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Research2026-08-08 · source-backed
arXiv 2608.05424 shows ImageNet- and LAION-scale pretrained encoders pick up metadata traces tied to camera and image-processing properties. Deliberately injecting metadata-semantics correlations during pretraining produces systematically higher metadata sensitivity and larger degradation under metadata distribution shift, and mitigation reduces sensitivity even to unseen metadata types without hurting downstream performance. The dual edge: this same sensitivity partly explains why encoders detect generated images, so removing it improves OOD generalization at a cost.
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Poisoned entries in persistent memory force unintended tool selection during retrieval — even against explicit user instructions. Unlike prompt injection targeting input, MCFA targets the memory store, making it persistent and harder to detect. If your agent has long-term memo...
UniTexture backpropagates gradients from a Vision-Language-Action policy's action outputs to the surface texture of a single 3D object through a differentiable renderer, optimizing one shared texture over a distribution of tasks, instructions, states and viewpoints. Tested on...
If you're tuning an agent system, upgrade the model doing the tuning before you rewrite a single line of the harness. That's the finding from HarnessOpt-Bench (arXiv 2608.06301, Scale AI), which tests whether frontier models can improve an agent *system* rather than write code...
Beyond Simply Environment Scaling from the Chinese Academy of Sciences tested the industry assumption that more agent environments is better and found it false. Ability-aware Environment Selection picked 30 environments producing 95.6% relative gain versus 43.4% for the full p...
arXiv 2607.24392 measured secondary costs across downstream task performance, over-refusal on benign inputs, and inference cost. Rule-based defenses best preserve task performance. Conservative self-reflective defenses drive the most over-refusal. Multi-round defenses carry th...
This study investigates dropping speculative decoding's lossless guarantee without any training, quantifying speed-ups against controlled capability drift. Standard spec decoding exactly preserves the sampling distribution. Relaxing it buys latency at a small distributional co...
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