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NeurIPS 2026U Virginia, CharlottesvilleCiscoCalifornia Institute of TechnoloPurdueU VirginiaMeta, transfer & few-shot learning

Predicting Plasticity in Deep Continual Learning: A Theoretical Perspective

Existing plasticity diagnostics fail to predict trainability, but optimization readiness, combining gradient strength and reliability, lower-bounds optimization gain and predicts plasticity more reliably.

Jiuqi Wang, Jayanth Srinivasa, Claire Chen, Shuze D Liu and 2 more

Atlanta Poster Session 4, Thu, Dec 10, 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 10/10
strict 2/5
74%Highly rated
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NeurIPS 2026New Jersey Institute of TechnoloNational University of SingaporeWake ForestCiscoThe Chinese University of Hong KProbing & representation analysis

Rep2Text: Decoding Full Text from a Single LLM Token Representation

Rep2Text recovers roughly half of tokens from single LLM representations via adapter-based decoding, showing sequence-length bottlenecks preserve semantics but reduce token recovery.

Haiyan Zhao, Zirui He, Yiming Tang, Fan Yang and 3 more

Atlanta Poster Session 4, Thu, Dec 10, 4:30 PM–7:30 PM, Hall C1 · Published 2026

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9/20 AI panelreviewers recommend it

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AI panel: 9 of 20 reviewers recommend it
lenient 4/5
medium 3/10
strict 2/5
83%Must read
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NeurIPS 2026U California, Santa BarbaraU Southern CaliforniaCiscoCisco ResearchRL for LLMs

Survive or Collapse: The Asymmetric Roles of Data Gating and Reward Grounding in Self-Play RL

Self-play RL stability depends mainly on a strict data gate over proposer tasks, not reward design; ground-truth access accelerates collapse via a self-consistent attractor.

Sophia Xiao Pu, Zhaotian Weng, Chengzhi Liu, Jayanth Srinivasa and 3 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 4/5
medium 7/10
strict 2/5
88%Must read
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NeurIPS 2026U California San DiegoU California, San DiegoCiscoCisco ResearchRL for LLMs

TIER: Trajectory-Invariant Execution Rewards for Multi-Step Tool Composition

TIER derives dense tool-use rewards from execution and schemas rather than reference paths, enabling over 90% accuracy on multi-step composition where trajectory supervision fails.

Anay Kulkarni, Chia En Lu, Dheeraj Mekala, Jayanth Srinivasa and 2 more

Atlanta Poster Session 4, Thu, Dec 10, 4:30 PM–7:30 PM, Hall C1 · Published 2026

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15/20 AI panelreviewers recommend it

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AI panel: 15 of 20 reviewers recommend it
lenient 4/5
medium 10/10
strict 1/5