrated Gradients Amplify Forgetting: Dominant-direction Projection for Continual Multimodal Learning

Chengxiang Huang, Haopeng Zhang, Yuzhe Han, Rui Dai and 3 more

Sydney Poster Session 6, Thu, Dec 10, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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AI panel: 1 of 20 reviewers recommend it
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Evaluating Neural Data Tokenizers: A Framework for Assessing Learned Representations of Spiking Activity

Federico D'Agostino, Alex Gilbert, Susanne Keller, Jaivardhan Kapoor and 16 more

Sydney Poster Session 4, Wed, Dec 9, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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AI panel: 0 of 20 reviewers recommend it
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57%Worth a look
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When Trackers Fail: VLM-Guided Verification and Recovery for Robust Video Object Segmentation

Valay Mahesh Bundele, Susmit Agrawal, Mehran Hosseinzadeh, The Nam Nguyen and 1 more

Sydney Poster Session 6, Thu, Dec 10, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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AI panel: 1 of 20 reviewers recommend it
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86%Must read
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Controlling for Omitted Variable Bias in Deep Neural Networks

A control-variable method using generalized additive modeling and cross-fitted ridge refitting removes omitted-variable bias from deep networks, yielding unbiased predictions.

Manuel Pfeuffer, Roshan P Rane, Kerstin Ritter, Sonja Greven

Paris Poster Session 2, Wed, Dec 9, 5:00 PM–7:00 PM, Paris Poster Hall · Published 2026

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14/20 AI panelreviewers recommend it

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AI panel: 14 of 20 reviewers recommend it
lenient 5/5
medium 8/10
strict 1/5
83%Must read
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AlphaQ: Calibration-Free Bit Allocation for Mixture-of-Experts Quantization

AlphaQ allocates MoE quantization bits without calibration using heavy-tailed spectral analysis, outperforming calibration-based methods and achieving near full-precision accuracy at 3.5-bit average precision.

Wanqi Yang, Yuexiao Ma, Alexander Conzelmann, Xiawu Zheng and 3 more

Sydney Poster Session 6, Thu, Dec 10, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026 · ▲ 2 on Hugging Face

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13/20 AI panelreviewers recommend it

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AI panel: 13 of 20 reviewers recommend it
lenient 5/5
medium 8/10
strict 0/5
80%Must read
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FutureSim: Replaying World Events to Evaluate Adaptive Agents

FutureSim replays real-world events chronologically to benchmark adaptive AI agents forecasting future news, finding top accuracy at only 25%.

Shashwat Goel, Nikhil Chandak, Arvindh Arun, Ameya Prabhu and 4 more

Sydney Poster Session 2, Tue, Dec 8, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026 · ▲ 6 on Hugging Face · Code ★ 56

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AI panel: 12 of 20 reviewers recommend it
lenient 5/5
medium 6/10
strict 1/5
83%Must read
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Split the Differences, Pool the Rest: Provably Efficient Multi-Objective Imitation

MA-BC partitions conflicting expert trajectories while pooling compatible data to recover Pareto-optimal policies in multi-objective imitation with minimax optimal rates.

Ziyad Sheebaelhamd, Luca Viano, Volkan Cevher, Claire Vernade

Paris Poster Session 4, Thu, Dec 10, 5:30 PM–7:30 PM, Paris Poster Hall · Published 2026

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AI panel: 13 of 20 reviewers recommend it
lenient 3/5
medium 8/10
strict 2/5
78%Highly rated
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Unlearning That Lasts: Utility-Preserving, Robust, and Almost Irreversible Forgetting in LLMs

JensUn uses Jensen-Shannon divergence to achieve stable, robust LLM unlearning with preserved utility and strong resistance to relearning.

Naman Deep Singh, Maximilian Mueller, Amit Peleg, Francesco Croce and 1 more

Paris Poster Session 1, Wed, Dec 9, 12:30 PM–2:30 PM, Paris Poster Hall · Published 2026

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11/20 AI panelreviewers recommend it

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AI panel: 11 of 20 reviewers recommend it
lenient 5/5
medium 5/10
strict 1/5
76%Highly rated
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From Topology to Retrieval: Decoding Embedding Spaces with Unified Signatures

Topological and geometric embedding measures are redundant, so Unified Topological Signatures holistically characterize spaces to predict model properties and retrieval performance.

Florian Rottach, William Rudman, Bastian Rieck, Harrisen Scells and 1 more

Paris Poster Session 3, Thu, Dec 10, 12:30 PM–2:30 PM, Paris Poster Hall · Published 2026

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AI panel: 10 of 20 reviewers recommend it
lenient 5/5
medium 5/10
strict 0/5