Multi-Object Tracking (MOT) has traditionally focused on a few specific categories, restricting its applicability to real-world scenarios involving diverse objects. Open-Vocabulary Multi-Object Tracking (OVMOT) addresses this by enabling tracking of arbitrary categories, including novel objects unseen during training. However, current progress is constrained by two challenges: the lack of continuously annotated video data for training, and the lack of a customized OVMOT framework to synergistically handle detection and association. We address the data bottleneck by constructing C-TAO, the […]

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The recent court decision in the United States has highlighted a serious growing conflict between tech companies and the government. Anthropic will remain excluded from defence-related work while the legal case continues, according to the appeals court’s refusal to block Pentagon’s blacklist. Even though this is a temporary decision, it still highlights how sensitive and complex issues around artificial intelligence have become especially when it comes to national security. Anthropic chose to set clear boundaries on how its […]

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We Rewrote JSONata with AI in a Day, Saved $500K/Year Bit of a hyperbolic framing but this looks like another case study of vibe porting, this time spinning up a new custom Go implementation of the JSONata JSON expression language – similar in focus to jq, and heavily associated with the Node-RED platform. As with other vibe-porting projects the key enabling factor was JSONata’s existing test suite, which helped build the first working Go version in 7 hours […]

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Table of Links Abstract and 1. Introduction Related Work and Background Analysis 3.1 Limitations about Existing ReLUficatio 3.2 dReLU Are Neurons in Expert still Sparsely Activated? dReLU Sparsification Experiments Results 6.1 Downstream Tasks Performance 6.2 Sparsity of Sparsified Models Practical Inference Speedup Evaluation 7.1 Experiments Setting 7.2 Pure CPU Inference and 7.3 Hybrid GPU-CPU Inference 7.4 Deploy LLMs on mobile phones Conclusion and References A. Appendix / supplemental material B. Limitation C. Broader Impact 5 dReLU Sparsification In […]

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Multi-objective reinforcement learning (MORL) seeks to learn policies that balance multiple, often conflicting objectives. Although a single preference-conditioned policy is the most flexible and scalable solution, existing approaches remain brittle in practice, frequently failing to recover complete Pareto fronts. We show that this failure stems from two structural issues in current methods: destructive gradient interference caused by premature scalarization and representational collapse across the preference space. We introduce $D^3PO$, a PPO-based framework that reorganizes multi-objective policy optimization to […]

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How are you, hacker? 🪐Want to know what’s trending right now?: The Techbeat by HackerNoon has got you covered with fresh content from our trending stories of the day! Set email preference here. ## Crawl, Walk, Run, Fly – The Four Phases of AI Agent Maturity By @denisp [ 5 Min read ] Don’t let your AI fly before it can walk. Why jumping straight to fully autonomous systems is a recipe for disaster (and what to do […]

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It’s another month in AI research, and it’s hard to pick favorites. This month, I am going over a paper that discusses strategies for the continued pretraining of LLMs, followed by a discussion of reward modeling used in reinforcement learning with human feedback (a popular LLM alignment method), along with a new benchmark. Continued pretraining for LLMs is an important topic because it allows us to update existing LLMs, for instance, ensuring that these models remain up-to-date with […]

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