Knowledge catalogue
arXiv:2607.25953v1 Announce Type: new Abstract: As LLMs increasingly mediate the political information citizens rely on, there is still no standardized way to assess whether they do so responsibly. We
arXiv:2509.19710v2 Announce Type: cross Abstract: Symbolic regression has emerged as a powerful tool for artificial intelligence-driven scientific discovery by learning interpretable analytical expres
If your GGUF has MTP/NextN tensors baked in (GLM-5.2, hy_v3, qwen35moe, step35, etc.), recent llama.cpp builds load them by default — even if you never pass --spec-type draft-mtp. Before, they were sk
we're experimenting with our own dynamic GGUF quants of kimi k3, made from the original weights with our llama.cpp fork. Q3_K_S is done and works 1114.76 GiB on disk. Q1 and Q2 are in progress, result
Right now we’re in the midst of a historic transition from traditional public-key algorithms based on EC-based cryptography and RSA, moving over to new post-quantum algorithms based on novel problems.
arXiv:2607.10137v2 Announce Type: replace Abstract: Post-training quantization (PTQ) of large language models degrades sharply below 4-bit precision. We identify the root cause as residual stream dist
arXiv:2607.25392v1 Announce Type: new Abstract: We introduce RDVSv2, a large-scale benchmark for RGB-D video salient object detection (RGB-D VSOD) with dense frame-level annotations. Existing datasets
arXiv:2607.25565v1 Announce Type: new Abstract: Recovering an editable design file from a raster image is a common and costly bottleneck in modern design workflows, yet remains challenging since edita
arXiv:2512.06276v3 Announce Type: replace-cross Abstract: Referring Expression Comprehension (REC) is a vision-language task that localizes a specific image region based on a textual description. Exis
arXiv:2607.09800v3 Announce Type: replace Abstract: Direct low-precision write-back can erase optimizer proposals, while aggregate update visibility need not identify parameters worth protecting. We s
arXiv:2601.20043v2 Announce Type: replace Abstract: Standard Bayesian Optimization (BO) assumes uniform smoothness across the search space an assumption violated in multi-regime problems such as molec
arXiv:2607.25970v1 Announce Type: cross Abstract: RL for code correctness is now established: have the model generate a program, run it against hidden test cases, and reward solutions that pass. Exten
arXiv:2607.25524v1 Announce Type: cross Abstract: Unmanned aerial vehicle (UAV)-satellite cross-view geo-localization matches UAV images against satellite imagery and has achieved impressive accuracy
arXiv:2607.24845v1 Announce Type: cross Abstract: Large language models (LLMs) have been applied to sequential recommendation by formulating it as a natural language task. Previous work has improved p
arXiv:2607.24868v1 Announce Type: new Abstract: This report studies noise-shaped one-bit coefficients in normalized discrete polynomial Fourier extension. For first-order Sigma-Delta quantization, the
arXiv:2607.25196v1 Announce Type: new Abstract: Contrastive decoding (CD) has been proposed as a training-free strategy for mitigating object hallucinations in multimodal large language models (MLLMs)
arXiv:2607.25299v1 Announce Type: cross Abstract: Optimization over the Stiefel manifold plays a significant role in various machine learning tasks. Existing methods either use the retraction operator
arXiv:2607.25199v1 Announce Type: cross Abstract: Automated code generation is becoming an important tool in quantitative finance, where large language models can generate option pricing implementatio
arXiv:2606.13040v2 Announce Type: replace Abstract: Vision-language models (VLMs) are increasingly explored as visual critics, reward generators, and failure detectors in robotic manipulation. These r
arXiv:2607.24810v1 Announce Type: new Abstract: Vision-language models (VLMs) have achieved strong performance on general remote sensing tasks. However, their capability for rare scenes remains insuff
arXiv:2607.25886v1 Announce Type: cross Abstract: Recursive self-improvement requires turning evidence of model failures into better models. Data-centric post-training research entails diagnosing capa
arXiv:2607.24772v1 Announce Type: new Abstract: Geoscience research requires complex analysis and domain expertise, with remote sensing (RS) observations as a key foundation. However, existing RS agen
arXiv:2606.18902v2 Announce Type: replace Abstract: Context engineering has emerged as a primary lever for improving AI systems without parameter updates. Recent work showing that textual gradients do
arXiv:2511.20027v2 Announce Type: replace Abstract: Open-vocabulary semantic segmentation (OVSS) aims to segment and recognize objects universally. Trained on extensive high-quality segmentation data,
arXiv:2607.25275v1 Announce Type: cross Abstract: Real-world Image Restoration (Real-IR) aims to recover high-quality (HQ) images from complex and unknown degradations. Although recent diffusion-based
arXiv:2607.25041v1 Announce Type: cross Abstract: A growing number of applications, such as biometrics and retrieval-augmented generation (RAG), rely on cosine similarity scores computed between vecto
arXiv:2607.25225v1 Announce Type: cross Abstract: LLMs are increasingly used for code generation in critical infrastructure, yet the security effect of domain-specific prompting is understudied. We pr
arXiv:2607.25314v1 Announce Type: new Abstract: Sensory advertising evokes human senses through visual cues, enabling audiences to mentally simulate experiences and increasing persuasive impact. Despi
arXiv:2607.25388v1 Announce Type: new Abstract: Autonomous multi-vehicle racing requires real-time planning of diverse competitive behaviors in intense interactions. Existing planners often struggle t
arXiv:2607.26001v1 Announce Type: new Abstract: Sharpness-Aware Minimization (SAM) aims to improve generalization by encouraging insensitivity to small, worst-case parameter perturbations. However, th
