Knowledge catalogue
arXiv:2602.21054v2 Announce Type: replace-cross Abstract: Large Vision-Language Models (LVLMs) frequently hallucinate, limiting their safe deployment in real-world applications. Existing LLM self-eval
arXiv:2602.06932v3 Announce Type: replace Abstract: Speculative decoding can significantly accelerate LLM serving, yet most deployments today disentangle speculator training from serving, treating spe
You need feedback to help power the data flywheel! This 'feedback' can come from many sources: - direct user feedback (thumbs up/down) - indirect user feedback (code suggestions accepted) - llm as jud
llama.cpp enables LLM inference in C/C++ , and release b9014 represents a build revision or commit snapshot from the llama.cpp repository on GitHub. This release would include bug fixes, feature impro
Release b9016 is a build of llama.cpp, an LLM inference implementation in C/C++ . This build designation represents a specific intermediate version in the project's development cycle, similar to other
B9018 is the latest version of llama.cpp released on May 4, 2026. Llama.cpp is a project for LLM inference in C/C++ , providing efficient large language model execution with broad hardware support inc
b9019 is a release of llama.cpp, a C/C++ implementation for LLM inference. As a release tag from the ggml-org/llama.cpp repository, it represents a specific commit or version of the project that inclu
b9025 is a release from llama.cpp, a C/C++ project for LLM inference . The release uses a build numbering system for intermediate versions of the library. As a llama.cpp release, it likely includes up
arXiv:2602.02443v2 Announce Type: replace Abstract: Test-time scaling improves LLM performance by generating multiple candidate solutions, yet token-level sampling requires temperature tuning that tra
arXiv:2605.00610v1 Announce Type: new Abstract: SFT and RLVR represent two fundamental yet distinct paradigms for LLM post-training, each excelling in distinct dimensions. SFT expands knowledge breadt
arXiv:2512.01020v2 Announce Type: replace-cross Abstract: Evaluating the quality of LLM-generated reasoning traces in expert domains (e.g., law) is essential for ensuring credibility and explainabilit
This post documents a 10-day project to build a local AI system using open-source tools and models, specifically combining Ollama (a local LLM framework), Gemma 4 (a language model), Claude Cowork 3P,
arXiv:2408.11349v2 Announce Type: replace Abstract: Mercari is the largest C2C e-commerce marketplace in Japan, having more than 20 million active monthly users. Search being the fundamental way to di
arXiv:2605.00702v1 Announce Type: new Abstract: Large language model (LLM) agents require long-term user memory for consistent personalization, but limited context windows hinder tracking evolving pre
NVIDIA cuOpt Agent Skills integrate LLM reasoning with GPU-accelerated solvers to enable AI agents to translate natural language supply chain problems into optimized mathematical models, with skills e
arXiv:2510.26020v2 Announce Type: replace Abstract: Multi-tool-integrated reasoning enables LLM-empowered tool-use agents to solve complex tasks by interleaving natural-language reasoning with calls t
arXiv:2501.00885v2 Announce Type: replace Abstract: The extraordinary success of recent Large Language Models (LLMs) on a diverse array of tasks has led to an explosion of scientific and philosophical
arXiv:2605.00528v1 Announce Type: cross Abstract: AI agents execute tens to hundreds of chained LLM calls per task, yet GPU schedulers treat each call as independent, discarding gigabytes of intermedi
arXiv:2605.00557v1 Announce Type: new Abstract: Scientific discovery is an extended process of ideation--surveying prior work, forming hypotheses, and refining reasoning--yet existing approaches treat
arXiv:2508.04086v2 Announce Type: replace Abstract: Prior work synthesizes tool-use LLM datasets by first generating a user query, followed by complex tool-use annotations like depth-first search (DFS
b9012 is a build version from the llama.cpp GitHub repository, which is an LLM inference implementation in C/C++ . The release likely contains updates, bug fixes, and improvements to the llama.cpp inf
that’s right. 😂 Finally, the great LeCun conversion arc is complete! “LLM agents = disaster” – said the guy who once defended Galactica like it was the Second Coming. Gary, you didn’t change… the time
b9000 is a build release of llama.cpp, a C/C++ implementation for LLM inference . The release follows the project's rapid development cycle where multiple releases can be published in a single day . A
B9002 is a build release of llama.cpp, a C/C++ implementation for LLM inference. The release includes compiled binaries and artifacts for multiple platforms and hardware configurations, as part of the
llama.cpp is an LLM inference framework in C/C++ that enables efficient local execution of large language models. Build b9004 is a recent release with optimizations and improvements for supporting var
B9008 is a build release of llama.cpp from May 2, 2026. Llama.cpp is a C/C++ implementation of LLM inference designed to enable large language model inference with minimal setup and high performance a
b9010 is a build release of llama.cpp, an open-source project for LLM inference in C/C++ . As a numbered build tag in the llama.cpp release system, it represents a specific development build containin
llama.cpp is a C/C++ implementation that enables LLM inference with minimal setup and state-of-the-art performance on a wide range of hardware locally and in the cloud. Release b8995 is a build/versio
llama.cpp b8999 is a build release from the llama.cpp project, which provides LLM inference in C/C++ . This build represents an intermediate development version in the project's rapid release cycle. T
arXiv:2604.27045v1 Announce Type: cross Abstract: As Large Language Model (LLM) agents transition from single-session tools to persistent systems managing longitudinal healthcare journeys, their memor
arXiv:2604.27275v1 Announce Type: cross Abstract: Large language model (LLM) reading assistants are increasingly used in settings that require interpretation rather than simple retrieval. In these con
I have been testing DeepSeek-V4-Pro with the Pi coding agent. I am mindblown by how well it works out of the box. A few notes: I spent a few hours building an LLM wiki with an agent powered entirely b
A Reddit discussion in the r/ollama community seeking recommendations for RAG (Retrieval-Augmented Generation) models, likely addressing model selection for local LLM-based document retrieval systems.
