Knowledge catalogue
arXiv:2609.17763v1 Announce Type: new Abstract: Next-day wildfire prediction requires models whose forecasts can be evaluated alongside the assumptions and historical evidence used in their computatio
arXiv:2609.19101v1 Announce Type: new Abstract: As models scale, reward hacking becomes more frequent, more sophisticated, and more consequential. Does it leave a telltale signature in model represent
arXiv:2609.18432v1 Announce Type: new Abstract: Extensive occlusions in real-world scenarios pose challenges to gait recognition due to missing and noisy information, as well as body misalignment in p
arXiv:2609.17633v1 Announce Type: new Abstract: The Graph Coloring Problem (GCP) is NP-hard and DSATUR stands as one of the fastest heuristics for it despite producing colorings that typically use mor
arXiv:2609.17709v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) systems typically employ fixed retriever and generator configurations across queries, despite substantial differe
OpenAI Group PBC today disclosed six new “concerning” incidents involving artificial intelligence agents behaving badly again. The agents made up data, moved files onto the public internet without per
arXiv:2609.19143v1 Announce Type: cross Abstract: Intelligent systems that act in the world require image understanding that is both comprehensive and spatially grounded. Current vision-language model
arXiv:2602.11220v2 Announce Type: replace-cross Abstract: Large language models are commonly adapted to downstream tasks through supervised fine-tuning (SFT), but substantial distribution mismatch bet
arXiv:2609.18329v2 Announce Type: new Abstract: Remote sensing recognition is often constrained by scarce observations of rare targets and costly annotations, making realistic synthetic augmentation p
arXiv:2609.18120v1 Announce Type: cross Abstract: AI-driven penetration testing has been demonstrated with premium frontier models such as GPT-4, but the per-engagement token cost makes continuous, au
arXiv:2609.19142v1 Announce Type: cross Abstract: World models endow perceptual systems with the ability to predict how scenes evolve under interaction. They are most beneficial when trained on divers
arXiv:2609.18056v1 Announce Type: new Abstract: Parameter-efficient fine-tuning (PEFT) has recently emerged as a pivotal research direction for adapting pre-trained point cloud transformers to diverse
arXiv:2609.17789v1 Announce Type: cross Abstract: Quantum machine learning offers a compelling representational perspective: angle-encoded states inhabit Hilbert spaces in which periodic similarities
Hey Reddit. This is a post asking for advice / user experience. The goal is simple: deploy a small private server for a developer to run a harness that rivals/beats Claude Opus (in perf/intelligence,
arXiv:2609.17728v1 Announce Type: new Abstract: Recent Vision-Language-Action (VLA) models for autonomous driving have incorporated world modeling by predicting future driving scenes alongside driving
arXiv:2609.19070v1 Announce Type: new Abstract: This paper introduces ``question archaeology'', a specific evaluation task focused on inferring the single, authentic 'genesis question' that motivated
arXiv:2609.18359v1 Announce Type: cross Abstract: Generalized morphology control requires a single policy to transform information across limbs with different physical roles, coordinate whole-body mot
arXiv:2609.18591v1 Announce Type: new Abstract: In-context learning (ICL) enables large language model (LLM) agents to improve decisions using interaction history, yet it remains unclear whether such
arXiv:2609.17653v1 Announce Type: cross Abstract: GUI agents execute long-horizon tasks on dynamic graphical user interfaces, where pop-ups, delayed loads, and relocated widgets routinely invalidate p
arXiv:2609.17542v1 Announce Type: new Abstract: Large language model services increasingly route each query to one of several models of differing capability, using a cheap estimate of query complexity
arXiv:2609.18207v1 Announce Type: cross Abstract: Reinforcement learning fine-tuning on top of large, pretrained Vision-Language-Action (VLA) models offers promise for highly reliable robot deployment
arXiv:2609.18262v1 Announce Type: new Abstract: Precise retrieval of scientific information is fundamentally constrained by long-tailed concepts and high fact-sensitivity of scientific corpora. These
arXiv:2609.18304v1 Announce Type: new Abstract: Large language model (LLM) agents increasingly tackle long-horizon tasks through multi-step environment interaction, yet a single erroneous action can a
arXiv:2609.19134v1 Announce Type: new Abstract: Scientific code repositories encode decades of human knowledge in executable models, methods, and tools. Yet fragmented toolchains, implicit domain conv
Choosing the right vector store for your Amazon Bedrock Knowledge Bases RAG application affects performance and cost. This post compares Amazon OpenSearch Service, Amazon Aurora PostgreSQL with pgvect
arXiv:2609.18037v1 Announce Type: new Abstract: Segment Anything Models provide reusable priors, yet they require user prompts and cannot support fully automatic instrument segmentation. Automatic pro
arXiv:2609.17855v1 Announce Type: new Abstract: Standardizing clinical language to SNOMED CT supports interoperability, analytics, and reusable phenotyping, but concept recommendation remains difficul
arXiv:2609.18642v1 Announce Type: new Abstract: Large language models (LLMs) often suffer from capability stagnation in self-improvement training because fixed difficulty levels fail to adapt to their
arXiv:2609.18045v1 Announce Type: new Abstract: Recovering latent interaction structures from multi-agent dynamics is important for understanding and predicting interacting systems. Trajectory-based s
arXiv:2609.18540v1 Announce Type: new Abstract: Most keyframe selection studies focus on offline settings, assuming access to the full video and query in advance. In contrast, real-world streaming sce
