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Showing papers from Qualcomm AI Research Show all papers

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Efficient Test-Time Adaptation For Robot Policies

Motasem Alfarra, Pietro Mazzaglia, Markus Peschl, Daniel Dijkman and 1 more

Sydney Poster Session 2, Tue, Dec 8, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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AI panel: 2 of 20 reviewers recommend it
lenient 2/5
medium 0/10
strict 0/5
45%Niche pick
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AC/DC on a Budget -- Alternating Sparse Phases

Rahul Nittala, Advait Gadhikar, Tom Jacobs, Rebekka Burkholz

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
lenient 0/5
medium 0/10
strict 0/5
45%Niche pick
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ArtCrafter: Feed-Forward Generation of Articulated 3D Object with Analytic Joint Derivation

Minh Tu, Quang-Binh Nguyen, Khoi Nguyen

Sydney Poster Session 1, Tue, Dec 8, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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AI panel: 0 of 20 reviewers recommend it
lenient 0/5
medium 0/10
strict 0/5
67%Highly rated
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Alleviating Hallucination with Training-Free Uncertainty-Guided Steering

Litian Liu, Yubing Jian, Qiqi Hou, Reza Pourreza and 4 more

Atlanta Poster Session 1, Wed, Dec 9, 10:00 AM–1:00 PM, Hall C1 · Published 2026

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2/20 AI panelreviewers recommend it

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AI panel: 2 of 20 reviewers recommend it
lenient 1/5
medium 1/10
strict 0/5
57%Worth a look
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Mix-Opt: Mixed Optimization for Memory-Efficient Personalization of Text-to-Image Diffusion Models

Seokeon Choi, Sunghyun Park, Hyoungwoo Park, Jeongho Kim 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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AI panel: 1 of 20 reviewers recommend it
lenient 1/5
medium 0/10
strict 0/5
71%Highly rated
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Enhancing Novel View Synthesis via Geometry Grounded Set Diffusion

SetDiff integrates explicit 3D geometry priors into a set-based diffusion model to improve novel-view synthesis from 3D Gaussian Splatting, reducing hallucinations and achieving state-of-the-art results.

Farhad G. Zanjani, Herbert Cai, Amirhossein Habibian

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

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6/20 AI panelreviewers recommend it

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AI panel: 6 of 20 reviewers recommend it
lenient 3/5
medium 3/10
strict 0/5
86%Must read
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Reasoning as Compression: Unifying Budget Forcing via the Conditional Information Bottleneck

Conditional Information Bottleneck frames reasoning as lossy compression with a semantic surprisal prior, improving LLM reasoning efficiency with minimal accuracy loss.

Fabio Valerio Massoli, Andrey Kuzmin, Arash Behboodi

Sydney Poster Session 2, Tue, Dec 8, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026 · ▲ 23 on Hugging Face

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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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Leech Lattice Vector Quantization for Efficient LLM Compression

Leech lattice vector quantization enables efficient LLM compression via structured high-dimensional packing, achieving state-of-the-art post-training quantization without rotation preprocessing.

Tycho F van der Ouderaa, Mart van Baalen, Paul Whatmough, Markus Nagel

Sydney Poster Session 1, Tue, Dec 8, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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13/20 AI panelreviewers recommend it

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AI panel: 13 of 20 reviewers recommend it
lenient 4/5
medium 8/10
strict 1/5
76%Highly rated
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MAPLE: Latent Multi-Agent Play for End-to-End Autonomous Driving

MAPLE trains vision-language-action driving models via latent multi-agent rollout and reinforcement learning, achieving state-of-the-art closed-loop performance without external simulators.

Rajeev Yasarla, Deepti Hegde, Hsin-Pai Cheng, Shizhong Han and 8 more

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

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10/20 AI panelreviewers recommend it

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AI panel: 10 of 20 reviewers recommend it
lenient 5/5
medium 5/10
strict 0/5
83%Must read
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Generative Scenario Rollouts for End-to-End Autonomous Driving

GeRo enables vision-language-action models to generate language-grounded future traffic scenes via autoregressive rollouts, improving Bench2Drive driving scores by 15.7 and success rates by 26.2.

Rajeev Yasarla, Deepti Hegde, Shizhong Han, Hsin-Pai Cheng and 10 more

Sydney Poster Session 1, Tue, Dec 8, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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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 7/10
strict 1/5
72%Highly rated
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Prune to Protect: Faster Training and Enhanced Privacy by Dynamic Data Pruning

WLIB dynamically prunes easy samples and reweights hard ones to reduce memorization, improve privacy, and speed up training.

Chinmay Joshi, Advait Gadhikar, Celia Rubio-Madrigal, Aneet Kumar Dutta and 2 more

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

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8/20 AI panelreviewers recommend it

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AI panel: 8 of 20 reviewers recommend it
lenient 5/5
medium 3/10
strict 0/5