How sparse attention solves the memory bottleneck in long-context LLMs
As AI agents take on longer tasks, the KV cache of LLMs has become a massive bottleneck. Discover how sparse attention techniques are freeing up GPU memory. The post How sparse attention solves the memory bottleneck in long-context LLMs first appeared on TechTalks .
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A Player Selection Network for Scalable Game-Theoretic Prediction and Planning
arXiv:2505.00213v3 Announce Type: replace Abstract: While game-theoretic planning frameworks are effective at modeling multi-agent interactions, they require solving large optimization problems where the number of variables increases with the number of agents, resulting in long computation times that limit their use in large-scale, real-time systems. To address this issue, we propose 1) PSN Game-a learning-based, game-theoretic prediction and planning framework that reduces game size by learning a Player Selection Network (PSN); and 2) a Goal Inference Network (GIN) that makes it possible to use the PSN in incomplete-information games where other agents' intentions are unknown to the ego agent. A PSN outputs a player selection mask that distinguishes influential players from less relevant
Control which domains your AI agents can access
In this post, we show you how to configure AWS Network Firewall to restrict AgentCore resources to an allowlist of approved internet domains. This post focuses on domain-level filtering using SNI inspection — the first layer of a defense-in-depth approach.
Moltbook risks: The dangers of AI-to-AI interactions in health care
A new report examines the emerging risks of autonomous AI systems interacting within clinical environments. The article, "Emerging Risks of AI-to-AI Interactions in Health Care: Lessons From Moltbook," appears in the Journal of Medical Internet Research. The work explores a critical new frontier: as high-risk AI agents begin to communicate directly with one another to manage triage and scheduling, they create a "digital ecosystem" that can operate beyond active human oversight.
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A social network for AI looks disturbing, but it s not what you think
A social network where humans are banned and AI models talk openly of world domination has led to claims that the "singularity" has begun, but the truth is that much of the content is written by humans
[P] Trained a small BERT on 276K Kubernetes YAMLs using tree positional encoding instead of sequential
I trained a BERT-style transformer on 276K Kubernetes YAML files, replacing standard positional encoding with learned tree coordinates (depth, sibling index, node type). The model uses hybrid bigram/trigram prediction targets to learn both universal structure and kind-specific patterns — 93/93 capability tests passing. Interesting findings: learned depth embeddings are nearly orthogonal (categorical, not smooth like sine/cosine), and 28/48 attention heads specialize on same-depth attention (up to 14.5x bias). GitHub: https://github.com/vimalk78/yaml-bert submitted by /u/vimalk78 [link] [comments]
Avoid Re-encoding Reference Images in Vision-LLM When Comparison Criteria Are User-Defined
Hi everyone, I’m working with a Vision-LLM (like Qwen-VL / LLaVA / llama.cpp-based multimodal models) where I need to compare new images against reference images. The key part of my use case is that users define the comparison criteria (e.g., fur length, ear shape, color patterns), and I’m using image-to-text models to evaluate how well a new image matches a reference according to these criteria. Currently, every time I send a prompt including the reference images, the model re-encodes them from scratch . From the logs, I can see: llama-server encoding image slice... image slice encoded in 3800–4800 ms decoding image batch ... Even for the same reference images, this happens every single request , which makes inference slow. Questions: Has anyone dealt with user-defined comparison criteria
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