Anthropic Races to Contain Leak of Code Behind Claude AI Agent - WSJ
Anthropic Races to Contain Leak of Code Behind Claude AI Agent WSJ
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My most common research advice: do quick sanity checks
Written quickly as part of the Inkhaven Residency . At a high level, research feedback I give to more junior research collaborators often can fall into one of three categories: Doing quick sanity checks Saying precisely what you want to say Asking why one more time In each case, I think the advice can be taken to an extreme I no longer endorse. Accordingly, I’ve tried to spell out the degree to which you should implement the advice, as well as what “taking it too far” might look like. This piece covers doing quick sanity checks, which is the most common advice I give to junior researchers. I’ll cover the other two pieces of advice in a subsequent piece. Doing quick sanity checks Research is hard (almost by definition) and people are often wrong. Every researcher has wasted countless hours
Are Benchmark Tests Strong Enough? Mutation-Guided Diagnosis and Augmentation of Regression Suites
arXiv:2604.01518v1 Announce Type: new Abstract: Benchmarks driven by test suites, notably SWE-bench, have become the de facto standard for measuring the effectiveness of automated issue-resolution agents: a generated patch is accepted whenever it passes the accompanying regression tests. In practice, however, insufficiently strong test suites can admit plausible yet semantically incorrect patches, inflating reported success rates. We introduce STING, a framework for targeted test augmentation that uses semantically altered program variants as diagnostic stressors to uncover and repair weaknesses in benchmark regression suites. Variants of the ground-truth patch that still pass the existing tests reveal under-constrained behaviors; these gaps then guide the generation of focused regression
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Cross-Scale MAE: A Tale of Multi-Scale Exploitation in Remote Sensing
arXiv:2401.15855v1 Announce Type: cross Abstract: Remote sensing images present unique challenges to image analysis due to the extensive geographic coverage, hardware limitations, and misaligned multi-scale images. This paper revisits the classical multi-scale representation learning problem but under the general framework of self-supervised learning for remote sensing image understanding. We present Cross-Scale MAE, a self-supervised model built upon the Masked Auto-Encoder (MAE).During pre-training, Cross-Scale MAE employs scale augmentation techniques and enforces cross-scale consistency constraints through both contrastive and generative losses to ensure consistent and meaningful representations well-suited for a wide range of downstream tasks. Further, our implementation leverages the
Tracking the emergence of linguistic structure in self-supervised models learning from speech
arXiv:2604.02043v1 Announce Type: cross Abstract: Self-supervised speech models learn effective representations of spoken language, which have been shown to reflect various aspects of linguistic structure. But when does such structure emerge in model training? We study the encoding of a wide range of linguistic structures, across layers and intermediate checkpoints of six Wav2Vec2 and HuBERT models trained on spoken Dutch. We find that different levels of linguistic structure show notably distinct layerwise patterns as well as learning trajectories, which can partially be explained by differences in their degree of abstraction from the acoustic signal and the timescale at which information from the input is integrated. Moreover, we find that the level at which pre-training objectives are d
My most common research advice: do quick sanity checks
Written quickly as part of the Inkhaven Residency . At a high level, research feedback I give to more junior research collaborators often can fall into one of three categories: Doing quick sanity checks Saying precisely what you want to say Asking why one more time In each case, I think the advice can be taken to an extreme I no longer endorse. Accordingly, I’ve tried to spell out the degree to which you should implement the advice, as well as what “taking it too far” might look like. This piece covers doing quick sanity checks, which is the most common advice I give to junior researchers. I’ll cover the other two pieces of advice in a subsequent piece. Doing quick sanity checks Research is hard (almost by definition) and people are often wrong. Every researcher has wasted countless hours
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