Knowledge catalogue
arXiv:2605.05593v2 Announce Type: replace Abstract: Despite the remarkable success of Multimodal Large Language Models (MLLMs) across diverse tasks, the internal mechanisms governing how they encode a
arXiv:2609.00619v1 Announce Type: new Abstract: In indoor environments, object positions frequently change due to human activities or embodied-agent interactions, causing previously constructed scene
arXiv:2609.01260v1 Announce Type: new Abstract: Robotic systems are deeply embedded in both industry and everyday life, where they are expected to act with speed, precision, and reliability. Classical
arXiv:2609.00935v1 Announce Type: cross Abstract: Deep Research agents tackle knowledge-intensive tasks through multi-round retrieval and decision-oriented generation. However, these agents suffer fro
arXiv:2609.01526v1 Announce Type: new Abstract: Scientific agents must learn not only how to reason, but also what to believe. However, existing LLM agents typically express scientific hypotheses in f
arXiv:2609.00745v1 Announce Type: new Abstract: Multi-view human reconstruction has been extensively studied under simplified settings, yet robust and efficient multi-person reconstruction in unconstr
arXiv:2602.14028v2 Announce Type: replace Abstract: While Group Relative Policy Optimization (GRPO) offers a powerful framework for LLM post-training, its effectiveness in open-ended domains like Mach
If this is true, OpenAI seems to be violating one of the few redlines that exist in the AI industry. Absolutely do not train your models like this - what is going on?? Really huge and extremely concer
arXiv:2602.19881v2 Announce Type: replace-cross Abstract: Unsupervised remote sensing change detection (UCD) aims to localise changes between two images of the same region without relying on labelled
arXiv:2603.03517v2 Announce Type: replace-cross Abstract: General-purpose large language models (LLMs) that rely on in-context learning do not reliably deliver the scientific understanding and perform
arXiv:2609.01341v1 Announce Type: cross Abstract: Do multilingual language models transfer factual knowledge across languages during continued pretraining, or do they mostly recall facts learned direc
Extended reasoning and post-training appear to be the keys used by DeepSeek, Qwen, and GLM to boost performance (leveraging higher token counts). And Qwen 4 hasn't even been released yet. Of course, w
arXiv:2609.00918v1 Announce Type: new Abstract: Large language models are increasingly used as interactive recommender assistants. Their evaluation should therefore go beyond plausible item recommenda
arXiv:2609.01579v1 Announce Type: new Abstract: We present SG-AMP, integrating robust depth completion with input-conditioned uncertainty, persistent panoptic mapping, plant scene-graph reasoning, and
arXiv:2609.00293v1 Announce Type: new Abstract: We investigate how vision-language models (VLMs) handle context-memory conflicts; that is, situations in which the model is given information in context
arXiv:2609.00427v1 Announce Type: new Abstract: The exponential growth of wireless devices is driving unprecedented spectrum demand, pushing spectrum management toward more fine-grained decisions acro
arXiv:2508.11857v3 Announce Type: replace-cross Abstract: Tokenization remains a persistent bottleneck in language modeling, especially when vocabulary learning is limited by whitespace boundaries. We
This 'recurrent depth' is essentially what's in Sec. 5.3 of the 2015 paper: On Learning to Think: Algorithmic Information Theory for Novel Combinations of Reinforcement Learning Controllers and Recurr
arXiv:2609.00455v1 Announce Type: new Abstract: Large language models (LLMs) are being used as policies for autonomous decision-making and planning in many domains. Despite their strong reasoning capa
arXiv:2609.00050v1 Announce Type: cross Abstract: Agentic AI is enabling cloud-based workflows in which autonomous agents reason over operational state, invoke authorized tools, modify software and in
arXiv:2609.00845v1 Announce Type: new Abstract: Recent advancements in Vision-Language Models (VLMs) have demonstrated impressive capabilities in static visual recognition and high-level semantic reas
arXiv:2609.01428v1 Announce Type: new Abstract: Large Language Model (LLM) agents based on the ReAct paradigm have demonstrated remarkable capabilities in tool use and task execution. However, ReAct s
arXiv:2609.00920v1 Announce Type: cross Abstract: Vision-and-Language Navigation (VLN) requires an agent to navigate through unseen 3D environments according to natural-language instructions. Explicit
arXiv:2505.13180v3 Announce Type: replace Abstract: Integrating Large Language Models with symbolic planners is a promising direction for obtaining verifiable and grounded plans, with recent works ext
we're begging to get our asses skynetted OpenAI’s Astra AI uses a new reasoning approach called “recurrent depth.” Though it can help model costs and performance, researchers are concerned bc it obscu
arXiv:2607.17883v2 Announce Type: replace-cross Abstract: Enterprises will not deploy AI agents they cannot trust, and the most-cited reason for distrust is hallucination: confident, fluent output tha
A research agent is only as good as the search layer behind it! I built a Deep Research Agent using LangChain Deep Agents with a search-first architecture designed around grounded, source-backed resea
arXiv:2605.22122v2 Announce Type: replace-cross Abstract: Collaborative perception (CP) enables connected and autonomous vehicles to share sensor data and jointly reason about their environment. To de
arXiv:2608.31025v1 Announce Type: new Abstract: Inferring object dynamics from visual observations is essential for intelligent agents to reason about and interact with the physical world, yet remains
arXiv:2608.30345v1 Announce Type: new Abstract: Generating multiple diverse and high-quality solutions is valuable for many applications, such as code-test generation and drug discovery. However, Larg
