Search: “benchmark”

Does Peer Observation Help? Vision-Sharing Collaboration for Vision-Language Navigation

arXiv:2603.20804v2 Announce Type: replace Abstract: Vision-Language Navigation (VLN) systems are fundamentally constrained by partial observability, as an agent can only accumulate knowledge from loca

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Local Ai

Does Personalization Break Trust? Open-Set Rejection Calibration Under Few-Shot On-Device Intent Adaptation

DGX agent

arXiv:2610.20652v1 Announce Type: new Abstract: Always-on virtual assistants increasingly personalize on-device to new user-specific commands via few-shot fine-tuning (e.g., LoRA), but this adaptation

local-aiarxiv-cs-cl

16 Oct 2026

DropKV: Decoupling Residual-Output Perturbation for Near-Optimal KV-Cache Eviction

arXiv:2610.22130v1 Announce Type: new Abstract: Key-value (KV) cache management has become a critical bottleneck for scaling Large Language Models (LLMs) to long-context scenarios due to linear memory

researcharxiv-cs-ai

Local Ai

DST-Net: Decomposed Spatio-Temporal Network for Time Series Forecasting

arXiv:2610.21123v1 Announce Type: cross Abstract: Series decomposition has emerged as a widely acknowledged paradigm in deep time series forecasting models. However, existing approaches predominantly

local-aiarxiv-cs-ai

EEG-Based Prediction of Music Enjoyment Using Deep Learning with Explainable Temporal Analysis

arXiv:2610.20795v1 Announce Type: new Abstract: Understanding why listeners enjoy music is difficult because the experience is subjective and changes as a piece unfolds. Electroencephalography (EEG) o

Efficient Pairwise Difference Learning: A Comparative Study of Encoding Strategies and Training Pair Selection

arXiv:2610.21644v1 Announce Type: cross Abstract: Pairwise Difference Learning for Classification (PDC) enhances supervised learning in low-data regimes by training a binary similarity classifier on p

EMO: A Closed-Loop Test-Time Computation Framework via Alternating Context Optimization and Logits Steering

arXiv:2610.22160v1 Announce Type: cross Abstract: Test-time context optimization adapts frozen language models through textual feedback, but ordinary conditional generation provides no separate contro

Enhancing Multimodal RLVR with Pairwise Token Contrast and Adaptive Trajectory Confidence

arXiv:2610.21671v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards (RLVR) improves multimodal reasoning, but group-relative policy optimization broadcasts a single sequence

safetyarxiv-cs-cl

EnsembleLink: Accurate Record Linkage Without Training Data

arXiv:2601.21138v3 Announce Type: replace Abstract: Record linkage, the process of matching records that refer to the same entity across datasets, is essential to empirical social science but remains

researcharxiv-cs-cl

EVA-Cap: Optimizing Audiovisual Video Captioning via Event-Centric Alignment

arXiv:2610.22186v1 Announce Type: cross Abstract: Recent advances in Omni Language Models (OLMs) provide a unified solution for audiovisual video captioning. Nevertheless, existing approaches predomin

safetyarxiv-cs-cl

Evolving Together, Failing Apart: The Skill-Policy Co-evolution Trap in Self-Improving Agents

arXiv:2610.20845v1 Announce Type: new Abstract: Skill-policy co-evolution, in which an agent's policy model and its skill bank improve each other during training, is a promising paradigm for self-impr

Failure Is the Mother of Success: Recovering Supervision with Grounding Hindsight

arXiv:2610.21018v1 Announce Type: new Abstract: When every rollout for a problem fails, GRPO receives neither a successful exemplar nor a nonzero group-relative advantage, so it learns nothing from ex

FANTF: Fuzzy attention network based transformers for time series analysis

arXiv:2504.00070v2 Announce Type: replace Abstract: This paper introduces Fuzzy Attention Network-Based Transformer (FANTF), an approach for time series forecasting, classification, and anomaly detect

Tutorials

FeaTok: Feature-Loss Tokenization for Image Generation

arXiv:2610.22060v1 Announce Type: new Abstract: Discrete image generators learn the distribution of tokens produced by an image tokenizer. These token representations should be easy for generators to

tutorialsarxiv-cs-cv

Flexible Doubly Robust Proximal Causal Inference for Censored Outcomes

arXiv:2610.20893v1 Announce Type: cross Abstract: Estimating causal effects on right-censored time-to-event outcomes is challenging under unmeasured confounding. Proximal causal inference addresses un

