Leaderboards, eval harnesses and ablations — the contested business of deciding which model is actually better.
Missing data are a fundamental challenge in statistical analysis and machine learning, as the choice of imputation method substantially impacts downstream inference. In this work, we propose two hybri...
Generating full-body co-speech motion for humanoid robots requires coordinating speech prosody, linguistic content, and embodiment-specific motion. To this end, we present ECHO-G, a framework jointly...
Recent advances have enabled unified omni-modal models in understanding audio, vision, and language. However, existing benchmarks, training data, and learning methods largely treat the modalities inde...
Speech transcripts used as long-term memory must preserve both words and stable speaker identities. Existing meeting-transcription metrics either ignore speakers or remap anonymous speakers independen...
Trustworthy Legal AI requires systems that can answer legal questions while grounding their responses in authoritative sources. However, existing Vietnamese legal benchmarks provide limited coverage o...
This paper studies \textbf{thinking--answer consistency} in vision-language models. We focus on Visual Intention Grounding, where a model infers a target object based on a human intention query and pr...
Satellite-ground localization estimates the planar position and yaw orientation of a ground camera within a geo-referenced satellite image. Most recent methods map ground and satellite features into a...
Runtime error healing lets a crashed program continue by generating code that repairs its live runtime state. Recent work shows that LLMs can generate such healing code, but it is evaluated only on sm...
Large language model (LLM) agents could help security operations centers (SOCs) investigate advanced persistent threats (APTs) by turning weak leads into evidence for intrusion scoping and response. Y...
Self-consistency samples many reasoning trajectories and aggregates their final answers, treating the LLM as a black box that returns one answer per trajectory. Yet the final answer of each trajectory...
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:2609.37529v1 Announce Type: cross Abstract: Pretrained denoisers provide a powerful way to incorporate image priors into restoration algorithms. Plug-and-Play and RED approaches exploit fixed-n...
arXiv:2609.36526v1 Announce Type: cross Abstract: Long-horizon agents require context compression to manage growing interaction histories. Compression quality, however, is ultimately determined by do...
arXiv:2609.32490v2 Announce Type: replace Abstract: LLM-based multi-agent systems (MASs) have shown strong potential for solving complex tasks, but most assume that task requirements are sufficiently...
The paper introduces the concept of aspectual flattening, where long‑term memory systems convert conversational statements into concise notes that omit temporal cues. Using the LAPSE benchmark, the authors show that several memory writer models consistently flatten progressive statements while preserving simple‑present ones, and this asymmetry persists across multiple configurations. Experiments reveal that the loss of temporal information can alter how downstream readers assess the validity of a fact and influence their decision‑making, sometimes leading them to act without further verification.
arXiv:2608. 22277v3 Announce Type: replace Abstract: Deep learning surrogates have become powerful tools for simulating and forecasting complex dynamical systems, yet their utility remains limited by catastrophic error accumulation during long-term autoregressive rollouts.
arXiv:2609.33772v2 Announce Type: replace Abstract: Executable environments are critical for post-training agents on tasks that require tool use and multi-step interaction, but constructing executabl...
Beacon is a new agentic visual reasoning model that improves multimodal large language models (MLLMs) by better deciding when to use tools and how to use them. It introduces two key concepts—Mode Adaptiveness, which ensures tools are invoked only when necessary, and Tool Effect, which measures the net benefit of tool use— and trains the model with supervised fine‑tuning and reinforcement learning that rewards necessity-aware decisions and expands capability through expert hints. Across 13 benchmarks, Beacon outperforms other open‑source models, achieving the highest average score and the largest net tool‑gain on diagnostic tests.
PARSEE-VAD is a training‑free online video anomaly detection framework that separates semantic evidence acquisition from score‑state evolution. It uses Proposition‑Aware Reasoning to extract structured propositional evidence from the current causal window and selectively activates more specific queries, while Streaming Evidence Escalation maps this evidence into a compact score‑domain event state and propagates only the bounded state to maintain temporal continuity. Experiments on four benchmarks show strong performance with reduced specialist computation and sparse score‑state propagation, supporting a current‑first principle for streaming multimodal inference.
PGL-3D introduces a progressive geometric learning framework for 3D visual query localization, where intermediate cuboids guide feature aggregation and refinement. The method predicts a complete cuboid for each proposal, selects reference observations via Query‑Tube‑Memory, pools query‑conditioned features, and re‑predicts refined cuboids. A training‑only objective, ST‑D9O, supervises cuboid geometry at every stage, yielding significant performance gains over prior baselines.