Search: “reasoning”

GaugeVLM: Structuring Spatial Supervision with Measured Geometric Interventions

arXiv:2609.38285v1 Announce Type: cross Abstract: Vision-language models (VLMs) can contradict themselves across views of the same spatial relation and fail to respond when that relation changes. Addr

agentsarxiv-cs-ai

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Generative sequence modeling for infinite memory processes via predictive states

DGX agent

arXiv:2609.38524v1 Announce Type: cross Abstract: We consider estimating the one-step-ahead conditional distribution of a multivariate stochastic process. Many existing approaches rely on assumptions

researcharxiv-cs-lg

1 Oct 2026

Local Ai

Grounding with Confidence: Controllable Generative Video Temporal Grounding

arXiv:2609.39883v1 Announce Type: new Abstract: Video temporal grounding supports applications such as video search, content review, and automated editing by localizing events described in natural lan

local-aiarxiv-cs-cv

Hyperspectral Image Dataset for Benchmarking on Salient Object Detection

arXiv:1806.11314v3 Announce Type: replace Abstract: Many works have been done on salient object detection using supervised or unsupervised approaches on colour images. Recently, a few studies demonstr

researcharxiv-cs-cv

Tutorials

InfoAgent: Traceable Generation and Repair of Evidence-Grounded Infographics

arXiv:2609.39380v1 Announce Type: new Abstract: Reliable infographic generation requires facts, symbols, and visual relations to remain consistent through rendering and revision. Correcting one elemen

tutorialsarxiv-cs-cv

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

Interpretable but Fragile? Robustness of Concept Bottlenecks under Geometric-Semantic Perturbations

arXiv:2609.38625v1 Announce Type: cross Abstract: Concept Bottleneck Models (CBMs) are designed to provide interpretable intermediate representations, yet how such bottlenecks affect robustness remain

Language-Conditioned World Modeling for Visual Navigation

arXiv:2603.26741v2 Announce Type: replace-cross Abstract: Goal-conditioned visual navigation has been a long-standing testbed for embodied AI. We study a natural language-conditioned variant, language

Learning Beyond Full Imitation: Task-Preserving Knowledge Distillation

arXiv:2609.39338v1 Announce Type: new Abstract: Knowledge distillation transfers knowledge by encouraging a student to match a teacher's predicted class probabilities. These probabilities express not

researcharxiv-cs-lg

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

Listening to the Wise Few: Query-Key Alignment Unlocks Latent Correct Answers in Large Language Models

model-releasesarxiv-cs-cl

arXiv:2410.02343v2 Announce Type: replace Abstract: Large language models (LLMs) routinely fail to output the correct option in multiple-choice question answering (MCQA) while encoding the answer inte

Looped Diffusion Transformer

model-releasesarxiv-cs-cv

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

Applications

Low-Discrepancy Dither for Quantized Recurrent State Caches

arXiv:2609.39185v1 Announce Type: new Abstract: Mamba-style and hybrid language models compress their past into a fixed-size recurrent state that is rewritten at every generated token. Storing this st

applicationsarxiv-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

NMIXX: Domain-Adapted Neural Embeddings for Cross-Lingual eXploration of Finance

arXiv:2507.09601v3 Announce Type: replace-cross Abstract: Financial text embeddings must distinguish changes in event status, perspective, and obligations even when passages share similar wording. NMI

Overlap, Unique and Conflict: Can LLMs Extract What They Can Recognize?

model-releasesarxiv-cs-cl

arXiv:2609.38799v1 Announce Type: new Abstract: Understanding multi-perspective alternative narratives requires identifying how their information agrees, conflicts, or differs across sources. Existing

PathAnchor: Path-Structured Evidence for Scientific Agents

arXiv:2609.38766v1 Announce Type: new Abstract: Scientific agents can retrieve relevant passages yet still lose functional order, mix evidence across sources, or state conclusions that exceed the retr

agentsarxiv-cs-ai

PhyProbe: Rethinking Physical Consistency Evaluation in Generated Videos

arXiv:2609.38377v1 Announce Type: new Abstract: Evaluating the physical consistency of generated videos remains a fundamental challenge. Existing approaches rely on off-the-shelf vision-language model

researcharxiv-cs-cv

Physis-Lang: Self-Evolving Language as a Physical Representation for Video World Model

arXiv:2609.40358v1 Announce Type: new Abstract: Video world models are expected to predict how the physical world evolves, yet they often produce visually plausible videos that violate basic physical

agentsarxiv-cs-cv

Prompt2Skill: Unsupervised Skill Optimization From Natural Language Instructions

arXiv:2609.38593v1 Announce Type: cross Abstract: Skills are external artifacts that Large Language Models (LLMs) consume at inference time to improve their performance on specialized domains by incor

