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Showing papers from Apple MLR Show all papers

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NeurIPS 2026AppleU Tübingen, AppleApple MLRWayveRL for LLMs

Expanding LLM Agent Boundaries with Strategy-Guided Exploration

Strategy-Guided Exploration improves LLM agent reinforcement learning by planning in language strategies rather than actions, boosting performance across UI, tool, coding, and embodied tasks.

Andrew Szot, Michael Kirchhof, Omar Attia, Alexander Toshev

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

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12/20 AI panelreviewers recommend it

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AI panel: 12 of 20 reviewers recommend it
lenient 5/5
medium 6/10
strict 1/5
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NeurIPS 2026Institute of Science and TechnolApple MLRInstitute of Science and TechnolAchira IncISTAControllable generation

Assessing Sample Quality in Conditional Generation under Compositional Shift

A per-sample trust score combining global realism and attribute-wise faithfulness evaluates conditional generations under compositional shift without reference data, enabling filtering and ranking that improves biological imaging and vision benchmarks.

Berker Demirel, Valentino Maiorca, Marco Fumero, Theofanis Karaletsos 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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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
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NeurIPS 2026SpotlightDeel, ANITI, Université Paul SabAppleU CambridgeMassachusetts Institute of TechnApple MLRUnified multimodal models

The Design Space of Tri-Modal Masked Diffusion Models

A tri-modal masked diffusion model pretrained from scratch on text, image-text, and audio-text data achieves strong cross-modal generation and introduces an SDE-based batch-size reparameterization.

Louis Bethune, Victor Guilherme Turrisi da Costa, Bruno Mlodozeniec, Pau Rodriguez and 20 more

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

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12/20 AI panelreviewers recommend it

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AI panel: 12 of 20 reviewers recommend it
lenient 3/5
medium 7/10
strict 2/5