Search: “benchmark”

Hyperbolic Prototype Routing for Rehearsal-Free Class-Incremental Learning

arXiv:2609.39550v1 Announce Type: new Abstract: Class-Incremental Learning (CIL) aims to continually learn new classes while preserving prior knowledge. Parameter-efficient fine-tuning with pre-traine

Content type

All Blog X Post Paper YouTube Reddit GitHub

Clear filters

Model Releases

Index-Translate: A Multilingual Translation Model Family -- Text, Speech, Controlled Dubbing, and Long-Document Translation

DGX agent

arXiv:2609.40181v1 Announce Type: new Abstract: We introduce Index-Translate, a multilingual translation model family that combines a shared multilingual foundation with specialized training for gener

model-releasesarxiv-cs-cl

1 Oct 2026

Inductive Visual Logic for Few-Shot Out-Of-Distribution Adaptation in VLMs

arXiv:2609.38362v1 Announce Type: new Abstract: Generative vision-language models (VLMs) such as Qwen-VL and LLaVA achieve strong zero-shot performance on tasks overlapping with their pretraining dist

Instruction Retrieval at Inference Time for Small Language Models

arXiv:2510.13935v3 Announce Type: replace-cross Abstract: The facts a language model stores are tied to its parameter count, so small models that fit on edge devices fail on expert problems, which nee

Invariant Shape Analysis of Surfaces with Spherical Topology

arXiv:2609.39567v1 Announce Type: new Abstract: Spherical harmonic descriptors of closed 3D shapes depend on the parameterization, the pose and the scale of the surface, and the standard rotation-inva

researcharxiv-cs-cv

Is Better Teacher Supervision Enough? Unlocking Student-side Learning in Multimodal On-Policy Distillation

arXiv:2609.39120v1 Announce Type: new Abstract: On-policy distillation (OPD) improves reasoning by providing token-level supervision from a teacher on a student's own trajectories. Existing methods pr

safetyarxiv-cs-cv

ITO: Multi-View Alignment and Training-Time Fusion for Image-Text Pretraining

arXiv:2603.02767v4 Announce Type: replace-cross Abstract: Image--text contrastive pretraining has become a dominant paradigm for visual representation learning, yet existing methods often yield repres

LatentHarness: Learning Latent Actions for Memory and Reasoning via Counterfactual Policy Distillation

arXiv:2609.39740v1 Announce Type: new Abstract: Long-context reasoning faces two complementary bottlenecks: retaining evidence across long inputs and sustaining computation across many reasoning steps

safetyarxiv-cs-cl

LBDU-VIO: Learned Bias Dynamics and Uncertainty for Visual-Inertial Odometry with Unreliable Vision

arXiv:2609.39125v1 Announce Type: new Abstract: Visual-inertial odometry (VIO) for aerial robots relies on high rate inertial measurement unit (IMU) propagation between visual updates. However, conven

safetyarxiv-cs-ro

Local Ai

LEAP: Learned Block-wise Evidence Retrieval for Long Audio-Video Perception

arXiv:2609.39938v1 Announce Type: cross Abstract: Hour-scale audio-visual question answering is constrained by a context dilemma: dense whole-recording encoding rapidly exhausts context limits, wherea

local-aiarxiv-cs-ai

Learning Adaptive and Visually Aligned Conditions for Generative Zero-Shot Learning

arXiv:2603.06281v3 Announce Type: replace Abstract: Generative zero-shot learning (ZSL) synthesizes visual features for unseen classes by learning a semantic-conditioned generator from seen classes. S

safetyarxiv-cs-cv

Tutorials

Learning Skills from Historical Action Trajectories: Action Experience Dictionary for World Action Models

arXiv:2609.40219v1 Announce Type: cross Abstract: World Action Models (WAMs) couple visual dynamics prediction with action generation, yet they do not explicitly support the reuse of action experience

tutorialsarxiv-cs-ai

Learning to Build: Autonomous Robotic Assembly of Stable Structures Without Predefined Plans

arXiv:2602.23934v2 Announce Type: replace-cross Abstract: This paper presents a novel autonomous robotic assembly framework for constructing stable structures without relying on predefined architectur

Learning to Reason with Compressed Context: Ground-Truth-Free Adaptation of OmniLLMs via Self-Distillation

arXiv:2609.39953v1 Announce Type: new Abstract: Omni-modal large language models (OmniLLMs) enable unified audio-video understanding, but their long multimodal token sequences make deployment computat

safetyarxiv-cs-cv

Learning to Route in Visual Space via Multi-Step Embedding Retrieval

arXiv:2609.38743v1 Announce Type: new Abstract: LLM agents rely on retrieval tools to access external knowledge, yet visual agentic search remains severely bottlenecked by standard single-step retriev

Lens Flare Removal and Reconstruction

arXiv:2609.39527v1 Announce Type: new Abstract: The presence of lens flares in images can significantly reduce the quality of downstream application results for tasks such as 3D scene reconstruction.

