Knowledge catalogue
arXiv:2610.04914v1 Announce Type: cross Abstract: Monitoring the chain-of-thought (CoT) of large reasoning models (LRMs) is a common way to detect misbehavior in real-world practice. However, current
arXiv:2610.04916v1 Announce Type: new Abstract: Urban navigation requires embodied agents to pursue long-horizon goals through local decisions based on egocentric observations. However, existing agent
arXiv:2610.04109v1 Announce Type: new Abstract: Real-world time series are frequently driven by exogenous events and structural shifts, rendering conventional forecasting based solely on historical nu
arXiv:2610.04686v1 Announce Type: cross Abstract: Multi-agent debate can improve reasoning, yet often fails to beat simple majority voting. We argue that successful debate requires two distinct mechan
arXiv:2602.10273v3 Announce Type: replace-cross Abstract: Reasoning ability in large language models is often attributed to distribution sharpening: concentrating output probability on high-likelihood
Semafor: Reflection unveils Beam, an open model it says rivals GLM 5.2 on reasoning while using 3x-4× less compute and approaches Qwen3.8-Max on coding and agentic tasks — Reflection AI, the startup b
Very good to see more competent labs training open-weight models. This Reflection's new Beam model seems to have strong reasoning efficiency, which I think is a big deal for long-running agents. 3-4x
arXiv:2610.00947v1 Announce Type: new Abstract: ABDA-NL adds a natural-language interface to ABDA, a system for argument-based discussion using ASPIC- knowledge bases under grounded semantics. Users s
arXiv:2610.00040v1 Announce Type: new Abstract: Recent advances in 3D Gaussian Splatting have enabled open-vocabulary and referring segmentation by distilling semantic knowledge from 2D foundation mod
arXiv:2610.00661v1 Announce Type: cross Abstract: Diffusion large language models (dLLMs) generate text by denoising a sequence or successive blocks, allowing several tokens to be revealed in parallel
arXiv:2610.01184v1 Announce Type: cross Abstract: Remote multimodal models offer strong numerical reasoning capabilities over charts and speech, but sending private inputs risks exposing sensitive con
arXiv:2610.01249v1 Announce Type: new Abstract: Conventional reasoning protocols present a fixed, preselected task, so they cannot test whether an agent propagates relevant updates, preserves unaffect
arXiv:2609.38962v1 Announce Type: new Abstract: Recent work on hallucination detection in large language models has shown that, for a fixed pre-trained model and reasoning task, it is possible to esti
arXiv:2609.39294v1 Announce Type: new Abstract: Precise numerical reasoning with Large Language Models (LLMs) is essential for expanding their applicability to complex real-world tasks. However, text-
arXiv:2609.39371v1 Announce Type: new Abstract: In hospital workflows, electronic health records (EHRs) are often noisy, and may not contain the evidence needed to confirm events or measurements refer
arXiv:2512.06174v3 Announce Type: replace Abstract: Generating realistic cast shadows for inserted foreground objects requires reasoning about scene geometry and illumination. However, most learning-b
arXiv:2511.22232v2 Announce Type: replace-cross Abstract: Multimodal large language models (MLLMs) are increasingly capable in medical imaging, yet most focus on single-image settings. Clinical interp
arXiv:2609.37788v1 Announce Type: cross Abstract: Rubrics support the structured evaluation of language models. We propose a rubric for assessing expressed clinical reasoning in model responses, drawi
arXiv:2609.37066v1 Announce Type: cross Abstract: Post-training is central to mathematical reasoning in modern large language models (LLMs), but endpoint pass@1 alone underidentifies what has changed.
arXiv:2609.37312v1 Announce Type: cross Abstract: Can reasoning models trick chain of thought (CoT) monitors and perform hidden computation without revealing it in their thinking traces? We show that
arXiv:2609.37432v1 Announce Type: new Abstract: Looped reasoning models repeatedly apply a shared set of parameters, enabling more computation without increasing the model size. These models also supp
arXiv:2609.37588v1 Announce Type: new Abstract: Users of language-based assistive agents often make ambiguous requests. In response, an assistant can either directly act on its interpretation of the r
arXiv:2609.36599v1 Announce Type: cross Abstract: We study whether scaling video generation enables models to reason about hidden information from the past frames, and at what computational cost. Our
arXiv:2609.36201v1 Announce Type: cross Abstract: Computer-use agents (CUAs), while capable of completing computer tasks in everyday and professional workflows, can cause unintended harm even under be
arXiv:2609.33323v1 Announce Type: cross Abstract: Recent advances in Large Language Models (LLMs) have enabled agentic systems capable of solving complex tasks through multi-turn planning, tool use, v
arXiv:2609.33838v1 Announce Type: new Abstract: Large language models are increasingly expected to support diverse chemical reasoning capabilities within a unified model. One approach is to develop sp
arXiv:2609.34148v1 Announce Type: new Abstract: Vision-Language Models (VLMs) are far more reliable at recognizing what appears in a video than at reasoning about its spatial and temporal properties,
arXiv:2609.32681v1 Announce Type: new Abstract: 4D radar provides geometric and motion cues that complement visual semantics, but integrating it into vision-language-action (VLA) models requires both
arXiv:2609.33616v1 Announce Type: new Abstract: Vision-language models (VLMs) can benefit from geometric priors for multi-view spatial reasoning, yet answer-only training does not directly supervise t
arXiv:2609.33462v1 Announce Type: new Abstract: Omnidirectional or 360 cameras provide embodied AI agents with a holistic, wide field-of-view (FoV) view of their surroundings, motivating the use of Mu