arXiv:2607.25366v1 Announce Type: cross Abstract: LLM-based Cross-Domain Sequential Recommendation (CDSR) leverages LLMs to enhance target performance via deep semantic reasoning, alleviating the depe
arXiv:2607.25857v1 Announce Type: new Abstract: We introduce Shieldstral, a 3B-parameter policy-adaptive multimodal safety classifier that matches or outperforms models nearly 7imes its size on text s
arXiv:2607.25063v1 Announce Type: new Abstract: Developers judge a model checkpoint by how it behaves. After supervised fine-tuning (SFT), two checkpoints that perform about the same across relevant b
arXiv:2607.25026v1 Announce Type: cross Abstract: In this work, we present a simulation-based parameter estimation framework for a model defined by a computational simulation of a physical system. We
arXiv:2607.24787v1 Announce Type: new Abstract: Sparse Mixture-of-Experts (MoE) models expand foundation model capacity through conditional expert activation, but their full expert pools remain diffic
arXiv:2607.25716v1 Announce Type: new Abstract: Federated learning (FL) enables privacy-preserving training of automatic speech recognition (ASR) systems across distributed data sources, yet its appli
arXiv:2607.26052v1 Announce Type: new Abstract: Mixture-of-Experts (MoE) variants of Low-Rank Adaptation (LoRA) route every token to a fixed number of experts k. Tokens differ in how uncertain the mod
arXiv:2511.16618v2 Announce Type: replace Abstract: Surgical scene understanding demands temporally consistent tracking of instruments and tissues. For clinical use, such tracking should generalize to
Israeli runtime security startup Sweet Security Ltd. today unveiled Agentic AI Blocking, a capability that stops artificial intelligence agents mid-action in production when they do something they wer
arXiv:2607.25182v1 Announce Type: cross Abstract: The ability to retrieve relevant tables for answering questions is a key task for structured information retrieval. Multi-stage retrieval systems rely
Today’s data lakehouse is no longer mere data repository, but increasingly a system of action, actively executing tasks via always-on, autonomous AI agents. Rather than waiting for static reports, the
The median task consumed nearly 6x more tokens in Claude Code than in Kimi Code: - 61k in Kimi Code - 67k in Hermes - 340k in Claude Code At K3's 3/M input rate (input tokens make up roughly 95% of ag
The number of times I have had to tell Opus 5 and Fable 5 to talk to me like an excited teenager and not a computer science phd is high. Was going through a Google auth thing and got multiple paragrap
OpenAI announced the release of GPT‑5.6 Sol after deployment, incorporating optimizations across its stack to enhance run‑time efficiency. The update delivers a roughly 20 % reduction in serving costs
This is a willfully misleading narrative from OpenAI. Sam’s earnest expressions are being deployed, again, to misdirect. If there’s a need to “pace AI development”, the OAI hack incident isn’t any evi
arXiv:2607.24781v1 Announce Type: cross Abstract: RAG systems rely on chunking, which destroys structural information in documents. Existing heading-based retrieval (Jeong et al., 2025) requires multi
Thrilled to have @gabepereyra speak at our @sequoia event tmrw on OWN YOUR AI: how to build your own Lab as an application company. Also featuring @FireworksAI_HQ @mercor_ai @LangChain @trajectorylabs
arXiv:2607.24750v1 Announce Type: new Abstract: Large Language Models (LLMs) are temporally overexposed: trained on vast contemporary corpora, they encode present-day concepts that make them unreliabl
arXiv:2607.10016v2 Announce Type: replace-cross Abstract: Large language models (LLMs) are increasingly used as backbone architectures for recommender systems because of their strong sequence modeling
arXiv:2607.25216v1 Announce Type: cross Abstract: Semantic ID-based generative recommendation tokenizes each item into a sequence of discrete semantic IDs and predicts the next item by generating sema
arXiv:2607.25446v1 Announce Type: new Abstract: Multi-agent frameworks built on large language models (LLMs) routinely entangle three logically distinct concerns: who is on the team (organization), ho
arXiv:2607.25789v1 Announce Type: new Abstract: Sentimental Image Captioning (SIC) requires balancing emotional expression with visual fidelity. Existing methods often struggle with this trade-off, le
arXiv:2607.24809v1 Announce Type: cross Abstract: Remaining useful life prediction for aircraft centrifugal air compressors in real commercial operations poses challenges that controlled benchmark dat
arXiv:2607.25091v1 Announce Type: new Abstract: The alignment of Small Language Models (SLMs) in the 70--500M parameter range using reinforcement learning is often considered unstable, though the unde
arXiv:2506.21571v3 Announce Type: replace-cross Abstract: Large Reasoning Models (LRMs), which autonomously produce a reasoning Chain of Thought (CoT) before producing final responses, offer a promisi
arXiv:2607.24852v1 Announce Type: new Abstract: Automated photogrammetric inspection emits metric measurements from a 3D reconstruction whose own correctness is normally unknown without an external su
arXiv:2607.25404v1 Announce Type: new Abstract: Long-horizon conversion prediction under delayed feedback creates a two-clock, two-window learning problem in online advertising. A short base observati
Two of the people most responsible for scaling the transformer are now betting on a next act. @MillionInt ran the Reasoning 🍓 team at OpenAI. @_arohan_ was a pre-training lead on Gemini after years at
arXiv:2607.24846v1 Announce Type: cross Abstract: Traditional conversational recommenders entangle retrieval and response generation within a single text interface, so exact entity cues fade as the di
Hi. Many people think uncensored models are basically the same model that just doesn't refuse, but... I was recently checking whether uncensored models would give me better answers for stock market pr