Implicit Goal Conditioning via Value Disaggregation

Shashwat Saxena, Mehul Goel, Sreyas Venkataraman, Sarvesh Patil and 1 more

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

MM-SCALE: Evaluating Evidence-Grounded Moral Judgment in Vision-Language Models

Eunkyu Park, Wesley Deng, Cheyon Jin, Matheus Kunzler Maldaner and 7 more

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

Vortex: Efficient and Programmable Sparse Attention Serving

Zhuoming Chen, Xinrui Zhong, Qilong Feng, Ranajoy Sadhukhan and 4 more

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

Witness Overlap: Directional Provenance Inside Open-Weight Model Families

Siyuan Li, Haoxuan Zeng, Xin Luo, Fernando Jia and 5 more

Atlanta Poster Session 6, Fri, Dec 11, 4:30 PM–7:30 PM, Hall C1 · Published 2026

Flow Map Language Models: One-step Language Modeling via Continuous Denoising

Continuous flow language models outperform discrete diffusion in quality and speed, and distilling their unique flow map enables one-step generation surpassing eight-step discrete diffusion.

Chanhyuk Lee, Jaehoon Yoo, Manan Agarwal, Sheel Shah and 5 more

Sydney Poster Session 4, Wed, Dec 9, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026 · ▲ 4 on Hugging Face · Code ★ 172

PACE: A Proxy for Agentic Capability Evaluation

PACE predicts agentic benchmark scores from small, selected non-agentic test subsets via regression, achieving under 4% error and over 0.80 correlation at under 1% evaluation cost.

Yueqi Song, Lintang Sutawika, Jiarui Liu, Lindia Tjuatja and 7 more

Sydney Poster Session 3, Wed, Dec 9, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026 · ▲ 18 on Hugging Face

PithTrain: A Compact and Agent-Native MoE Training System

PithTrain is a compact agent-native MoE training framework that matches production throughput while reducing agent turns by 62% and GPU time by 64% on framework tasks.

Ruihang Lai, Hao Kang, Haozhan Tang, Akaash R Parthasarathy and 5 more

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

Evaluating Test-Time Scaling of General LLM Agents

Realistic benchmark reveals LLM agents suffer scaling plateaus and verification gaps that prevent meaningful test-time compute gains.

Xiaochuan Li, Tianshi Ming, Pranav Setlur, Abhijay S Paladugu and 5 more

Sydney Poster Session 6, Thu, Dec 10, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026 · ▲ 10 on Hugging Face · Code ★ 25

Offline Reinforcement Learning for Plasma Control in Nuclear Fusion: Codebase and Benchmark

RL4F introduces an offline RL benchmark for tokamak plasma control using DIII-D dynamics, finding model-based methods perform best but no method dominates all tasks.

YANG FU, Haomin Bao, Rohit Sonker, Xiaoyan Hu and 3 more

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

Interpreting Neural Combinatorial Optimization via Evolving Programmatic Bottlenecks

EPB distills black-box neural combinatorial optimization models into interpretable program portfolios and reveals stage-dependent heuristic-like behavior.

Haocheng Duan, Yuxin Guo, Jieyi Bi, Anqi Xie and 3 more

Atlanta Poster Session 6, Fri, Dec 11, 4:30 PM–7:30 PM, Hall C1 · Published 2026