arXiv:2609.32813v1 Announce Type: new Abstract: Large vision-language models (VLMs) have emerged as a powerful paradigm for urban and spatial AI. However, current state-of-the-art large VLMs still str
arXiv:2609.30768v1 Announce Type: new Abstract: Thinking in reasoning language models (RLMs) has been subject to debate on whether it resolves or amplifies bias. Prior works have shown competing concl
arXiv:2609.31112v1 Announce Type: new Abstract: Vision-language models (VLMs) enable open-vocabulary reasoning for robot manipulation, but their high inference latency limits responsiveness in dynamic
arXiv:2609.29934v1 Announce Type: new Abstract: Does progress on spatial reasoning benchmarks translate into better navigation? Existing benchmarks test isolated inferences from images or videos, with
arXiv:2609.28956v1 Announce Type: new Abstract: Recent advances in video object segmentation with Multimodal Large Language Model (MLLM) reasoning have demonstrated the effectiveness of using a single
arXiv:2609.28798v1 Announce Type: new Abstract: Long-horizon manipulation often requires reasoning about state absent from the current view, such as a vanished object's location, temporal identity, or
arXiv:2609.29073v1 Announce Type: new Abstract: Vision-Language Models (VLMs) describe scenes well but reason poorly about metric 3D structure such as absolute distances, physical sizes, or egocentric
arXiv:2511.16043v2 Announce Type: replace Abstract: Large Language Model (LLM) Agents, often trained with Reinforcement Learning (RL), are constrained by a dependency on human-curated data, limiting s
arXiv:2609.26704v1 Announce Type: new Abstract: Large language models increasingly tackle hard reasoning problems by spending more test-time compute, yet the dominant strategy remains naive repeated s
arXiv:2609.26094v1 Announce Type: new Abstract: Multi-step causal reasoning requires chaining inferences where each step constrains the next. An early error propagates silently, and a correct answer r
arXiv:2609.25055v1 Announce Type: new Abstract: In this report we present results of the ICDAR2026 Competition on Multimodal Reasoning over Documents in Multiple Domains. This competition aimed to adv
arXiv:2609.26708v1 Announce Type: new Abstract: Quantization-aware distillation (QAD) restores much of the short-form question-answering performance lost to sub-3-bit quantization, yet leaves mathemat
arXiv:2609.22788v1 Announce Type: new Abstract: Video foundation models now reach human-level accuracy on physical-reasoning benchmarks, yet such tasks require predicting unobserved physical outcomes.
arXiv:2609.22700v1 Announce Type: new Abstract: Step-level reasoning evaluators are commonly based on autoregressive language models, whose causal attention restricts each step representation to the p
arXiv:2609.24071v1 Announce Type: new Abstract: Chart reasoning agents are increasingly used to extract actionable insights in critical domains, achieving state-of-the-art performance on multiple benc
arXiv:2609.24187v1 Announce Type: cross Abstract: Autonomous endoscopic navigation requires the policy model to predict actions from texture-poor monocular observations, make safe control decisions, a
arXiv:2609.23841v1 Announce Type: new Abstract: Long-horizon robotic search must resolve natural language against heterogeneous, incomplete, and often ambiguous evidence: textual information, prior ma
arXiv:2606.22570v2 Announce Type: replace Abstract: Reinforcement Learning from Verifiable Rewards (RLVR) has emerged as a promising framework for enhancing the reasoning ability of large language mod
arXiv:2609.21402v1 Announce Type: new Abstract: Surgical instrument segmentation (SIS) plays a critical role in robotic assistance and surgical workflow analysis. However, most existing SIS methods fo
🚀 Meet Qwen3.8-Omni-Flash, Qwen's first omni-modal model built around agentic capabilities! Native audio-video understanding, reasoning, and tool use come together in one model: understand the content
arXiv:2609.17804v1 Announce Type: new Abstract: Large language models solve grade-school math word problems with high accuracy, yet a single irrelevant clause inserted into the problem can collapse it
arXiv:2609.18461v1 Announce Type: new Abstract: Personalized agents are required to reason over long-term history interactions to infer both explicit preferences and implicit behavioral evidence. Whil
arXiv:2609.17552v1 Announce Type: new Abstract: Moral-reward RL can make language-model agents more cooperative, but whether that alignment survives adversarial persona pressure is unknown. Such attac
arXiv:2609.17686v1 Announce Type: cross Abstract: Three recent results describe what look like unrelated LLM reliability problems. Yin et al. (2026) show reasoning RL collapses tool-reliability repres
arXiv:2512.24100v2 Announce Type: replace Abstract: Current state-of-the-art paradigms predominantly treat Text-to-Motion (T2M) generation as a direct translation problem, mapping symbolic language di
arXiv:2603.15547v2 Announce Type: replace-cross Abstract: Modeling student misconceptions in a realistic manner is critical for AI in education. In this work, we examine how large language models (LLM
arXiv:2609.16646v1 Announce Type: new Abstract: When strong multimodal models are widely available, progress requires new scientific methodologies beyond benchmark scores---using models as instruments
arXiv:2609.15668v1 Announce Type: cross Abstract: Through pre-training on extensive text and image datasets, current multi-modal large language models (MLLMs) achieve strong performance on general tas
arXiv:2609.14302v1 Announce Type: new Abstract: Can financial vision-language models (VLMs) turn chart evidence into reliable action recommendations? Existing hallucination evaluations are mostly clai
arXiv:2609.14073v1 Announce Type: cross Abstract: Counterfactual world models (CWM) extract motion from pretrained video predictors by comparing factual and intervened predictions. However, responses