arXiv:2608.28725v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used as graders, verifiers, and process auditors, but most mathematical evaluations still emphasize final-
arXiv:2608.25937v2 Announce Type: replace Abstract: Multi-agent systems (MAS) sometimes already have the potential to answer correctly, but still report a wrong answer. Explaining this outcome is diff
arXiv:2608.30109v1 Announce Type: new Abstract: Large Language Models (LLMs) trained on extensive scientific research are increasingly integrated as assistants for scientific discovery. However, most
arXiv:2608.29123v1 Announce Type: new Abstract: Training a video-generation model from scratch is hard for reasons that precede model design. The feedback loop is long: a failure that appears only aft
arXiv:2608.28691v1 Announce Type: cross Abstract: Wearable VLM pipelines promise continuous multimodal assistance from egocentric visual capture: a user asks a task-driven question about the surroundi
arXiv:2608.30543v1 Announce Type: new Abstract: Large Language Models (LLMs) offer new possibilities for scaling qualitative analysis, but existing applications often provide limited methodological tr
arXiv:2608.29646v1 Announce Type: new Abstract: Large language model (LLM)-based agents have shown strong potential in solving complex tasks through multi-step reasoning, yet they remain vulnerable to
arXiv:2608.30294v1 Announce Type: new Abstract: Streaming video understanding requires answering questions at arbitrary times over a continuously growing visual stream. The central challenge is to com
arXiv:2601.16690v2 Announce Type: replace Abstract: We introduce EMemBench, a programmatic benchmark generator for evaluating long-term episodic memory of agents through interactive games. Rather than
arXiv:2608.29896v1 Announce Type: new Abstract: A robot's effective ``mind'' need not reside in a single policy. It can emerge when specialized components perceive, reason, predict, act, verify, and r
arXiv:2512.23765v2 Announce Type: replace-cross Abstract: Speculative decoding (SD) accelerates large language model (LLM) inference by using a lightweight draft model to propose tokens and a stronger
arXiv:2608.30429v1 Announce Type: new Abstract: LLM-based agent systems increasingly adopt skill-based architectures to reduce repetitive reasoning costs and improve stable, efficient task execution.
arXiv:2608.28591v1 Announce Type: new Abstract: Recent advancements in AI are helping scientists achieve breakthroughs in fields such as mathematics, medicine, and materials sciences. New evaluation d
arXiv:2510.03204v2 Announce Type: replace Abstract: Web agents powered by large language models (LLMs) must process lengthy web page observations to complete user goals; these pages often exceed tens
arXiv:2608.31005v1 Announce Type: new Abstract: Existing long-video agents acquire evidence through one uniform behavior, ignoring whether the required evidence is concentrated, requires broad occurre
arXiv:2608.29995v1 Announce Type: cross Abstract: Large language models can generate fluent clinical case vignettes, but fluency alone does not ensure fidelity to a specifiable clinical structure. We
arXiv:2608.29483v1 Announce Type: cross Abstract: Modern Vision-Language Models (VLMs) perform well above the human baseline in image geolocalization, a task critically important in disaster response,
arXiv:2608.30712v1 Announce Type: new Abstract: Large multimodal models follow instructions about what to generate, but not necessarily about what evidence to rely on. Hence, models may continue to de
arXiv:2608.29193v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) can assign similar confidence to answers that fail for different reasons. We propose HalluPrism, a behavioral d
arXiv:2608.30672v1 Announce Type: new Abstract: Recent advances in large language models and multimodal models have pushed remote sensing (RS) processing from simple perception models to agentic syste
arXiv:2608.29884v1 Announce Type: new Abstract: We show that sequence-level distillation from a capable long-context teacher model is a simple, annotation-free, and data-efficient strategy for improvi
arXiv:2608.28629v1 Announce Type: cross Abstract: Existing methods lack a generalized approach to efficiently identify and resolve the diversity of design defects in BIM. Therefore, this study propose
arXiv:2512.16391v2 Announce Type: replace-cross Abstract: Attention is the dominant source of latency during long-context LLM inference, an increasingly popular workload with reasoning models and RAG.
arXiv:2512.07019v4 Announce Type: replace-cross Abstract: The proliferation of Large Language Models (LLMs) necessitates valid evaluation methods to provide guidance for both downstream applications a
arXiv:2608.30584v1 Announce Type: new Abstract: Despite the impressive progress of recent MLLMs on spatio-temporal video grounding (STVG), existing evaluations and training data focus primarily on sim
arXiv:2608.31028v1 Announce Type: cross Abstract: Scientific discovery often requires reasoning over competing hypotheses that are consistent with experimental observations. For mixed-variable and com
arXiv:2608.28624v1 Announce Type: cross Abstract: Accurate interpretation of single-visit and longitudinal clinical assessments for Parkinson's disease is time-consuming and often depends on specialis
arXiv:2608.29865v1 Announce Type: new Abstract: Manga visual question answering requires models to answer questions over panel-based visual narratives, where relevant evidence is distributed across or
arXiv:2608.29286v1 Announce Type: new Abstract: As Multimodal Large Language Models (MLLMs) evolve into sophisticated interactive assistants, their reliability depends not only on following instructio
arXiv:2608.29477v1 Announce Type: cross Abstract: Document question-answering systems increasingly answer questions over collections of retrieved documents rather than one clean source, so robustness