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Showing papers from Samsung Research America Show all papers

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NeurIPS 2026U New South WalesU California, San DiegoUCSDSamsung Research AmericaCausal inference

PACO: Partial-order-Augmented Continuous Optimization for Differentiable Causal Discovery

Xiaoxuan Li, Junda Wu, Julian McAuley, Lina Yao 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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57%Worth a look
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NeurIPS 2026U California, San DiegoUCSDU California San DiegoNew YorkPurdueLLM reasoning & chain of thought

Reasoning Pathologies in Large Language Models: A Diagnostic Perspective

Rohan Surana, Junda Wu, Sheldon Yu, Gagan Mundada and 5 more

Atlanta Poster Session 5, Fri, Dec 11, 10:00 AM–1:00 PM, Hall C1 · Published 2026

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45%Niche pick
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NeurIPS 2026U California, San DiegoU Michigan - Ann ArborNew YorkUCSDU New South WalesLLM agents & planning

The Geometry of Agent Skills: Non-Commutative Composition in Representation Space

Junda Wu, Yifan Wang, Zihan Huang, Xunyi Jiang and 5 more

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

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NeurIPS 2026Shanghai JiaotongShanghai Jiao TongSamsung Research AmericaOptimization

On the Necessity of Guidance Decay: From Three-Phase Analysis in Gaussian Mixture Models to Dynamic Optimization

Yiyu Qiu, Ruofeng Yang, Tong Yu, Shuai Li

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

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76%Highly rated
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NeurIPS 2026U California, San DiegoSamsung Research AmericaDukeUCSDVision-language models

AMPS: Adaptive Modality Preference Steering via Functional Entropy

AMPS uses instance-aware functional entropy to adaptively steer multimodal model modality preferences, improving control while minimizing inference errors.

Zihan Huang, Xintong Li, Rohan Surana, Tong Yu and 4 more

Atlanta Poster Session 2, Wed, Dec 9, 4:30 PM–7:30 PM, Hall C1 · Published 2026

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lenient 4/5
medium 6/10
strict 0/5
88%Must read
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NeurIPS 2026Samsung Research AmericaUncertainty & calibration

Actionable Hallucination Detection: Translating Latent Uncertainty into Agentic Critique

Latent Critic is a lightweight LoRA adapter that translates LLM latent uncertainty into real-time, localized natural-language hallucination feedback, achieving 0.966 AUROC and enabling agent self-correction with negligible latency.

Sanidhya Vijayvargiya, Rahul Lokesh

Atlanta Poster Session 2, Wed, Dec 9, 4:30 PM–7:30 PM, Hall C1 · Published 2026

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AI panel: 15 of 20 reviewers recommend it
lenient 5/5
medium 9/10
strict 1/5