Knowledge catalogue
arXiv:2609.18676v1 Announce Type: new Abstract: Generative artificial intelligence (GenAI) is changing how students learn, yet the roles of course context, cognitive reliance, evaluation literacy, and
arXiv:2609.17984v1 Announce Type: new Abstract: Machine-learning libraries such as Weka and scikit-learn were designed for human programmers. Language-model agents now use these same libraries by reca
I find another new model at HF: https://huggingface.co/XingChen-AGI/Xing4.0-29B-A4B 'Xing4.0-29B-A4B is a next-generation large language model in the Xing series (formerly TeleChat), developed by Chin
arXiv:2609.16625v1 Announce Type: cross Abstract: How and why does a recommender system fail the users it serves? Oftentimes, practitioners are left to improve their algorithms based on a combination
arXiv:2609.16991v1 Announce Type: new Abstract: Natural language arguments are compelling before they are formally explicit. A premise supports a claim through defeasible warrants, background commitme
Change max context length for auto-fitting with unified KV (#28849) Website: https://llama.app Attestations: https://github.com/ggml-org/llama.cpp/attestations/47906437 macOS/iOS: macOS Apple Silicon
arXiv:2609.16006v1 Announce Type: cross Abstract: Large language models (LLMs) increasingly serve users whose expectations are shaped by their cultural context, yet most cultural evaluations test what
Hey experts! I genuinely have this question and would love a general consensus from people actually using these models. LLMs have advanced a lot in benchmarks and in practical usage. Even GPT-4o and G
arXiv:2609.16091v1 Announce Type: new Abstract: Tabular foundation models deliver strong zero-training predictive performance via in-context learning, but their high inference latency makes them impra
arXiv:2609.16070v1 Announce Type: cross Abstract: Generative recommendation reformulates item prediction as semantic identifier generation, yet episodic content introduces a fundamentally different se
arXiv:2609.16603v1 Announce Type: new Abstract: Full-context neural visual geometry is impractical for thousands of images, while sequence-based chunking poorly captures irregular non-local overlap in
Google is opening up its smart home to AI agents, letting tools like Claude and Open Claw access and control your connected devices and analyze your home's data using the standardized Model Context Pr
If you build agent harnesses, this is important. Should you avoid subagents, or can they be useful? My thoughts as a harness builder: I remember using subagents in Claude Code, and I mostly found them
arXiv:2609.16795v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) can answer knowledge-intensive visual questions by combining visual evidence from images with facts retrieved f
arXiv:2609.16396v1 Announce Type: new Abstract: Negation is typically modeled through its linguistic realization, although spoken interaction is accompanied by tightly coordinated nonverbal behavior.
arXiv:2609.16737v1 Announce Type: cross Abstract: Generative video models can serve as a promising backbone for robot navigation by predicting future observations as video plans. Recent approaches oft
arXiv:2609.17035v1 Announce Type: new Abstract: Soft and continuum robots enable manipulation through distributed body deformation and contact, yet translating language and visual context into executa
arXiv:2609.16858v1 Announce Type: new Abstract: Prompt learning adapts vision-language models, such as CLIP, by adjusting a small set of context tokens. However, under few-shot supervision, even moder
arXiv:2609.17010v1 Announce Type: new Abstract: Lifelong conversational agents rely on memory systems to maintain deep, context-aware interactions with users. However, existing explicit textual memory
arXiv:2609.17260v1 Announce Type: new Abstract: Cyber-Physical Systems (CPS) operate through a continuous sense-compute-act loop within an open context environment, making it impossible to anticipate
arXiv:2609.16266v1 Announce Type: cross Abstract: In recent years, the utility of parameterized quantum circuits as function approximators has been widely studied. In the context of reinforcement lear
arXiv:2609.17515v1 Announce Type: new Abstract: Pruning can reduce the deployment cost of large language models (LLMs), but its impact on context-grounded tool calling remains poorly understood. We sy
arXiv:2609.16074v1 Announce Type: cross Abstract: Robots operating in open environments act under partial observability, physical constraints, and dynamic task contexts. Beyond mapping observations an
arXiv:2609.14819v1 Announce Type: cross Abstract: Large language models (LLMs) have a growing range of applications in medicine, and their evaluation is critical for ensuring they provide benefit and
arXiv:2609.14236v1 Announce Type: cross Abstract: With the rapid proliferation of large language model (LLM)-based systems, AI companions have emerged as conversational agents designed to cultivate em
example: https://huggingface.co/audnai/penclaw-GLM-5.3-abliterated-for-offensive-cyber. edit: good context for why this was done here https://www.reddit.com/r/LocalLLaMA/comments/1whbvpx/comment/pa16g
arXiv:2609.13202v1 Announce Type: new Abstract: Feature engineering has long been a cornerstone of tabular machine learning. Tabular foundation models (TFMs) are pretrained on a wide range of tabular
arXiv:2609.14779v1 Announce Type: new Abstract: Performing deliberate mathematical reasoning in visual contexts is a hallmark of advanced Multimodal Large Language Models (MLLMs) and requires a sophis
I have tried using qwen 3.6 and 3.8 q5 64k context and I'm just not getting the experience everyone here seems to get. I'm using open code and it will loop, forget what it's doing, and mess up the ui.
