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Contents

AI Agent Papers·2026
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Agent Memory Distillation: Empowering Small LLM Agents with Hierarchical Teacher Memory

Taeil Kim, Kangsan Kim +1

Transfers hierarchical, structured memory from a large teacher agent into small LLM agents to improve tool-use success. Constructs Workflow, Subtask and Function memories with proactive/reactive injection; training-free and validated on multiple tool-use benchmarks.

#distillation#agent-skills#ai-agent#llm#benchmarks+1
Hugging Face
AI Dataset·2026
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EgoStandard

LightwheelAI

Provides 90,000 hours of head-mounted egocentric video paired with synchronized 3D hand pose and an optional 3D full‑body pose add-on, with event-level semantic labels available as a complimentary layer — designed for embodied AI and robotics training at scale.

#robotics#video#multimodal#mcap#parquet+3
AI Agent Papers·2026
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Ouroboros: A Self-Developing Frontier Coding Agent with Reviewed Core Evolution

Anton Razzhigaev, Andrei Gritsaev +4

Enables a coding agent to self-develop by evolving its harness, prompts, tools, and core code via reviewed commits — supporting recursive free evolution and experience-driven evolution. Demonstrated a 161-day live lineage and state-of-the-art scores on multiple coding benchmarks while foregrounding operational safety.

#coding-agents#ai-agent#ai-coding#benchmarks#evaluation+3
Hugging Face
AI Dataset·2026
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Masri 100h — Egyptian Arabic Single-Speaker Speech Corpus

Ehab Negm·Hugging Face

100-hour, single-narrator Egyptian Arabic speech corpus with 15,653 aligned clips at 24 kHz for TTS and ASR fine-tuning; studio-consistent audio, machine-generated undiacritized transcripts, CC BY-NC 4.0 (research/non-commercial use).

#huggingface#tts#ASR#speech#audio+3
Natural Language Processing Papers·2026
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Can LLM Agents Stick to the Script? A Benchmark for Long-Horizon Consistency in Interactive Narratives

Yingpeng Ma, Jianhao Yan +7·Affiliation: NLP2CT Lab, University of Macau, Macau, China, Affiliation: Westlake University, Hangzhou, China +3

Evaluates whether LLM-driven storyteller agents preserve long-horizon logical consistency under adversarial player interventions. Introduces NCP-Bench (100 movie-derived narrative environments) with structured trajectory/commitments and automatic violation checks; finds strong LLMs often contradict themselves across multi-turn interactions.

#LLM#NLP#evaluation#benchmark#benchmarks+6
Hugging Face
AI Model·2026
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Qwen3.8-2.4T-A95B

Qwen Team

A MoE causal large language model for long-horizon agents, coding, and multi-step reasoning: 2.4T parameters (95B activated), native 262,144-token context (extensible to 1,010,000), multi-token prediction, and configurable thinking-mode reasoning controls.

#qwen#foundation-model#LLM#transformers#huggingface+7
Hugging Face
AI Model·2026
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Qwen3.8-2.4T-A95B-FP8

Qwen

FP8-quantized checkpoint of the Qwen3.8 text-only causal LLM (2.4T params, 95B activated) for text-generation; preserves near-original performance, supports very long contexts (262k–1M), Mixture-of-Experts architecture, and is compatible with vLLM/SGLang/TokenSpeed. Thinking mode and preserve_thinking are enabled by default.

#qwen#transformers#safetensors#huggingface#llm+7
Hugging Face
AI Dataset·2026
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Slop classifier dataset

bench-labs

Human-annotated text dataset that labels perceived “AI slop” with a continuous human slop_score (-1 / 0 / +1) plus provenance metadata (source_dataset, source_row_id, content_hash). Collected via Bench Labs SlopFinder from public datasets for training classifiers and studying subjective perception.

#huggingface#nlp#evaluation#benchmark#json+2
Hugging Face
AI Model·2026
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Muse Glimmer-30B

Meta Superintelligence Lab

A 29.6B-parameter multimodal causal language model with a dedicated ViT-G/14 perception encoder for running agentic, tool-using, multimodal reasoning locally on consumer hardware. Offers 4-bit quantized weights and a DFlash drafter for speculative decoding to reduce memory and speed up generation.

#meta-ai#huggingface#transformers#multimodal#vision+5
Hugging Face
AI Model·2026
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Muse Glimmer-30B-GGUF

Meta Superintelligence Lab, meta-models

A GGUF release of Meta's Muse Glimmer 30B optimized for local multimodal agent inference; includes two quantized text builds, a perception encoder for image input, and an optional DFlash drafter for speculative decoding—fits on 24–32 GB VRAM.

#multimodal#llm#llama.cpp#meta-ai#huggingface+7
Hugging Face
AI Dataset·2026
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DDB Newspaper Corpus

Stefan Schweter·Deutsche Digitale Bibliothek, Hugging Face +5

Provides OCR full text for 11.55M public-domain German newspaper pages (1638–1964) with per-page IIIF scans and ALTO XML coordinates; suited for historical NLP, language-model training, and OCR research. Pages carry explicit per-page public-domain licenses.

#nlp#ocr#parquet#polars#huggingface+1
Hugging Face
AI Model·2026
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dots3-note Preview

Dots Studio, Xiaohongshu

Multimodal Mixture-of-Experts text-generation model that accepts text, images, video and audio and returns text; preview open-weight release with 280B total params, 16B activated params, up to 512K token context and BF16/FP8 checkpoints under Apache-2.0.

#multimodal#llm#transformers#vllm#safetensors+7
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