Discover the Best AI Resources
Curated essentials, no noise — just what matters
Decouples perception and reasoning for hours-long videos by streaming inputs into a three-tier Hierarchical Graph Memory and using an agentic Observation–Reason–Action retrieval loop; reduces reasoning context to ~2% of full video while improving benchmark accuracy.
GGUF-format QAT (quantization-aware training) build of Gemma 4 12B that reduces memory needs for local or lightweight inference while preserving near bfloat16 quality. Ready for any-to-any conversational pipelines and ecosystem deployment.
Removes the subspace of frequent, uninformative tokens that LLMs inject into text embeddings via the model's unembedding matrix. EmbedFilter is a lightweight linear transform that refines LLM-derived embeddings to improve zero‑shot semantic retrieval, enable dimensionality reduction, and speed up indexing; code on GitHub.
Provides a comprehensive benchmark for instruction-based audio editing across seven audio modalities and eight operation types, with 2,000 high-fidelity samples and a rubric that decomposes tasks into 17,741 verifiable criteria for multi-dimensional evaluation.
Simulates egocentric, embodied human–world interactions and enables customizable, self-evolving local scenes by defining anchor views and text-driven evolution. Uses exogenous viewpoints and full-body motion supervision to improve spatial grounding and interaction consistency.
Provides a virtual, durable filesystem and pluggable execution runtimes for agents running inside Cloudflare Durable Objects. Offers container, isolate-shell, and isolate-JS backends; preview-stage API with ~10GB Durable Object backing and FUSE-mounted container trade-offs.
Analyzes the parameter-space geometry of on-policy distillation (OPD) for LLM training, showing OPD updates affect fewer weights, avoid principal directions, and rapidly lock into a low-dimensional update subspace. Compares OPD with supervised fine-tuning (SFT) and reinforcement learning with verifiable rewards (RLVR) and studies implications for optimization and objective mixing.
Provides page-level relevance judgments and full OCR'd annual-report text for KPI question answering and page retrieval benchmarking — supports retrieval (per-page qrels) and needle‑in‑a‑haystack numeric extraction over long documents, with eval and train configs.
Pairs OCR-extracted annual-report text with ground-truth financial KPI values to benchmark LLM/table-QA and needle-in-a-haystack extraction tasks. Includes Markdown OCR (.mmd), page images for eval, and 31 KPI columns across multiple years—suited for KPI extraction, retrieval, and robustness testing.
A GGUF release of Gemma 4 26B A4B (QAT) packaged by Unsloth for local multimodal inference — quantization-aware trained to keep near-bfloat16 quality while significantly lowering memory requirements, compatible with Transformers and Unsloth tooling.
Code-focused sparse Mixture-of-Experts LLM designed for agentic coding and terminal/tool use, offering very long context (256K) and long outputs. Released with open weights under Apache-2.0 and optimized for transformers/vLLM workflows.
Experimental, uncensored fine-tune of Google Gemma-4-12B-it that applies an 'abliteration' technique to remove refusal behaviors; intended for research and testing only and carries elevated safety and legal risks.