arXiv Computer Vision
Oct 27
VGGT-$\Omega$
arXiv:2605.15195v2 Announce Type: replace Abstract: Recent feed-forward reconstruction models, such as VGGT, have proven competitive with traditional optimization-based reconstructors while also prov...
Benchmarks and evaluation
Leaderboards, eval harnesses and ablations — the contested business of deciding which model is actually better.
arXiv Computer Vision
Oct 27
Detector Confidence Signals Presence Rather Than Occlusion in Cluttered Manipulation
arXiv:2607.13361v2 Announce Type: replace Abstract: An open-vocabulary detector's confidence that a named object is present barely changes when clutter hides all but an eighth of it, and can even ris...
arXiv AI
Oct 27
IRON-Meme: A Benchmark for Structured Understanding of Identities and Roles in Online Meme Narratives
arXiv:2610.33097v1 Announce Type: new Abstract: As polarization grows on social media, understanding how political memes shape online narratives has become increasingly important. Previous research h...
arXiv AI
Oct 27
CoEvolve: Counterfactual-Guided Co-Evolution of Memory and Collaboration in Multi-Agent Systems
arXiv:2610.33539v1 Announce Type: new Abstract: Large language model (LLM)-based multi-agent systems have demonstrated remarkable capabilities across diverse tasks. Yet their memory and collaboration...
arXiv AI
Oct 27
AlphaIdeaBench: Benchmarking Automated Research Idea Generation under Feasibility Constraints with Peer Review Alignment
arXiv:2610.33644v1 Announce Type: new Abstract: Research ideas are always proposed under resource constraints: a plan must fit the available compute, API credit, and time. Yet evaluations of research...
arXiv AI
Oct 27
PACE: Research Proving Agent for Adaptive Conjecture Exploration
arXiv:2610.33690v1 Announce Type: new Abstract: Many mathematical research agents focus on solving problems with fixed statements. However, conjecture research, especially in applied mathematics, may...
arXiv AI
Oct 27
WebViSE: Scaling Visual Design Intelligence from the Open Web for LLM Website Generation
arXiv:2610.33929v1 Announce Type: new Abstract: Large language models have made remarkable strides in website development, enabling complete and interactive web experiences to be built from natural-l...
arXiv AI
Oct 27
Variational Cognitive Memory Abstraction for Long-Horizon Vision-Language-Action Models
arXiv:2610.34251v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models have shown remarkable success in robotic manipulation, yet their ability to execute long-horizon tasks remains limi...
arXiv AI
Oct 27
Matched Capabilities, Different Behaviors: A Controlled Study of MCP and CLI Interfaces for Language Agents
arXiv:2610.34298v1 Announce Type: new Abstract: Language agents increasingly act through external tools and interactive environments, making the interface between the model and its executable capabil...
arXiv AI
Oct 27
SlowLab: Benchmarking Autonomous AI Agents on a Year of Experiments They Cannot Undo
arXiv:2610.34644v1 Announce Type: new Abstract: AI agents are increasingly proposed as autonomous experimenters, yet the benchmarks that test them mostly offer cheap experiments that answer at once....
arXiv AI
Oct 27
TrustJev: Separating Policy Authority from Untrusted Evidence in LLM-Based Decisions
arXiv:2610.34826v1 Announce Type: new Abstract: Decision models enable software to select from predefined actions using natural language inputs. However, the external evidence that supplies facts for...
arXiv AI
Oct 27
Why Are LLMs Confidently Wrong? Correcting Overconfident Errors via Causal Head Intervention
arXiv:2610.34994v1 Announce Type: new Abstract: Modern large language models (LLMs) often exhibit overconfident errors, assigning high confidence to incorrect predictions and thereby undermining reli...
arXiv AI
Oct 27
When Fusion Order Matters: Insights on Progressive Multimodal Representation Learning for Cross-modal Retrieval
arXiv:2610.35125v1 Announce Type: new Abstract: Multimodal video retrieval combines visual, acoustic, and speech information, yet how their fusion order should depend on the data and the retrieval qu...
arXiv AI
Oct 27
From Denoising Dynamics to Endpoint Uncertainty in Diffusion Language Models
arXiv:2610.35425v1 Announce Type: new Abstract: Predictive uncertainty in stochastic language-model inference can be characterized by repeatedly sampling an input and measuring the distribution of ou...
arXiv AI
Oct 27
Evolve on Graph: Self-Evolving Knowledge Graph Reasoning without Question Annotations
arXiv:2610.35598v1 Announce Type: new Abstract: Knowledge graphs (KGs) provide reliable external knowledge for multi-hop factual reasoning with large language models (LLMs), yet most KGs in real-worl...
arXiv AI
Oct 27
Retriever-Free Retrieval: Navigating Documents with Decision Models
arXiv:2610.35868v1 Announce Type: new Abstract: While language models have become dramatically more capable, retrieval still largely relies on lexical matching or similarity in fixed embedding spaces...
arXiv AI
Oct 27
SLICE-Mem: Salience-Gated Lightweight Information Curation for Efficient LLM Agent Memory
arXiv:2610.33015v1 Announce Type: cross Abstract: Long-term memory enables large language model (LLM) agents to maintain personalized and temporally coherent interactions, but repeated LLM inference...
arXiv AI
Oct 27
DRA-SAM: Disagreement-Driven Referential Active SAM for Low-Altitude Drone Referring Segmentation
arXiv:2610.33057v1 Announce Type: cross Abstract: Referring segmentation in low-altitude drone imagery requires distinguishing small, visually similar instances under complex linguistic and spatial c...
arXiv AI
Oct 27
Evolving to Match Capabilities: Improving Harness Self-Evolution via Skill Adaptation
arXiv:2610.33024v1 Announce Type: cross Abstract: Harness self-evolution offers a practical route for LLM agents to improve from execution experience without updating model parameters. Its effectiven...
arXiv AI
Oct 27
MarineLife-16K: An Attribute-Rich Video-Text Dataset for Motion-Aware Marine Video Understanding
arXiv:2610.33478v1 Announce Type: cross Abstract: Marine video understanding is crucial for a wide range of real-world applications. However, existing marine video datasets suffer from limitations, i...