Less Data Approximates More: Earning Faithful Confidence in High-Stakes Domains

arXiv:2604.08454v2 Announce Type: replace Abstract: Large language models are increasingly deployed in high-stakes domains, where confident yet incorrect inferences may cause severe real-world harm, b

Less Data, Better Timing: Student-Curriculum Coupling for VLM On-Policy Distillation in Temporal Video Grounding

arXiv:2609.40055v1 Announce Type: cross Abstract: On-policy distillation (OPD) provides dense supervision directly on student-generated trajectories, making it an effective post-training strategy for

LIBERO-Agent: Evaluating General-Purpose Agents for Direct Embodied Manipulation

model-releasesarxiv-cs-ro

arXiv:2609.39507v1 Announce Type: new Abstract: General-purpose agents can plan, use tools, and revise their behavior from feedback, but it remains unclear whether these capabilities transfer from dig

LOCI: Spatial Linear Memory for Streaming World Models

arXiv:2609.40222v1 Announce Type: new Abstract: When a camera revisits a previously observed region, a video world model should reproduce what was there before. This requires both remembering past obs

LongEmo: Towards Emotion Understanding and Reasoning in Long Videos

arXiv:2609.40079v1 Announce Type: cross Abstract: While recent Multimodal Large Language Models (MLLMs) have shown promise in affective computing, their reasoning capabilities are largely confined to

Loop-Free Inverse Reinforcement Learning via Sequential Value Recovery with Q-Score Matching

arXiv:2609.38955v1 Announce Type: cross Abstract: Inverse Reinforcement Learning (IRL) aims to recover a reward function that explains expert demonstrations. Existing IRL methods typically rely on a b

Looped Diffusion Transformer

arXiv:2609.40305v1 Announce Type: new Abstract: Improving text-to-image models has traditionally relied on increasing model size or the number of denoising steps. In this work, we explore an alternati

LoopVL: Recurrent Visual Intelligence

arXiv:2609.38426v1 Announce Type: cross Abstract: We introduce LoopVL to study whether Loop Transformers can be effectively extended to vision- language models. LoopVL combines Module-Loop and Model-L

researcharxiv-cs-cl

MatGPTQ: Efficient and Accurate Inference over Nested Quantized Models

arXiv:2602.03537v2 Announce Type: replace Abstract: Matryoshka Quantization (MatQuant), Any-Precision-LLM (AP) and AnyBCQ (AB) are recent quantization approaches showing that a single integer-quantize

researcharxiv-cs-lg

MCD: Causal Distillation of Multimodal In-Context Learning in Large Vision-Language Models

arXiv:2609.39920v1 Announce Type: cross Abstract: Large vision-language models (LVLMs) exhibit strong multimodal in-context learning (ICL) capabilities, yet this ability degrades substantially as mode

safetyarxiv-cs-cl

Local Ai

MetaSteer: Context-Conditioned, nonlinear Steering via Attention-Projection Adaptation

arXiv:2609.38718v1 Announce Type: new Abstract: Steering large language models typically relies on linear, context-independent interventions in activation space, an assumption that recent work has cha

local-aiarxiv-cs-cl

Mid-Harness: Scaling Actions Between Model and Harness for Terminal Agents

arXiv:2609.39982v1 Announce Type: cross Abstract: Terminal agents act through stochastic model generations, yet the ability to generate a useful action does not ensure its reliable execution. A poor c

Tutorials

MIND: Marginal-Invariant Neural Dependency Diffusion for Mixed-Type Tabular Generation

arXiv:2609.39628v1 Announce Type: new Abstract: This paper proposes MIND, a marginal-invariant neural dependency diffusion model for mixed-type tabular data. MIND does not directly learn the joint dis

tutorialsarxiv-cs-lg

Mitigating Object Hallucination in Large Vision-Language Models via False Discovery Controlled Visual Data Splitting

arXiv:2609.38979v1 Announce Type: new Abstract: Multiple object hallucination, where large vision-language models (LVLMs) generate objects not supported by the visual input, is a persistent challenge

researcharxiv-cs-cv

MOBA-VL: Event-Localized Multi-Turn Reinforcement Learning for Real-Time MOBA Commentary

arXiv:2609.38428v1 Announce Type: new Abstract: Real-time commentary for Multiplayer Online Battle Arena (MOBA) esports requires a vision-language model (VLM) to narrate a live match second by second,