Revisiting scaling laws for reward optimization

arXiv:2609.38526v1 Announce Type: cross Abstract: Scaling laws for optimization against reward models in AI alignment have pinned down how performance depends on optimization effort---measured by a KL

safetyarxiv-cs-ai

Applications

Robust Transfer Learning for Paper ECG Recognition

arXiv:2609.39581v1 Announce Type: cross Abstract: Paper ECG recognition is challenging because real-world ECG images vary in layout, physical artifacts, and label availability. We introduce RobECG-CL,

applicationsarxiv-cs-ai

Safety of Latent Communication in Multi-Agent Systems

arXiv:2609.39788v1 Announce Type: new Abstract: Latent communication enables multi-agent systems to exchange information directly in internal representation space, reducing the token, computation, and

safetyarxiv-cs-ai

Scaling Laws for Looped Mixture of Experts

arXiv:2609.40316v1 Announce Type: cross Abstract: Looped transformers and Mixture-of-Experts (MoE) offer complementary routes to efficient scaling: recurrence increases computational depth at fixed pa

Searching for BSM Experimental Signatures with Large Lagrangian Models

arXiv:2609.38309v1 Announce Type: cross Abstract: The search for physics Beyond the Standard Model (BSM) is generally limited not by the supply of theory descriptions but by the lack of discriminating

Applications

SOLAR: SVD-Optimized Lifelong Attention for Recommendation

arXiv:2603.02561v2 Announce Type: replace-cross Abstract: Attention mechanism remains the defining operator in Transformers since it provides expressive global credit assignment, yet its quadratic cos

applicationsarxiv-cs-cv

STARS: From Spatiotemporal Dynamics to Social Representations in Human-Robot Interaction

model-releasesarxiv-cs-lg

arXiv:2609.40245v1 Announce Type: cross Abstract: Robot navigation in dynamic, human-centered environments requires socially-compliant decisions grounded in robust scene understanding. Recent Vision-L

Structure-aware Reinforcement Learning for Protein Directed Evolution

arXiv:2609.39048v1 Announce Type: cross Abstract: Protein optimization remains a longstanding goal in life sciences. Existing machine learning-assisted directed evolution (MLDE) methods primarily rely

Training LLM Judges from Language Feedback via Position-Selective Self-Distillation

arXiv:2609.38792v1 Announce Type: new Abstract: We study training LLM judges from natural language feedback, especially for subjective tasks where the verdict depends strongly on which evaluation crit

researcharxiv-cs-cl

Tutorials

TripleFlow: Training-Free Video Object Removal by Bridging Residual Editing and Native Generation

arXiv:2609.39157v1 Announce Type: new Abstract: Video object removal presents a uniquely difficult editing challenge. Because a removal prompt specifies only what to erase rather than what to generate

tutorialsarxiv-cs-cv

Local Ai

Trust the Critic More

arXiv:2609.39247v1 Announce Type: cross Abstract: Standard language model RL algorithms credit every token of a long rollout with the same advantage determined by the terminal reward. Actor-critic met

local-aiarxiv-cs-ai

Trustworthy Runtime Error Healing in Real-World Repositories: A Benchmark and Guardrail

arXiv:2609.39086v1 Announce Type: cross Abstract: Runtime error healing lets a crashed program continue by generating code that repairs its live runtime state. Recent work shows that LLMs can generate

Vision-Language-Action Autonomous Driving Agent with Language-based Memory

arXiv:2609.38641v1 Announce Type: new Abstract: Vision-Language-Action (VLA) foundation models have recently emerged as one of the prevailing solutions for autonomous driving, as they can utilize know

agentsarxiv-cs-cv

WARP: A Unified Benchmark for Invisible Image Watermarking -- Robustness and Protection Against Attacks

arXiv:2609.40031v1 Announce Type: cross Abstract: Digital image watermarking is increasingly critical in media contexts, as emerging regulations and industry practices require marking AI-generated con