arXiv:2609.13154v1 Announce Type: new Abstract: Recent advances in large language models (LLMs) have made prompts increasingly large and complex. Techniques such as chain-of-thought reasoning (Wei et
arXiv:2503.06868v2 Announce Type: replace-cross Abstract: Existing long-text generation methods produce lengthy outputs from short inputs, leaving long-input-to-long-output generation underexplored. A
arXiv:2512.24565v4 Announce Type: replace Abstract: Large Language Models (LLMs) are increasingly serving as autonomous agents, and their utilization of external tools via the Model Context Protocol (
arXiv:2609.15635v1 Announce Type: cross Abstract: A radiology report can already answer a clinical question, so it is hard to tell whether a vision-language model also uses the image. ModaLens, a pair
Prompt caching in Amazon Bedrock can cut input token costs by up to 90% when you repeatedly send the same context to foundation models. This post walks through six practical prompt caching scenarios u
arXiv:2609.13595v1 Announce Type: cross Abstract: Predicting potential dangers is core to safety. Forecasting the presence of other traffic agents is core to danger prediction. Occluded traffic agents
arXiv:2609.15679v1 Announce Type: cross Abstract: This paper studies projection-free algorithms for stochastic constrained multi-level compositional optimization. In this context, the objective functi
arXiv:2410.14083v2 Announce Type: replace Abstract: This paper describes a new spatial correspondence representation based on paired regions-of-interest (ROIs), for medical image registration. The dis
arXiv:2609.13353v1 Announce Type: cross Abstract: Agent skills are reusable units for language-model agents, but their risks emerge through model decisions, user context, tool calls, and execution fee
Database startup Keewano today launched KeewanoDB, an event-oriented database designed to give artificial intelligence agents real-time context for analytics and decision-making. The startup also anno
arXiv:2609.14565v1 Announce Type: new Abstract: LLM-based sequential recommenders usually cast next-item prediction as text generation, but this interface is poorly matched to full-catalog top-K ranki
arXiv:2609.15982v1 Announce Type: cross Abstract: Skills extend an LLM agent beyond its parametric knowledge, and the gain they promise rests on picking the right one. Deployed harnesses route by prel
arXiv:2609.14780v1 Announce Type: cross Abstract: Multi-tenant tools commonly accept a tenant identifier and validate it against the caller's entitlement. For a large language model (LLM) agent, that
arXiv:2609.13356v1 Announce Type: new Abstract: In this work, we present ZGCM-1, a fully open 7B dense foundation model trained from scratch with extreme data, system, and algorithmic efficiency. ZGCM
arXiv:2609.12431v1 Announce Type: new Abstract: Automated crack inspection increasingly relies on deep learning, yet its reliability is limited by scarce and weakly controllable defect data. Existing
arXiv:2609.12162v1 Announce Type: cross Abstract: Draft-verify-revise is a common LLM orchestration pattern for scaling inference-time compute. One LLM drafts, a second critiques the draft and provide
arXiv:2609.12791v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) has empowered Large Language Models (LLMs) to tackle knowledge-intensive tasks. However, navigating global, heterog
Release: commit-rewriter 0.1 I built this little web app the other day to help edit the commit messages for the Datasette security releases. The initial commits were full of coding agent cruft and ref
arXiv:2609.11998v1 Announce Type: new Abstract: Diffusion language models decode tokens in parallel, but their bidirectional denoiser rules out the naive key--value (KV) cache behind fast autoregressi
arXiv:2609.13076v1 Announce Type: cross Abstract: Conversational voice agents have advanced significantly, offering increasingly natural human-machine interactions through both cascaded and end-to-end
arXiv:2609.11936v1 Announce Type: cross Abstract: Energy supply and heat dissipation are two of the main challenges with modern GPU deployments. While typically discussed in the context of new datacen
arXiv:2601.10657v3 Announce Type: replace-cross Abstract: Self-evolving agents powered by Large Language Models (LLMs) have emerged as a promising direction across diverse domains, including code opti
arXiv:2609.12658v1 Announce Type: new Abstract: Streaming video understanding requires models to process continuous multimodal input while maintaining temporal context. Existing evaluations are predom
Hello Reddit. Posting this for fun. I thought it was a lonely and silly journey to set up Qwen 3.8 Next on a system that doesn't really run it properly—it was a challenge that might help the community
deepseek v4.1 flash has arrived on together ai it beats gpt-5.6 sol on agentic benchmarks at one third the cost per task the model supports a 1m context window and native multimodal input, with 552b t
Want to help me with a tiny ChatGPT experiment? I stumbled onto something interesting after seeing how differently ChatGPT users got responses to the exact same underspecified prompt, and now I’m curi
I've been keeping journals on and off for more than 10 years. There were stretches when I wrote pretty regularly, then I'd stop for a few months. Usually I'd write down what happened, what I did, mayb
Another new model dropped in the course of this week that is well deployable on consumer hardware: Nex N2.5 Mini I went with the recommended settings for the best generation quality and ran a few benc
arXiv:2609.10962v1 Announce Type: cross Abstract: Studies of the Model Context Protocol (MCP) server ecosystem draw their samples in ways that quietly select for servers that work: reference sets, pop
arXiv:2609.11126v1 Announce Type: new Abstract: Verification and validation (V&V) of classification models is crucial to enable a wide range of sensor processing applications. Currently, the V&V proce
arXiv:2609.11206v1 Announce Type: cross Abstract: Cryptocurrency forecasting presents a distinctive combination of extreme cross-asset scale heterogeneity, non-stationary dynamics, and structural depe