Search: “reasoning”

arXiv:2608.24017v1 Announce Type: cross Abstract: The emerging W3C WebMCP proposal enables LLM agents to invoke tools exposed by web pages. In multi-party web environments, however, integrating agent

We’re excited to announce that 𝗛𝗮𝗿𝗿𝗶𝘀𝗼𝗻 𝗖𝗵𝗮𝘀𝗲 @hwchase17, Co-Founder & CEO of @LangChain, will be speaking at VapiCon 2026! 🎙️ Harrison co-founded LangChain with the goal of making it easier to use LL

arXiv:2608.24022v1 Announce Type: cross Abstract: LLM agents integrated with external resources gain complex task capabilities, yet the unified natural-language context channel makes them vulnerable t

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:2608.24492v1 Announce Type: cross Abstract: Uncertainty quantification (UQ) methods are widely used for hallucination detection in large language models (LLMs) in closed-book settings where grou

With hopes of a Qwen3.8-35B-A3B release now mostly dashed, many people including myself are looking at fine-tunes and other variants of Qwen3.6-35B-A3B to run on VRAM-limited hardware. I decided to tr

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:2608.21400v1 Announce Type: new Abstract: Dense traffic is inherently interactive. The ego vehicle and surrounding agents continuously influence each other's reactions, making 'what-if' reasonin

arXiv:2608.22444v1 Announce Type: new Abstract: The unit of AI safety evaluation is still the individual model, yet language-model agents are increasingly deployed in interacting populations that read

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:2608.22160v1 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:2608.23311v1 Announce Type: new Abstract: Policy optimization (PO) for Large Language Models faces a stability--exploration trade-off, currently mediated by an action-side Policy-KL regularizer.

arXiv:2608.21447v1 Announce Type: new Abstract: Structured-language models such as SceneScript reconstruct a scene as a short program of parametric commands, an inherently editable and semantically ex

arXiv:2608.23336v1 Announce Type: new Abstract: Developing competitive deep learning baselines for medical imaging remains a highly iterative process requiring literature review, implementation, exper

arXiv:2608.22376v1 Announce Type: new Abstract: Large language models generate tokens sequentially, but can they execute multiple tasks concurrently while forming each token? Broader attention allocat

arXiv:2608.22808v1 Announce Type: new Abstract: When can an agent failure be caught? An audit is usually limited by the record rather than by the method. CatchBench therefore puts one auditor's questi

arXiv:2608.22899v1 Announce Type: new Abstract: Unlike static medical question answering, long-horizon diagnosis captures the sequential nature of clinical practice: evidence is progressively acquired

arXiv:2608.22281v1 Announce Type: cross Abstract: Medical image segmentation requires high accuracy and robustness, yet practical commercial deployment also demands privacy preservation and computatio

arXiv:2608.22967v1 Announce Type: new Abstract: Practical molecular inverse design is rarely a one-shot generation problem; it often takes the form of closed-loop candidate-pool enrichment, where unde

arXiv:2608.21914v1 Announce Type: new Abstract: We align three upper ontologies that sit under UK and NATO defence data infrastructure: the Information Exchange Standard (IES), the Higher Quality Data

arXiv:2608.23244v1 Announce Type: cross Abstract: Large language models (LLMs) often produce fluent but incorrect answers with unwarranted confidence. A central limitation is that standard LLMs repres

arXiv:2403.14830v2 Announce Type: replace-cross Abstract: Deep clustering partitions complex high-dimensional data using deep neural networks for clustering. It involves projecting data into lower-dim

arXiv:2608.22191v1 Announce Type: new Abstract: Software-engineering agents solve repository-level tasks through long, stochastic tool-use trajectories, and repeated attempts often find fixes missed b

arXiv:2608.21364v1 Announce Type: cross Abstract: Both human and AI systems that process narrative or long-form content operate incrementally: input is received over time, and internal representations

