Knowledge catalogue
arXiv:2608.23885v1 Announce Type: cross Abstract: Real-time optimization (RTO) relies on process models to locate economically optimal operating conditions. Because developing first-principles models
arXiv:2608.24229v1 Announce Type: cross Abstract: Quantum noise is expected to degrade quantum machine learning by driving circuits away from their noiseless implementations. Yet recent studies show m
arXiv:2608.23798v1 Announce Type: new Abstract: Speciation in generative diffusion models denotes the emergence of distinct stable branches during denoising, through which initially undifferentiated t
arXiv:2608.24010v1 Announce Type: new Abstract: Supervised proxy-based deep cross-modal hashing has become the dominant paradigm for large-scale retrieval. However, prevalent methods model class proxi
arXiv:2608.23796v1 Announce Type: cross Abstract: Problem definition: Solar electricity generation is a strategic component of energy portfolios designed to meet growing demand and reduce carbon emiss
arXiv:2608.24103v1 Announce Type: new Abstract: Commercial design platforms increasingly edit documents through large language model (LLM) agents, but two practical problems block reliable deployment:
arXiv:2608.24568v1 Announce Type: cross Abstract: Deep neural networks generalize well despite their highly nonconvex, overparameterized loss landscapes, a phenomenon often associated with the geometr
arXiv:2601.19435v2 Announce Type: replace-cross Abstract: Sustainable monetization of large language models (LLMs) remains a critical open challenge. Traditional search advertising, which relies on st
arXiv:2402.11215v4 Announce Type: replace Abstract: The choice of batch size in minibatch stochastic gradient optimization is critical for both optimization and generalization performance in large-sca
arXiv:2502.21099v3 Announce Type: replace-cross Abstract: This paper proposes {sf AEPG-SPIDER}, an Adaptive Extrapolated Proximal Gradient (AEPG) method with variance reduction for minimizing composit
arXiv:2511.02605v3 Announce Type: replace Abstract: Shielding is widely used to enforce safety in reinforcement learning (RL), ensuring that an agent's actions remain compliant with formal specificati
arXiv:2608.24361v1 Announce Type: new Abstract: Multi-agent LLM systems are increasingly deployed in real-world applications, where failures can be costly and difficult to localize. Despite growing ef
arXiv:2509.13577v3 Announce Type: replace Abstract: Trustworthy trajectory prediction grounds autonomous vehicle (AV) safety, yet deployed models inevitably face out-of-distribution (OOD) scenes. Prio
arXiv:2512.00342v2 Announce Type: replace Abstract: Real-world intelligence systems usually operate by combining offline learning and online adaptation with highly correlated and non-stationary system
arXiv:2603.10068v2 Announce Type: replace-cross Abstract: Most adversarial evaluations of large language model (LLM) safety assess single prompts and report binary pass/fail outcomes, which fails to c
arXiv:2608.23864v1 Announce Type: new Abstract: Visual tokenizers increasingly inject semantic supervision into latent spaces to make downstream diffusion easier. Yet how these semantics should be org
arXiv:2608.23740v1 Announce Type: new Abstract: Concurrent multi-agent coding promises division of labor across modules, robustness through redundancy, and parallel exploration at the natural granular
arXiv:2608.24076v1 Announce Type: new Abstract: Evaluation of agentic information retrieval remains limited to scripted interactions with uniform users, missing both natural personality diversity and
arXiv:2608.24114v1 Announce Type: new Abstract: Training multi-turn LLM agents with reinforcement learning typically relies on trajectory-level rewards, which assign a uniform advantage to every step