Flow-CLIP: Image-Conditioned Residual Scoring for CompositionalImage-Text Matching

arXiv:2610.21937v1 Announce Type: new Abstract: Vision--language pre-training has enabled substantial progress in image--text retrieval and zero-shot recognition by learning transferable cross-modal r

FocusOPD: Structure-Guided On-Policy Distillation for Diffusion Models

arXiv:2610.21250v1 Announce Type: new Abstract: Multi-teacher on-policy distillation enables rectified-flow models with diffusion-transformer architectures to combine task-specific capabilities within

From Long Context to Selective Memory:A Survey of Memory-Centric Compression for Large Language Models

arXiv:2610.21165v1 Announce Type: new Abstract: Long-context capability has become essential for large language models (LLMs) to handle complex tasks, yet continuously expanding the context window doe

From Observation to Intervention: Distilling Self-Generated In-Context Demonstrations via Executable Causal Intervention

arXiv:2610.21149v1 Announce Type: new Abstract: Self-generated In-Context Learning (ICL) enables LLMs to synthesize their own few-shot demonstrations, often outperforming manual prompts. However, exis

researcharxiv-cs-cl

Functional Subnetwork Localization for Efficient Continual Learning in Large Language Models

arXiv:2610.21537v1 Announce Type: new Abstract: Biological intelligence continually adapts to new information while retaining prior skills through localized plasticity. In contrast, continual learning

FuseAdapt: Parameter-Efficient Multimodal Fusion for Semantic Segmentation with Missing Modalities

arXiv:2610.21779v1 Announce Type: new Abstract: Multi-modal semantic segmentation benefits from complementary sensors, but this benefit is fragile when some modalities are degraded or missing at test

FusionAudit: Pathway-Conditioned Robustness Auditing for Native Multimodal Models

arXiv:2610.21667v1 Announce Type: new Abstract: As vision-language models (VLMs) transition from frozen-encoder pipelines to native multimodal architectures, the visual channel increasingly serves as

researcharxiv-cs-cv

Gibbs-Gap Policy Optimization for Off-Policy RL with Verifiable Rewards

arXiv:2610.20814v1 Announce Type: cross Abstract: Reinforcement learning with verifiable rewards often trains language models on responses generated by a different, possibly stale behavior policy. Cor

Agents

GRADS: Graph-Regulated Agentic Data Synthesis with Controllable Difficulty

arXiv:2610.21167v1 Announce Type: new Abstract: Deep research agents are increasingly deployed to retrieve precise information from large corpora, yet multi-hop, long-horizon tasks remain challenging.

agentsarxiv-cs-ai

Local Ai

Graph Additive Models disentangling feature, topology, and interactions

arXiv:2610.20662v1 Announce Type: cross Abstract: Graph Neural Additive Networks (GNANs) have emerged as effective, interpretable-by-design architectures for graph learning. They achieve transparency

local-aiarxiv-cs-lg

Grid-Bench: Can LLMs Predict How Discrete Worlds Change?

arXiv:2610.22312v1 Announce Type: new Abstract: Spatial instructions describe how to move objects, transform regions, or modify their surroundings. When several operations are applied in sequence, eac

Hear, Localize, and Reason: Spatially Aware Scene Understanding for Audio-Visual LLMs

arXiv:2610.21588v1 Announce Type: new Abstract: Audio-visual large language models (AV-LLMs) have made strong progress in multimodal understanding, yet most treat audio as monaural semantic content, l

Hierarchical Interpretable Multitask Audio Representation Learning: An Analysis of Event-Scene Relationships through the Information Bottleneck

arXiv:2610.21520v1 Announce Type: cross Abstract: Joint Acoustic Scene Classification (ASC) and Audio Tagging (AT) models traditionally rely on unconstrained multihead architectures. However, this des

Human Interventions as Labels: Localizing, Steering, and (Sometimes) Editing the Computations Behind LLM Agent Failures

arXiv:2610.21708v1 Announce Type: cross Abstract: When an LLM agent misbehaves, a human steps in, and that intervention is usually discarded or replayed as training data. We treat it instead as a meas

Hy-Memory-30B-A3B: Attributed Reinforcement Learning for Long-Term Memory Writing

arXiv:2610.22196v1 Announce Type: new Abstract: External-memory systems extend language-model applications beyond a context window, but their quality depends critically on the model executing the writ

Imperfect Is Perfect: Learned Column Prefilling for Branch-Price-and-Cut

arXiv:2610.21001v1 Announce Type: new Abstract: Column generation folklore warns that initial columns hurt: a warm start may bias the dual and slow the root of branch-price-and-cut. We show the opposi

