A Goal-Conditioned Inverse Dynamics Model amortizes planning in pretrained world model latents, matching or exceeding CEM in seven of eight settings at 100-130x lower per-decision cost.
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4 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 4representative citing papers
ANCHOR applies simulated human supervision to self-evolving agents and shows limited oversight reduces safety degradation while maintaining performance on coding, math, and safety tasks.
DGS-Net decomposes gradients into harmful and beneficial directions, projects task updates onto the orthogonal complement of harmful ones, and aligns with distilled signals from frozen CLIP to fine-tune for AI-image detection while preserving priors.
NS-Net uses null-space projection on CLIP features plus contrastive learning and patch selection to improve generalization of AI-generated image detectors across 40 unseen generative models.
citing papers explorer
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Latent Geometry Beyond Search: Amortizing Planning in World Models
A Goal-Conditioned Inverse Dynamics Model amortizes planning in pretrained world model latents, matching or exceeding CEM in seven of eight settings at 100-130x lower per-decision cost.
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Towards Healthy Evolution: Exploring the Role and Mechanisms of Human-Agent Interaction in Self-Evolving Systems
ANCHOR applies simulated human supervision to self-evolving agents and shows limited oversight reduces safety degradation while maintaining performance on coding, math, and safety tasks.
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DGS-Net: Distillation-Guided Gradient Surgery for CLIP Fine-Tuning in AI-Generated Image Detection
DGS-Net decomposes gradients into harmful and beneficial directions, projects task updates onto the orthogonal complement of harmful ones, and aligns with distilled signals from frozen CLIP to fine-tune for AI-image detection while preserving priors.
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NS-Net: Decoupling CLIP Semantic Information through NULL-Space for Generalizable AI-Generated Image Detection
NS-Net uses null-space projection on CLIP features plus contrastive learning and patch selection to improve generalization of AI-generated image detectors across 40 unseen generative models.