arXiv:2608.23642v1 Announce Type: new Abstract: AI agents pose significant risks as they are granted increasing autonomy. A commonly proposed solution is human oversight and keeping a ''human in the l
Kate Clark / Wall Street Journal: AI assistant Instinct is raising a 250M Series B co-led by Index Ventures and Benchmark at a 2.5B valuation, bringing its total raised to 350M — Buzzy startup Instinc
arXiv:2608.23875v1 Announce Type: new Abstract: Artificial Intelligence (AI) algorithms frequently learn creative and unexpected solutions, surprising even expert researchers who develop and study the
Puzzles and games have been central to AI development since the very beginning. Just as we humans like to test our smarts with crosswords or logic puzzles, developers can test how far models have adva
arXiv:2608.24046v1 Announce Type: new Abstract: When an AI algorithm makes decisions that affect more than one person, aligning it becomes a problem of social choice: how should people's divergent pre
Luz Ding / Bloomberg: Alibaba releases Qwen3.8-Flash, an open-weight, 125B-parameter model built on its next-gen Qwen 4 architecture, saying it rivals Opus 4.6 and V4-Flash — Alibaba Group Holding Ltd
Ollama’s latest release retains all existing functionalities while adding a built‑in web‑search capability. Users can now run research tasks directly through open models that automatically query the i
arXiv:2608.24051v1 Announce Type: new Abstract: Can a sequence model remain competitive with only a few thousand parameters and an explicitly auditable prediction interface? We introduce ALPHABET, a c
Mahadev Konar co‑hosted a 2‑hour live broadcast titled “Amazing deep dive on K3!!” with Together Compute. The session explored scaling the Kimi K3 language model for inference, detailing deployment st
Amazing! 🥳 Thanks @vllm_project for getting Qwen3.8-Flash-Next running on NVIDIA and AMD from day 0. Qwen3.8-Flash-Next from @Alibaba_Qwen has day-0 support in vLLM, verified on NVIDIA and AMD GPUs. 🎉
Despite rapid advances in artificial intelligence, the enterprise world is still dealing with a data pipeline problem. More than 80% of enterprise data is unstructured, and 99% of this data is dark to
America is LLM-pilled. China is World-Model-Pilled. China’s booming ultra-short drama industry is being reshaped by the rapid rise of AI-generated video. In Zhengzhou, one of the country’s major produ
arXiv:2608.24175v1 Announce Type: new Abstract: Iterated Function Systems (IFS) generate self-similar fractals from a few contractive affine maps. The forward map from parameters to images is computat
arXiv:2601.19509v2 Announce Type: replace Abstract: In loosely coupled strapdown inertial navigation system/Doppler velocity log (SINS/DVL) integration, the body-frame DVL velocity is projected into t
arXiv:2608.24127v1 Announce Type: cross Abstract: Telephone fraud is pervasive and costly, but its inner workings are rarely observed at scale. We analyze a complete corpus of 10,211 inbound scam and
Another special guest is hitting the stage at #FullyConnected26! @jerryjliu0 built @llama_index, the framework thousands of teams use to connect LLMs to their data. Come see Jerry and many more Sept 2
Frederic Lardinois / The New Stack: Anthropic gives Claude Cowork its own built-in browser on the desktop app separate from users' day-to-day browser, rolling out to paying subscribers — Anthropic add
Upgraded to a 5070 Ti so I could run Qwen 3.8 27B, which works perfectly, but didn't want to let the old 4070 Ti go to waste. The cards would touch if I put them both in the PC and I knew the heat wou
arXiv:2602.03702v2 Announce Type: replace-cross Abstract: Large language models are increasingly trained in continual or open-ended settings, where the total training horizon is not known in advance.
