Knowledge catalogue
arXiv:2608.24310v1 Announce Type: new Abstract: Search-augmented reasoning remains difficult for small language models. On-policy distillation (OPD) from trained teachers offers a promising direction,
arXiv:2606.13856v2 Announce Type: replace Abstract: Fine-tuning a vision-language-action model (VLA-JEPA) on a single GPU should be simple: load a pretrained checkpoint, run training, deploy. There is
arXiv:2608.24658v1 Announce Type: new Abstract: Scaling test-time reasoning has substantially improved the problem-solving ability of large language models (LLMs), but standard autoregressive decoding
arXiv:2608.24082v1 Announce Type: cross Abstract: Large Language Models (LLMs) have shown strong capabilities in table reasoning, but their effectiveness degrades as tables grow in size and complexity
arXiv:2608.24049v1 Announce Type: new Abstract: Neural operators provide efficient surrogates for spatiotemporal PDE systems, but purely data-driven formulations often accumulate substantial errors du
arXiv:2608.24585v1 Announce Type: new Abstract: Automated high-density storage systems (warehouses, robotic parking, plant logistics, etc.) require fleets of agents to move through scarce task-critica
arXiv:2608.23893v1 Announce Type: new Abstract: Learning systems deployed over long periods must adapt not only to statistical changes in incoming data, but also to revisions of the definitions that g
arXiv:2608.24207v1 Announce Type: new Abstract: Semantic identifiers (SIDs) represent entities as hierarchical token sequences for generative retrieval and recommendation. Residual-quantization tokeni
Yet another vLLM fork thread here, but this time its for older INT8-centric hardware. This is a complete INT8 serving stack for Qwen3.8 27B based on vLLM, AITER, and a 27B GPTQ INT8 quant w/ DFlash2.
arXiv:2512.13979v2 Announce Type: replace Abstract: Large reasoning models achieve strong performance on diverse tasks by producing extended chains of thought. Self-reflection, the ability to review a
arXiv:2608.24015v1 Announce Type: new Abstract: The Planner-Operator-Reflector (POR) framework is widely used in GUI agents to maintain objective alignment in complex tasks through modular collaborati
arXiv:2608.23568v1 Announce Type: new Abstract: Memory and RAG evaluations often treat the answering model's input as an implementation detail, even though systems may render the same history as a mem
arXiv:2608.24275v1 Announce Type: new Abstract: Safeguarding language model agents requires assessing complete execution trajectories under context-dependent safety policies. Existing policy-aware saf
arXiv:2608.24790v1 Announce Type: new Abstract: Clinicians read chain-of-thought (CoT) rationales as evidence of medical reasoning, but whether the visible chain plays that role is rarely tested. Gene
arXiv:2608.24135v1 Announce Type: new Abstract: Reinforcement learning from verifiable rewards (RLVR) has emerged as a pivotal technique for enhancing the code generation capabilities of Large Languag
arXiv:2602.23553v2 Announce Type: replace Abstract: Long-form video question answering (LVQA) requires answering natural-language queries about videos spanning minutes to hours, demanding temporal rea
arXiv:2608.24138v1 Announce Type: new Abstract: Large vision-language models have shown strong progress in UI-to-code generation, yet their test-time self-evolution remains unstable. We first identify
arXiv:2608.24561v1 Announce Type: new Abstract: Rapid earthquake magnitude estimation is central to earthquake early warning, yet many operational systems depend on dense regional seismic networks and
arXiv:2608.23873v1 Announce Type: new Abstract: Everything a language model sees is tokens. The serving stack knows what each span is -- user input, tool output, instructions -- but the model must kee
arXiv:2608.23807v1 Announce Type: new Abstract: Masked diffusion language models (dLLMs) can in principle generate text faster than autoregressive (AR) models, since they denoise many tokens at once.
arXiv:2608.23840v1 Announce Type: cross Abstract: Training large-scale AI models often outgrows a single data center, demanding sharded, multi-cluster, and decentralized training. However, the huge sp
arXiv:2603.00188v2 Announce Type: replace-cross Abstract: Training-free KV cache compression is essential for deploying vision-language GUI agents under memory and latency constraints, yet existing me
arXiv:2608.24804v1 Announce Type: new Abstract: We present StarHarness, a framework for evolving environment-specific agent harnesses while keeping model weights fixed. The evolved harness can include
arXiv:2512.16715v3 Announce Type: replace-cross Abstract: In recent years, Predictive Process Mining (PPM) techniques based on artificial neural networks have evolved as a method for monitoring the fu
arXiv:2608.24101v1 Announce Type: new Abstract: Robot actions are inherently embodiment-specific and only weakly aligned with image-space visual changes, limiting their effectiveness as conditioning s
arXiv:2608.24119v1 Announce Type: cross Abstract: Visual demonstrations provide a natural interface for specifying image transformations that are difficult to describe exhaustively with text. However,
arXiv:2608.24019v1 Announce Type: cross Abstract: Underactuated systems pose a challenge for convex motion planning because their dynamically feasible motions lie on a manifold of trajectories in func
arXiv:2511.08873v3 Announce Type: replace Abstract: Large language models (LLMs) are shifting from answer providers to intelligent tutors in educational settings, yet current supervised fine-tuning me
arXiv:2608.24366v1 Announce Type: new Abstract: End-to-end multimodal driving has progressed rapidly by fusing camera and LiDAR streams. Existing pipelines remain fragile under asymmetric sensor degra
arXiv:2608.24302v1 Announce Type: new Abstract: Long-video understanding depends critically on how a limited model context is constructed from a much longer video. Existing approaches improve this pro