arXiv:2608.23525v1 Announce Type: new Abstract: Earth-system analysis reconstructs changing physical processes from observations that differ in source, scale, timing, and modality. Natural hazards mak

arXiv:2608.22909v1 Announce Type: new Abstract: Dowker homology is a topological tool that may be used to analyze the relative position of two point clouds living in a common space. We investigate whe

arXiv:2608.21839v1 Announce Type: new Abstract: Reliable reward models are essential for text-to-video evaluation and alignment. However, the trade-off between evaluation accuracy and inference effici

arXiv:2608.22055v1 Announce Type: new Abstract: Suppose one embodied agent knows what must be built, while its teammate alone knows which transformation its workcell can perform. Neither local view de

arXiv:2608.21928v1 Announce Type: new Abstract: In embodied AI, safety risk can be latent: a benign instruction and a safe scene become hazardous only when composed. Prior work has advanced embodied s

arXiv:2608.22485v1 Announce Type: new Abstract: Current visual tokenizers in Multimodal Large Language Models (MLLMs) predominantly rely on patch-based partitioning, which causes severe semantic mixtu

arXiv:2608.23286v1 Announce Type: cross Abstract: Federated learning on non-IID data seeks flat minima to generalize across clients, and existing methods borrow sharpness-aware minimization from centr

arXiv:2608.23566v1 Announce Type: cross Abstract: Group-based reinforcement learning methods such as GRPO for large language models avoid training a critic by sampling multiple responses for each prom

arXiv:2608.22817v1 Announce Type: new Abstract: Industrial technical reports contain high-value knowledge for maintenance, troubleshooting, and product engineering, but their heterogeneous structure (

arXiv:2608.22990v1 Announce Type: new Abstract: Vision-language-action (VLA) models have made general-purpose robot manipulation increasingly plausible by conditioning robot actions on natural-languag

arXiv:2608.23538v1 Announce Type: cross Abstract: Modern AI models such as tabular foundation models and gradient-boosted ensembles can outpredict classical methods, but provide little basis for reaso

arXiv:2509.23155v3 Announce Type: replace Abstract: Robotic manipulation benefits from foundation models that describe goals, but today's agents still lack a principled way to learn from their own mis

arXiv:2608.22765v1 Announce Type: new Abstract: Coupled-dynamics environments expose the one-step outcomes that would follow from several possible counterfactual actions under a common realization of

arXiv:2510.11358v3 Announce Type: replace-cross Abstract: Retrieval-augmented generation (RAG) is typically optimized for topical relevance, yet its success ultimately depends on whether retrieved pas

arXiv:2608.23200v1 Announce Type: new Abstract: Large language models are increasingly expected to execute complex workflows whose success depends on maintaining interdependent constraints and produci

arXiv:2608.22035v1 Announce Type: new Abstract: Robot foundation models have substantially advanced perception and control, but natural human-robot collaboration requires more than executing isolated

arXiv:2608.22138v1 Announce Type: new Abstract: Language models are usually judged by a single accuracy score, which does not reveal how their performance degrades as inputs are perturbed. We present

arXiv:2608.22323v1 Announce Type: new Abstract: The application of Large Language Models (LLMs) to diagnostic decision-making has garnered growing interest. However, existing benchmarks largely focus

arXiv:2608.23234v1 Announce Type: new Abstract: In this technical report, we present a training-free framework for audio-guided video object segmentation, which integrates Multimodal Large Language Mo

arXiv:2608.23104v1 Announce Type: cross Abstract: Molecular science represents an important frontier for LLM-based agents. Unlike general agents that mainly operate over natural language, code, or web

arXiv:2608.22346v1 Announce Type: cross Abstract: This data article describes a multimodal collection of scanned examination answers paired with expert-designed Outcome-Based Education (OBE) grading m

arXiv:2608.23179v1 Announce Type: cross Abstract: Large language model (LLM) agents are increasingly attractive for automating network configuration, yet their reliability and failure patterns are poo