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NeurIPS 2026The University of Hong KongU WaterlooUCSCMetaMassachusetts Institute of TechnUnified multimodal models

Tuna-2: Pixel Embeddings Beat Vision Encoders for Multimodal Understanding and Generation

Tuna-2 replaces vision encoders with patch embeddings for end-to-end pixel-space multimodal understanding and generation, achieving state-of-the-art results that outperform encoder-based designs at scale.

Zhiheng Liu, Weiming Ren, Xiaoke Huang, Shoufa Chen and 11 more

Sydney Poster Session 4, Wed, Dec 9, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026 · ▲ 70 on Hugging Face · Code ★ 756

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lenient 4/5
medium 6/10
strict 1/5
88%Must read
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NeurIPS 2026Massachusetts Institute of TechnFacebookThe Ohio StateMetaBytedance Inc.Unified multimodal models

Unifying Contrastive and Generative Objectives for Visual Understanding and Text-to-Image Generation

DREAM unifies contrastive and generative objectives via Masking Warmup, yielding joint visual understanding gains and faster, higher-quality text-to-image generation.

Chao Li, Tianhong Li, Sai V Nuthalapati, Hong-You Chen and 8 more

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

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lenient 4/5
medium 9/10
strict 2/5
71%Highly rated
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NeurIPS 2026New YorkMetaFacebookMeta AIGoogle DeepMind / NYUText-to-image

RepFusion: Leveraging Multimodal Priors for Denoising in Representation Space

RepFusion conditions a diffusion transformer on multimodal LLM outputs to denoise visual representations, outperforming comparable newly initialized denoisers.

Xichen Pan, Satya Narayan Shukla, Aashu Singh, Shlok K Mishra and 1 more

Atlanta Poster Session 4, Thu, Dec 10, 4:30 PM–7:30 PM, Hall C1 · Published 2026 · ▲ 17 on Hugging Face

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lenient 3/5
medium 4/10
strict 0/5
83%Must read
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NeurIPS 2026FacebookMeta, FAIRMetaMeta AIResearch, FacebookWatermarking & AI-text detection

TextSeal: A Localized LLM Watermark for Provenance & Distillation Protection

TextSeal is a localized LLM watermark using dual-key generation and entropy-weighted scoring for robust provenance and distillation detection without inference overhead.

Tom Sander, Pierre Fernandez, Hongyan Chang, Tomáš Souček and 5 more

Paris Poster Session 5, Fri, Dec 11, 11:30 AM–1:30 PM, Paris Poster Hall · Published 2026 · ▲ 1 on Hugging Face

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lenient 5/5
medium 7/10
strict 1/5
92%Must read
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NeurIPS 2026Meta AIMeta FAIR & Université ParisMeta / Ecole Normale SupérieureInriaFacebook AI ResearchRL for LLMs

Reinforcement Learning for Code Optimization

Reinforcement learning for code optimization fails due to noisy, sparse execution-time rewards, so a calibrated three-stage pipeline improves strict pass rates by up to 125% while preserving correctness.

Pierre Chambon, Kunhao Zheng, Juliette Decugis, Benoît Sagot and 1 more

Sydney Poster Session 5, Thu, Dec 10, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026 · ▲ 13 on Hugging Face

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19/20 AI panelreviewers recommend it

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AI panel: 19 of 20 reviewers recommend it
lenient 4/5
medium 10/10
strict 5/5
91%Must read
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NeurIPS 2026Meta FAIR & Université ParisMeta / Ecole Normale SupérieureMeta AIMeta AIMeta / FAIRRL for LLMs

Extrapolative Weight Averaging Reveals Correctness–Efficiency Frontiers in Code RL

Nested unit-test coverage in code RL reveals a correctness, efficiency frontier that extrapolative weight averaging extends, enabling complementary checkpoints that improve pass@250 by 3.3%.

Kunhao Zheng, Juliette Decugis, Pierre Chambon, Jonas Gehring and 3 more

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

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17/20 AI panelreviewers recommend it

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AI panel: 17 of 20 reviewers recommend it
lenient 5/5
medium 8/10
strict 4/5
78%Highly rated
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NeurIPS 2026Rochester Institute of TechnologU RochesterU Missouri - Kansas CityRutgersMeta AILLM reasoning & chain of thought

On the Token Value Inequality in Efficient Reasoning

Token value inequality in reasoning traces enables identifying core versus redundant tokens via log probabilities, yielding 76% token reduction with preserved accuracy via selective compression.

Runjia Zeng, Hang Hua, Yiyang Liu, Zhiqiang Tao and 4 more

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

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AI panel: 11 of 20 reviewers recommend it
lenient 4/5
medium 7/10
strict 0/5
88%Must read
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NeurIPS 2026Meta AIFacebookMETAMetaÉcole Normale Supérieure, ParisVision-language models

Boosting Brain-to-Image Decoding with TRIBE v2 Data Augmentation

TRIBE v2 synthetic fMRI augmentation improves brain-to-image decoding by up to 68%, though optimal synthetic-to-real ratios vary by dataset, and synthetic-only training achieves above-chance zero-shot decoding.

Yohann Benchetrit, Marlene Careil, Simon Dahan, Hubert Banville and 2 more

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

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AI panel: 15 of 20 reviewers recommend it
lenient 5/5
medium 8/10
strict 2/5
71%Highly rated
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NeurIPS 2026NortheasternU California, San DiegoU Maryland, College ParkMeta AIU WashingtonVideo-language models

ThinkJEPA: Empowering Latent World Models with Large Vision-Language Reasoning Model

ThinkJEPA combines dense JEPA dynamics with sparse VLM reasoning via dual pathways to improve long-horizon latent world modeling and trajectory prediction.

Haichao Zhang, Yijiang Li, Shwai He, Tushar Nagarajan and 4 more

Atlanta Poster Session 5, Fri, Dec 11, 10:00 AM–1:00 PM, Hall C1 · Published 2026 · ▲ 21 on Hugging Face · Code ★ 58

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AI panel: 7 of 20 reviewers recommend it
lenient 4/5
medium 2/10
strict 1/5
88%Must read
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NeurIPS 2026U Science and Technology of ChinMeta AIMetaPurdueFacebookRL for LLMs

Objective Shaping with Hard Negatives: Windowed Partial AUC Optimization for RL-based LLM Recommenders

GRPO for LLM recommenders maximizes AUC but beam-search negatives reshape objectives toward partial AUC; proposed WPAUC with TAWin optimization improves top-K alignment and achieves state-of-the-art results.

Wentao Shi, Qifan Wang, Chen Chen, Fei Liu and 6 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: 15 of 20 reviewers recommend it
lenient 4/5
medium 8/10
strict 3/5