arXiv:2608.24169v1 Announce Type: new Abstract: 3D geometry editing is a critical yet labor-intensive part of the graphics pipeline, requiring artists to translate creative intent into precise operati
arXiv:2608.24479v1 Announce Type: new Abstract: Massively parallel simulation changes the data regime in which off-policy reinforcement learning (RL) is trained, challenging stabilizers designed for d
arXiv:2607.18467v2 Announce Type: replace Abstract: Many operational decisions rely on predictive models that estimate uncertain outcomes conditional on observable contexts. Training such models, howe
arXiv:2606.13705v2 Announce Type: replace-cross Abstract: The Gemma 4 instruction-tuned models share a reproducible failure: on long factual enumeration prompts, such as TV episodes, the 88 IAU conste
arXiv:2608.24631v1 Announce Type: cross Abstract: Similarity in many decision systems is governed not by distance alone but by interactions among variables. In fraud and anomaly detection, small local
arXiv:2608.22910v1 Announce Type: cross Abstract: We develop a finite-width geometric framework describing how learned feature geometries are organized, transported, and selectively aligned in deep ne
arXiv:2608.23421v1 Announce Type: new Abstract: Natural Language Processing (NLP) has grown rapidly over the past decade, driven by digital transformation in the Arab world, social media, and large la
arXiv:2506.20073v2 Announce Type: replace-cross Abstract: Spatio-temporal data mining plays a pivotal role in informed decision making across diverse domains. However, existing models are often restri
arXiv:2608.22945v1 Announce Type: new Abstract: Federated learning has traditionally been formulated as a single-objective optimization problem, primarily focused on maximizing model utility. In real-
arXiv:2608.23235v1 Announce Type: new Abstract: Event extraction aims to identify event triggers, classify event types, and extract arguments to construct structured event representations. Despite str
arXiv:2608.22235v1 Announce Type: new Abstract: Pore-scale flow governs transport and permeability behaviour in porous media engineering applications, yet repeated lattice Boltzmann method (LBM) simul
arXiv:2608.21570v1 Announce Type: new Abstract: Deploying a safety layer for large language models on commodity hardware is constrained by the guards available to do it: current open guard models hold
arXiv:2608.22713v1 Announce Type: new Abstract: Clinical diagnosis requires progressive integration of patient history, physical examination, laboratory findings, medical images, and diagnostic-inform
arXiv:2608.23167v1 Announce Type: new Abstract: Diffusion Language Models (DLMs) exhibit strong parallel decoding capabilities by denoising multiple tokens in a single generation step. However, this p
arXiv:2608.23478v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models can turn multimodal context into robot actions, but their action decoders are still trained largely by behavior cl
arXiv:2608.22313v1 Announce Type: new Abstract: Learning multi-label image classification with incomplete annotations is a challenging task that has been widely studied for its superior trade-off betw
arXiv:2608.23400v1 Announce Type: cross Abstract: Discrete Diffusion Models (DDMs) have recently been introduced to recommendation systems, modeling user history as a token generation process via iter
arXiv:2602.09555v3 Announce Type: replace Abstract: Recent advances in block diffusion language models have demonstrated competitive performance and strong scalability on reasoning tasks. However, the
arXiv:2512.17730v2 Announce Type: replace Abstract: Detectors of AI-generated images tend to inherit the biases of the data they are trained on: models fitted to GAN imagery learn to treat GAN-specifi
arXiv:2507.13727v2 Announce Type: replace Abstract: Adversarial training is a promising strategy for enhancing robustness against adversarial attacks, but its impact on generalization under substantia
arXiv:2603.12031v3 Announce Type: replace-cross Abstract: Cloud-native container orchestration requires resource schedulers capable of balancing infrastructure expenditure, fault resilience, and node
arXiv:2509.01479v3 Announce Type: replace-cross Abstract: Explainable systems expose information about why certain observed effects are happening to the agents interacting with them. We argue that thi
arXiv:2608.22906v1 Announce Type: new Abstract: Recent monocular 3D Gaussian Splatting (3DGS) streaming reconstruction methods have achieved impressive performance by balancing reconstruction quality
arXiv:2608.22478v1 Announce Type: cross Abstract: Implied volatility surfaces summarise the option market and are central to many financial applications. Forecasting their future evolution requires mo
arXiv:2608.22029v1 Announce Type: new Abstract: Single-particle cryo-electron microscopy (cryo-EM) pose estimation is traditionally solved anew for each dataset, where iterative refinement is done fro
arXiv:2603.29902v2 Announce Type: replace Abstract: Interleaved text-and-image generation represents a significant frontier for Multimodal Large Language Models (MLLMs), offering a more intuitive way
arXiv:2608.22160v1 Announce Type: new Abstract: Physical automation is scaling toward fleets of embodied machines commanded by an AI brain. Early deployments already run factories and warehouses at pr
arXiv:2608.21847v1 Announce Type: new Abstract: Low-light image enhancement (LLIE) must correct ambiguous exposure without overwriting structure already supported by the input. Generative transport ca
arXiv:2608.22322v1 Announce Type: new Abstract: Low-precision optimizer-state methods are commonly designed for dense Adam-style moments, but memory-efficient optimizers maintain factored, confidence-
arXiv:2608.22101v1 Announce Type: new Abstract: Modern voice-agent systems rely on streaming speech recognition models that operate under stringent latency constraints. This study shows that, due to t