API is live on QwenCloud: https://www.qwencloud.com/models/qwen3.8-flash 🙌Let's build something! ⚡Meet Qwen3.8-Flash, a multimodal MoE and an early preview of the Qwen4 architecture, now open-weight!
arXiv:2608.23816v1 Announce Type: new Abstract: Quantized fine-tuning (QLoRA) saves memory but not time. It dequantizes every 4-bit weight on the fly, so it trains more slowly than fp16 LoRA. We prese
arXiv:2608.24099v1 Announce Type: new Abstract: GUI agents often encounter dynamic anomalies when deployed on Android devices, from unexpected pop-ups to action misuse, yet existing benchmarks lack sy
The news about Qwen 3.8 Flash Next is the first I'm reading about n-gram tables. I may be completely misunderstanding how they work but it seems they could open the door for 1T+ parameter models to be
arXiv:2512.23245v3 Announce Type: replace Abstract: Recent text-to-image diffusion models have significantly improved visual quality and text alignment. However, generating a sequence of images while
arXiv:2506.02825v3 Announce Type: replace-cross Abstract: We present the OmniMatch algorithm for seeded multiple graph matching. In the setting of d-dimensional Random Dot Product Graphs (RDPG), we pr
At a 1M-token context length, QSA’s attention kernel is up to 7.6× faster in prefill and 4.9× faster in decode. With a 90% prefix-cache hit rate, Qwen3.8-Flash-Next delivers 8.6× the prefill throughpu
arXiv:2608.23640v1 Announce Type: new Abstract: When a large language model (LLM) is asked to write a person's life, how much of what it writes actually happened? We present a scene-level case-study a
arXiv:2510.21536v5 Announce Type: replace-cross Abstract: Free-space segmentation is essential for autonomous robots to identify drivable regions and navigate safely across indoor, outdoor, and road-s
arXiv:2608.23670v1 Announce Type: new Abstract: LLM-based agents execute multi-step tasks, but their behavioral structure remains opaque: long unstructured traces resist the safety auditing and runtim
arXiv:2608.23842v1 Announce Type: cross Abstract: DevOps programming (e.g., using CLI/API scripts or IaC frameworks) is key to cloud infrastructure management. Unlike traditional programming tasks, De
arXiv:2608.24807v1 Announce Type: cross Abstract: Model cards are structured documents that summarize key information about machine learning models to improve transparency, usability, and accountabili
arXiv:2608.23691v1 Announce Type: new Abstract: We study autonomous mathematical discovery in the Station, an open-world multi-agent environment in which AI agents from different model families pursue
arXiv:2608.24364v1 Announce Type: new Abstract: Self-supervised pretraining enables transferable representations for medical imaging, yet most CT encoders remain biased toward coarse semantic understa
ggml-meta: propagate buffer usage and call init on the new tensors (#27586) Website: https://llama.app Attestations: https://github.com/ggml-org/llama.cpp/attestations/43044002 macOS/iOS: macOS Apple
ggml-metal: add chunked SSD MMA for Mamba-2 prefill optimization (#26647) metal: WIP chunked SSD SSM_SCAN kernels for multi-token prefill metal: drop scalar SSD path; MMA + sequential tail drop WIP ss
cuda: unblock mmq for MoE on sm_60 (#26264) cuda: unblock mmq for MoE on sm_60 cuda: duplicate mmq-config-pascal for dp4a and older cuda: reduce occupancy on non-dp4a pascal for Q2_K, Q4_K, Q5_K, Q6_K
ci: Clean up UI builds from releases (#27706) ci : inline UI version resolution into ui-build.yml ci : build UI once and reuse the artifact in release jobs Server jobs now extract the ui-build artifac
Implemented vulkan cross_entropy_loss and cross_entropy_loss_back (#27216) Website: https://llama.app Attestations: https://github.com/ggml-org/llama.cpp/attestations/43166320 macOS/iOS: macOS Apple S
vulkan: warptiles currently assume warp sizes <= 64, clamp to work around larger warps (#27726) Website: https://llama.app Attestations: https://github.com/ggml-org/llama.cpp/attestations/43173045 mac
llama: add token ID tracking to KV cell (#27762) kv: track token id rm get_prev_tokens, move it to the main pr nits add get_prev_tokens Website: https://llama.app Attestations: https://github.com/ggml
arXiv:2608.24627v1 Announce Type: new Abstract: We study adversarial bandit maximization of monotone submodular functions under a matroid constraint. For a rank-k matroid on n elements, we give a rand