Ammu-1.0-FactCheck-Need: Lightweight and Efficient Models for Fact Check Need Classification
kalyan-ks
• • 1NEW Articles from Team or Enterprise organizations will get promoted to the main section.
RAGFS: a filesystem interface for agents, and the query JSON I keep as a retrieval receipt
gianlucamazza
• Where do the evidence pages go?
mihirinamdar
• Measuring Texture Alignment in Image-to-3D with MeshyBench
meshy-ai-team
• AtMem From remembering relevant text to assembling usable evidence
javadtaghia
• Choosing how to adapt an EEG foundation model
Twu31
• Your Agent Has the Company Name. How Much Can it Actually Tell You About It?
Tavily
• • 1What "AI controls your phone" actually requires: observation paths, input paths, and verification
NatalieY
• With MemOS, your support Agent remembers exactly where you left off.
MemTensor
• • 1大模型的记忆管理：从记忆外挂到主动管理
Junrulu
• • 3Build a Tiny Language Model, Day 4: Save the Experiment
ericmey
• Leading the System One Mosaic Benchmark: What Darwin-27B-ZTC-v2's #1 Means
FINAL-Bench
• • 6Testing selected-token J-lens directions and a fixed-symbol autoencoder on Qwen3-8B
kotlarmilos
• 【观察】Opus 5.5 做视频，强在哪一层？——一个小团队的制作笔记
BreadStudio
• Understanding Jev: confidence is only useful if it works
stephen-solka
• Using Token Data to Improve the AI Agent Experience
phranzia
• • 2FEM-ASM: Modular Language Computation
Bochkov
• Introducing Superfluid: the local-AI native LLM server for multi-agent inference
basecompute
• • 3JOSIE-2 Technical Report: Curated Post-Training for Reasoning and Behavior in Small Language Models
Goekdeniz-Guelmez
• • 1LightOnOCR-3: High-Performance OCR and Layout Extraction in One Model
lightonai
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