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NeurIPS 2026U California, Santa BarbaraUCSB / AppleCaltech/GatechGoogleAccentureLLM agents & planning

Ares: Adaptive Reasoning Effort Selection for Efficient LLM Agents

Ares uses a lightweight router to select per-step reasoning effort for LLM agents, cutting reasoning tokens by up to 52.7% with minimal accuracy loss.

Jingbo Yang, Bairu Hou, Jiayun (Peter) Wang, Wei Wei and 2 more

Atlanta Poster Session 3, Thu, Dec 10, 10:00 AM–1:00 PM, Hall C1 · Published 2026 · ▲ 1 on Hugging Face

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AI panel: 12 of 20 reviewers recommend it
lenient 5/5
medium 7/10
strict 0/5
89%Must read
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NeurIPS 2026UC Santa BarbaraU California, Santa BarbaraUCSBFeature attribution

Revealing the Gap in Human and VLM Scene Perception through Counterfactual Semantic Saliency

Counterfactual Semantic Saliency reveals VLMs diverge from human scene perception via size, center, and saliency biases while underweighting people.

Ziqi Wen, Parsa Madinei, Miguel Eckstein

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

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16/20 AI panelreviewers recommend it

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AI panel: 16 of 20 reviewers recommend it
lenient 4/5
medium 9/10
strict 3/5
80%Must read
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NeurIPS 2026UC Santa BarbaraU Roma "La Sapienza"Universitat Politècnica de CatalU FribourgAalto University, CGIHypergraphs

HOPSE: Scalable Higher-Order Positional and Structural Encoder for Combinatorial Representations

HOPSE replaces higher-order message passing with Hasse-graph encodings to scale linearly on combinatorial domains while matching or exceeding state-of-the-art performance.

Guillermo Bernárdez, Marco Montagna, Louis Van Langendonck, Martin Carrasco and 6 more

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 4/5
medium 8/10
strict 0/5