Task planning for robotic manipulation with large language models (LLMs) is an emerging area. Prior approaches rely on specialized models, fine tuning, or prompt tuning, and often operate in an open loop manner without robust environmental feedback, making them fragile in dynamic settings. MALLVI presents a Multi Agent Large Language and Vision framework that enables closed-loop feedback driven ro
Modern language models are trained almost exclusively on token sequences produced by a fixed tokenizer, an external lossless compressor often over UTF-8 byte sequences, thereby coupling the model to that compressor. This work introduces proxy compression, an alternative training scheme that preserves the efficiency benefits of compressed inputs while providing an end-to-end, raw-byte interface at
Large language models (LLMs) have demonstrated remarkable performance due to their large parameter counts and extensive training data. However, their scale leads to significant memory bottlenecks during training, especially when using memory-intensive optimizers like Adam. Existing memory-efficient approaches often rely on techniques such as singular value decomposition (SVD), projections, or weig
We present Key-Value Means ("KVM"), a novel block-recurrence for attention that can accommodate either fixed-size or growing state. Equipping a strong transformer baseline with fixed-size KVM attention layers yields a strong $O(N)$ chunked RNN, while adding only an insignificant number of new parameters. We train a transformer with a growable KVM cache and show it performs competitively on long-co
Remote sensing object detection is a critical technology for real-world applications such as natural resource monitoring, traffic management, and UAV-based rescue. Detecting tiny objects in high-resolution aerial imagery remains challenging due to weak visual cues and insufficient global context modeling in complex scenes. Existing methods often suffer from delayed contextual interaction and limit
Capital is clustering at the top end, with one dominant entry dwarfing the rest of the cycle. The mix also shows nontraditional flows alongside AI-linked funding, underscoring how concentrated this tape is.
Capital is clustering at the top end, with one dominant entry dwarfing the rest of the cycle. The mix also shows nontraditional flows alongside AI-linked funding, underscoring how concentrated this tape is.
Capital is clustering at the top end, with one dominant entry dwarfing the rest of the cycle. The mix also shows nontraditional flows alongside AI-linked funding, underscoring how concentrated this tape is.
Named moves are sparse, but the desk still matters when leadership changes land at a major platform. Repeated entries around the same role suggest a noisy feed, not a broad hiring wave.
Named moves are sparse, but the desk still matters when leadership changes land at a major platform. Repeated entries around the same role suggest a noisy feed, not a broad hiring wave.
Named moves are sparse, but the desk still matters when leadership changes land at a major platform. Repeated entries around the same role suggest a noisy feed, not a broad hiring wave.
A small set of benchmark results adds fresh reference points across agentic and reasoning-style tasks. The signal is less about a leaderboard shake-up than about which evaluation surfaces are now being tracked.
A small set of benchmark results adds fresh reference points across agentic and reasoning-style tasks. The signal is less about a leaderboard shake-up than about which evaluation surfaces are now being tracked.
A small set of benchmark results adds fresh reference points across agentic and reasoning-style tasks. The signal is less about a leaderboard shake-up than about which evaluation surfaces are now being tracked.