Agents

MoFlow: Multi-Objective Agentic Workflow Generation

arXiv:2609.38294v1 Announce Type: new Abstract: We study the generation of agentic workflows that jointly optimize multiple objectives, such as accuracy, cost, latency, robustness, and consistency. Ex

agentsarxiv-cs-ai

Multi-agent discussion gains less when dissent is withheld

model-releasesarxiv-cs-cl

arXiv:2609.38324v1 Announce Type: cross Abstract: Multi-agent systems of LLMs add discussion to majority voting and are therefore expected to be more capable. However, empirical reports conflict on wh

Multi-LLM Collaborative Alignment via Stackelberg Games

arXiv:2609.39076v1 Announce Type: new Abstract: A pool of language models can collaborate and improve collectively by learning from one another's responses. These interactions depend on the instructio

NeurDuo-EEG: A Long-Sequence EEG Foundation Model with Persistent State and Explicit Memory

arXiv:2609.38587v1 Announce Type: new Abstract: Electroencephalography (EEG) is recorded continuously over hours, with relevant dynamics spanning timescales from milliseconds to hours. Most EEG founda

researcharxiv-cs-lg

Neuro-Symbolic Predicate Learning for Semantic Safe Robot Control

model-releasesarxiv-cs-ro

arXiv:2609.39594v1 Announce Type: new Abstract: As robots are increasingly deployed in everyday environments, ensuring their safety has become a central challenge. Existing methods often encode safety

Applications

NEXUS: Perceptive Whole-Body Control for Terrain-Adaptive Teleoperation

arXiv:2609.39000v1 Announce Type: new Abstract: Whole-body teleoperation requires a humanoid robot to reproduce a human operator's behavior even when their terrains differ. This demands that the robot

applicationsarxiv-cs-ro

No Corners Cut: State-Grounded Transitions for Mid-Stream Prompt Switches in Video Generation

arXiv:2609.38691v1 Announce Type: new Abstract: Streaming video generators allow users to dynamically modulate video synthesis via mid-stream prompt switching. Existing streaming methods can respond t

Offline Guidance, Online Reasoning: Reusing LLM Feedback for Small Language Models

model-releasesarxiv-cs-cl

arXiv:2609.39346v1 Announce Type: new Abstract: Large language models (LLMs) offer strong reasoning capabilities but are often costly to access through commercial APIs, while small language models (SL

OGPO: One-Step Generative Policy Optimization for Real-Time Robot Control

arXiv:2601.20701v2 Announce Type: replace Abstract: Real-time robot control demands fast action generation. Diffusion and flow matching policies for robot control require multi-step sampling, limiting

safetyarxiv-cs-ro

Local Ai

On the Relaxation of Conditional Independence Assumption for Image Segmentation

arXiv:2609.38930v1 Announce Type: cross Abstract: In semantic segmentation, a recent line of RankSEG methods directly optimizes Dice/IoU scores at inference time, improving alignment with evaluation m

local-aiarxiv-cs-ai

OpenCollab: A Multi-Agent Coding Framework with Programmable Collaboration and Controllable Runtime

arXiv:2609.38345v1 Announce Type: cross Abstract: Multi-agent coding systems are designed to tackle complex software engineering tasks through collaboration. However, existing evaluations typically as

OpenTSLM TeeMoE: A Unified Time-Series Language Model for Forecasting, Contextual Prediction, and Reasoning

arXiv:2609.40265v1 Announce Type: new Abstract: Real-world time-series applications increasingly require models that can handle time series forecasting, context-conditioned prediction, and language-ba

OPSRD: On-Policy Self-Role Distillation

arXiv:2609.39884v1 Announce Type: cross Abstract: Role prompting elicits specialized behavior from large language models through an expert identity, offering a lightweight way to guide reasoning on de

Over-Personalization Is a Decision Failure: Generation-Induced Apply Bias in LLMs

arXiv:2609.34284v2 Announce Type: replace Abstract: Personalized LLMs must decide, for each stored preference, whether the current context calls for applying or suppressing it, which we call its appli

safetyarxiv-cs-cl

Overconfidence and Calibration in Medical VQA: Empirical Findings and Hallucination-Aware Mitigation

arXiv:2604.02543v2 Announce Type: replace Abstract: As vision-language models (VLMs) are increasingly deployed in clinical decision support, more than accuracy is required: knowing when to trust their

researcharxiv-cs-cv

PANDA: A Decentralized Architecture with Flexible Orchestration for Scalable, Fault-Tolerant Multi-Agent Systems

arXiv:2609.38482v1 Announce Type: cross Abstract: Existing architectures for LLM-based multi-agent systems (MAS) cannot reliably and efficiently solve multi-step tasks at scale: they struggle to suppo

PassGPT+: Leveraging Linguistic Priors for Password Modeling

arXiv:2609.39880v1 Announce Type: cross Abstract: Passwords remain the dominant online authentication mechanism, and understanding how humans choose them is essential for defensive strength estimation