Good Papers

Showing papers from University of Arkansas - Fayetteville Show all papers

71%Highly rated
?Highly ratedVote to see the score
NeurIPS 2026U ArkansasU Arkansas - FayettevilleCarnegie MellonLLM agents & planning

Agentic Multi-Turn Reasoning: A Fairness Approach

Fair-MPO improves multi-turn agentic reasoning via multi-level preference optimization and a fairness objective that fixes long-horizon credit assignment and data imbalance, achieving state-of-the-art benchmark results.

Thanh-Dat Truong, Sankalp Pandey, Hugh Churchill, Jackson Cothren and 2 more

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

– ReadersNo votes yet
7/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 7 of 20 reviewers recommend it
lenient 4/5
medium 3/10
strict 0/5
83%Must read
?Must readVote to see the score
NeurIPS 2026Massachusetts Institute of TechnU Arkansas - FayettevilleUniversity Medical Center HamburComputational neuroscience

Decomposing the modulation of interactions between neuronal populations

A low-rank tensor extension of communication subspaces decomposes how third variables multiplicatively gate interactions between neural populations via multiplicative interaction channels.

Marco Celotto, J. S Sooter, Sofie Ährlund-Richter, Kyle R Jenks and 2 more

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

– ReadersNo votes yet
13/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 13 of 20 reviewers recommend it
lenient 5/5
medium 6/10
strict 2/5
80%Must read
?Must readVote to see the score
NeurIPS 2026U ArkansasU Arkansas - FayettevilleGoogle ResearchU LiverpoolDFKI & Max Planck Research SVision-language models

OmniSpace: Efficient Geometry Awareness for Autonomous Vehicles MLLMs

OmniSpace improves autonomous vehicle MLLM spatial reasoning via camera pose injection, multi-view epipolar attention, and 3D geometric distillation without auxiliary 3D models, surpassing existing methods across planning, risk detection, and language benchmarks.

Anh Hao Vo, Phu Loc Nguyen, Khoa Vo, Sieu Tran and 6 more

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

– ReadersNo votes yet
12/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 12 of 20 reviewers recommend it
lenient 5/5
medium 7/10
strict 0/5
91%Must read
?Must readVote to see the score
NeurIPS 2026U ArkansasU Arkansas - FayettevilleGoogle ResearchU LiverpoolDFKI & Max Planck Research SVision-language models

DriveSpatial: A Benchmark for Spatiotemporal Intelligence in VLMs for Autonomous Driving

DriveSpatial benchmarks vision-language models' spatiotemporal autonomous driving intelligence, finding a 28.4-point human gap with cognitive scene construction as the key bottleneck.

Anh Hao Vo, Khoa Vo, Phu Loc Nguyen, Sieu Tran and 9 more

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

– ReadersNo votes yet
18/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 18 of 20 reviewers recommend it
lenient 5/5
medium 10/10
strict 3/5