IneqAgent: Agentic Inequality Proving in Lean with Symbolic Computation

arXiv:2610.20681v1 Announce Type: new Abstract: Formal inequality proving requires mathematical reasoning and exact computation, often connected through intermediate lemmas and goal reformulations. We

Interfaces and Modules: A Scalable Representation for Weight-Space Learning across Architectures

arXiv:2610.21995v1 Announce Type: new Abstract: Weight-space learning across architectures requires representations that respect parameter symmetries and scale to large networks. Existing equivariant

Tutorials

Is Text All You Need? Text as a Universal Information Bottleneck for Speech LLMs

arXiv:2606.09366v2 Announce Type: replace Abstract: Large language models (LLMs) provide a powerful reasoning backbone for speech understanding, but integrating continuous acoustic signals into a pret

tutorialsarxiv-cs-cl

Isolating a Language Model's Judgment with a Label-Free Subspace

arXiv:2610.21256v1 Announce Type: cross Abstract: Language models are often asked to make judgments: whether a proposed answer is correct, whether one statement follows logically from another, or whet

Iterative Neural Variational Solver of Committor Function in Under-damped Langevin Dynamics

arXiv:2610.20617v1 Announce Type: cross Abstract: The committor function describes the probability that a stochastic dynamical system, while transitioning between two metastable states, will first rea

Agents

Jev-LiteMAS: Separating Decisions from Generation in Multi-Agent Workflows

arXiv:2610.21411v1 Announce Type: new Abstract: Multi-agent LLM systems rely on generative models for expert execution, workflow planning, and artifact review. Yet many coordination operations require

agentsarxiv-cs-ai

Tutorials

Latent Annealed Posterior Sampling with a Variance-Preserving Bridge for Sparsely Observed Random Fields

arXiv:2610.20659v1 Announce Type: cross Abstract: Posterior inference for random fields from sparse observations presents two major challenges: accurately characterizing complex spatial uncertainty an

tutorialsarxiv-cs-lg

Learning at the Right Pace: Adaptive Data Scheduling Improves LLM Reinforcement Learning

arXiv:2606.22305v2 Announce Type: replace Abstract: Large Language Models (LLMs) achieve remarkable reasoning capabilities through reinforcement learning (RL) post-training. However, existing RL post-

safetyarxiv-cs-cl

Learning from Unverified Peers via Shared Residual Distillation

arXiv:2610.22080v1 Announce Type: new Abstract: A language model can generate several solutions to the same problem, some leading to correct answers and others to incorrect ones. Many post-training me

Learning Lean Feedback for Efficient Proof-Candidate Evaluation on GPUs

arXiv:2610.21881v1 Announce Type: new Abstract: Training language models for formal theorem proving requires large numbers of verifier calls. Slow checks and timeouts often leave GPUs waiting and with

Learning to Reason with Selective Internal Feedback

arXiv:2610.21392v1 Announce Type: new Abstract: Reinforcement learning from internal feedback can improve language models without ground-truth answers or external verifiers. However, confidence and co

Learning to Think and Explore in 3D World

arXiv:2610.21783v1 Announce Type: new Abstract: We study how vision-language models (VLMs) can learn to think and actively explore in 3D space. Instead of providing multi-view images, videos, or 3D re

Learning When to Think: Utility-Guided Adaptive Reasoning for Efficient Text Retrieval

arXiv:2610.21030v1 Announce Type: cross Abstract: Reasoning-conditioned embeddings can improve retrieval by progressively refining semantic constraints. However, additional reasoning may introduce ove

Agents

MAI-Gym: An Open All-in-One Platform for GUI Agent Evaluation and RL Training across Mobile and Computer Environments

arXiv:2610.20939v1 Announce Type: new Abstract: Online reinforcement learning (RL) with verifiable rewards has driven rapid progress in mathematics, coding, and tool use, but scaling this success to G

agentsarxiv-cs-ai

Market Incentives for AI Safety Investment

arXiv:2610.21607v1 Announce Type: new Abstract: Despite their practical significance, modern AI systems pose significant societal risks, including toxicity, malicious use, and misinformation. Their mi

safetyarxiv-cs-lg

MBBQ: A Dataset for Cross-Lingual Comparison of Stereotypes in Generative LLMs

arXiv:2406.07243v4 Announce Type: replace Abstract: Generative large language models (LLMs) have been shown to exhibit harmful biases and stereotypes. While safety fine-tuning typically takes place in

Mind the Tail: Data Selection Through Sparse Concepts

arXiv:2610.21007v1 Announce Type: new Abstract: Data selection reallocates training support across visual concepts, including objects, attributes, color, texture, and backgrounds. Different allocation

researcharxiv-cs-cv