arXiv:2604.27820v1 Announce Type: new Abstract: Every document format in existence was designed for a human reader moving linearly through text. Autonomous LLM agents do not read - they retrieve. This
arXiv:2604.27233v1 Announce Type: new Abstract: Tool-calling agents are evaluated on tool selection, parameter accuracy, and scope recognition, yet LLM trajectory assessments remain inherently post-ho
arXiv:2604.27358v1 Announce Type: new Abstract: As large language model (LLM) agents are deployed in high-stakes environments, the question of how safely to delegate subtasks to specialized sub-agents
arXiv:2604.27093v1 Announce Type: cross Abstract: Current LLM safety alignment techniques improve model robustness against adversarial attacks, but overlook whether and how LLMs can recover helpfulnes
arXiv:2604.27167v1 Announce Type: cross Abstract: LLM agents are known to deviate from Nash equilibria in strategic interactions, but nobody has looked inside the model to understand why, or asked whe
arXiv:2603.16496v2 Announce Type: replace Abstract: Large language model (LLM) agents increasingly rely on external memory to support long-horizon interaction, personalized assistance, and multi-step
arXiv:2604.26103v1 Announce Type: cross Abstract: All current LLM serving systems place the GPU at the center, from production-level attention-FFN disaggregation to NVIDIA's Rubin GPU-LPU heterogeneou
b8983 is a release of llama.cpp, a C/C++ implementation for LLM inference. The release represents a specific build/commit version in the active development of the llama.cpp project, which supports run
B8984 is a build/release version of llama.cpp, an open-source C/C++ library for large language model inference. Llama.cpp enables LLM inference with minimal setup and state-of-the-art performance on a
llama.cpp b8990 is a release version of a C/C++ LLM inference tool from the ggml-org project. The release likely contains bug fixes, performance improvements, or new features for local language model
llama.cpp is a C/C++ implementation for LLM inference , and b8991 is a specific release version from the project's ongoing development cycle. The search results indicate that the project doesn't follo
b8992 is a build release from the llama.cpp project, which publishes multiple releases in a single day . llama.cpp enables LLM inference with minimal setup and state-of-the-art performance on a wide r
Building a document processing pipeline at scale is hard, and is one of the reasons that it's hard to DIY your own document OCR solution by relying on LLM APIs. Your orchestration pipeline needs to ha
arXiv:2604.26256v1 Announce Type: new Abstract: Reinforcement learning (RL) has become a critical paradigm for LLM post-training, yet the rollout phase -- accounting for 50--80% of total step time --
arXiv:2604.26235v1 Announce Type: cross Abstract: We introduce LATTICE, a benchmark for evaluating the decision support utility of crypto agents in realistic user-facing scenarios. Prior crypto agent
arXiv:2604.26622v1 Announce Type: new Abstract: Autonomous LLM agents increasingly operate in long-horizon, interactive settings where success depends on reusing experience accumulated over extended h
arXiv:2604.26573v1 Announce Type: new Abstract: Improving large language model (LLM) reasoning requires supervision that is both aligned with the model's own test-time states and informative at the to
arXiv:2603.04337v2 Announce Type: replace-cross Abstract: Constructing computer-aided design (CAD) models is labor-intensive but essential for engineering and manufacturing. Recent advances in Large L
arXiv:2603.15262v2 Announce Type: replace Abstract: Modern e-commerce search is evolving to resolve complex user intents. While Large Language Models (LLMs) offer strong reasoning, existing LLM-based
arXiv:2604.26412v1 Announce Type: new Abstract: Speculative decoding accelerates LLM inference, but SOTA hidden-state-based drafters suffer from long-range decay: draft accuracy degrades as the specul
b8969 is a release build of llama.cpp (commit bdc9c74), published on April 29, 2026. Llama.cpp is a C/C++ implementation designed to enable LLM inference with minimal setup and state-of-the-art perfor
b8971 is a build release of llama.cpp, an open-source C/C++ project for LLM inference . As a numbered build identifier from the llama.cpp repository, this release likely contains bug fixes, performanc
b8972 is a release of llama.cpp, a C/C++ implementation for LLM inference . The release identifier follows llama.cpp's development versioning scheme with frequent builds published to the GitHub releas
b8973 is a release of llama.cpp that added SVE tuned code for the gemm_q8_0_4x8_q8_0() kernel and changed arrays to static const in repack.cpp . The llama.cpp project enables LLM inference with minima
b8978 is a release of llama.cpp, a project designed to enable LLM inference with minimal setup and state-of-the-art performance on a wide range of hardware. The llama.cpp project uses sequential build
b8981 is a release version of llama.cpp, a C/C++ implementation for LLM inference . Build versions in llama.cpp releases typically contain updates to the underlying ggml library, performance optimizat
arXiv:2604.25120v1 Announce Type: new Abstract: We study clinical trial table reasoning, where answers are not directly stored in visible cells but must be reasoned from semantic understanding through