When Does a Spoken Agent Have Enough Evidence to Act? The PACT-SLM Contract Test

arXiv:2609.38232v1 Announce Type: cross Abstract: Streaming spoken agents may take an external action before the available speech supports it, yet final-turn scores do not reveal whether each observed

agentsarxiv-cs-cl

When Does Randomized Oversight Align AI Agents That Can Conceal?

arXiv:2609.38262v1 Announce Type: cross Abstract: Oversight changes the evidence it relies on. We ask when randomized audits and scoring align AI agents that can conceal misconduct and alter records.

agentsarxiv-cs-ai

X-Planner: Event-Structured Task Planning for Embodied Intelligence

arXiv:2609.25187v2 Announce Type: replace Abstract: Task planning bridges high-level instructions and executable behavior in long-horizon manipulation, yet modern Vision-Language-Action (VLA) systems

ABC: Advantage-Based Control Variates for Reinforcement Learning with Verifiable Rewards

arXiv:2609.36058v1 Announce Type: new Abstract: Recent progress in reinforcement learning with verifiable rewards (RLVR) has highlighted the effectiveness of simple critic-free policy-gradient methods

safetyarxiv-cs-lg

AdvancedMathBench: A Benchmark Suite for Advanced Mathematical Proof Generation and Verification

model-releasesarxiv-cs-cl

arXiv:2607.11849v2 Announce Type: replace Abstract: Large language models (LLMs) have achieved remarkable performance on high-school and competition-level mathematics, yet their capabilities on advanc

Agentic Commerce Bench: Measuring Fraud Detection for Agents That Spend Money

arXiv:2609.35886v1 Announce Type: cross Abstract: AI agents now hold spend authority and settle payments without per-action human confirmation. The resulting loss is often not a security failure: a co

Auditable Long-Term Memory: A Deterministic Retrieval Chain Measured at 479/475 of 500 on LongMemEval-S

arXiv:2609.38021v1 Announce Type: cross Abstract: We evaluate an auditable long-term memory system on LongMemEval-S. Its retrieval chain uses hybrid candidate retrieval, cross-encoder reranking, cover

Beam Search as Test-Time Self-Distillation via Counterfactual Contexts

arXiv:2609.37041v1 Announce Type: cross Abstract: Self-Distillation Fine-Tuning (SDFT) enables a language model to act as its own teacher: by conditioning on a demonstration, the model produces an imp

Beyond Low-Rank Parameterization: Narrowing the Gap Between LoRA and Full Fine-Tuning via Gradient Decomposition

arXiv:2609.37027v1 Announce Type: new Abstract: Low-Rank Adaptation (LoRA) is a widely used approach to parameter-efficient fine-tuning (PEFT), yet a performance gap can remain relative to full fine-t

BRIDGE: Bilevel Retrieval-Credit-Aware Agentic Reinforcement Learning

arXiv:2609.36505v1 Announce Type: new Abstract: Agentic reinforcement learning (ARL) with verifiable rewards improves the ability of large language models (LLMs) to tackle knowledge-intensive tasks by

safetyarxiv-cs-ai

Code4Scene: Benchmarking Coding Agents for Constructing and Editing 3D Scenes

arXiv:2609.36777v1 Announce Type: new Abstract: Frontier coding agents can now write and execute code that authors 3D environments, but whether they reliably understand 3D structure and precisely cont

Concealing LLM-Based Multi-Agent Topology via Phantom Structure Injection

arXiv:2609.37567v1 Announce Type: cross Abstract: Driven by the rapid advancement of large language models (LLMs), LLM-based multi-agent systems (MAS) have emerged as a powerful paradigm for collabora

Dating the Model: Hidden Dates in System Prompts Affect LLM Evaluation

arXiv:2609.36931v1 Announce Type: new Abstract: Reproducibility is essential for scientific research, yet prior work shows that LLM outputs vary with hardware and batching. We identify an overlooked f

researcharxiv-cs-cl

Dynamic Optimizations of LLM Ensembles with Two-Stage Reinforcement Learning Agents

arXiv:2502.04492v3 Announce Type: replace Abstract: The advancement of LLMs and their accessibility have triggered renewed interest in multi-agent reinforcement learning as robust and adaptive framewo

safetyarxiv-cs-cl