arXiv:2609.18128v1 Announce Type: new Abstract: Large language model (LLM) agents augmented by tools can automate complex, multi-step tasks, such as web navigation, code generation, and workflow orche
arXiv:2609.19010v1 Announce Type: cross Abstract: Urban Land Cover (ULC) classification plays a crucial role in urban planning, environmental monitoring, and sustainable development. We study this tas
arXiv:2609.18407v1 Announce Type: cross Abstract: Weekly influenza surveillance counts guide vaccine distribution and public-health alerts, yet they are hard to forecast. Each region offers only a few
arXiv:2609.18966v1 Announce Type: new Abstract: Linear RNNs with input-dependent Householder-product transitions (DeltaNet/DeltaProduct-class) can provably represent hard state-tracking automata, yet
arXiv:2408.07702v2 Announce Type: cross Abstract: Schema linking is a crucial step in Text-to-SQL pipelines. Its goal is to retrieve the relevant tables and columns of a target database for a user's q
arXiv:2609.18453v1 Announce Type: new Abstract: A calibrated Vision-Language Model (VLM) can repeatedly self-correct, say 'Wait, I should recheck,' arrive at the wrong answer, and still report high co
arXiv:2609.18063v1 Announce Type: new Abstract: Mixture-of-experts (MoE) inference on consumer hardware is bounded by weight memory: a 35B-class model is 19.5GB at 4-bit, and sparsity shrinks the comp
This is basically an Opus 4.5/4.6 level model that will work on anything with 8 Gb RAM Today, we’re announcing Ternary Bonsai 2 27B. Based on Qwen3.8 27B, Bonsai 2 27B is 9x smaller than its full-prec
arXiv:2609.18700v1 Announce Type: new Abstract: This paper investigates multi-material 3D printing as a method to fabricate non-planar structures with 3D-printed electrode patterns for electroadhesion
arXiv:2609.18776v1 Announce Type: new Abstract: Multi-robot navigation in human-shared spaces is inherently interactive: coordinated robot motions influence how nearby entities respond, while those re
arXiv:2609.18321v1 Announce Type: cross Abstract: Offline on-policy distillation gains efficiency by collecting student trajectories and teacher supervision once and reusing them throughout optimizati
arXiv:2609.18525v1 Announce Type: new Abstract: Modern evaluation of learning pipelines often reduces to downstream accuracy, leaving open the question of why tasks succeed or fail. TriProbe addresses
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
Two weeks. That's how long it took to go from GLM-5.3-Flash's first run on domestic accelerators to serving all of its production traffic, with 3.2× end-to-end throughput along the way. What I keep th
arXiv:2602.00408v3 Announce Type: replace-cross Abstract: This paper proposes a novel Variational Graph-to-Scheduler (VG2S) framework for solving the Job Shop Scheduling Problem (JSSP), a critical tas
arXiv:2609.18077v1 Announce Type: cross Abstract: Open-source video generative models ship almost exclusively as PyTorch/CUDA reference implementations. This leaves Cloud TPU pods without a production
arXiv:2609.18677v1 Announce Type: new Abstract: Speech language models enable richer spoken interactions between humans and machines than cascaded systems, allowing access to paralinguistic informatio
arXiv:2609.18812v1 Announce Type: new Abstract: Time-series language models provide a shared natural-language interface across temporal tasks, but plausible text does not guarantee reliable task outpu
arXiv:2609.18612v1 Announce Type: cross Abstract: We analyze the learned input-output behavior of GLU-based neurons in large language models (LLMs). We propose a simple analysis method: For each neuro
arXiv:2609.18182v1 Announce Type: new Abstract: Real-world agents fundamentally require persistent non-parametric knowledge for dynamic reasoning, i.e., long-term memory and retrieval-augmented genera
arXiv:2609.18283v1 Announce Type: new Abstract: As telecommunication networks evolve toward autonomous 5G-Advanced and 6G operations, agentic artificial intelligence (AI) workflows, where large langua
arXiv:2609.18772v1 Announce Type: new Abstract: Sign language processing advances rapidly for high-resource languages such as American Sign Language (ASL), yet most of the world's sign languages lack
arXiv:2609.16067v1 Announce Type: new Abstract: In order to solve the high-dimensional (d geq 30) expensive black-box problems within budget, an efficient global optimization (EGO) algorithm with a dy
arXiv:2609.16824v1 Announce Type: new Abstract: Decentralized bandit systems often contain heterogeneous agents: rewards can change at individual agents even when the best action for the network stays
arXiv:2609.16309v1 Announce Type: new Abstract: Despite the rapid progress of LLM-based agents for planning, code generation, and debugging, their practical value for tabular machine learning remains
arXiv:2609.16213v1 Announce Type: new Abstract: Artificial intelligence is reshaping biological research across an increasingly connected digital-to-physical workflow. General-purpose large language m
arXiv:2609.16880v1 Announce Type: new Abstract: Space robots are increasingly expected to perform long-duration, contact-rich, and multi-stage operations with limited human intervention. Recent advanc
arXiv:2609.16346v1 Announce Type: cross Abstract: This paper presents Auto-HSI, a method for generating personalized human-swarm interaction (HSI) interfaces on demand. The objective is to enable untr
metal: fix NaN in mul_mm_id when activations exceed f16 range (#26223) test-backend-ops: reproduce MUL_MAT_ID NaN for activations beyond f16 The Metal mul_mm_id path narrows src1 to half for the simdg
Banger paper from NVIDIA. It's on the topic of choosing which models go into a multi-agent system. The team compared eight selection strategies, based on size, accuracy, answer diversity and error div